From 842412d23ff3518091174f371b80f3c6363f4834 Mon Sep 17 00:00:00 2001
From: zhaohan xiong <zhaohanxiong@github.com>
Date: Fri, 3 Mar 2023 22:59:12 +1100
Subject: [PATCH] all tasks

---
 notebooks/Module 1.1.1 - Overview.ipynb       |   30 +-
 notebooks/Module 1.1.2 - Distributions.ipynb  | 1253 ++++-
 .../Module 1.1.3 - Moments and Z-Scores.ipynb |  582 ++-
 .../Module 1.1.4 - Normality Tests.ipynb      |  160 +-
 notebooks/Module 1.2.1 - Covariance.ipynb     |  100 +-
 notebooks/Module 1.2.2 - Correlation.ipynb    |  290 +-
 .../Module 1.2.3 - Cross Correlation.ipynb    |  458 +-
 ...odule 1.3.1 - Ordinary Least Squares.ipynb |  217 +-
 .../Module 1.3.2 - Multivariate OLS.ipynb     |  467 +-
 .../Module 1.3.3 - Regression Tests.ipynb     |  111 +-
 .../Module 1.3.4 - Multicolinearity.ipynb     | 2329 ++++-----
 .../Module 1.4.1 - PCA Introduction.ipynb     |  254 +-
 .../Module 1.4.2 - PCA Interpretation.ipynb   |  272 +-
 ...1 - Bayesian Statistics Introduction.ipynb |  124 +-
 ...le 1.5.2 - Bayesian Inference Basics.ipynb |  349 +-
 ...5.3 - Representing Prior information.ipynb |  269 +-
 ...e 1.6.1 - Time Series moving average.ipynb |  456 +-
 notebooks/Module 1.6.2 - Stationarity.ipynb   |  277 +-
 notebooks/Module 1.6.3 - White Noise.ipynb    |  188 +-
 notebooks/Module 1.6.4 - ARMA.ipynb           |  629 ++-
 ...e 1.7.1 - Kalman Filter Introduction.ipynb |  417 +-
 .../Module 2.1.1 - Hypothesis Testing.ipynb   |  412 +-
 ...ule 2.2.1 - Linear regression models.ipynb |  167 +-
 notebooks/Module 2.2.2 - ARIMA.ipynb          | 1392 ++++--
 notebooks/Module 2.2.3 - GARCH.ipynb          |  626 ++-
 ...ule 2.3.1 - Statistical Significance.ipynb |  131 +-
 .../Module 2.3.2 - Confidence Intervals.ipynb |  308 +-
 .../Module 2.4.1 - Residual Analysis.ipynb    |  556 ++-
 ...5.1 - Model and Estimate Instability.ipynb | 4401 ++++++++++++++++-
 notebooks/Module 2.6.1 - Overfitting.ipynb    |  493 +-
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diff --git a/notebooks/Module 1.1.1 - Overview.ipynb b/notebooks/Module 1.1.1 - Overview.ipynb
index a993d0d..3e24078 100644
--- a/notebooks/Module 1.1.1 - Overview.ipynb	
+++ b/notebooks/Module 1.1.1 - Overview.ipynb	
@@ -51,7 +51,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.7.4 (default, Aug  9 2019, 18:34:13) [MSC v.1915 64 bit (AMD64)]\n"
+      "3.10.3 (tags/v3.10.3:a342a49, Mar 16 2022, 13:07:40) [MSC v.1929 64 bit (AMD64)]\n"
      ]
     }
    ],
@@ -88,6 +88,15 @@
     "Try adding a cell now. Click this cell, so that it has an outline around this paragraph. Then click <kbd>ESC</kbd>, and then <kbd>b</kbd>. This should create a new cell below this one."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# this is a code cell"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -97,6 +106,14 @@
     "If you want to write notes, like these ones, click <kbd>ESC</kbd>, and then <kbd>m</kbd>. This changes the cell type to \"markdown\", letting you add notes to the notebook as we are going."
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### this is a markdown cell"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -106,7 +123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -181,7 +198,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -195,7 +212,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3 (tags/v3.10.3:a342a49, Mar 16 2022, 13:07:40) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.1.2 - Distributions.ipynb b/notebooks/Module 1.1.2 - Distributions.ipynb
index 94db2e6..10fa62b 100644
--- a/notebooks/Module 1.1.2 - Distributions.ipynb	
+++ b/notebooks/Module 1.1.2 - Distributions.ipynb	
@@ -34,9 +34,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import random\n",
     "\n",
@@ -54,7 +65,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -65,16 +76,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Counter({3: 16580, 1: 16818, 2: 16464, 5: 16581, 6: 16845, 4: 16712})"
+       "Counter({1: 16908, 3: 16509, 4: 16664, 2: 16802, 5: 16473, 6: 16644})"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -87,9 +98,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RendererRegistry.enable('default')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "import altair as alt\n",
     "import vega\n",
@@ -98,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -106,25 +128,25 @@
      "output_type": "stream",
      "text": [
       "   state  count\n",
-      "0      1  16818\n",
-      "1      2  16464\n",
-      "2      3  16580\n",
-      "3      4  16712\n",
-      "4      5  16581\n",
-      "5      6  16845\n"
+      "0      1  16908\n",
+      "1      2  16802\n",
+      "2      3  16509\n",
+      "3      4  16664\n",
+      "4      5  16473\n",
+      "5      6  16644\n"
      ]
     },
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-fcf84721b6e042bab1d8a5cedcbe638a\"></div>\n",
+       "<div id=\"altair-viz-cec599379c094f3190c7e0ffa62be46c\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-fcf84721b6e042bab1d8a5cedcbe638a\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-fcf84721b6e042bab1d8a5cedcbe638a\");\n",
+       "    if (outputDiv.id !== \"altair-viz-cec599379c094f3190c7e0ffa62be46c\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-cec599379c094f3190c7e0ffa62be46c\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -170,14 +192,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-72e0286a2ef38f96d66916aa519e4f44.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"state\", \"type\": \"ordinal\"}, \"y\": {\"field\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-f62ca775fa0367bfb328aab4a2658d7b\"}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"field\": \"state\", \"type\": \"ordinal\"}, \"y\": {\"field\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-f62ca775fa0367bfb328aab4a2658d7b\": [{\"state\": 1, \"count\": 16908}, {\"state\": 2, \"count\": 16802}, {\"state\": 3, \"count\": 16509}, {\"state\": 4, \"count\": 16664}, {\"state\": 5, \"count\": 16473}, {\"state\": 6, \"count\": 16644}]}}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.Chart(...)"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -227,9 +249,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<!-- Run me, but there is  need to worry about this code - \n",
+       "    it makes the equation in the next cell larger and more readable -->\n",
+       "<style>\n",
+       "    .big_function {font-size: 200%;}\n",
+       "</style>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "%%html\n",
     "<!-- Run me, but there is  need to worry about this code - \n",
@@ -240,6 +279,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -255,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -277,12 +317,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -300,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -309,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -318,17 +358,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ 0.28912919,  1.52371025, -0.64598402, -0.00686155,  1.41223634,\n",
-       "       -2.3430656 , -0.41657553, -0.45794799, -1.18153368,  0.24811689])"
+       "array([-0.12412694,  0.78286318, -0.2536435 , -1.07421581, -0.1162626 ,\n",
+       "        0.963849  ,  1.15496496,  0.63165475, -0.3517302 ,  1.9828123 ])"
       ]
      },
-     "execution_count": 154,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -340,7 +380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -351,18 +391,106 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({0.10703127193584497: 1,\n",
+       "         0.14236308643132137: 1,\n",
+       "         0.5542097050075205: 1,\n",
+       "         -0.690753480114846: 1,\n",
+       "         -1.1183858165422245: 1,\n",
+       "         -1.2827622508167755: 1,\n",
+       "         0.1999070306558162: 1,\n",
+       "         -1.4312531428899704: 1,\n",
+       "         0.6642871661116427: 1,\n",
+       "         0.6559095747173593: 1})"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "Counter(results)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-e0520d383cc3468cb37f2379ad1573b4\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-e0520d383cc3468cb37f2379ad1573b4\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-e0520d383cc3468cb37f2379ad1573b4\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-2aef0e774bd33af97e1dab05d65d6121\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"#287E1E\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-2aef0e774bd33af97e1dab05d65d6121\": [{\"value\": -1.6386318759684122}, {\"value\": -0.8582311486959374}, {\"value\": -0.5903440397949479}, {\"value\": -1.6503160661248877}, {\"value\": 0.01857024849691058}, {\"value\": 0.6500880501572796}, {\"value\": 0.6416271160069997}, {\"value\": -0.30707732317814185}, {\"value\": -0.7659060177188053}, {\"value\": 0.3631719018794969}, {\"value\": -0.9602105646082241}, {\"value\": 0.35493473018866034}, {\"value\": -0.36264168635657473}, {\"value\": 0.9111643671616019}, {\"value\": 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-0.5339373662765551}, {\"value\": 0.14467898921204325}, {\"value\": -0.011789707520337275}, {\"value\": -0.9462291776919283}, {\"value\": 0.19905701951250457}, {\"value\": 0.12792156221013448}, {\"value\": 0.4990498157999495}, {\"value\": 0.6272776291228624}, {\"value\": 0.4038144713063295}, {\"value\": 0.9555574858479657}, {\"value\": 0.06691369666455775}, {\"value\": 0.4704768644979534}, {\"value\": 0.9679029431924971}, {\"value\": 0.12626485667003232}, {\"value\": -0.3070048271417868}, {\"value\": 1.268077368713437}, {\"value\": -0.5213089184803078}, {\"value\": -0.8888851436641004}, {\"value\": 0.13224266975724547}, {\"value\": 0.49661861069018787}, {\"value\": -1.3625419355392983}, {\"value\": 0.715548610580947}, {\"value\": -0.8501173820385984}, {\"value\": 0.2475362080606603}, {\"value\": 0.4724539818373629}, {\"value\": -0.06425019967818803}, {\"value\": -0.5105797096631487}, {\"value\": 0.5642441217817075}, {\"value\": 1.1342920866612418}, {\"value\": 0.8444468015365895}, {\"value\": -1.845156460352024}, {\"value\": 0.39812839513513354}, {\"value\": 0.3088966362040935}]}}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# Histograms work better here\n",
     "\n",
@@ -386,11 +514,79 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {
     "scrolled": false
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-3bd4abc44ac14216b866d6e05ca3f2e3\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-3bd4abc44ac14216b866d6e05ca3f2e3\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-3bd4abc44ac14216b866d6e05ca3f2e3\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-79123025a1cc105512c1a907f8298ea0.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "if not alt.data_transformers.active == 'json':  # Check json isn't already active\n",
     "    import os\n",
@@ -436,115 +632,613 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# (1) (doing is from scratch so just this chunk can be run)\n",
-    "import pandas as pd\n",
-    "from scipy import stats\n",
-    "import altair as alt\n",
-    "alt.renderers.enable('default')\n",
-    "\n",
-    "mean_std_list = [(1,7), (10,1), (-10,5)]\n",
-    "\n",
-    "for vals in mean_std_list:\n",
-    "    norm_dist_func = stats.norm(vals[0], vals[1])\n",
-    "    results = norm_dist_func.rvs(1000)\n",
-    "    normal_values = pd.DataFrame({\"value\": results})\n",
-    "    norm_plot = alt.Chart(normal_values, title=\"Normal Distribution histogram with mean \" + str(vals[0]) + \" and standard deviation \" + str(vals[1])).mark_bar().encode(\n",
-    "    alt.X(\"value\", bin=alt.Bin(maxbins=100)),\n",
-    "    y='count()',)\n",
-    "    norm_plot.display()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# (2)\n",
-    "def display_norm(mean, std):\n",
-    "    norm_dist_func = stats.norm(mean, std)\n",
-    "    results = norm_dist_func.rvs(1000)\n",
-    "    normal_values = pd.DataFrame({\"value\": results})\n",
-    "    norm_plot = alt.Chart(normal_values, title=\"Normal Distribution histogram with mean \" + str(mean) + \" and standard deviation \" + str(std)).mark_bar().encode(\n",
-    "    alt.X(\"value\", bin=alt.Bin(maxbins=100)),\n",
-    "    y='count()',)\n",
-    "    norm_plot.display()\n",
-    "    \n",
-    "display_norm(1,2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# (3) Compound Histogram Altair\n",
-    "df = pd.DataFrame({'Norm 1': stats.norm(1,7).rvs(6000),\n",
-    "                   'Norm 2': stats.norm(10,1).rvs(6000),\n",
-    "                   'Norm 3': stats.norm(-10,5).rvs(6000)})\n",
-    "\n",
-    "alt.Chart(df).transform_fold(\n",
-    "    ['Norm 1', 'Norm 2', 'Norm 3'],\n",
-    "    as_=['Columns', 'Values']\n",
-    ").mark_area(\n",
-    "    opacity=0.8,\n",
-    "    interpolate='step'\n",
-    ").encode(\n",
-    "    alt.X('Values:Q', bin=alt.Bin(maxbins=300)),\n",
-    "    alt.Y('count()', stack=None),\n",
-    "    alt.Color('Columns:N'))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*For solutions, see `solutions/normal_distributions.py`*"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Probability Density Functions\n",
-    "\n",
-    "The histograms we were looking at above are approximations of what is known as a *Probability Density Function*, or PDF. A PDF shows us the probability of a given value, for a discrete random variable.\n",
-    "\n",
-    "For a continuous random variable, any *exact* value has a probability of 0 (think about why this is the case). We can only give probabilities for ranges. For a lower bound of $a$ and an upper bound of $b$, we can obtain the probability of a random value lying between $a$ and $b$ by integrating the PDF between those values. For example, if our PDF function is $f(x)$, then the probability of random variable $X$ lying between $a$ and $b$ is given by:\n",
-    "\n",
-    "$P[a <= X <= b] = \\int_a^b f(x) dx$\n",
-    "\n",
-    "As an aside, and very informally, you can think of \"the probability of $X=2$\" as being a range *about* 2, say any value between 1.99 and 2.01 (or whatever precision makes sense in the context). Be clear on this definition when you do go to present your findings though, as a formal definition will be needed to replicate your results.\n",
-    "\n",
-    "We can generate the PDF through the `.pdf` method on a `scipy.stats` distribution. Earlier in this notebook we computed this manually through the equation. Now we let a library do the work for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "n = stats.norm(0, 1)"
-   ]
-  },
-  {
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-f8e5835516674161b91136a550b512da\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-f8e5835516674161b91136a550b512da\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-f8e5835516674161b91136a550b512da\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-72fd843685a60fe52ef1b6d49255f97a.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"title\": \"Normal Distribution histogram with mean 1 and standard deviation 7\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-a72efa38475248e7a57f796e7f0cbc81\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-a72efa38475248e7a57f796e7f0cbc81\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-a72efa38475248e7a57f796e7f0cbc81\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-3bfe9cc51f7934b29bf1a09eeb0ce0a8.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"title\": \"Normal Distribution histogram with mean 10 and standard deviation 1\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-c113db7e95a1480fa47c1b0cb7da7fb7\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-c113db7e95a1480fa47c1b0cb7da7fb7\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-c113db7e95a1480fa47c1b0cb7da7fb7\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-fe30c565244867acbe1677769e4cb545.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"title\": \"Normal Distribution histogram with mean -10 and standard deviation 5\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# (1) (doing this from scratch so just this chunk can be run)\n",
+    "import pandas as pd\n",
+    "from scipy import stats\n",
+    "import altair as alt\n",
+    "alt.renderers.enable('default')\n",
+    "\n",
+    "mean_std_list = [(1,7), (10,1), (-10,5)]\n",
+    "\n",
+    "for vals in mean_std_list:\n",
+    "    norm_dist_func = stats.norm(vals[0], vals[1])\n",
+    "    results = norm_dist_func.rvs(1000)\n",
+    "    normal_values = pd.DataFrame({\"value\": results})\n",
+    "    norm_plot = alt.Chart(normal_values, title=\"Normal Distribution histogram with mean \" + str(vals[0]) + \" and standard deviation \" + str(vals[1])).mark_bar().encode(\n",
+    "    alt.X(\"value\", bin=alt.Bin(maxbins=100)),\n",
+    "    y='count()',)\n",
+    "    norm_plot.display()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-6143459c27ea44bfa9784b43c47c5f13\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-6143459c27ea44bfa9784b43c47c5f13\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-6143459c27ea44bfa9784b43c47c5f13\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-b72bcd4fa2daedea1bea96255f357fe2.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"title\": \"Normal Distribution histogram with mean 1 and standard deviation 7\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-26e533c73fd644528e469f3ab66aa7c1\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-26e533c73fd644528e469f3ab66aa7c1\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-26e533c73fd644528e469f3ab66aa7c1\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-61d853e42914629e3ccb99d082941587.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"title\": \"Normal Distribution histogram with mean 10 and standard deviation 1\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-af2ce4bcb0b7440795d67c8bbf9f19dc\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-af2ce4bcb0b7440795d67c8bbf9f19dc\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-af2ce4bcb0b7440795d67c8bbf9f19dc\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-39c057fd57634838b29c4db3d28a0cff.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"title\": \"Normal Distribution histogram with mean -10 and standard deviation 5\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# (2)\n",
+    "def display_norm(mean, std):\n",
+    "    norm_dist_func = stats.norm(mean, std)\n",
+    "    results = norm_dist_func.rvs(1000)\n",
+    "    normal_values = pd.DataFrame({\"value\": results})\n",
+    "    norm_plot = alt.Chart(normal_values, title=\"Normal Distribution histogram with mean \" + str(mean) + \" and standard deviation \" + str(std)).mark_bar().encode(\n",
+    "    alt.X(\"value\", bin=alt.Bin(maxbins=100)),\n",
+    "    y='count()',)\n",
+    "    return(norm_plot)\n",
+    "    \n",
+    "c1 = display_norm(1, 7)\n",
+    "c2 = display_norm(10, 1)\n",
+    "c3 = display_norm(-10, 5)\n",
+    "\n",
+    "c1.display()\n",
+    "c2.display()\n",
+    "c3.display()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-fb5f00c835734ece8eee0b82fa67d6e0\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-fb5f00c835734ece8eee0b82fa67d6e0\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-fb5f00c835734ece8eee0b82fa67d6e0\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"data\": {\"url\": \"altair-temp-data/-479b7050cc41ebb03c0fadc15da731d7.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"grey\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}}, {\"data\": {\"url\": \"altair-temp-data/-1554e0bd61f559ce139152f5791228ed.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"red\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}}, {\"data\": {\"url\": \"altair-temp-data/-d40775a3e6309d0c22db66c356bdb7a7.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"green\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.LayerChart(...)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# (3) Compound Histogram Altair\n",
+    "df = pd.DataFrame({'Norm 1': stats.norm(1,7).rvs(6000),\n",
+    "                   'Norm 2': stats.norm(10,1).rvs(6000),\n",
+    "                   'Norm 3': stats.norm(-10,5).rvs(6000)})\n",
+    "\n",
+    "colors = [\"grey\", \"red\", \"green\"]\n",
+    "\n",
+    "# alt.Chart(df).transform_fold(\n",
+    "#     ['Norm 1', 'Norm 2', 'Norm 3'],\n",
+    "#     as_=['Columns', 'Values']\n",
+    "# ).mark_area(\n",
+    "#     opacity=0.8,\n",
+    "#     interpolate='step'\n",
+    "# ).encode(\n",
+    "#     alt.X('Values:Q', bin=alt.Bin(maxbins=300)),\n",
+    "#     alt.Y('count()', stack=None),\n",
+    "#     alt.Color('Columns:N'))\n",
+    "\n",
+    "def display_norm(distribution, col):\n",
+    "    normal_values = pd.DataFrame({\"value\": distribution})\n",
+    "    norm_plot = alt.Chart(normal_values).mark_bar().encode(\n",
+    "    alt.X(\"value\", bin=alt.Bin(maxbins=100)), color=alt.value(col),\n",
+    "    y='count()',)\n",
+    "    return(norm_plot)\n",
+    "\n",
+    "c = []\n",
+    "for i in range(df.shape[1]):\n",
+    "    c.append(display_norm(df.iloc[:, i], col = colors[i]))\n",
+    "\n",
+    "c[0] + c[1] + c[2]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*For solutions, see `solutions/normal_distributions.py`*"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Probability Density Functions\n",
+    "\n",
+    "The histograms we were looking at above are approximations of what is known as a *Probability Density Function*, or PDF. A PDF shows us the probability of a given value, for a discrete random variable.\n",
+    "\n",
+    "For a continuous random variable, any *exact* value has a probability of 0 (think about why this is the case). We can only give probabilities for ranges. For a lower bound of $a$ and an upper bound of $b$, we can obtain the probability of a random value lying between $a$ and $b$ by integrating the PDF between those values. For example, if our PDF function is $f(x)$, then the probability of random variable $X$ lying between $a$ and $b$ is given by:\n",
+    "\n",
+    "$P[a <= X <= b] = \\int_a^b f(x) dx$\n",
+    "\n",
+    "As an aside, and very informally, you can think of \"the probability of $X=2$\" as being a range *about* 2, say any value between 1.99 and 2.01 (or whatever precision makes sense in the context). Be clear on this definition when you do go to present your findings though, as a formal definition will be needed to replicate your results.\n",
+    "\n",
+    "We can generate the PDF through the `.pdf` method on a `scipy.stats` distribution. Earlier in this notebook we computed this manually through the equation. Now we let a library do the work for us."
+   ]
+  },
+  {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "n = stats.norm(0, 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<scipy.stats._distn_infrastructure.rv_frozen at 0x1fa81dca200>"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -554,9 +1248,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 63,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "plt.plot(x, y);"
    ]
@@ -581,6 +1288,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -596,7 +1304,7 @@
     "\n",
     "This finding is why the normal distribution is so important, and so commonly found in nature.\n",
     "\n",
-    "**Hint:** See this example for how to do independent axes with Altair: https://altair-viz.github.io/gallery/layered_plot_with_dual_axis.html?highlight=resolve_scale\n",
+    "**Hint:** See this example for how to do independent axes with Altair: https://altair-viz.github.io/gallery/layered_plot_with_dual_axis.html?highlight=resolve_scale (link not working)\n",
     "\n",
     "#### Extended exercise\n",
     "\n",
@@ -605,9 +1313,208 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 71,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-1915adb2ff864c86b4c9c10e8f242c65\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-1915adb2ff864c86b4c9c10e8f242c65\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-1915adb2ff864c86b4c9c10e8f242c65\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-f13eaca54854f98b8d65440d40f5888f.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"normal\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-ead00c0420914199a7bec4fae16fd936\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-ead00c0420914199a7bec4fae16fd936\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-ead00c0420914199a7bec4fae16fd936\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-97e2ca6aaf5699d20cdae09b0073a6ba.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"cosine\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "<div id=\"altair-viz-b7c75f24c791443981b94056ea02139a\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-b7c75f24c791443981b94056ea02139a\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-b7c75f24c791443981b94056ea02139a\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
+       "\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
+       "\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
+       "\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
+       "\n",
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-temp-data/-4e2ca29028f7d7794f239be958a9507a.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"uniform\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
+      ],
+      "text/plain": [
+       "alt.Chart(...)"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# (1)\n",
     "\n",
@@ -624,7 +1531,7 @@
     "\n",
     "\n",
     "distributions = dict([(\"normal\", stats.norm(0,1)),\n",
-    "                 (\"poisson\", stats.poisson(1,1)),\n",
+    "                 (\"cosine\", stats.cosine(0, 1)),\n",
     "                 (\"uniform\", stats.uniform(0, 1))\n",
     "                ])\n",
     "\n",
@@ -658,7 +1565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -667,7 +1574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -677,12 +1584,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAcmklEQVR4nO3deXzV9b3n8dcn+0JIWAICSQgIFFFRMERwK3UbtFamiwodbVUqnbbe2/V27GY7zty1tXPbSseht4pYFdFeW6bFi3bQVlEgQTYBkbCEJBAICQkh2zk55zt/JNIYgznASX7n/M77+XjwyPktyXmfNrz98lu+P3POISIi8S/J6wAiIhIdKnQREZ9QoYuI+IQKXUTEJ1ToIiI+keLVG48cOdIVFxd79fYiInFp06ZNx5xz+X1t86zQi4uLKS8v9+rtRUTikplVnm6bDrmIiPiECl1ExCdU6CIiPqFCFxHxCRW6iIhP9FvoZvaYmR01s7dPs93M7OdmVmFm28xsZvRjiohIfyIZoS8D5n3I9puAyd1/FgP/+9xjiYjImer3OnTn3F/MrPhDdpkPLHdd8/CuN7M8MxvjnDscrZAiEv+CoTCtgRCtgU5aOkK0BUIEQmGCoTCdIUcwHCbYGaYz7AiGwgRDjs5Q17JzDgc4B2HnTn2l5zr+ug0gHP7gukid8aTiZ/gG110wmksK8870XfoVjRuLxgFVPZaru9d9oNDNbDFdo3iKioqi8NYi4pVAZ5jDTW3UHG+jurGNuuYO6k8GaGjpoL4lwPHWAMdbgrQEOmnt6CpvPzOLfN9RQzNittAj5pxbCiwFKCkp0ZM1ROJAR2eIXYeb2V17gnePnOTdI81UHD1J7Yn2DwxMs9OSGT4kjeHZ6YzKyWDK6Bxy0lPITEshOy2ZrPSur5lpyWSlpZCabKQlJ5GSnETKqddGSlJSj9eGmWEGSWYYXV8xPrDuvVJN6rW/GdiZNG6cikah1wCFPZYLuteJSBxqDXTyRkU9ZQcaKK88zvbqplOj64zUJCaPymHOxBEUDs9i3LBMCvIyKRiWxaih6WSkJnucPrFFo9BXAfeb2QrgcqBJx89F4suRE+2s3n6Yte8cZcO+BgKhMGnJSVxckMvdVxYzozCPaWOHUjAsi+Qk/49041W/hW5mzwBzgZFmVg38EEgFcM49CqwGbgYqgFbgnoEKKyLR0x4M8cdth3lhcw3r9h7DOZiYn83n5ozn2qmjmDl+mEbccSaSq1wW9rPdAV+JWiIRGVC1Te08uf4AT284yPHWIIXDM/mbj01i/oxxnJ8/xOt4cg48mz5XRAbXocY2frF2D8+VVxNyjhsuGM09V05g9sThCXHCMBGo0EV8rqktyM/+tIffbKjEOcfC0iLuu3oiRSOyvI4mUaZCF/Ep5xwvbK7hH1bvoqElwKdnFvDV6ydTMExF7lcqdBEfOtTYxree28obe+u5tDCPZfeUctG4XK9jyQBToYv4zP/deojvvbCdUNjx95+8iIWzikjSpYYJQYUu4hMdnSF+tGoHz2ys4tLCPH624FLGj8j2OpYMIhW6iA8cbW7nS795i02Vx/ny3PP5+g1TSE3W4w4SjQpdJM7tOnyCe5eV0dgaZMlnZ/Lx6WO8jiQeUaGLxLFNlQ3c83gZWWkpPP+lOVw4Vic+E5kKXSRO/fndOr74ZDljcjN5clGpLkcUFbpIPHp191HuW17O5FE5LF9Uysgh6V5HkhigQheJM+v31fPFJzcxZXQOT983m9zMVK8jSYzQaXCROLKlqpFFy8ooHJ7F8ntLVebyPip0kThxsL6Ve5eVMWJIOk994XJG6DCL9KJCF4kDTW1B7n2ijLBzPHFvKaOHZngdSWKQCl0kxnWGwtz/9FtU1rfw6J2XMWGk7v6UvumkqEiM+4fV7/DanmP8y6enM3viCK/jSAzTCF0khr24/TCPrdvP3VcUc/uswv6/QRKaCl0kRh041sK3n9/GJYV5fPfmC7yOI3FAhS4Sg9qDIb781FskJRlLPjuDtBT9VZX+6Ri6SAz68Zrd7Dx8gl9/vkS39EvE9J99kRjz5t56fv36fu6aPZ7rLhjtdRyJIyp0kRjS3B7kW89tpXhEFt+5earXcSTO6JCLSAz5n3/YxeGmNp77r3PIStNfTzkzGqGLxIjX9xzj2fIqFl9zPpeNH+51HIlDKnSRGNAeDPGD379N8Ygsvnb9ZK/jSJzSv+lEYsAvX93L/mMt/GbR5WSkJnsdR+KURugiHttbd5JHX93L/EvHctXkkV7HkTimQhfxkHOO77/wNhmpSXz/49O8jiNxToUu4qHV22t5c1893543lfwczW8u50aFLuKR9mCIf3xxF1PPy2FhaZHXccQHIip0M5tnZrvNrMLMHuhje5GZvWJmm81sm5ndHP2oIv7y+LoDVB9v4we3TCM5ybyOIz7Qb6GbWTKwBLgJmAYsNLPeB/u+D6x0zs0AFgC/jHZQET+pa+5gySsVXH/BKK6cpBOhEh2RjNBLgQrn3D7nXABYAczvtY8Dhna/zgUORS+iiP/89OXdtAdDmhZXoiqSQh8HVPVYru5e19OPgDvNrBpYDfxNXz/IzBabWbmZldfV1Z1FXJH49+6RZp4tq+KuOeOZmD/E6zjiI9E6KboQWOacKwBuBp40sw/8bOfcUudciXOuJD8/P0pvLRJfHn5pN1lpKfzttbojVKIrkkKvAXo++6qge11Pi4CVAM65N4EMQAcGRXrZWtXImh1H+MLVExiWneZ1HPGZSAq9DJhsZhPMLI2uk56reu1zELgOwMwuoKvQdUxFpJefvLSbYVmpLLpqgtdRxIf6LXTnXCdwP7AG2EXX1Sw7zOwhM7u1e7dvAveZ2VbgGeBu55wbqNAi8WjDvnpe23OML809n5yMVK/jiA9FNDmXc241XSc7e657sMfrncCV0Y0m4h/OOX7y0m5GD03nc3OKvY4jPqU7RUUGwesVxyg7cJz7r52s2RRlwKjQRQbBL9ZWMCY3g9tLCryOIj6mQhcZYBv21bNxfwOLr5lIeopG5zJwVOgiA+yRVyoYOSSNBbM0AZcMLBW6yADaUtXIa3uO8YWrJ5KZptG5DCwVusgAemRtBbmZqdw5e7zXUSQBqNBFBsiuwyf4064j3HvlBIak6/G9MvBU6CID5NE/7yU7LZm7ryj2OookCBW6yACoaWzjD9sOs7C0iNws3RUqg0OFLjIAlq3bD8A9mrNFBpEKXSTKTrQHeWZjFTdfPIZxeZlex5EEokIXibJnN1ZxsqOT+67W6FwGlwpdJIqCoTCPr9vP5ROGM70gz+s4kmBU6CJRtHr7YQ41tXPf1RO9jiIJSIUuEiXOOX712j4m5mdz7dRRXseRBKRCF4mSjfsbeLvmBF+4aiJJSeZ1HElAKnSRKFn+ZiW5mal8csY4r6NIglKhi0RBbVM7/7GjljtmFWoSLvGMCl0kCp7aUEnYOe68XJNwiXdU6CLnqKMzxDMbD3Ld1FEUjcjyOo4kMBW6yDl6cXstx04G9PBn8ZwKXeQcPfHmASaOzOaqSSO9jiIJToUucg62VTey+WAjd80Zr0sVxXMqdJFzsPzNSrLTkvnMZQVeRxFRoYucrYaWAKu2HuJTMwvIydCc5+I9FbrIWVpZXkWgM8zn5uhSRYkNKnSRs+CcY8XGg5ROGM7k0TlexxEBVOgiZ2XD/gYO1LeyYFah11FETlGhi5yFFRsPkpORws0Xj/E6isgpKnSRM9TUGmT127V8csY4MlI1b4vEjogK3czmmdluM6swswdOs8/tZrbTzHaY2dPRjSkSO17YXE2gM8wdOtwiMSalvx3MLBlYAtwAVANlZrbKObezxz6Tge8AVzrnjpuZZvcXX3LOsaKsiukFuVw4NtfrOCLvE8kIvRSocM7tc84FgBXA/F773Acscc4dB3DOHY1uTJHYsK26iXdqmzU6l5gUSaGPA6p6LFd3r+tpCjDFzNaZ2Xozm9fXDzKzxWZWbmbldXV1Z5dYxEMryg6SmZrMrZeM9TqKyAdE66RoCjAZmAssBH5lZnm9d3LOLXXOlTjnSvLz86P01iKDo6Wjk1VbDnHL9DG6M1RiUiSFXgP0/PdlQfe6nqqBVc65oHNuP/AuXQUv4ht/2HaIlkCIBaU63CKxKZJCLwMmm9kEM0sDFgCreu3zO7pG55jZSLoOweyLXkwR760oq2LyqCHMLBrmdRSRPvVb6M65TuB+YA2wC1jpnNthZg+Z2a3du60B6s1sJ/AK8HfOufqBCi0y2HbXNrP5YCN3zCrETNPkSmzq97JFAOfcamB1r3UP9njtgG90/xHxnRVlB0lLTuJTMzVNrsQu3Skq0o/2YIgXNtdw44WjGZ6d5nUckdNSoYv0Y82OWhpbgyyYVeR1FJEPpUIX6cezZVUUDs/kivNHeB1F5EOp0EU+RGV9C2/sreeOkkI9M1Ringpd5EM8W1ZFksFnLtO15xL7VOgip9EZCvPcpmqunTqK83IzvI4j0i8VushprH3nKHXNHdyhk6ESJ1ToIqfxbFkVo3LS+dhHNO+QxAcVukgfapvaeWX3UW4rKSAlWX9NJD7oN1WkD8+VVxF2cHuJToZK/FChi/QSDjueLa/iykkjGD8i2+s4IhFToYv0sm7vMaqPt+lkqMQdFbpILyvKqsjLSuXGaaO9jiJyRlToIj00tAR4aUctn5pRQEZqstdxRM6ICl2kh39/q5pgyOmpRBKXVOgi3ZxzrCirYmZRHlNG53gdR+SMqdBFum2qPE7F0ZOaJlfilgpdpNuKsiqy05L5+PQxXkcROSsqdBHgRHuQP247zK2XjiM7PaInM4rEHBW6CLBqyyHagiEWzNLJUIlfKnQRuibiumDMUKYX5HodReSsqdAl4b1d08T2miYWzCrETE8lkvilQpeE92xZFekpSfznS8d5HUXknKjQJaG1BUL8bksNN188htysVK/jiJwTFboktNXbD9Pc3qlpcsUXVOiS0FaUHWTCyGxmTxzudRSRc6ZCl4S150gzZQeO62So+IYKXRLWirIqUpONT19W4HUUkahQoUtCag+G+O1b1dw47TxGDkn3Oo5IVKjQJSGt2VFLY2uQhaWaiEv8Q4UuCemZjQcpHJ7JFeeP8DqKSNREVOhmNs/MdptZhZk98CH7fdrMnJmVRC+iSHTtqzvJ+n0NLJhVRFKSToaKf/Rb6GaWDCwBbgKmAQvNbFof++UAXwU2RDukSDQ9W1ZFcpJxm06Gis9EMkIvBSqcc/uccwFgBTC/j/3+B/DPQHsU84lEVaAzzPObqrlu6ihGDc3wOo5IVEVS6OOAqh7L1d3rTjGzmUChc+6PH/aDzGyxmZWbWXldXd0ZhxU5Vy/vPEJ9S4CFl+tkqPjPOZ8UNbMk4KfAN/vb1zm31DlX4pwryc/PP9e3FjljK8oOMi4vk2sm6/dP/CeSQq8Bek50UdC97j05wEXAq2Z2AJgNrNKJUYk1VQ2tvLbnGLeXFJKsk6HiQ5EUehkw2cwmmFkasABY9d5G51yTc26kc67YOVcMrAdudc6VD0hikbO0ouwgSQa3z9LJUPGnfgvdOdcJ3A+sAXYBK51zO8zsITO7daADikRDoDPMyvJq5n5kFGNyM72OIzIgInoarnNuNbC617oHT7Pv3HOPJRJdL+2spa65g7tmj/c6isiA0Z2ikhCWv1lJ4fBMrpmik6HiXyp08b3dtc1s3N/AnZeP18lQ8TUVuvjek+sPkJaSxG16KpH4nApdfK25PcgLb9XwieljGZ6d5nUckQGlQhdfe2FzDS2BEHfN0clQ8T8VuviWc47lb1YyvSCXSwvzvI4jMuBU6OJb6/c1UHH0JHfqUkVJECp08a0n1x8gNzOVWy8Z63UUkUGhQhdfqm1qZ82OI9xeUkBGarLXcUQGhQpdfGn5mwcIO8dds4u9jiIyaFTo4jttgRBPbzzIjdNGUzQiy+s4IoNGhS6+89u3qmlsDbLoqoleRxEZVCp08ZVw2PHYuv1cPC6XWcXDvI4jMqhU6OIrf363jn11LSy6agJmmrdFEosKXXzlsXX7GT00nZsvHuN1FJFBp0IX39hd28xre47xuTnFpKXoV1sSj37rxTcee30/GalJfLa0yOsoIp5QoYsvHDnRzguba/j0zAKGaVZFSVAqdPGFX7++n85wmC9ec77XUUQ8o0KXuNfUGuSp9ZXcMn2sbiSShKZCl7i3/M0DtARCfGmuRueS2FToEtfaAiEef+MA104dxQVjhnodR8RTKnSJa8+WHaShJaDRuQgqdIljwVCYX722n1nFw5hVPNzrOCKeU6FL3PrtpmpqGtv48scmeR1FJCao0CUuBTrD/GJtBZcW5jF3Sr7XcURiggpd4tJzm6qoaWzja9dP1iRcIt1U6BJ3OjpDPLK2gplFeXxUo3ORU1ToEndWllVxuKmdr98wRaNzkR5U6BJX2oMhlryyl1nFw7hq0kiv44jElIgK3czmmdluM6swswf62P4NM9tpZtvM7P+Z2fjoRxXpuiu09oRG5yJ96bfQzSwZWALcBEwDFprZtF67bQZKnHPTgeeBf4l2UJHG1gCPrK1g7kfyueJ8jc5FeotkhF4KVDjn9jnnAsAKYH7PHZxzrzjnWrsX1wMF0Y0pAkteqaC5o5MHbprqdRSRmBRJoY8DqnosV3evO51FwIt9bTCzxWZWbmbldXV1kaeUhFfV0MoTb1TymZkFTD1Pc7aI9CWqJ0XN7E6gBPhxX9udc0udcyXOuZL8fF1uJpF7+KXdmME3bpzidRSRmJUSwT41QGGP5YLude9jZtcD3wM+6pzriE48Edha1cjvthziy3PPZ0xuptdxRGJWJCP0MmCymU0wszRgAbCq5w5mNgP4P8Ctzrmj0Y8piSocdjy4agcjh6RrRkWRfvRb6M65TuB+YA2wC1jpnNthZg+Z2a3du/0YGAI8Z2ZbzGzVaX6cyBl5flM1W6sa+e7NU8nJSPU6jkhMi+SQC8651cDqXuse7PH6+ijnEqGpNcg//cc7lIwfxidnfNh5eBEB3SkqMeynL++msTXAf59/oW4iEomACl1i0paqRp5cX8mds8dz4dhcr+OIxAUVusScQGeYbz+/ldFDM/i7//QRr+OIxI2IjqGLDKYlr1Tw7pGTPHZ3iU6EipwBjdAlprxTe4JfvlrB/EvHcu3U0V7HEYkrKnSJGYHOMN96bis5Gan88BMXeh1HJO7okIvEjIdf3s3bNSd49M7LGJ6d5nUckbijEbrEhDcqjrH0L/tYWFrEvIvO8zqOSFxSoYvnjrcE+PrKLUwYmc0PbrnA6zgicUuFLp4KhR1fX7mFhpYAP18wg6w0HQUUOVsqdPHUv/7pXV7dXccPP3EhF43TDUQi50KFLp5Zs6OWX6yt4I6SQv7L5UVexxGJeyp08cQ7tSf45sqtXFKQq7laRKJEhS6D7lBjG3c/VkZ2ejKP3nUZGanJXkcS8QUVugyqprYgdz++kZaOTpbdU6onEIlEkS4pkEHTFgixeHk5+4+18MQ9pVwwRg97FokmjdBlULQGOrl3WRllBxr4yW2XcMWkkV5HEvEdFboMuNZAJ4uWlbNhfz0P334J8y/V04dEBoIOuciAamwNcN/ycjZVHufh2y/hkzMKvI4k4lsqdBkwVQ2tfP7xjVQ3tPHzhTO4ZfpYryOJ+JoKXQbEWwePs3h5OcGQ4zdfuJzSCcO9jiTieyp0iSrnHMveOMDf/3EXY/IyWHH3LCaNyvE6lkhCUKFL1DS1Bvnu77bzx22Huf6CUTx826XkZukRciKDRYUuUfGnnUf47gvbqW8J8N/mTeWL10wkKUm384sMJhW6nJPapnb+8cVd/H7LIaael8OvPz+Liws0a6KIF1ToclbaAiGW/mUfj/55LyHn+Op1k/nKxyaRlqJbG0S8okKXM9LcHuTJ9ZU89vp+jp0M8PGLx/DATVMpHJ7ldTSRhKdCl4hU1rfwzMYqnt5QyYn2Tq6Zks/fXjuJkmJdjigSK1ToclrN7UHWvnOU58qreb3iGEkGN047jy9/7HymF+R5HU9EelGhy/tUNbSyruIYa3bUsq6inkAozLi8TL5xwxRuLynkvNwMryOKyGmo0BNYR2eIPUdOsuNQE2UHjvPm3npqGtsAGJeXyefmjOemi89jRuEwXYIoEgciKnQzmwf8DEgG/s0590+9tqcDy4HLgHrgDufcgehGlbMRDjvqWwIcbGihsr6VyvpWDja0sru2mT1HmwmGHAB5WanMnjCC+66ewJzzRzJl9BA9Fk4kzvRb6GaWDCwBbgCqgTIzW+Wc29ljt0XAcefcJDNbAPwzcMdABPY75xzBkKMzHCbY6QiEwgR7/Al0OtqCIU52dHKyvZOTHUFOdoROvT7eGqSuuYNjJzuoa+6gviVAKOxO/XwzGJubycT8bK6ZMpGLxg3lwrG5jB+epVG4SJyLZIReClQ45/YBmNkKYD7Qs9DnAz/qfv088IiZmXPOEWUry6pY+to+3vvRp97A/fV1723vpXDda04t90gX8fe87/s+uO10P6uvvD2Xw851l/bZ/0+WkZpEXmYa+TnpjMpJ58KxQ8nPSSd/SDpFI7IoGp5N4fBM0lP0DE8RP4qk0McBVT2Wq4HLT7ePc67TzJqAEcCxnjuZ2WJgMUBRUdFZBR6WncZHRndP9mTv+4KZ9Xj9wW09l//6vdbHvn/d9r7lHt8c8ff0ev/T7WtAWkoSqclJ3V+N1OTu5eQkUlO6llOSkshMS2ZIekrXn4wUhqSlkJ2eTEqybuoRSWSDelLUObcUWApQUlJyVkPRG6aN5oZpo6OaS0TEDyIZ0tUAhT2WC7rX9bmPmaUAuXSdHBURkUESSaGXAZPNbIKZpQELgFW99lkFfL779WeAtQNx/FxERE6v30Mu3cfE7wfW0HXZ4mPOuR1m9hBQ7pxbBfwaeNLMKoAGukpfREQGUUTH0J1zq4HVvdY92ON1O3BbdKOJiMiZ0GURIiI+oUIXEfEJFbqIiE+o0EVEfMK8urrQzOqASk/e/NyMpNcdsAkg0T5zon1e0GeOJ+Odc/l9bfCs0OOVmZU750q8zjGYEu0zJ9rnBX1mv9AhFxERn1Chi4j4hAr9zC31OoAHEu0zJ9rnBX1mX9AxdBERn9AIXUTEJ1ToIiI+oUI/B2b2TTNzZjbS6ywDycx+bGbvmNk2M3vBzPK8zjRQzGyeme02swoze8DrPAPNzArN7BUz22lmO8zsq15nGixmlmxmm83sD15niRYV+lkys0LgRuCg11kGwcvARc656cC7wHc8zjMgejwQ/SZgGrDQzKZ5m2rAdQLfdM5NA2YDX0mAz/yerwK7vA4RTSr0s/e/gG/T47nPfuWce8k519m9uJ6up1b50akHojvnAsB7D0T3LefcYefcW92vm+kquHHephp4ZlYAfBz4N6+zRJMK/SyY2Xygxjm31essHrgXeNHrEAOkrwei+77c3mNmxcAMYIPHUQbDv9I1IAt7nCOqBvUh0fHEzP4EnNfHpu8B36XrcItvfNjndc79vnuf79H1T/SnBjObDDwzGwL8Fviac+6E13kGkpndAhx1zm0ys7kex4kqFfppOOeu72u9mV0MTAC2mhl0HX54y8xKnXO1gxgxqk73ed9jZncDtwDX+fh5sZE8EN13zCyVrjJ/yjn3717nGQRXArea2c1ABjDUzH7jnLvT41znTDcWnSMzOwCUOOficda2iJjZPOCnwEedc3Ve5xkoZpZC10nf6+gq8jLgs865HZ4GG0DWNSp5Amhwzn3N4ziDrnuE/i3n3C0eR4kKHUOXSDwC5AAvm9kWM3vU60ADofvE73sPRN8FrPRzmXe7ErgLuLb7/9st3SNXiUMaoYuI+IRG6CIiPqFCFxHxCRW6iIhPqNBFRHxChS4i4hMqdBERn1Chi4j4xP8HFUPeb68yaHUAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -709,7 +1616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
@@ -721,7 +1628,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -779,9 +1686,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 88,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
@@ -791,7 +1722,7 @@
     },
     {
      "data": {
-      "image/png": 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bZWYvmlmMkilJNDRAVZUX6rEQ7Nq5IvMoAH/asDc27ysiUTVsoJtZGvAQcC0wC7jVzGYdd9gKoNY5Nxf4FfC9SBea1GI1wiUkuIBGxe5GZpQV8OJ69aOLJINwWujzgQbn3BbnXDfwNHDjwAOccy8559qDu28BlZEtM4k5F7sx6CGh99q0iatmjmNZ40H2H9WydCKJLpxArwB2DNhvCj43lI8Dvx/sBTO7w8zqzKyuubk5/CqT2YED0NIS2xZ6YSGUlEBDA9fNLaffwe/XaBUjkUQXTqAPtnzOoMMizOyvgVrggcFed8494pyrdc7VlpSUhF9lMov1kMWQmhrYtIkZZQVMLcnj+Xd2xfb9RSTiwgn0JmDCgP1K4ISffjO7CvgacINzTn+/h8uvQK+uhoYGzIy/OHM8SxsPsre1M7Y1iEhEhRPoy4AaM5tsZpnAQmDRwAPM7CzgYbww1xW2U7F5s7eG6OTJsX3fmhrYuRPa27l+7nicg/9atTu2NYhIRA0b6M65XuBO4AVgPfCMc26tmd1nZjcED3sAyAd+aWYrzWzREKeT4zU0wIQJ3tS5sRT6i2DzZqpL85lRVsDvVqnbRSSRhTXw2Tm3GFh83HNfH/D4qgjXlTpiPWQxJPSeDQ1wxhncdFYF9/9+Aw37jlJdmh/7ekTktOlOUb+F5kGPtYGBDnzw7ArSA8YzdTtO8kUiEs8U6H46eBD274fp02P/3kVFUFwMmzYBUFqQzZUzS/n18ia6e7U0nUgiUqD7qb7e206b5s/719S8O8oGWDh/IgfaunlxvaYCEElECnQ/hQLdjxY6eN0uwRY6wCU1JZQXZvPUMnW7iCQiBbqfNm70JuSK9ZDFkOpqaGryFtcA0gLGwnMn8mp9Mw37jvpTk4iMmALdT/X1MGUKZGT48/7BSbrYsuXYU399/kQy0wM89vpWf2oSkRFToPtp40b/+s/h3ZEuA7pdxuZn8aGzK/j18iYOtnX7VJiIjIQC3S/9/V6Q+tV/DoMGOsDHLpxMV28/T7y1zYeiRGSkFOh+2bEDOjv9baGPHu0NXQxdnA2qGVfA5dNLePyNRtq6en0qTkROlQLdL36PcAmZORM2bDjh6buurOFgWzc/fbMx5iWJyMgo0P2ycaO39bOFDjBjBqxff8LTZ00czWXTS3jk1S0cVStdJCEo0P1SXw8FBVBW5m8dM2d6i2zs33/CS3dfNY3D7T389I3G2NclIqdMge6X+nqvdW6DrR8SQzNmeNtBWunzJhRx5YxS/t/Lm7VEnUgCUKD7xe8hiyEzZ3rbQfrRAb7ygZm09/Tx/T/WD/q6iMQPBbofOjth2zb/L4gCTJwIOTmDttABqkvz+ZvzJ/HU0u1s2NMa4+JE5FQo0P1QXw/OxUegBwJeHUO00AHuvqqGguwMvvX8OpwbdDlZEYkDCnQ/rF3rbWfP9reOkCFGuoQU5Wbyhaun8XrDAZ5bsTOGhYnIqVCg+2HdOkhLi48+dPD60bdtg/b2IQ/5q/Mmcc6k0dz3/DpdIBWJUwp0P6xd6912H+t1RIcyY4bXBVQ/9IXPQMD43x86g/auPr75u3UxLE5EwqVA98PatfHT3QLDjnQJqS4t4M4rqvndO7tY9I4WlBaJNwr0WOvq8lYJiqdAr6nxLo6epB895B8um8rZE4v42rOr2X5g6C4aEYk9BXqsbdzozbQ4a5bflbwrO9tbZCOMQM9IC/DDhWeBwV1Pr6CnT+uPisQLBXqsxdsIl5DZs2HNmrAOnTAml/s/OJeVOw5z/+9P3k0jIrGjQI+1eBvhEjJ3rndRtLMzrMOvm1vO7RdU8ehrW/llndYgFYkHCvRYi7cRLiFz50JfX1jdLiH/dN1MLqwey9eeW8PybQejWJyIhEOBHmvxNsIlZO5cb7tqVdhfkp4W4KGPns34omzu+Nlytu5vi1JxIhIOBXostbd7I1zmzPG7khNVV3sXR08h0MG7i/Sx288F4K9/soTdLR3RqE5EwqBAj6U1a7wRLmed5XclJ0pL8/5yOMVAB5hSks9PPzaf1o4e/ubRpVpcWsQnCvRYWrHC286b528dQ5k7d0SBDjCnopCf3FbLjoPtfPTHb2l6ABEfKNBjaeVKKCyESZP8rmRwc+fCvn2wd++Ivvy8KWN59LZz2XagnY88/CZ7WsIbMSMikRFWoJvZAjPbaGYNZnbPIK9fYmZvm1mvmX048mUmiZUrvda536sUDSV0YXT16hGf4qKaYn728fnsa+3iloff0IVSkRgaNtDNLA14CLgWmAXcambH3+a4HbgdeDLSBSaNvj6vOyNeu1sAzjjD277zzmmd5tyqMTz5ifNo6+rj5n9/nbe2HIhAcSIynHBa6POBBufcFudcN/A0cOPAA5xzjc65VYDuAx9KQ4M3yiWeA72kBCoq3u3rPw1zK4v4zacvZGxeJn/z6BLdfCQSA+EEegUw8KexKfjcKTOzO8yszszqmpubR3KKxLVypbeN50AHqK2FurqInGri2Fye/fSFnDd5LF/81Sr+6Ter6ezpi8i5ReRE4QT6YB2+I1qHzDn3iHOu1jlXW1JSMpJTJK6VKyEjI74m5RpMba03gVhLS0ROV5iTwX/83bl88pIpPPHWdj7472/QqH51kagIJ9CbgAkD9isBTYZ9qurqvHHemZl+V3Jy53o3CfH22xE7ZUZagK98YCaP3lbLzsMdXP9/X+OXdTu0PqlIhIUT6MuAGjObbGaZwEJgUXTLSjL9/bB0KZx3nt+VDK+21tsuWxbxU185cxyLP3sxs8pH8cVfreLvHl+mO0tFImjYQHfO9QJ3Ai8A64FnnHNrzew+M7sBwMzONbMm4BbgYTNbG82iE86GDdDaCuef73clwxs71psbPUL96MerKMrh6TvO5xt/MYslWw5y9YOv8uSS7fT1q7UucrrSwznIObcYWHzcc18f8HgZXleMDGbJEm+bCC108Lpdli6N2ukDAeP2Cydz+YxSvvzrVXz1udU8uXQb37xhNudMGhO19xVJdrpTNBaWLPHuEJ0+3e9KwnPuudDYCFEeiTRpbB5PfeJ8frhwHvuPdPOhH73J3U+vYOdhdcOIjIQCPRaWLPFCMpAg/7lDF0aj2EoPMTNunFfBn75wKXdeXs3iNXu4/IGXufe3a9jXqqkDRE5FgiRMAmtv926lT5TuFoD5870hln/+c8zeMjcznS9cM52Xv3AZHzqngieWbOeSB17iO4vXs++Igl0kHAr0aFu2zLvtPxEuiIbk5HijXWIY6CHji3L47gfn8uLnLuUDc8r58Z+3cNH9L/HlX62iYd+RmNcjkkgU6NH2yiveZFwXXuh3Jafmkku8X0Yd/vRnVxXn8eBfzuNPn7+Mj5xbyW9W7uSqB1/l448v45X6Zvo1KkbkBAr0aHv5Ze92/9Gj/a7k1Fx8MfT0vDtCxyeTi/P49k1n8MY9V3D3VTWs3HGY2x5byqX/8hIPvdSg7hiRARTo0dTZCW++CZdd5nclp+7CC72/LHzodhnM2Pws7r5qGm985Qr+9dazqCzK5YEXNnLBd//EHT+r4/erd2ueGEl5YY1DlxFautQL9UQM9KIib370V1/1u5L3yEpP44Yzx3PDmePZ0nyUp5Zu57kVu/jDur0UZKVzzZwybpw3nvdNGUt6mtorkloU6NH08steK/fii/2uZGQuuwweftjrR8/J8buaE0wpyedr183iywtm8OaWAyxauYv/XrOHXy1vYkxeJlfMKOWqmeO4ZFoxuZn6X12Sn/k1QVJtba2ri9Lt5XHjkkvg6NGITnQVU//933Dttd72mmv8riYsnT19vLxxH79fs4eXNuyjtbOXrPQAF1UXc9WscVxcU0zl6Fy/yxQZMTNb7pyrHew1NVui5dAheOMNuOeEFfsSx6WXQnZ2QgV6dkYaC+aUs2BOOT19/SzbepA/rNvLH9ft5cUN+wDvQutF1cVcWF3M+6aOpTAnw+eqRSJDLfRo+cUvYOFCeP11uOACv6sZuQULvGkANmzwu5LT4pyjfu9RXmvYz2ubmlmy9SDt3X0EzFtdaf7kMdROGs05k0YzNj/L73JFhnSyFroCPVr+9m/hv/4L9u2DtDS/qxm5H/wA/vEfYcsWbxbGJNHd28+K7Yd4vWE/r28+wOqmFrr7vBUUp5TkUTtpNLVVYzh74mimFOcRCMTpwt6SchTosdbXB+Xl8P73w3/+p9/VnJ76em9SsR/8AD77Wb+riZrOnj5W72xhWeNBljceom7bIVo6egDIy0xj9vhCzqgs5IwKbzt5rEJe/KE+9Fj785+9mQpvvHH4Y+PdtGlwxhnwy18mdaBnZ6RxbtUYzq3ypu/t73dsbj7Kyh2HWb2zhdU7W3jirW109Xqt+PysdGaVj2J6WQHTygqYUVbAtNICCnPVHy/+UaBHw9NPQ24uXHed35VExkc+Av/8z9DUBJWpMe19IGDUjCugZlwBt9R6KzD29vWzad9RL+CbWli7q4XfrNjJka7eY19XNiqbaWUFTB+Xz7RxBUwpyWdKcR6j8+J86UFJCupyibSeHhg/Hq66Cp56yu9qImPjRpgxA77/fbj7br+riSvOOXa3dLJxzxE27j1CfXC7ad9RuoOtefAWy55cnMeU4jwmF+dRFdxOLs4jL0vtKgmf+tBjKTR2+7nn4Kab/K4mcubNg6ws3+d2SRR9/Y7tB9vZuv8oW5rb2Lq/jcYDbWxtbmNXy3vnnynOz6RidC6Vo3OC/7zHE0bnUFGUS05mAl9Ul4hTH3os/eQn3rqcCxb4XUlk3X67N9rlnXfgzDP9ribupQXsWAv8ihnvfa2ju49tB71w37K/jaZDHTQdamf9rlb+uHbvsdE2IaHAryjKZtyobMpGZVNW+N7H2RkKfVELPbJ27YKJE+Fzn4Pvfc/vaiLr4EGvK+nv/x7+7d/8riZp9fc79h/tYkcw5L2w9x7vPNzB3pZO2rpPnISsMCeDslHZlI7KOhbypQVZjM3Pojg/i+L8TMbmZzEqOx0zjc5JZGqhx8pjj3lDFu+4w+9KIm/MGLjlFvj5z+H++yE/3++KklIgYJSOyqZ0VDbnTBp8yuUjnT3sbe1kT0uXt23tDO572/q9R2g+0sVgU8ZnpgcozssMBn0mxflZxx6XFGQxJi+T0bmZFOVmMDo3k9zMNP0CSCBqoUdKR4d34828eV4/ejJautRbSu9f/gU+/3m/q5GT6O3r52B7NweOdrP/aNexbfOAxwOf7+kbPAcy0wIU5mYwOjeDotxMb5uTSVGeF/ih54tyMhidl8mo7AwKstP1iyCK1EKPhZ/8BPbuha99ze9Komf+fLjySi/QP/3puJyBUTzpaQFKC7IpLcge9ljnHK2dvew/2sXBtm4OtXVzuL2HQ+3dHGrvoaWjm0Nt3n7j/nYOtR/mcHvPCX39A6UFjILs9GMBf2ybc+L+qGP7GYzKSSc/K528rHSy0gP6pXCK1EKPhLY27wac6mpvyblk9vLLcPnl8OCD3kVSSUnOOdq7+zjc0fOeXwBHOns50tlDa2cPRzp7ae0Ibo/bHzh2fyjpASM3M438rHRygyGfl5lGXpYX+qHX8gY8zs1KJz8rjbzM4PHBr8nNSic7PZAUc+SrhR5t99/vXRB95hm/K4m+Sy+Fq6+Gb34T/uqvoLTU74rEB2Z2LDArik79L7W+fsfRrmD4d4R+CXjbI529tHX30tbVS1tXn7ft7uVoVx/tXb0cbGunrbuX9q4+jnb1Hrt7NxyZaQGyMwLkZKaRm5lOdkYaOcH9nIw0cjLTvf2MNLKDz+UGt9kZacGvCz4O7odey05PIysj4OtfFgr007VxIzzwAHz0o4m3EPRImMEPf+hNB/ClL8Hjj/tdkSSgtIBRmJPhTV18msvt9vT1094dDP6uXtqCj4929dIe/EXQ2d1He3cfHT19dPb00RF83N4d3O/p41BbD5097x7X0dP3npvDTkVWeoDsjLRj2+yM9+7ffkEVV84cd3offBAK9NPR3e0FeV6eF+qpYsYML8y/8x1veoNbbvG7IklhGWkBCnMCUZnXvq/feeE+IPhDvwwG/lLo6u2js6efzp4+unr66Or1Hnf29L/7Wm8fXT39HO3qpeck1x9Oh/TCdAUAAAX8SURBVAJ9pJyDO+/0ViN69llvjHYq+cY34MUX4ROfgNmzYdYsvysSibi0gJEf7LNPBIl/hcAPzsG998KPfwxf/SrcfLPfFcVeRoY3CVlOjten3tjod0UiKU+Bfqq6u+Ezn4FvfQs+9jH49rf9rsg/VVXwwgveKJ/zz/fGqYuIbxTop2LpUm8s9o9+BF/8otdCT/VxsnPnesvs5eR4F4XvvRfa2/2uSiQlhRXoZrbAzDaaWYOZnbDqsZllmdkvgq8vMbOqSBfqm+5uWLQIrr/eu0ty92747W+9uVoC+n0IeP3ndXXeGqr33ee13L/1Ldi82e/KRFLKsDcWmVkaUA+8H2gClgG3OufWDTjm08Bc59ynzGwhcLNz7i9Pdt64urGoqwtaWuDwYe9uz82boaHBa5G/8YbXpVBSAnfd5a3aU1Dgd8Xx67XXvDD/wx+8/enTvUWy586FKVO8sB8zBgoLvflgUv0vHJFTdFrzoZvZ+4BvOOeuCe5/BcA5990Bx7wQPOZNM0sH9gAl7iQnH3GgP/aYd+t5f793cXKo7cleG7jt7obOzhPfJxCAOXPgkku8qXCvvtq7ECjh2b7dW7bulVe8X4oHDpx4TCDgreyUnu79tw1tMzK818480zuHiBxzuneKVgA7Buw3AecNdYxzrtfMWoCxwP7jCrkDuANg4sSJYRV/grFjvaA1837oh9sOd0xmptdaDP0rLvZu4Z80SQF+OiZO9Cbw+vznvV+eBw7Ali1e0B8+/O6/jg5vlaeeHujtffdxfz9Mner3pxBJKOEE+mB/Ex/f8g7nGJxzjwCPgNdCD+O9T3Tjjcmx+HIqMfN+URYXexeVRSQqwrmq1wRMGLBfCewa6phgl0shcDASBYqISHjCCfRlQI2ZTTazTGAhsOi4YxYBtwUffxj408n6z0VEJPKG7XIJ9onfCbwApAGPOefWmtl9QJ1zbhHwKPBzM2vAa5kvjGbRIiJyorAmKHDOLQYWH/fc1wc87gQ0Q5OIiI90Z4yISJJQoIuIJAkFuohIklCgi4gkCd8WiTazZmDbCL+8mOPuQk0B+sypQZ85NZzOZ57knCsZ7AXfAv10mFndUHMZJCt95tSgz5waovWZ1eUiIpIkFOgiIkkiUQP9Eb8L8IE+c2rQZ04NUfnMCdmHLiIiJ0rUFrqIiBxHgS4ikiQSNtDN7BtmttPMVgb/fcDvmqJluEW6k5GZNZrZ6uD3Nk4Wn40sM3vMzPaZ2ZoBz40xsz+a2abgdrSfNUbaEJ85aX+WzWyCmb1kZuvNbK2ZfTb4fFS+zwkb6EHfd87NC/5bPPzhiSe4SPdDwLXALOBWM5vlb1Uxc3nwe5usY5QfBxYc99w9wIvOuRrgxeB+MnmcEz8zJO/Pci/weefcTOB84DPBn9+ofJ8TPdBTwXygwTm3xTnXDTwNaA2+JOCce5UTV/a6Efhp8PFPgZtiWlSUDfGZk5Zzbrdz7u3g4yPAerw1mKPyfU70QL/TzFYF/4xLqj9NBxhske4Kn2qJJQf8wcyWBxcXTxXjnHO7wQsDoNTnemIl6X+WzawKOAtYQpS+z3Ed6Gb2P2a2ZpB/NwI/AqYC84DdwP/xtdjoCWsB7iR0oXPubLyups+Y2SV+FyRRk/Q/y2aWD/wauNs51xqt9wlrxSK/OOeuCuc4M/sx8HyUy/FLOIt0Jx3n3K7gdp+ZPYfX9fSqv1XFxF4zK3fO7TazcmCf3wVFm3Nub+hxMv4sm1kGXpj/p3Pu2eDTUfk+x3UL/WSC/xFCbgbWDHVsggtnke6kYmZ5ZlYQegxcTfJ+f483cMH124Df+lhLTCTzz7KZGd6ay+udcw8OeCkq3+eEvVPUzH6O9yeaAxqBT4b6pJJNcBjXD3h3ke7/5XNJUWVmU4DngrvpwJPJ+JnN7CngMrypVPcC9wK/AZ4BJgLbgVucc0lzEXGIz3wZSfqzbGYXAX8GVgP9wae/itePHvHvc8IGuoiIvFfCdrmIiMh7KdBFRJKEAl1EJEko0EVEkoQCXUQkSSjQRUSShAJdRCRJ/H/m5d0earlXDwAAAABJRU5ErkJggg==\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -809,6 +1740,7 @@
     "y_1 = np.array([distribution.cdf(1) - distribution.cdf(0)\n",
     "               for distribution in distributions_1])\n",
     "plt.plot(x_values_1, y_1, \"r\")\n",
+    "plt.show()\n",
     "\n",
     "# Exercise 2\n",
     "x_values_2 = np.linspace(0.1, 20, 1000)\n",
@@ -816,7 +1748,15 @@
     "y_2 = np.array([distribution.cdf(1) - distribution.cdf(0)\n",
     "               for distribution in distributions_2])\n",
     "plt.plot(x_values_2, y_2)\n",
-    "print(\"The area between 0 and 1 decreases as the standard deviation increases are values will spread out and hence have less between 0 and 1.\")"
+    "plt.show()\n",
+    "print(\"The area between 0 and 1 decreases as the standard deviation increases are values will spread out and hence have less between 0 and 1.\")\n",
+    "\n",
+    "# Extended exercise\n",
+    "n_samples = 100\n",
+    "outcome = np.sort([random.randint(1, 6) + random.randint(1, 6) for _ in range(n_samples)])\n",
+    "cdf = np.cumsum(outcome)/np.sum(outcome)\n",
+    "plt.bar(outcome, cdf)\n",
+    "plt.show()\n"
    ]
   },
   {
@@ -829,7 +1769,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -843,7 +1783,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.1.3 - Moments and Z-Scores.ipynb b/notebooks/Module 1.1.3 - Moments and Z-Scores.ipynb
index bd6f06b..05b1bfe 100644
--- a/notebooks/Module 1.1.3 - Moments and Z-Scores.ipynb	
+++ b/notebooks/Module 1.1.3 - Moments and Z-Scores.ipynb	
@@ -28,7 +28,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -46,11 +46,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<scipy.stats._continuous_distns.norm_gen at 0x10fbe57fd60>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "stats.norm?"
+    "stats.norm"
    ]
   },
   {
@@ -67,65 +78,70 @@
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"layer\": [{\"data\": {\"url\": \"altair-data-9a57b8270dd7c5d23697562f1ec379e6.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"red\"}, \"x\": {\"type\": \"quantitative\", \"bin\": {\"maxbins\": 100}, \"field\": \"value\"}, \"y\": {\"type\": \"quantitative\", \"aggregate\": \"count\"}}}, {\"data\": {\"url\": \"altair-data-1bbc6c52161ece64f5df04a499f7ae82.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"blue\"}, \"x\": {\"type\": \"quantitative\", \"bin\": {\"maxbins\": 100}, \"field\": \"value\"}, \"y\": {\"type\": \"quantitative\", \"aggregate\": \"count\"}}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\"};\n",
-       "var opt = {};\n",
-       "var type = \"vega-lite\";\n",
-       "var id = \"65f48e9a-5712-4029-aef5-51abc020cfe8\";\n",
+      "text/html": [
        "\n",
-       "var output_area = this;\n",
+       "<div id=\"altair-viz-f6871a0fdb304007b07346586e6424f6\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-f6871a0fdb304007b07346586e6424f6\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-f6871a0fdb304007b07346586e6424f6\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
        "\n",
-       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
-       "  var target = document.createElement(\"div\");\n",
-       "  target.id = id;\n",
-       "  target.className = \"vega-embed\";\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
        "\n",
-       "  var style = document.createElement(\"style\");\n",
-       "  style.textContent = [\n",
-       "    \".vega-embed .error p {\",\n",
-       "    \"  color: firebrick;\",\n",
-       "    \"  font-size: 14px;\",\n",
-       "    \"}\",\n",
-       "  ].join(\"\\\\n\");\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
        "\n",
-       "  // element is a jQuery wrapped DOM element inside the output area\n",
-       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
-       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
-       "  element[0].appendChild(target);\n",
-       "  element[0].appendChild(style);\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
        "\n",
-       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
-       "}, function (err) {\n",
-       "  if (err.requireType !== \"scripterror\") {\n",
-       "    throw(err);\n",
-       "  }\n",
-       "});\n"
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"data\": {\"url\": \"altair-data-af3edf80d361e13a35223bf1cfe6e480.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"red\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}}, {\"data\": {\"url\": \"altair-data-820733db60fa440b427479be379c68a1.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"blue\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
       "text/plain": [
-       "<vega.vegalite.VegaLite at 0x7f1c960d6588>"
+       "alt.LayerChart(...)"
       ]
      },
-     "metadata": {
-      "jupyter-vega": "#65f48e9a-5712-4029-aef5-51abc020cfe8"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "jupyter-vega": "#65f48e9a-5712-4029-aef5-51abc020cfe8"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -154,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,7 +190,7 @@
     {
      "data": {
       "text/plain": [
-       "57.01233080225524"
+       "56.986620720479934"
       ]
      },
      "execution_count": 7,
@@ -188,15 +204,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The actual mean was 57, while the computed mean was 57.012\n",
-      "This gives an error of 0.012\n"
+      "The actual mean was 57, while the computed mean was 56.987\n",
+      "This gives an error of -0.013\n"
      ]
     }
    ],
@@ -216,16 +232,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "56.97738700021971"
+       "56.97910350110642"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -247,16 +263,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "57.263528793928465"
+       "9.938938143766496"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -286,70 +302,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "application/javascript": [
-       "var spec = {\"config\": {\"view\": {\"width\": 400, \"height\": 300}}, \"layer\": [{\"data\": {\"url\": \"altair-data-e75ffcab4dfd2209e43a0c3330a9a744.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"green\"}, \"x\": {\"type\": \"quantitative\", \"bin\": {\"maxbins\": 100}, \"field\": \"value\"}, \"y\": {\"type\": \"quantitative\", \"aggregate\": \"count\"}}}, {\"data\": {\"url\": \"altair-data-da752fe5e60b3b58726b5d9502702ef2.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"orange\"}, \"x\": {\"type\": \"quantitative\", \"bin\": {\"maxbins\": 100}, \"field\": \"value\"}, \"y\": {\"type\": \"quantitative\", \"aggregate\": \"count\"}}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.6.0.json\"};\n",
-       "var opt = {};\n",
-       "var type = \"vega-lite\";\n",
-       "var id = \"4fde3383-90ff-41d9-be50-7cde1e576727\";\n",
+      "text/html": [
        "\n",
-       "var output_area = this;\n",
+       "<div id=\"altair-viz-eef943e0d0764abb9951be13399eca2f\"></div>\n",
+       "<script type=\"text/javascript\">\n",
+       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
+       "  (function(spec, embedOpt){\n",
+       "    let outputDiv = document.currentScript.previousElementSibling;\n",
+       "    if (outputDiv.id !== \"altair-viz-eef943e0d0764abb9951be13399eca2f\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-eef943e0d0764abb9951be13399eca2f\");\n",
+       "    }\n",
+       "    const paths = {\n",
+       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
+       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
+       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
+       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
+       "    };\n",
        "\n",
-       "require([\"nbextensions/jupyter-vega/index\"], function(vega) {\n",
-       "  var target = document.createElement(\"div\");\n",
-       "  target.id = id;\n",
-       "  target.className = \"vega-embed\";\n",
+       "    function maybeLoadScript(lib, version) {\n",
+       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
+       "      return (VEGA_DEBUG[key] == version) ?\n",
+       "        Promise.resolve(paths[lib]) :\n",
+       "        new Promise(function(resolve, reject) {\n",
+       "          var s = document.createElement('script');\n",
+       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
+       "          s.async = true;\n",
+       "          s.onload = () => {\n",
+       "            VEGA_DEBUG[key] = version;\n",
+       "            return resolve(paths[lib]);\n",
+       "          };\n",
+       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
+       "          s.src = paths[lib];\n",
+       "        });\n",
+       "    }\n",
        "\n",
-       "  var style = document.createElement(\"style\");\n",
-       "  style.textContent = [\n",
-       "    \".vega-embed .error p {\",\n",
-       "    \"  color: firebrick;\",\n",
-       "    \"  font-size: 14px;\",\n",
-       "    \"}\",\n",
-       "  ].join(\"\\\\n\");\n",
+       "    function showError(err) {\n",
+       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
+       "      throw err;\n",
+       "    }\n",
        "\n",
-       "  // element is a jQuery wrapped DOM element inside the output area\n",
-       "  // see http://ipython.readthedocs.io/en/stable/api/generated/\\\n",
-       "  // IPython.display.html#IPython.display.Javascript.__init__\n",
-       "  element[0].appendChild(target);\n",
-       "  element[0].appendChild(style);\n",
+       "    function displayChart(vegaEmbed) {\n",
+       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
+       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
+       "    }\n",
        "\n",
-       "  vega.render(\"#\" + id, spec, type, opt, output_area);\n",
-       "}, function (err) {\n",
-       "  if (err.requireType !== \"scripterror\") {\n",
-       "    throw(err);\n",
-       "  }\n",
-       "});\n"
+       "    if(typeof define === \"function\" && define.amd) {\n",
+       "      requirejs.config({paths});\n",
+       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
+       "    } else {\n",
+       "      maybeLoadScript(\"vega\", \"5\")\n",
+       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
+       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
+       "        .catch(showError)\n",
+       "        .then(() => displayChart(vegaEmbed));\n",
+       "    }\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"data\": {\"url\": \"altair-data-62d3b50fd04f5c264262b68b797baba6.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"green\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}}, {\"data\": {\"url\": \"altair-data-6ca143425559551b6ead5d75459b039f.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"orange\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}}], \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "</script>"
       ],
       "text/plain": [
-       "<vega.vegalite.VegaLite at 0x7f1c91175048>"
+       "alt.LayerChart(...)"
       ]
      },
-     "metadata": {
-      "jupyter-vega": "#4fde3383-90ff-41d9-be50-7cde1e576727"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/plain": []
-     },
-     "execution_count": 9,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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"
-     },
-     "metadata": {
-      "jupyter-vega": "#4fde3383-90ff-41d9-be50-7cde1e576727"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
@@ -381,16 +402,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<scipy.stats._distn_infrastructure.rv_frozen at 0x29677ba7e08>"
+       "<scipy.stats._distn_infrastructure.rv_frozen at 0x10fa5f52ce0>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -401,16 +422,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<scipy.stats._continuous_distns.skew_norm_gen at 0x10fbe6f75b0>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "stats.skewnorm?"
+    "stats.skewnorm"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -428,20 +460,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-59babccc1eac493581c67818ed5f5ac8\"></div>\n",
+       "<div id=\"altair-viz-bfedb874238b4309a6b80c664667d24f\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-59babccc1eac493581c67818ed5f5ac8\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-59babccc1eac493581c67818ed5f5ac8\");\n",
+       "    if (outputDiv.id !== \"altair-viz-bfedb874238b4309a6b80c664667d24f\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-bfedb874238b4309a6b80c664667d24f\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -487,14 +519,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-68e8bbe891cf272fd6a4a1cf9e456ae5.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"blue\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-91689be383ea8a8e6ee25c38ddf542dd.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"blue\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"value\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.Chart(...)"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -512,16 +544,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 83,
    "metadata": {},
    "outputs": [],
    "source": [
-    "aapl = pd.read_hdf(\"data/AAPL.h5\")"
+    "#aapl = pd.read_hdf(\"data/AAPL.h5\") # this function generates error, so using another method to load data\n",
+    "\n",
+    "import h5py\n",
+    "f_aapl = h5py.File(\"data/AAPL.h5\")[\"AAPL\"]\n",
+    "aapl = pd.DataFrame(np.array(f_aapl[\"block0_values\"])) \n",
+    "aapl.columns = [\"Open\", \"High\", \"Low\", \"Close\", \"Close.Adj\"]\n",
+    "aapl[\"Volume\"] = np.array(f_aapl[\"block1_values\"])\n",
+    "aapl = aapl.set_index(pd.to_datetime(np.array(f_aapl[\"axis1\"])))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
@@ -549,80 +588,76 @@
        "      <th>High</th>\n",
        "      <th>Low</th>\n",
        "      <th>Close</th>\n",
+       "      <th>Close.Adj</th>\n",
        "      <th>Volume</th>\n",
-       "      <th>Adj Close</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Date</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
+       "      <th>increase</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>2000-01-03</td>\n",
+       "      <th>2000-01-03</th>\n",
        "      <td>104.87</td>\n",
        "      <td>112.50</td>\n",
        "      <td>101.69</td>\n",
        "      <td>111.94</td>\n",
-       "      <td>19144400</td>\n",
        "      <td>27.74</td>\n",
+       "      <td>19144400</td>\n",
+       "      <td>7.07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2000-01-04</td>\n",
+       "      <th>2000-01-04</th>\n",
        "      <td>108.25</td>\n",
        "      <td>110.62</td>\n",
        "      <td>101.19</td>\n",
        "      <td>102.50</td>\n",
-       "      <td>18310000</td>\n",
        "      <td>25.40</td>\n",
+       "      <td>18310000</td>\n",
+       "      <td>-5.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2000-01-05</td>\n",
+       "      <th>2000-01-05</th>\n",
        "      <td>103.75</td>\n",
        "      <td>110.56</td>\n",
        "      <td>103.00</td>\n",
        "      <td>104.00</td>\n",
-       "      <td>27818000</td>\n",
        "      <td>25.77</td>\n",
+       "      <td>27818000</td>\n",
+       "      <td>0.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2000-01-06</td>\n",
+       "      <th>2000-01-06</th>\n",
        "      <td>106.12</td>\n",
        "      <td>107.00</td>\n",
        "      <td>95.00</td>\n",
        "      <td>95.00</td>\n",
-       "      <td>27443200</td>\n",
        "      <td>23.54</td>\n",
+       "      <td>27443200</td>\n",
+       "      <td>-11.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2000-01-07</td>\n",
+       "      <th>2000-01-07</th>\n",
        "      <td>96.50</td>\n",
        "      <td>101.00</td>\n",
        "      <td>95.50</td>\n",
        "      <td>99.50</td>\n",
-       "      <td>16463200</td>\n",
        "      <td>24.66</td>\n",
+       "      <td>16463200</td>\n",
+       "      <td>3.00</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "              Open    High     Low   Close    Volume  Adj Close\n",
-       "Date                                                           \n",
-       "2000-01-03  104.87  112.50  101.69  111.94  19144400      27.74\n",
-       "2000-01-04  108.25  110.62  101.19  102.50  18310000      25.40\n",
-       "2000-01-05  103.75  110.56  103.00  104.00  27818000      25.77\n",
-       "2000-01-06  106.12  107.00   95.00   95.00  27443200      23.54\n",
-       "2000-01-07   96.50  101.00   95.50   99.50  16463200      24.66"
+       "              Open    High     Low   Close  Close.Adj    Volume  increase\n",
+       "2000-01-03  104.87  112.50  101.69  111.94      27.74  19144400      7.07\n",
+       "2000-01-04  108.25  110.62  101.19  102.50      25.40  18310000     -5.75\n",
+       "2000-01-05  103.75  110.56  103.00  104.00      25.77  27818000      0.25\n",
+       "2000-01-06  106.12  107.00   95.00   95.00      23.54  27443200    -11.12\n",
+       "2000-01-07   96.50  101.00   95.50   99.50      24.66  16463200      3.00"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -660,20 +695,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 87,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-fdbd9f21dbdc4c249bb72c528a4d2c71\"></div>\n",
+       "<div id=\"altair-viz-f23e668803484ed7962b157189d1784f\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-fdbd9f21dbdc4c249bb72c528a4d2c71\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-fdbd9f21dbdc4c249bb72c528a4d2c71\");\n",
+       "    if (outputDiv.id !== \"altair-viz-f23e668803484ed7962b157189d1784f\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-f23e668803484ed7962b157189d1784f\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -719,7 +754,7 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-ffc129b5e5987ef92a23c6ab888126df.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"red\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"Price_Change\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-6507fe2eb490a6e37ec23578e3efae75.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"value\": \"red\"}, \"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"Price_Change\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
@@ -739,6 +774,7 @@
     }
    ],
    "source": [
+    "# Exercise 1\n",
     "aapl['Price_Change'] = aapl['Close'] - aapl['Open']\n",
     "aapl.head()\n",
     "\n",
@@ -753,6 +789,136 @@
     "print(\"Kurtosis: \" + str(stats.kurtosis(aapl['Price_Change'])))"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WIKI/IBM\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skew: -0.01722639664430268\n",
+      "Kurtosis: 1.3863770581535748\n",
+      "WIKI/GOOG\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skew: -0.997185747702747\n",
+      "Kurtosis: 6.860219399039256\n",
+      "WIKI/AAPL\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skew: -0.14127274911011609\n",
+      "Kurtosis: 6.757625341815192\n",
+      "WIKI/AMZN\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Skew: -0.8616838510166817\n",
+      "Kurtosis: 22.24936002978405\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Extended exercise\n",
+    "import quandl\n",
+    "quandl.ApiConfig.api_key = 'hPGPuToY_GBPFpx5zsyM'\n",
+    "\n",
+    "tickers = ['WIKI/IBM', \"WIKI/GOOG\", \"WIKI/AAPL\", \"WIKI/AMZN\"]\n",
+    "\n",
+    "def return_moments(ticker):\n",
+    "\n",
+    "    data = quandl.get(ticker, trim_start = \"2010-01-01\", trim_end = \"2022-12-30\")\n",
+    "    increase = data[\"Close\"] - data[\"Open\"]\n",
+    "\n",
+    "    plt.hist(increase, bins = 100)\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print(\"Skew: \" + str(stats.skew(increase)))\n",
+    "    print(\"Kurtosis: \" + str(stats.kurtosis(increase)))\n",
+    "\n",
+    "for t in tickers:\n",
+    "    print(t)\n",
+    "    return_moments(t)\n",
+    "    "
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- The skewness tells us which direction the stock has greater tendency to move\n",
+    "- The kurtosis tells us how volatile the stock is, shows us that the distributions are non-normal\n",
+    "- However we also have to keep in mind the magnitude is non-normalized"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -771,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 109,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -789,7 +955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 110,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -798,7 +964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 111,
    "metadata": {},
    "outputs": [
     {
@@ -808,7 +974,7 @@
        "       -0.39197144, -0.3288956 ], dtype=float32)"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -830,14 +996,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 112,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Overwriting my_secrets.py\n"
+      "Writing my_secrets.py\n"
      ]
     }
    ],
@@ -849,7 +1015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 113,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -860,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 114,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -872,7 +1038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 115,
    "metadata": {},
    "outputs": [
     {
@@ -881,7 +1047,7 @@
        "pandas.core.frame.DataFrame"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -900,7 +1066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 116,
    "metadata": {},
    "outputs": [
     {
@@ -912,7 +1078,7 @@
        "Name: adj_close, dtype: float64"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -930,20 +1096,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 117,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-e6acb6fc1eac473395965c0de9823361\"></div>\n",
+       "<div id=\"altair-viz-dae286e9a21a4f2fae9cbed161f281ae\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-e6acb6fc1eac473395965c0de9823361\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-e6acb6fc1eac473395965c0de9823361\");\n",
+       "    if (outputDiv.id !== \"altair-viz-dae286e9a21a4f2fae9cbed161f281ae\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-dae286e9a21a4f2fae9cbed161f281ae\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -996,7 +1162,7 @@
        "alt.Chart(...)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1019,7 +1185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 118,
    "metadata": {},
    "outputs": [
     {
@@ -1028,7 +1194,7 @@
        "Index(['ticker', 'date', 'adj_close'], dtype='object')"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1039,7 +1205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 119,
    "metadata": {},
    "outputs": [
     {
@@ -1074,27 +1240,27 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>2017-01-03</td>\n",
+       "      <th>2017-01-03</th>\n",
        "      <td>-2.399152</td>\n",
        "      <td>-1.330589</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2017-01-04</td>\n",
+       "      <th>2017-01-04</th>\n",
        "      <td>-2.406966</td>\n",
        "      <td>-1.356847</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2017-01-05</td>\n",
+       "      <th>2017-01-05</th>\n",
        "      <td>-2.371503</td>\n",
        "      <td>-1.356847</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2017-01-06</td>\n",
+       "      <th>2017-01-06</th>\n",
        "      <td>-2.293364</td>\n",
        "      <td>-1.306206</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2017-01-09</td>\n",
+       "      <th>2017-01-09</th>\n",
        "      <td>-2.228449</td>\n",
        "      <td>-1.324962</td>\n",
        "    </tr>\n",
@@ -1112,7 +1278,7 @@
        "2017-01-09 -2.228449 -1.324962"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1125,20 +1291,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 120,
    "metadata": {},
    "outputs": [
     {
      "data": {
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        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
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-       "    if (outputDiv.id !== \"altair-viz-62e043d3cd544dec8ed6c25194f9558c\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-62e043d3cd544dec8ed6c25194f9558c\");\n",
+       "    if (outputDiv.id !== \"altair-viz-7d4fb3edf05d4d668e9719758290665a\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-7d4fb3edf05d4d668e9719758290665a\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -1184,14 +1350,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-c30b316f8117097027d792220517ecd7.json\", \"format\": {\"type\": \"json\"}}, \"mark\": {\"type\": \"bar\", \"opacity\": 0.4}, \"encoding\": {\"color\": {\"field\": \"ticker\", \"type\": \"nominal\"}, \"x\": {\"bin\": {\"maxbins\": 30}, \"field\": \"z_score_adj_close\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"stack\": null, \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-0d1a14e757701cfffa714c83b5e98240.json\", \"format\": {\"type\": \"json\"}}, \"mark\": {\"type\": \"bar\", \"opacity\": 0.4}, \"encoding\": {\"color\": {\"field\": \"ticker\", \"type\": \"nominal\"}, \"x\": {\"bin\": {\"maxbins\": 30}, \"field\": \"z_score_adj_close\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"stack\": null, \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.Chart(...)"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1223,20 +1389,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 124,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-f062721bcfdc4d82b04e82a4f197ea66\"></div>\n",
+       "<div id=\"altair-viz-7cca0de38b234f08a27287221feb2e1e\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-f062721bcfdc4d82b04e82a4f197ea66\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-f062721bcfdc4d82b04e82a4f197ea66\");\n",
+       "    if (outputDiv.id !== \"altair-viz-7cca0de38b234f08a27287221feb2e1e\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-7cca0de38b234f08a27287221feb2e1e\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -1282,14 +1448,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-be5783c31777849ca5f78ac125b3bfc1.json\", \"format\": {\"type\": \"json\"}}, \"mark\": {\"type\": \"bar\", \"opacity\": 0.4}, \"encoding\": {\"color\": {\"field\": \"ticker\", \"type\": \"nominal\"}, \"x\": {\"bin\": {\"maxbins\": 30}, \"field\": \"z_score_returns\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"stack\": null, \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-641e7b6d4ecd39574045517733423317.json\", \"format\": {\"type\": \"json\"}}, \"mark\": {\"type\": \"bar\", \"opacity\": 0.4}, \"encoding\": {\"color\": {\"field\": \"ticker\", \"type\": \"nominal\"}, \"x\": {\"bin\": {\"maxbins\": 30}, \"field\": \"z_score_returns\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"stack\": null, \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.Chart(...)"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1301,11 +1467,20 @@
     "alt.Chart(z_rets.melt(value_name=\"z_score_returns\")).mark_bar(opacity=0.4).encode(\n",
     "    x=alt.X(\"z_score_returns\", bin=alt.Bin(maxbins=30)),\n",
     "    y=alt.Y('count()', stack=None),\n",
-    "    # column='ticker',\n",
     "    color='ticker',\n",
     ")"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- MST has wider variation, showing more volatility\n",
+    "- AAPL has a narrower distribution, showing less extreme movements\n",
+    "- MSFT is also less skewed, showing less proportion of days where price moves negatively, however, this doesnt show its has better returns as its lower tail is longer than AAPL"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1316,7 +1491,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1330,7 +1505,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3 (tags/v3.10.3:a342a49, Mar 16 2022, 13:07:40) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.1.4 - Normality Tests.ipynb b/notebooks/Module 1.1.4 - Normality Tests.ipynb
index 9ae4591..ae16515 100644
--- a/notebooks/Module 1.1.4 - Normality Tests.ipynb	
+++ b/notebooks/Module 1.1.4 - Normality Tests.ipynb	
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,16 +39,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
-    "aapl = pd.read_hdf(\"data/AAPL.h5\")"
+    "#aapl = pd.read_hdf(\"data/AAPL.h5\") # this function generates error, so using another method to load data\n",
+    "\n",
+    "import h5py\n",
+    "f_aapl = h5py.File(\"data/AAPL.h5\")[\"AAPL\"]\n",
+    "aapl = pd.DataFrame(np.array(f_aapl[\"block0_values\"])) \n",
+    "aapl.columns = [\"Open\", \"High\", \"Low\", \"Close\", \"Adj Close\"]\n",
+    "aapl[\"Volume\"] = np.array(f_aapl[\"block1_values\"])\n",
+    "aapl = aapl.set_index(pd.to_datetime(np.array(f_aapl[\"axis1\"])))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -58,16 +65,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.4991757323146919"
+       "0.49966243998511045"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -78,16 +85,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "20.446941304720326"
+       "20.448973653158085"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -105,20 +112,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-f8ab423273c84e4c85a4994733d23082\"></div>\n",
+       "<div id=\"altair-viz-6faa89e405e040be8cdba39243a296e6\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-f8ab423273c84e4c85a4994733d23082\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-f8ab423273c84e4c85a4994733d23082\");\n",
+       "    if (outputDiv.id !== \"altair-viz-6faa89e405e040be8cdba39243a296e6\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-6faa89e405e040be8cdba39243a296e6\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -164,14 +171,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-8d4b91037c6feceeefff91e21fd845d7.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"Gain\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-f0a48047bfc3a304e2b7898a50ec04dd.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"bar\", \"encoding\": {\"x\": {\"bin\": {\"maxbins\": 100}, \"field\": \"Gain\", \"type\": \"quantitative\"}, \"y\": {\"aggregate\": \"count\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.Chart(...)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -194,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -203,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -212,7 +219,7 @@
        "0.0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -223,7 +230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -274,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -317,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -348,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -362,7 +369,7 @@
    ],
    "source": [
     "statistic_chi, p_c = stats.normaltest(heights) #chi squared test statistic\n",
-    "statistic_k, p_k = stats.kstest(heights,cdf = 'norm') #another one?\n",
+    "statistic_k, p_k = stats.kstest(heights, cdf = 'norm') #another one?\n",
     "print(str(p_c), str(p_k))\n",
     "\n",
     "print(\"From Stack overflow, the Kolmogorov-Smirnov is for a completely specified distribution, while the Shapiro-Wilk is for normality, with unspecified mean and variance.\")"
@@ -386,7 +393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -395,7 +402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -404,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -412,7 +419,7 @@
      "output_type": "stream",
      "text": [
       "The data looks like it was drawn from a normal distribution\n",
-      "p=0.200\n"
+      "p=0.395\n"
      ]
     }
    ],
@@ -435,14 +442,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.6714923453511482\n"
+      "0.6714923453511482 0.19052726425258604 2.785620140256962\n",
+      "Cannot reject h0\n"
      ]
     }
    ],
@@ -450,7 +458,12 @@
     "from statsmodels.stats import stattools\n",
     "\n",
     "jbstat, pvalue, skew, kurtosis = stattools.jarque_bera(heights)\n",
-    "print(pvalue)"
+    "print(pvalue, skew, kurtosis)\n",
+    "\n",
+    "if pvalue < 0.05:\n",
+    "    print(\"Reject h0 that data is normal\")\n",
+    "else:\n",
+    "    print(\"Cannot reject h0\")"
    ]
   },
   {
@@ -476,6 +489,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -496,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,28 +543,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1450: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
-      "  \"anyway, n=%i\" % int(n))\n"
+      "C:\\Users\\86155\\AppData\\Local\\Temp\\ipykernel_2100\\848363905.py:10: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  sample_sizes = np.linspace(10, max_sample_size, 20, dtype=np.int)\n",
+      "c:\\env\\lib\\site-packages\\scipy\\stats\\_stats_py.py:1477: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
+      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
      ]
     },
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-bc78b7eca1e04044a2f1c88b69e40734\"></div>\n",
+       "<div id=\"altair-viz-f231020751934015a9bee3cd7fd2dd35\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-bc78b7eca1e04044a2f1c88b69e40734\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-bc78b7eca1e04044a2f1c88b69e40734\");\n",
+       "    if (outputDiv.id !== \"altair-viz-f231020751934015a9bee3cd7fd2dd35\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-f231020751934015a9bee3cd7fd2dd35\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -596,14 +613,14 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"url\": \"altair-data-29bceff9ea88745720485d406629109a.json\", \"format\": {\"type\": \"json\"}}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"Test\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Sample\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"Failed\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-94c8132557d7badce8815e9d635825a8\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"Test\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Sample\", \"type\": \"quantitative\"}, \"y\": {\"field\": \"Failed\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-94c8132557d7badce8815e9d635825a8\": [{\"Test\": \"sw\", \"Sample\": 10, \"Failed\": 0.08}, {\"Test\": \"cs\", \"Sample\": 10, \"Failed\": 0.04}, {\"Test\": \"ks\", \"Sample\": 10, \"Failed\": 0.08}, {\"Test\": \"jb\", \"Sample\": 10, \"Failed\": 0.0}, {\"Test\": \"sw\", \"Sample\": 62, \"Failed\": 0.02}, {\"Test\": \"cs\", \"Sample\": 62, \"Failed\": 0.05}, {\"Test\": \"ks\", \"Sample\": 62, \"Failed\": 0.07}, {\"Test\": \"jb\", \"Sample\": 62, \"Failed\": 0.04}, {\"Test\": \"sw\", \"Sample\": 114, \"Failed\": 0.05}, {\"Test\": \"cs\", \"Sample\": 114, \"Failed\": 0.07}, {\"Test\": \"ks\", \"Sample\": 114, \"Failed\": 0.02}, {\"Test\": \"jb\", \"Sample\": 114, \"Failed\": 0.06}, {\"Test\": \"sw\", \"Sample\": 166, \"Failed\": 0.05}, {\"Test\": \"cs\", \"Sample\": 166, \"Failed\": 0.07}, {\"Test\": \"ks\", \"Sample\": 166, \"Failed\": 0.06}, {\"Test\": \"jb\", \"Sample\": 166, \"Failed\": 0.04}, {\"Test\": \"sw\", \"Sample\": 218, \"Failed\": 0.06}, {\"Test\": \"cs\", \"Sample\": 218, \"Failed\": 0.03}, {\"Test\": \"ks\", \"Sample\": 218, \"Failed\": 0.07}, {\"Test\": \"jb\", \"Sample\": 218, \"Failed\": 0.06}, {\"Test\": \"sw\", \"Sample\": 270, \"Failed\": 0.05}, {\"Test\": \"cs\", \"Sample\": 270, \"Failed\": 0.09}, {\"Test\": \"ks\", \"Sample\": 270, \"Failed\": 0.05}, {\"Test\": \"jb\", \"Sample\": 270, \"Failed\": 0.05}, {\"Test\": \"sw\", \"Sample\": 322, \"Failed\": 0.08}, {\"Test\": \"cs\", \"Sample\": 322, \"Failed\": 0.05}, {\"Test\": \"ks\", \"Sample\": 322, \"Failed\": 0.08}, {\"Test\": \"jb\", \"Sample\": 322, \"Failed\": 0.04}, {\"Test\": \"sw\", \"Sample\": 374, \"Failed\": 0.02}, {\"Test\": \"cs\", \"Sample\": 374, \"Failed\": 0.05}, {\"Test\": \"ks\", \"Sample\": 374, \"Failed\": 0.03}, {\"Test\": \"jb\", \"Sample\": 374, \"Failed\": 0.06}, {\"Test\": \"sw\", \"Sample\": 426, \"Failed\": 0.05}, {\"Test\": \"cs\", \"Sample\": 426, \"Failed\": 0.04}, {\"Test\": \"ks\", \"Sample\": 426, \"Failed\": 0.02}, {\"Test\": \"jb\", \"Sample\": 426, \"Failed\": 0.08}, {\"Test\": \"sw\", \"Sample\": 478, \"Failed\": 0.02}, {\"Test\": \"cs\", \"Sample\": 478, \"Failed\": 0.06}, {\"Test\": \"ks\", \"Sample\": 478, \"Failed\": 0.09}, {\"Test\": \"jb\", \"Sample\": 478, \"Failed\": 0.04}, {\"Test\": \"sw\", \"Sample\": 531, \"Failed\": 0.06}, {\"Test\": \"cs\", \"Sample\": 531, \"Failed\": 0.04}, {\"Test\": \"ks\", \"Sample\": 531, \"Failed\": 0.09}, {\"Test\": \"jb\", \"Sample\": 531, \"Failed\": 0.06}, {\"Test\": \"sw\", \"Sample\": 583, \"Failed\": 0.02}, {\"Test\": \"cs\", \"Sample\": 583, \"Failed\": 0.09}, {\"Test\": \"ks\", \"Sample\": 583, \"Failed\": 0.05}, {\"Test\": \"jb\", \"Sample\": 583, \"Failed\": 0.03}, {\"Test\": \"sw\", \"Sample\": 635, \"Failed\": 0.03}, {\"Test\": \"cs\", \"Sample\": 635, \"Failed\": 0.08}, {\"Test\": \"ks\", \"Sample\": 635, \"Failed\": 0.07}, {\"Test\": \"jb\", \"Sample\": 635, \"Failed\": 0.07}, {\"Test\": \"sw\", \"Sample\": 687, \"Failed\": 0.05}, {\"Test\": \"cs\", \"Sample\": 687, \"Failed\": 0.05}, {\"Test\": \"ks\", \"Sample\": 687, \"Failed\": 0.07}, {\"Test\": \"jb\", \"Sample\": 687, \"Failed\": 0.05}, {\"Test\": \"sw\", \"Sample\": 739, \"Failed\": 0.04}, {\"Test\": \"cs\", \"Sample\": 739, \"Failed\": 0.01}, {\"Test\": \"ks\", \"Sample\": 739, \"Failed\": 0.04}, {\"Test\": \"jb\", \"Sample\": 739, \"Failed\": 0.04}, {\"Test\": \"sw\", \"Sample\": 791, \"Failed\": 0.04}, {\"Test\": \"cs\", \"Sample\": 791, \"Failed\": 0.05}, {\"Test\": \"ks\", \"Sample\": 791, \"Failed\": 0.03}, {\"Test\": \"jb\", \"Sample\": 791, \"Failed\": 0.06}, {\"Test\": \"sw\", \"Sample\": 843, \"Failed\": 0.02}, {\"Test\": \"cs\", \"Sample\": 843, \"Failed\": 0.03}, {\"Test\": \"ks\", \"Sample\": 843, \"Failed\": 0.07}, {\"Test\": \"jb\", \"Sample\": 843, \"Failed\": 0.03}, {\"Test\": \"sw\", \"Sample\": 895, \"Failed\": 0.04}, {\"Test\": \"cs\", \"Sample\": 895, \"Failed\": 0.05}, {\"Test\": \"ks\", \"Sample\": 895, \"Failed\": 0.09}, {\"Test\": \"jb\", \"Sample\": 895, \"Failed\": 0.06}, {\"Test\": \"sw\", \"Sample\": 947, \"Failed\": 0.03}, {\"Test\": \"cs\", \"Sample\": 947, \"Failed\": 0.06}, {\"Test\": \"ks\", \"Sample\": 947, \"Failed\": 0.04}, {\"Test\": \"jb\", \"Sample\": 947, \"Failed\": 0.04}, {\"Test\": \"sw\", \"Sample\": 1000, \"Failed\": 0.04}, {\"Test\": \"cs\", \"Sample\": 1000, \"Failed\": 0.05}, {\"Test\": \"ks\", \"Sample\": 1000, \"Failed\": 0.06}, {\"Test\": \"jb\", \"Sample\": 1000, \"Failed\": 0.04}]}}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.Chart(...)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -615,7 +632,9 @@
     "import altair as alt\n",
     "import numpy as np\n",
     "\n",
-    "sample_sizes = [10, 30, 50, 100, 1000, 5000]\n",
+    "\n",
+    "#sample_sizes = [10, 30, 50, 100, 1000, 5000]\n",
+    "max_sample_size = 1000\n",
     "sample_sizes = np.linspace(10, max_sample_size, 20, dtype=np.int)\n",
     "\n",
     "test_types = [\"sw\", \"cs\", \"ks\", \"jb\"]\n",
@@ -630,8 +649,6 @@
     "\n",
     "df = pd.DataFrame(data, columns=['Test', 'Sample', 'Failed'])\n",
     "\n",
-    "\n",
-    "\n",
     "alt.Chart(df).mark_line().encode(\n",
     "    x='Sample',\n",
     "    y='Failed',\n",
@@ -639,32 +656,42 @@
     ")"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- You can see that the failure rate seems to be fairly random, and lies between 0.02 - 0.09 for varying samples\n",
+    "- The number of samples doesn't seem to affect the failure rate too much"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "16 passed and 4 failed\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "sample_size = 30\n",
-    "passed = 0\n",
-    "failed = 0\n",
-    "for i in range(20):\n",
-    "    distribution = stats.norm()\n",
-    "    data = distribution.rvs(sample_size)\n",
-    "    stat, p = stats.normaltest(data)\n",
-    "    if p > 0.05:\n",
-    "        passed += 1\n",
-    "    else:\n",
-    "        failed += 1\n",
-    "print(\"{} passed and {} failed\".format(passed, failed))"
+    "df = np.array(df)\n",
+    "\n",
+    "plt.plot(df[df[:,0] == \"sw\", 1], df[df[:,0] == \"sw\", 2], alpha=0.5, label='scipy.normaltest')\n",
+    "plt.plot(df[df[:,0] == \"cs\", 1], df[df[:,0] == \"cs\", 2], alpha=0.5, label='scipy.kstest')\n",
+    "plt.plot(df[df[:,0] == \"ks\", 1], df[df[:,0] == \"ks\", 2], alpha=0.5, label='statsmodels.kstest_normal')\n",
+    "plt.plot(df[df[:,0] == \"jb\", 1], df[df[:,0] == \"jb\", 2], alpha=0.5, label='statsmodels.jarque_bera')\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -677,7 +704,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -691,7 +718,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3 (tags/v3.10.3:a342a49, Mar 16 2022, 13:07:40) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.2.1 - Covariance.ipynb b/notebooks/Module 1.2.1 - Covariance.ipynb
index b6f5d92..b7c4661 100644
--- a/notebooks/Module 1.2.1 - Covariance.ipynb	
+++ b/notebooks/Module 1.2.1 - Covariance.ipynb	
@@ -116,7 +116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -125,7 +125,7 @@
        "3.3"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,7 @@
        "[(1, 4), (2, 3), (3, 5), (4, 1), (5, 5), (6, 2)]"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -172,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -203,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -216,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -225,7 +225,7 @@
        "3.3000000000000003"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -255,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +264,7 @@
        "3.5"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -305,19 +305,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "E[x] = (a + b) / 2\n"
+      "E[x] = (a + b) / 2: 10.0\n",
+      "E[x] Formal mean: 10.000000000000002\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -332,12 +333,16 @@
     "# (1)\n",
     "a = 0\n",
     "b = 20\n",
-    "x = np.linspace(a,b , 200)\n",
-    "y = [1/(b-a)] * 200\n",
+    "n = 200\n",
+    "x = np.linspace(a, b , n)\n",
+    "y = [1/(b-a)] * n\n",
     "plt.plot(x, y)\n",
     "\n",
     "# \n",
-    "print(\"E[x] = (a + b) / 2\")\n"
+    "intutive_mean = (b + a) / 2\n",
+    "formal_mean = np.sum(x * np.array(y)) / n * (b - a)\n",
+    "print(\"E[x] = (a + b) / 2: \" + str(intutive_mean))\n",
+    "print(\"E[x] Formal mean: \" + str(formal_mean))"
    ]
   },
   {
@@ -375,22 +380,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x20522e7a4c8>]"
+       "[<matplotlib.lines.Line2D at 0x20fbcfae560>]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -409,17 +414,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[34.35023637,  1.92047043],\n",
-       "       [ 1.92047043,  0.18891988]])"
+       "array([[34.35023637,  1.61837407],\n",
+       "       [ 1.61837407,  0.16973562]])"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -438,16 +443,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(34.00673400673401, 0.1563957161127152)"
+       "(34.00673400673401, 0.1680382669275329)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -475,23 +480,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.09361065179379917\n"
+      "-0.661653068121233\n",
+      "-0.21871116272915572\n"
      ]
     }
    ],
    "source": [
     "# (1)\n",
-    "# For the solutions a regression is used. Haven't done OLS in Python yet, if I remember I'll come back to this.\n",
-    "\n",
+    "# can generate a distribution based on the first with a negative in front to flip the correlation\n",
+    "# this will result in the correlation always being negative\n",
+    "x = np.random.random(100)\n",
+    "y = -1 * (x + np.random.random(100))\n",
+    "correlation, p_value = stats.pearsonr(x, y)\n",
+    "print(correlation) \n",
     "\n",
     "# (2)\n",
+    "# two randoml distriubutions should not have any correlation\n",
     "x = np.random.random(100) #+ np.linspace(0, 1, 100)\n",
     "y = np.random.random(100)\n",
     "\n",
@@ -517,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -533,7 +544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -556,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -565,7 +576,7 @@
        "73.26405232380192"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -594,7 +605,7 @@
        "11.384729794575746"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -612,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -621,7 +632,7 @@
        "(156.75665944575758, 179.52611903490907)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -642,7 +653,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -656,7 +667,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3 (tags/v3.10.3:a342a49, Mar 16 2022, 13:07:40) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.2.2 - Correlation.ipynb b/notebooks/Module 1.2.2 - Correlation.ipynb
index 38ad70d..bdf3341 100644
--- a/notebooks/Module 1.2.2 - Correlation.ipynb	
+++ b/notebooks/Module 1.2.2 - Correlation.ipynb	
@@ -140,65 +140,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The correlation of something with itself is 1 intuitively, i.e. perfect correlation.\n",
-      "1.0\n"
+      "(1)\n",
+      "Intuitively the answer is 1.\n",
+      "Estimated is 1.0\n",
+      "(2)\n",
+      "Intuitively the answer is -1.\n",
+      "Estimated is -0.9999999999999998\n",
+      "(3)\n",
+      "Intuitively the answer is 0.\n",
+      "Estimated is -0.07178695246257226\n"
      ]
     }
    ],
    "source": [
-    "# (1)\n",
-    "print(\"The correlation of something with itself is 1 intuitively, i.e. perfect correlation.\")\n",
+    "# exercise 1\n",
+    "print(\"(1)\")\n",
+    "print(\"Intuitively the answer is 1.\")\n",
     "\n",
-    "vals = np.random.rand(1, 10)\n",
-    "correl = np.corrcoef(vals,vals)\n",
+    "x = np.random.rand(1, 100)\n",
+    "corr1 = np.corrcoef(x, x)\n",
+    "print(\"Estimated is \" + str(corr1[0, 1]))\n",
     "\n",
-    "print(correl[0,0])"
+    "# exercise 2\n",
+    "print(\"(2)\")\n",
+    "print(\"Intuitively the answer is -1.\")\n",
+    "x = np.random.rand(1, 100)\n",
+    "corr2 = np.corrcoef(x, -x)\n",
+    "print(\"Estimated is \" + str(corr2[0, 1]))\n",
+    "\n",
+    "# exercise 3\n",
+    "print(\"(3)\")\n",
+    "print(\"Intuitively the answer is 0.\")\n",
+    "x = np.random.rand(1, 100)\n",
+    "y = np.random.rand(1, 100)\n",
+    "corr3 = np.corrcoef(x, y)\n",
+    "print(\"Estimated is \" + str(corr3[0, 1]))"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "corr(x,x) = E(X-E[X])^2/var(x)\n",
+    "Mathematically:\n",
     "\n",
-    "Since E(X-E(X))^2 = var(x), corr(x,x) = 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Intuitively the answer is -1.\n",
-      "-1.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "# (2)\n",
-    "print(\"Intuitively the answer is -1.\")\n",
+    "Given $corr(X, Y) = \\frac{E[(X-E[X])(Y-E[Y])]}{\\sigma_X \\sigma_Y}$\n",
     "\n",
+    "- exercise 1\n",
     "\n",
-    "print(np.corrcoef(vals,np.negative(vals))[0,1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "###### (3)\n",
-    "$corr(X, Y) = \\frac{E[(X-E[X])(Y-E[Y])]}{\\sigma_X \\sigma_Y}$"
+    "$corr(x,x) = E(X-E[X])^2/{\\sigma_X \\sigma_Y} = E(X-E[X])^2/{\\sigma_X \\sigma_X} = E(X-E[X])^2/var(x)$\n",
+    "\n",
+    "Since $E(X-E(X))^2 = var(x)$ Then $corr(x,x) = 1$\n",
+    "\n",
+    "- exercise 2\n",
+    "\n",
+    "$corr(x, -x) = E(X-E[X]) E(-X-E[-X])/{\\sigma_X \\sigma_X} = E(X-E[X]) -E(X-E[X])/var(x) = -E(X-E[X])^2/var(x)$\n",
+    "\n",
+    "Then following from the equation in (1) we know that $corr(x, -x) = -var(x)/var(x) = -1$\n",
+    "\n",
+    "- exercise 3\n",
+    "\n",
+    "$corr(X, Y) = E(X-E[X])E(Y-E[Y])/{\\sigma_X \\sigma_Y}$\n",
+    "\n",
+    "If both X and Y are random, then their normalized expectations are 0. The numerator thus becomes 0, therefore the overall term = 0\n"
    ]
   },
   {
@@ -212,16 +223,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.9999999999999999"
+       "1.0"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -250,16 +261,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-1.0"
+       "-0.9999999999999998"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -277,22 +288,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1e3b8e21a48>]"
+       "[<matplotlib.lines.Line2D at 0x17bb70b7820>]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -309,22 +320,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1e3bab03208>]"
+       "[<matplotlib.lines.Line2D at 0x17ba8cbbb80>]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -384,17 +395,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
     "import quandl\n",
+    "quandl.ApiConfig.api_key = 'hPGPuToY_GBPFpx5zsyM'\n",
     "gold_price = quandl.get(\"LBMA/GOLD\")['USD (AM)']"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -409,7 +421,7 @@
        "Name: USD (AM), dtype: float64"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -420,7 +432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -429,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -444,7 +456,7 @@
        "Name: Last, dtype: float64"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -455,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -490,27 +502,27 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>2005-01-04</td>\n",
+       "      <th>2005-01-04</th>\n",
        "      <td>426.80</td>\n",
        "      <td>318.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2005-01-05</td>\n",
+       "      <th>2005-01-05</th>\n",
        "      <td>425.50</td>\n",
        "      <td>319.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2005-01-06</td>\n",
+       "      <th>2005-01-06</th>\n",
        "      <td>425.80</td>\n",
        "      <td>328.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2005-01-07</td>\n",
+       "      <th>2005-01-07</th>\n",
        "      <td>423.15</td>\n",
        "      <td>329.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2005-01-10</td>\n",
+       "      <th>2005-01-10</th>\n",
        "      <td>421.25</td>\n",
        "      <td>327.50</td>\n",
        "    </tr>\n",
@@ -528,7 +540,7 @@
        "2005-01-10  421.25  327.50"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -541,22 +553,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1e3bb15f448>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -573,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -603,12 +615,12 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>gold</td>\n",
+       "      <th>gold</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.339939</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>wheat</td>\n",
+       "      <th>wheat</th>\n",
        "      <td>0.339939</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
@@ -622,7 +634,7 @@
        "wheat  0.339939  1.000000"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -657,7 +669,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -687,12 +699,12 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>gold_detrended</td>\n",
+       "      <th>gold_detrended</th>\n",
        "      <td>1.000000</td>\n",
        "      <td>0.642194</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>wheat_detrended</td>\n",
+       "      <th>wheat_detrended</th>\n",
        "      <td>0.642194</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
@@ -706,13 +718,13 @@
        "wheat_detrended        0.642194         1.000000"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -726,10 +738,12 @@
    "source": [
     "from scipy.signal import detrend\n",
     "\n",
+    "# normalize the magnitude so two prices can be comparable\n",
     "prices['gold_detrended'] = detrend(prices['gold'])\n",
     "prices['wheat_detrended'] = detrend(prices['wheat'])\n",
-    "prices[['gold_detrended', 'wheat_detrended']].plot()\n",
     "\n",
+    "# plot\n",
+    "prices[['gold_detrended', 'wheat_detrended']].plot()\n",
     "prices[['gold_detrended', 'wheat_detrended']].corr()"
    ]
   },
@@ -755,14 +769,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation is: 0.13316599458501419\n",
+      "Correlation is: -0.12373696331249749\n",
       "Did not find a high correlation\n"
      ]
     }
@@ -790,7 +804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -804,7 +818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -872,14 +886,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "62.1 ms ± 2.7 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
+      "62.4 ms ± 270 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
      ]
     }
    ],
@@ -896,27 +910,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-85-31609e899bfb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;31m# Relationship between data size and FP rate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[0mdata_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mfp_rate_datapoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromiter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_many_experiments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_datapoints\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp_rate_datapoints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-85-31609e899bfb>\u001b[0m in \u001b[0;36m<genexpr>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     17\u001b[0m \u001b[1;31m# Relationship between data size and FP rate\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[0mdata_size\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlogspace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 19\u001b[1;33m \u001b[0mfp_rate_datapoints\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromiter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_many_experiments\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_datapoints\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0md\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mdata_size\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     21\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata_size\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp_rate_datapoints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-85-31609e899bfb>\u001b[0m in \u001b[0;36mrun_many_experiments\u001b[1;34m(n_experiments, n_datapoints, threshold)\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m     \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_experiments\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[0mrun_experiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_datapoints\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn_datapoints\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m             \u001b[0mfalse_positives\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mfalse_positives\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mn_experiments\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-85-31609e899bfb>\u001b[0m in \u001b[0;36mrun_experiment\u001b[1;34m(n_datapoints, threshold)\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mx_random\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_datapoints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0my_random\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_datapoints\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m     \u001b[0mcorr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcorrcoef\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx_random\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_random\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mcorr\u001b[0m \u001b[1;33m>\u001b[0m \u001b[0mthreshold\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mcorrcoef\u001b[1;34m(x, y, rowvar, bias, ddof)\u001b[0m\n\u001b[0;32m   2520\u001b[0m         warnings.warn('bias and ddof have no effect and are deprecated',\n\u001b[0;32m   2521\u001b[0m                       DeprecationWarning, stacklevel=2)\n\u001b[1;32m-> 2522\u001b[1;33m     \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcov\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrowvar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2523\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2524\u001b[0m         \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdiag\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36mcov\u001b[1;34m(m, y, rowvar, bias, ddof, fweights, aweights)\u001b[0m\n\u001b[0;32m   2448\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2449\u001b[0m         \u001b[0mX_T\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mT\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2450\u001b[1;33m     \u001b[0mc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_T\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconj\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2451\u001b[0m     \u001b[0mc\u001b[0m \u001b[1;33m*=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrue_divide\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfact\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2452\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msqueeze\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\86155\\AppData\\Local\\Temp\\ipykernel_9252\\2630705584.py:22: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
+      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
+      "  data_size = np.logspace(1, 7, 7, dtype=np.int)\n"
      ]
     }
    ],
    "source": [
+    "# randomly choose 2 distributions to compare their correlations for a given threshold\n",
+    "#   return true if \"accidentally\" correlating with 2 random distributions\n",
     "def run_experiment(n_datapoints=20, threshold=0.8):\n",
     "    # Note that all data here is randomly chosen\n",
     "    x_random = np.random.random(n_datapoints)\n",
@@ -924,8 +933,10 @@
     "    corr = np.corrcoef(x_random, y_random)[0,1]\n",
     "    return corr > threshold\n",
     "\n",
-    "\n",
-    "def run_many_experiments(n_experiments=1000, n_datapoints=20, threshold=0.8):\n",
+    "# run X number of the experiments above to generate a probabily over many simulations\n",
+    "#   increment the number of false positives each time 2 random distributions happens\n",
+    "#   to be correlated. limit to 100 as my computer is slow\n",
+    "def run_many_experiments(n_experiments=100, n_datapoints=20, threshold=0.8):\n",
     "    false_positives = 0\n",
     "\n",
     "    for i in range(n_experiments):\n",
@@ -935,9 +946,61 @@
     "\n",
     "# Relationship between data size and FP rate\n",
     "data_size = np.logspace(1, 7, 7, dtype=np.int)\n",
-    "fp_rate_datapoints = np.fromiter((run_many_experiments(n_datapoints=d) for d in data_size), dtype=np.float32)\n",
+    "threshold = np.linspace(0, 1, 100)\n",
     "\n",
-    "plt.plot(data_size, fp_rate_datapoints)\n"
+    "# compute false positive (fp) rates for both\n",
+    "fp_rate_datapoints = np.fromiter((run_many_experiments(n_datapoints=d) for d in data_size), dtype=np.float32)\n",
+    "fp_rate_threshold = np.fromiter((run_many_experiments(threshold=t) for t in threshold), dtype=np.float32)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Relationship between data size and false positive rate\n",
+    "plt.plot(data_size, fp_rate_datapoints)\n",
+    "plt.xscale(\"log\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Relationship between threshold for considering \"high correlation\" and false positive rate\n",
+    "plt.plot(threshold, fp_rate_threshold)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Number of datapoints vs false positive rate shows larger number of datapoints results in less false positives\n",
+    "- Threshold used vs false positive rate shows higher threshold (stricter) results in less false positives"
    ]
   },
   {
@@ -950,9 +1013,26 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.3 (tags/v3.10.3:a342a49, Mar 16 2022, 13:07:40) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.2.3 - Cross Correlation.ipynb b/notebooks/Module 1.2.3 - Cross Correlation.ipynb
index 80c6a74..c0aea70 100644
--- a/notebooks/Module 1.2.3 - Cross Correlation.ipynb	
+++ b/notebooks/Module 1.2.3 - Cross Correlation.ipynb	
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,6 +64,7 @@
    "outputs": [],
    "source": [
     "import quandl\n",
+    "quandl.ApiConfig.api_key = 'hPGPuToY_GBPFpx5zsyM'\n",
     "interest_rates = quandl.get(\"RBA/F13_FOOIRATCR\")"
    ]
   },
@@ -84,7 +85,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8b59fadc8>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
      "execution_count": 4,
@@ -93,7 +94,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -126,7 +127,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8b5de7188>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
      "execution_count": 6,
@@ -135,7 +136,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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KZsjV43d77MWlFdLIDWNG+iF9pkSTYjHgGEkAX+Scv8oY8wDYwRh7jHNecbOQLtVjBtJSRKkeOeccgaiqkatdFgcnIxhVve9lnW68MRxCSuY4cCaIDl3qIQC0aWX6RkgrMkwM2szILq+tjGAneY/VIvLGHRIFO4nmpmqPnHN+mnP+qvpzEMA+AD3VHFMJRgqPXJVWSvSmpuMppGQOr90Kv88JIO2RuyQzujw2dKmBx72nAxmphwDgc6odEA3yyO1WszaftFvV5ovtA1AeuRFoWSsWyiMnmhtDNXLG2BIA5wJ4qZrjdHltGA7EwHm6V0qpHnlQLc/32K3wOizw2Cz4u4f34e4XjqG30w3GmJZBEowmZ3rkqmEXqYmlcHwsjMu+/yReOTae8Xg0mdJu6wHlTmM4GINcwDPUa+Q0iKI6tKZZEuWRE82NEdIKAIAx5gZwP4DPcc4DOZ6/EcCNALBo0aKCx+r2pKsgh9TgYKmasejT4nVYwBjD7e/v0yYPXbZC6bAocrqBzEAnALhsFvR2uvDikTH89eXnFH09zjm+8bs9OD42jft3DOCCJW3ac9GEDLslfa3s9tqRlDnGp+MzLiACIa1wrgTnLGaWczuiOMJw2yykkRPNjSGGnDFmhWLE7+Gc/2+ubTjndwK4EwA2bdpU0DXSV0FqGnmJJ6Hos+KxKxLJNX3zcU3f/IxtOtw2MKYYy/YcBvWq1d2467mjCEQT8KrHyccje87gqYMjaHFYsXXfMGSZw6SWjwppJf2+RFFQNK8hF9IKoOjkujgsUSbJVLpFAkAeOdG8GJG1wgD8J4B9nPN/rH5JyKiC1LJWSmwiJXqRe+35r1FWs0nrQd6R5ZEDwJY13UikOJ4+WLh5VziWxG0P7MWqeR7ccu0ajIZi6NfNHc025EKbHy4Q8BRZKwDp5NUiAsZWM4PFxCj9kGhajNDI3wLgTwFcwRjbqf57ZzUHFB75yfFpLXukVKOWllYKe9LiYpHLMz5vUStanVZs3Vu4edeD/adweiqKb757Lbas7oLZxDL2iSZkrc+H8prFqzun43qPnAx5NYisFYvZBLOJab1XCKLZMCJr5VnOOeOc93HON6r/HqrmmKIKcrfq3TJWerAzoAU7C6tGwqhmZ60AonlXN548MFJQV31s7zB6fA5ctLQNPqeEC5a0ZnRuzPbIRd/zQimIkQQZcqMQHrnFpHrklLVCNCl1V9kJQKuC7B9QDPk8tQVsKQTU0vpi2rbwyHNp5ABw1ZouTEUS2H58IufzkXgKzx4ewZbVXVp64ZbV3dh/JoiT6tzR7KwVyWJCu0sq2AExEk8PxaCe5NWR1KQVEyxmE2nkRNNSl4YcUDJXDg4pjbIWtjpL9k6DUaUXud6A5qLLo3jkuTRyALh0eSckswmP5+mN/tzhUUQTMras6dYe27Ja+fkJde5otrQCQOvzIpiKJPC+f30Ou9SLlt4jJ428OpKyDMaUOyyLidFgCaJpqVtD3uW1aR6Uv9VRhrSSgNdRPBnngxcsxHeuW6eV5GfjslmwYWELXj0xmfP5rfuG4LZZcNHSdu2xJR0uOKzmtEeeSGnFKILurOrOJ/YP4dUTk3j1hOL5RxLp90nSSnUkUhxWk/IVt5gZFQQRTUvdGnKhYVvNDF1ee+l55JGElnpYiB6fAx+5eHHBbdb3+LDn1NQMnVyWOR7fP4zLVnZCsmR+hG0uSQvQRhMp2KUsQ+5J95EBgK17Fe9ddFvMkFbIkFdFMiVrefgWE0krRPNSt4a8Sw14dnnssFlMSKR4wYpIQTCaLJh6WA59/hZEEzIOjyhDk4cDUfzP9pP4t6fewEgwhqtWd8/Yx+e0ap0TlYKgmR75SDCGlMwRS6bwlJriKKYSRRIprTcLGfLqSMocFjWnX/HI6fMkmhPDKjuNRnjkXV6b5vUmZBk2U2HtW5FWinvkpbDe3wIA6B+Ywqp5Xnzrwb14sP80AMApmbF5ZeeMfRSPXBjy1AyNfEmHCzIHth0YhtVsQiimeOBpjzwFr92KsXCcGmdVSSIlw6rObjWbGHnkRNNStx65yCrp9ti1Qcql6OTBaLJo6mGpLG13wW2zYNfAFOJJGdsOjOC6jQvw7FcuxwtfvTKnvu5zSpgIx5FMyUjKfEbQ9dq+BTiny41vPrAHf+g/DbvVhN4OV4Yhb1EvROSRV0cylW5xYDWZSCMnmpa6NeSiCrLba9NJDcVPxECkeFl9qZhMDOt6vNg1OIWXj44jFEvi2r4F8Lc6NWObTZvTionpBKLJzMG/AsliwrfesxYnxyO4b/tJXHJOJ7q9dkxFFC8+kkjBQ4bcEBKyDIuJPHKi+albQ56WVuyQVJ25FI9cGbxsnGLU5/dh7+kAHt6teM9vOaej4PY+p4RANIGwKpnkSoN887IOXLdxAQAlX73FYU175Im0Rx6vkzzyw8MhfPSul7U+No1CMsU1J8BiZjSzk2ha6taQL2ix44tXrcC7NywoOfgXT8qIJmTDPHIAWN/TgnhSxq93DOCSczq0IQX5aHNJ4Dxdhp8d7BTccu0afOKSpbimb4FmyGWZq+tXLkT14pG/8MYonj44gu3HchdH1StJWYZFleUs5JETTUzdGnLGGD595XIsbHNqwc5YEY883fnQSI+8RXvtLTmyVLIRgylOTaqGPI/hb3fb8PVr18Bts6DFacXkdALRpFIM5K0zaUXkvYtK20YhkdJlrZBGTjQxdWvI9YhgZzHDJoZKGJW1AgCL2pyah3zF6q6i24vBFGemIgCQ0Y88Hy0OK2JJWcs/r7dgp7i72DU4WduFlElSl7ViMTd2ZeeZqWjR6VLE2Uvdph/qER55MY18cFIxnm05GmFVCmMMF/W2IxhNaGX9hWhVM1lOT6keeZFWAUDacJ9R9xHSULxOPEgx3KPRPPKkbjCH2cQQSdTH51kJn79vJxySGXd97IJaL4WoQxrCkFtL9Mif2D8MyWLKmNJjBD/60LmQSxy7JqSVSgy58HxFi4FEiW0JZhvhCQ4Hlf7wIhBd7yRSslaibzU3trQyMDkNp7UhTleiBjSGtCI88gKGnHOOrfuG8OZl7XDZjP3COyRzyccUdwOnhbRiLU1aUfaJZvxeT9LKqnkeAI3llevzyBs9/XAsFMdYuPQ5ssTZRUMYcmsJBUFvjIRwfGy6pIDkbOKwmiFZTOlgZwkeufDiha4upJV6MOSxZAoT0wlcvqoLJgZt/mkjkJC5lrVibeAS/el4EtPxFMbD8bqecvSN3+3GS0fGar2MhmbPqSl8+devl/13bghDbhMl+gVujR9Tm09dWUJAcjZhjKHNKWkyiaMcjVzNDnGrwdV60MjFWLol7U4s7/Jg18BkbRdUBsmUDEnzyE11bQQLMRZSisVkDq39Q70RiafwsxeOay0siMp47vAofrV9AGfKDGw3hCEvxSPfum8I63taML/FMVfLyovPaU1PcC9DWhEeuVMyQzKb6sIjF50au7x2rPe3YNfgFHiJ8YJak0xxrbKzkfPIR0NpSUUY9XpDXGBEwgFRGeI7WmgcZC4awpBrTbPyGLbRUAyvnpiouawi0GfNlCKtiLa7QiN3WM2wmlldBDuFR97tsaPP34LRUFxbZ72TkPVtbI2VVgLRRIaBnU30xnusitcMxZI4NUuGVjPkE2TIq0EE5MtNNW0IQy4qO/N55Pe9chKcA1etqQ9D3qprppWvslOP2cTgtVs0o+mQzLBa6sMjF55Bt9eGjQt9AICHdjXG7bNSoq/PIzfOI7/t93tx48+2G3a8QuiDnKPhyjxyzjn+6r934N0/fm5WYgUTYaUGYnAy0jB3bPWI+NsUmuubi4Yw5IWyVk6OT+OfnziEd6ybhzULvHO9tJy0uhQP28TSF6FitDit2vtTPHJTXWjkQ8EYrGaGVqeE9T0tuGxFJ3649VBDFKckU3JmZaeBhvzEeLjsk61SRg3wyB/oP41nD4+qd6+TBq0sjfDIQ7EkApFkka2JfCSaWlopoJHf9sBemBjDLdeumetl5UV45HarWRvMXAx9N0WHtX408qFAFF0eO0wmBsYYbnv3WsRTMr7zh321XlpR9FkrZoOlldFQPGO+6mwyGorBJZlhMbGK5JxANIFvP7gXq+d7IZlN2JpnDm016IOwA5PThh//bOGs8MizDdtTB0ewdd8QPnvlcizw1T7IKdAb8lIRhlxSJ75bzawuDPlwIIYutTc8oAzGuOmyZfj966fqPtVMGSyh9iM3WFoZDcUwHZ8bz3MsFEenx4Y2l5Q32BlNpHDz/f0YCaYNwA+3HsRHfvISPvBvL2A0FMPt71uPi5e1Y+veWTDk4XRnTNLJK0dk5unHQZZCQxjyfFkrLx0Zg9nE8PFLltZiWXkR0kopqYcCn0MY/3QlYj0Y8qFAFN1ZrQlu2rwMVjPDNnVMXb2iz1oxGyitxJIpBKNJRBNySeMHq2UsHEO724Z2ty1DZtGz73QAv3zlJJ7W/U1+8sxRHB4OwW234JZr1mDDQh+2rO7CkdEw3lDHFxrFxHQcZlXGosyVyhH9gJpSWrGYGBib6ZFPRRLwOayaoa8XhEdeSuqhQDT6Em1yrWZTXfQjV0rybRmP2a1mLGpz4ojBxsBoZnjkBl0Yx3UBx7mQV0aDcbS7JHS4pbzVneGYso4h1ZMLxZIIxZL42FuW4P6b3qw5O1eqmV1Ge+UT03H0+BywW03kkVeByFppSmmFMQar2YRY1ok4GUnkndRTSzRppYSMFYF4H05JKQaqh6yVSDyFQDSpTWvSs7TDjSMj4RqsqnSym2bJHJBljmA0gUi8cgM8Gkwb8ukqjlMqmkfukvJq5GL2q8h8GtZlG+np8Tmwer7XcJ18PBxHq0tCj89Rlkc+GopRlosOcdc4FUkgWoaTYIghZ4zdxRgbZoztNuJ4ubCZTUhkeaiBiHGDlo0krZGX/vGKMn2hq0t1oJFrxUAe24znlnW6cHxsum6rJTnnSMmZBUEAkOIcf3bXy7jld5V/VUd1XnE1F4RSSMkc4+E4OtwSOty2vBq5mEglbsmHdPn/2VyxqhM7jk+UZSiKMTmdQJvTip5WJwZK9MhHQzG8+fYn8MieM4ato9HR3zUOl+GVG+WR/xeAqw06Vk6sFhPiqcwv3lS9euSuTKNcCmmPPC2t1NqQa8Ygh0fe2+lCPCVjYKI+MxRE0Cg96k35qidTHMfHpvHS0coDtXpjOp2Y3YDn5HQcMgc6VI18Op7KGWQNxzMNub4iN5v5LQ7IHNp4QSOYmI6j1VmeR35kJIx4UsaR0fq+s5tLEjrHaKiMgKchhpxz/jSAcSOOlQ+l0jHT+5uKJDRPtp5w2yywmFhFhlwESOshjzxdDJTLkLsBoG7lFRE00o96A5RahInpOE6ORzBRYXGNPpdbaNOzhQhutrsltLsl9fVnrluTVtSslaE80gqQnqAlBrEYwYQqrfhbHRgPx0vK6BlU0xTrte1ALUimZC1oXAuPfNaRLKYZBUGT0/XpkTPG0OqSyspaEe/DrjPktS7RL2QMejtcAGB49oNRCI88XRCk/D8RVjxcoPJOjmP6YGcF0spzh0fxq1dOzng8mkjhOw/uxdR02lMWF412lw0dqiHPpZOHdRo55xxDgRickhnuHO2XxWPC+FdLLJlCOJ5Cq9OKHjUNuJRWAAPjyjaFcuP7BybxT1sPnTU6ejLFMU91nMrJXJkzQ84Yu5Extp0xtn1kpPy0NcVDTRs2WeYIROvTkAPAhy5YWFbLgGxpRbLUXiM/PRWF3WrK+Rm3uSS0OKx1e1sstEaR0WRW/x/RGY1KDfmoLle7klzynz53DH//yP4Zj79wZAw/efZoRiBSlOQLjRzI7cGKO4N4SsbkdEIbAJKrIE309gkZ5JFPqheeVpeEnlbFkJeikwsJppBHft8rJ3HH1oP4/eunDFhp/ZOQOTrcEiSzae6llVLgnN/JOd/EOd/U2dlZ9v6S2ZSRRx6MJcE56taQf/FtK3HduT0lb59LWqm1IR+ciKDH58hpDBhj6O101W0Kooj+i6wVq+qR6wtm+itsyTsajsOjerWVpB8OB6MYDcVnBBuFTHVkNP2ZCo9caOQAcqYg6r3roWBUKeTKEaQG9B65MRq5qOoUGjlQWi652KaQRy62+faD+xCIGqfp1ytizmyX19a80oresE1lDSpudFqcM/PIC/VfnwsGJyPoaXXmfb63w42jNfbIEykZsWTaIHLOEY4lte+K1ZQu0QfShnxxuxO7BwMZx+KcY2BiGsdGwwVva8dCMfjblM+lkvRDcexsYycuivrPdDQUg9nE0OKwot0lpJWZHqz+zmAoEMNQMP9IvnI08lgyVTS7ReTVtzoldHvtsJhYSbnkYpuxArGKwYkIejtdGAvH8I+PHix6zEZHpMx2e+1zL60wxu4F8AKAlYyxAcbYXxhxXD3ZHrmIuDeLIffYLHBKZi11MVtKqgWDkxHNw8pFb6cLQ4GYYVprJXz51/248Wc7tN9/u3MQF353q5Zxo3nkQlpRDflbl3dicDKS4Q0+tOsMLvnek9j8g2246G8fx47jEzlfcywUx0JVQijXkKdkrq0h29gJA64PII+F4mhzSTCpwXO3zZLTgw3FUpqGPjQVzVnIJShVI5dljo/85CX8+U9fKbhdWlqxwmximNdiL+qRc861bcbD8ZwVsmKbzSu6cMNFi/CzF441RLO2ahAeebfXNveGnHP+Yc75fM65lXPu55z/pxHH1ZMtNTSbIWeM4f6b3ow/v2QJgNrnkU/HkxgPx+FvzW/Il3UqAc+jNcxc2T04hRePjGma+HOHxxCOp/Compusb5oF6Az5CkXe0+vkh4aDAIAffGADJLMJD+do18s5x1g4hkWqRx4pUyMfC8W0YOtMj1z5HI+OhjXDNhqKa544oGjluTXyJJa0K3+PwyMhRBNyXo9czJ8tppHft/0kXjk2gRePjhXs/aH3yAHA3+oo6pGPhuKIJWUs7XAhJXNM5kiFnJhOYDqeQk+rA+891w+ZAztPThY8bqOTlDnMJoYuj715pRW9Rz4ZUb48Pl3v70Zn9XyvNq+z1lkrIuugsEeupiCO1kYnV6SQCGJJGYeGlTXsUodDP7xbMeTWrKyV0VAMFhPDxb1tYCy9PaBIEh1uCdef78eblrVj676hGdkSgUgSiRTHvBY7rGaGcJkeub70Wp+DH44lcSYQhb/VgVhSTgcCwzF06rTudrctp0YejiXR6pLgc1o17T9XDjmgnEs2iwnBAh75WCiG2x/ejyXtTnAOPLl/OO+2k9PiXFS+uz2+4kVB4v1t8Ldorzdjm4n0d3DNfC/MJtZQM2MrIaH2B+r22hGMJbVspGI0jCHPzqtuNo88G6VEv3qN/PBwEP/94vGStn10zxk8c0jJKBInYk8Bj3xxuxMmBrxRI498YjqhBRt3DUxhOp7EoeEgJLMJJ8YVI6nlkeuyVnxOCR67Fb0dLvTrDPlIUGnZCwBb1nTj2Ng03hgJg3OOu549ij2nprSqzg63DQ6rOW/6oSxz/Ou2wzPS8PS3y3qvVcgqYsqVyAYaDcUyPPL2PB0QQ7Ek3DYLuj12TfvvzhPsBJTMlUIa+fcfOYBwLIk7P7oJPT4Htu7Lb8jHwwm4JDNsakuKnlYHhoLRgqMZxUWsz+9T3+fM9yTyzP2tDjgkM5Z3uTP+XsV4YySEe14q7btfLyTV/kBCFtN/X77y6/68+zWMIbdZTIjrglpNb8hVjbza/Nmfv3gCt/x2d0nFL7c9sBff+6OSFicMeSFpxWYxY0m7C/tPB/JuM5voDWH/4CT2ngpA5sCHLlyoPa4f9QYo0kqr6jmumu/FYVVOARRvWZxAW9Qh3lv3DeGxvUP41oN78U9bD2lGtN0twSlZ8qYfvnpiAn//xwP4ZVa+uEgpW9zuzJBWRD6+SFk9MhLCeDiOwYkIFquSifK6uTsghmNJuGxmdHltmvadT1oBlIBnPo08JXP8/vVTeP95fqzo9mDL6i48c2gkb9BzclopBhL4fQ5wDpwpMBJQ/O02LFQ98hx3GQMTmXeFfWXOjP35i8fxtd/szgiG1ztJtYe+uNsVdyBHRkK4b/vM2gNBwxhypT93pkcuWUxl9TNpJMT092pbr4qTodgt6VgohsHJCA6cCSKaSGFwMgKLqtUVYl1PC3bX6HZXeGytTit2DUxp3tpfXtoLl8j+Eb1WzDpD7kpruacmo5oeLXKvAaWMfV2PF3/oP43bHtgLAHjm0KjmYbe7bHBK5rzBTuHB7spKcRwKxMAYsMHvy7gQHRkJgzHg/MWt8NgsODISxpP7hyFz4Er1ogIAnW4J4+HYjB434VgKLpslw3h35Ql2AkrAM5Qnne/ISAjT8RQuXNoGQOmYGE3IeO7waM7tRXm+QMslLzBgYnAyAo/doun6+tx8/TYuyaxJNuv9PuXiVmILAPH5jldYwVsLEikZVhPD+p4WdLgl7Xv0eIE7IqCBDHm2Rj6lVnWWOoGn0RBZFrla946FSs8UEV/6YoZcPJ9IcRw4E8TgRATzfXYtSJiPPn8LTk1FM/Kz5wpxkbpqTTf2nQ5ix4kJdHttWNjmxGUrlWCmvvshoFwYhUfu9zkQT8kYDcWQVP/X68pXrurGrsEpDE5G8OkrzkEkkdIKUzo8EhxSfmlFFPVke5DDgSg63DYsbnfiTCCq/X2PjIbhb3XAbjUr+fmjITy+fwjdXhvWLWjR9m932yDztC4NKH364ykZbsmi3VF47Batk2Yu3Lb8Hrm4IK5X9euLetvgtlnydkwcn05keORaLnkBnVzUKPicEkwsdwri4EQEPa3pOoa+HmU9Iq5RrC2xlqceNNaQyzIv+0651O2TKSX90GxiuGJVF7YdGEY8KeOxfUNYPT//KMuGMeS5slaaVVYBdIZc11/m0T1nsOG2R3H+d7Ziw22P4sCZYL7dNQZVLXJXEW1R/3y/arwKBToF69WTqxZe+eBkBE7JjEuXdyKekrF17xDW9/gApLVmfRMyQZtqdNKeYwRjaum+PmVPyBzXn+/Hp644By7JjG0HFM+ozSnl9ciPjoZxeDiEZZ0ujIbiOKPTOUVaYI9PaVwl5IcjIyH0dii3072dbhwcCuGpAyO4cnU3TLqLqdZvRWf4REBM75EXklUAwG235NXIdw1OwWE1Y5l6e2+zmHHZik48smcIwRxe/OR0XLs4AsB8n/LahTznwckI/K0OmE0Mba7cctHAROZ3cNV8D6xmhv7BKRwZCeHcbz9WsHOiZsjz9HCvhOl4Epu+uxW/3jFQ8j7DgSjWfOMRvHy0eDsqIa0Aync4GE3i0b1nsP3YuCb35aJhDPkMj7zZDXmOgdOvnpiE1czw6SvOQUrmRfucBKMJBNSTtRSPvLfDhTaXhF0Dk6rHlL8YSLC2pwWMoawglFEMTijGYIMaMIslZfSpXuR7Nvbg//voJu1Co7+zEJlO4v0NTETSfWV0UtK6nhb85KObcNu718JmMeOtKzohc0XKsZhNcEgWTOfQjR9XPdfPblkBIPOzGQrE0O2xw9+afm3OOY6OhrFU7V/T2+HCSDCGcDw14+RtdykXGn0uufCs3TaLJoXlyyEXeAp45LsGp7Cux5vxmf3lW3sxMR3HD7cemrH9eDhTWrFZzOjy2PJ65JxzzSMHREplbmlFH2y3WcxYOc+D/oFJfON3exCMJvM6KOFYUstvN7Ip195TAYyH4/jNa4Ml73N4OIRIIqUlEhQiKctaptUlyztgs5jwt3/YB5mnnZNcNI4hzyqQmZxWpgM1K0Ij19+FHBkJYVGbE3/6psUAik9U11K8FvpmFL9ks2twCn3+FqzvacGrJyYxFIwWzFgRuG0WLOt01yQtTNw1LGxzaBd1veG+ak23dlsutHJA8aaBtEc+OBHJ27J3y5puLe9anEiiVN5pNefMI39s7xBWzfPgbWu6lZQ5nbEZDkbR5bWnX3sygjOBKKbjKS0vXwS6HFYz3rysI+PY6cZZOo88rvfIlbUVi2148njkyZSMPaemtDsbwcaFPnz4wkX4r+ePYe+pdHA7kZIRjCYzDDmg5pLn8cgDkSSCsaR2MWt3zxyYEYolMRVJzHAm1vf48MIbY3hW1evzvYb+8WLnSTmIi/JLR8czmpsVQgS4S3F2kikOs/pddUoWXHJOB05NRdHlsWnf7Vw0jiG3ZGZxNL1HnkMjPzoaRm+nG21OCYzlTtnSI7rLvWPdPAD5vfLhYBSnp6JY7/dhfU8LDg+HwLmiIZfC+p4W7BqcBKD0L8lVSFMOQ4Fozu6A2QzoNFTxJV+X58ue6ZEr3xu3zQKf04rByemCnR4Fl6/qgolBSwd02sxas6qxUAzf++N+fPvBvdh+fAJbVnfDbjVjRbcH/Vr8QcZoKI5urw3zW1T5YSKiFQIJA96rGvRLl3fMaIWcbpylb6UrDLlZ0/gLBToBRVoJxZIztFtRTCTubPR8+e0r0eKw4vP37cR3HtyL7zy4F99SA8GiB7+gp9WZ18iKIKi4mLW7bDM08sE86a/re1ogc2DtAi82LW7N6/Xrc/QLtQB44PVT2Jcj6+r4WBi/eOnEjMd3DU7BbGJIyRzbDhYOQAqEk6CPlzy86zRez1HcpB9PCCiOBIAZEls2DWPIrWYTOIcWra/X6UBGkW3IU7IyEKG30wWL2YRWZ/75jQJxIr197bwZxS96hL7d52/RAlxA4dRDPet7WjAUiOHoaBj/97934Iv/83pVk4Pue+Ukvnx/P06O5896yPbYru2bj8tXdmYUz+ix6E6OtqzA3OBEBMOBqGKk3fkNYJtLwvvP8+PS5YqX7JTMWh77w7vP4N+2vYF7Xz6BVqcV7964AIASoNs1MAnO06X53V477FYzOj02DE5O456XjsNmMWnBrN5OF9b3tODDFy6asYYWh1IGr5cLQurFRMkjt2HT4lZc3Nue930o21qRkjmiicyAYXagU4/PKeG7163DcDCKe18+gXtfPoH/fXUArU7rjAtoj8+BU5ORnKX3Ii4gLma5Jh/pc8j1XLq8A70dLvzd+9ZjUVv+i4Uw8HarKWdGjOAr9/fjr36+IyO1MiVzfObe1/A3v9k1o5dQ/8AkNq/oRIdbKppJIhBOgsi4mY4n8bn7duL//veOGfKWfjwhALxtTTeWd7nxgU3+gq+RP6xdZ0hZmnEwljwrPHIxgHlgYhrxlIxlakCs3SUVjcYPTkYgWUxY3OacUfyip39gCiYGrJnvzegwV4q0AkDz3j5976s4rQveLe/2lLR/NsKb6h+YwsK23Dp9tsf2oQsX4UM5DJ/AkkMjBxSDc2wsjC6PHZ0eW9Esne9/YIP2sz6PXEgDr9/6tozA6np/C+7bfhKDkxGdIbdpr/34vmGMheP40ttWaBcYm8WMBz59Sc7XN5kY2lyZF3F9sNNiNuHXN7254HsAFI8cAIKxhNaoDVAu9m6bBUt1uet63rF+Pt6xfn7R4/e0OpBIcQwHY5jXkinzCA9VPN7ulhCKJRFNpLQ7EPH3zb4rXNjmxBNf2qy9xpnXo0imZC1AKBiYjEAym7C8y6O1As5mOp7EdDyF42PT+Pen3sDn1JjGvS+fwOvqufL4viF84tJeAErM6choGO/Z2IN2t4SHd59RPejC/vBwIKZ58bsGFI8+lpRxJhDFjx4/hL9552oAM8cTKp+NDY994bKCxwcazCMHlCwOEcCrx+lARiFZMjXy9O23coK1F5ioLhABJZOJoc/vQ//AJBIpeYa3vGtgCss63XDZLJjntaPDbQNjSi51KaxZ4IWJAbsHA7hwiZJ7XE3wU3hZ/apck3sb9fa8RPlHf3JkeOStDgxMKDp1MV05G4fVjGhChixzjIXi8DmtM05qcZHbNTClGTDxOj2tDoyF4+jtcOEv39pb8uu2uySMBPUeeTrYWSqePP1WRKCz0G18Kfi1drYz76qGAlEwlpaJcg3MEIa4o8AdUo/PgZTMM7KCBCJ9tstjy6uRi7uAFocV/7rtDRwZCWE4EMX3HzmAN/W2Y2W3B4/tTadc7jkVAOfKxVlklLxSQibKUCCKjQt9sJqVFgNb9w3BY7fgfef14K5nj2rZZ9njCcuhYQy53iNv9qpOYKa0IjJUhI5aaBCvYEBN8QIU+WM4GMPyrz2M1d/4I/afSeuCuwanNI2ZMYYN/hbM89q1z7wYTsmCFd0e+JxW/MsN58EpmasKfgpvrFDKpOaxlXjXoL9d1afK9fgcmI6ncHAoWDTTIxuR2hhJpJRJ9y5pxjYr5ykpc88eHtUaT4mA6kI12Pet96zTyttLodNjy+uRl0quDogpmWPv6UDBoFqpFBowMRyMot1l077jIhNH/33eeyqAnlZHwQuKPlidjQiEt+uajA1MTOOC727FoSHFcIoLx1ffsQqS2YQr/uEpXPi3j2M6nsS3r1uLq9Z0Y/vxCS1nX3wf1/e0aBklpWSvDAWjWNjqwMp5Huw8OYnH9w1j88oufP2aNXDbLfjHxw4ASMvG2XcXpdA40oo5PXPxbDLkcV3BiM9p1bzJDretYBYKoHzBV6vpa+8/349ESsZ4OI7/ePoIdg1MYdU8LyLxFIaDMc3TB4C/uWZ12Slbt7+/D4BiZNYu8FZsyGWZ49SkYvBEcChX0Zfw2DoLeGx6hLRiYtAakwHpC8HpqSguX5U/TzcXwpBPx1MYDcZz6us2ixnXn78Qv3zlJC5f2QmziWkG/8/fsgQbF/pwyfKOGfsVot0l4fhYZsMtQAl2loroSa73yEdDMcSTMhblkVXKodCACX0rBADo8GQOzNh2YBjPHBrFl962oqTXGJiI4KKs5wYnIrhsRafWZIxzjldPTGIkGEP/wBSWd3u07/iaBV78/BMX4Vk1PfC8xa04p8uDLWu68eMnD2PbgRFcd24Pdg1Oocfn0O4SbrhoMX76/FF85OLF2LDQl3ONYuxet9cOh2TBL185Ac6VFhBtLgkb/D4tZpAQc2YruBtqOI88kZRndFtrRtIeuXKVVgpGdD03XBIC0WTePhLRRAqjoZj2ZW9xWPF/L1uGL75tJRhLn2Dif70evqzTrZVnl8rGhT5sVL/M63t82HNqqmjlXS5GQjHEUzLWLvAiGE1mGCw94ta5VAlAeDk+p5Sxjz69rbtcaUWtnIzEUxgNx/JeVL789pXw2i3Yum8YXR6b9vrdXjuuVjOKyqHdnSkXhGIpWM2sLK8+rZGnDblom1qo2VapuERGUA5vWd8KAUDGwIxoIoVbf7+nJLlpQZ6LRSypOCf+VifaXRISKY5AJKkN7hADqoUj1O62YeNCHz51xXJ86orlWspnX08LOj02PKar0tXfrXz+quXodNtwy+925w3uT0USiCdldHntWN/TAs4VQ715heI0SBYTYmp9TDJrzmw5NIwh13uoZ4NHLpnTFy5A0ciFrAKksyvy9ZE4lcNAA8oXR1+sIU4Cf4FJQOXS529BNCHjcAVj4MStuEiZ7B+cQkrm+J/tJzNkgEGdbFQKIojZmnXx1x+jUmllOpHEWCiuVV1m0+qS8NV3KAGtfK1ly6HdLSEcT2ntAZSGWeXdXHtsyuegzyVPp2BWv0Ygfy55tkcuPrcH+0/jC7/aieNj0yXJTXarGR3umYVHp9U7up5Wh5bFNBqOaXEm8T5FWmIuSQxQAstXrurCUwdGcMdjB3F0NJyRzeOxW/H1a9egf2AKv3g5nao4HIhqrRzS9Qk2LV5ywZI2bSKYvj5GOD6VSCuNZ8iTMgKqIW/q9ENdsDMYTcyQP7RS7TwSiOZp5wgG9vjSJ5i+57NRiC97JQFPsa7NK7sgWUzYNTCJn71wDP/v1/0Z+enZ5dvFEAGk7MIVn9OqGeRyDZjI9piaTmAqktC03lxcf74fl63oxKbFrWW9Ri7Erb3wKMOxJFwF+qrkwq1JK+kspaGgsYa8t8ONPacCGSmIiZSMsXAsI7DslCxY2e3B0wdH8NCuM/jIxYtKlpt6clws9N99vf4u+uaLWMVoKAa3zTIjV1/Pezb2IJZM4Z8ePwTJYsIl52Su61198/HmZe34/h/3YzSkSDifu28nPnPvaxgKRDMujiu6PTiny40PXpBOJdRXrCfkyoOdjaORn6XBznhK1nJZRS8OYObJnE2hfuI9rU5t+MDg5DQsJmbYyQsAS9tdcNss2DUwhQ9uWlh8Bx3iwrKkw4U18714+uCodmKK9xRNpDASjJXUQkCgeeRZ3hdjDD0+Bw4Nh4oW0WTjVA2AWFeHJ/+QE5OJ4e6PX1jW8fPRoeu3srDNqfUiLwehp2cMbVY7M3bkubMol8tXdeL3r5/CrsEpTUNWjN3Mi8UfP3cpRG1SORkzfp8De7MKekT6qr/Vob2/0VBMm2QlvOSxULzoe33TsnYc+PY7tN+z18YYw7fesw7v+Ken8XcP7cdbV3Tg+TfGACiOjJCBuz1K8sDWrFRCSddDSvPITU3skeulhsnpBBxWc1maYKMh6TRycUu4TOeR5yrV1jM4EVHmJ+Yw0PpijcGJCOa1FO9yWA4mE8O6Hq9W0VgOg5PTaHFY4bZZ0OdvwYGhIOIpGR6bRTOYIle91Dx3IH1yZEsr+uOUezETcsZJ1XAU8siNJO1lqh55PFlWoBNQgrBS1pQg0Zmxklv7XFy+sgtmE8vomqiXGvQwxmAysbLTHoVHrvf6ByciMDElT13cue49FUA4ngJjemklVrAATCDWlW9t53S58ZeX9uL+Vwdwy293Y818JR1318CkpsfncxIyPHKhkZ9N6YfN7I0DmemHR0bDMDFgUXvaA23PUaqt58BQEPO89pwnpV9XrFGuRFEqfX4fXj85iVW3PIwLv7u14MzHv3toH2757W4A6UZYQLpvyk2XLcOKeR4tJ7kSOchsYrConfay8bc6YDUzrQdLqQhpRUwjMsqTLUZ7Vt51SO1FXi5euyUja6XQwOZK8DklbFrcmpGLLYxouTn7+ejxORBPyhkdDg8MBdHttcNqNmntLETnwbULvBgOKBLIaDCeVx8vl09fsRw9PgeCsSRuf/96LO9SWjMMB6JocVjzyjdW3VD5pJq1UqzAKBcNY8j1hu2MwV+4esSqa5o1MD6N+S2OjDsQZbSWKWcfie3HxvHY3iG8a8OCnMdON2yantFhzig++qbFuGnzMly3sQfDwRheOZp7Ij0APHVwBPdtP4lwLJnRPved6+fjG9euwU2bl2Xq+nnKt4vxow+fi49cPLP68y8v7cU/f/jcsr1Boa2LnjaleHdGkJbVlL99uAJpBZjZk1x0ZjSSq9Z0Y/+ZoNZuYbiEnjbl4M/KJd9xfAKP7BnCu9XvvmhnsVOVEt/U2454SrmrL9UjLwWHZMZdH7sA//6R89Hn92G9Xxm4UsxWiR5SwFmStSLpgp2i4Xwzo7WxTcoYyNEbnDGWM5c8mZLx9d/uxoIWOz59xTk5jy2q7o6NKs2iSm2OVQ7+Vie+cvUq3PaetZDMpoJVmsNBJX/5mUMjGX9bl82Cj1+yFHarGT2tDpyejCKlykHi1rkc3rl+fs7snMXtLly9rnjZeTZOa6a0Mlceud1qhttm0QLdlWStAGrjLJ1HLjozGsmVasdI0dp3KBAr2tOmHPSFR+K7P89rx2euXK5t0+6SEE/KcFjN2ozQU1MRjIeLa+TlsHKeB29fq2Rb9flbMBqK4/WTUwUlO0mdzcs516aBNbVHLkrWxYTx2ZAD6gm9Rp7vwtXhlmZo5P/1/DHsPxPEN961Nu/JLY6148QEZF6e1lwuNosZq+Z78lZpxpIpLYXy1zsGEY6ncv5t/a0OJGWO4WAUA5MR7da5lghp5UwgCsliqsgrrhR9i4ZKgp2A4pELjVzfmdFIlna4sKzThcf3Kw2mhgLRknralIr4rjzYfwq3/G439p0O4NZ3rcn47gspammHS2vUdXAoCJmjYAuAahCyYLHWDzadZCyCnZV8No1jyM3qSTMVRSwpN70hF0YqkkjhTCCa8/1mF4YAwC9ePoGLlrbh7WvzN6F3Sha0Oq2ablhO9kclKG1up3J2whONpCSLCY/vV7y2XJKJfnzY4Czp+uUiWUywmBg4Bzpc0pyOHWx3KaXnnHNt8HK5uG1WLY9c35nRaLas6caLR8YQiCYwFIwZ+hoeuxW9HS48smcI9758Eu9cP29GkZXw/ns7Xdpri57q+XL/q2X1fK8mkRSUVnRKw1kR7BR51SIVz8gClnrEbGIwMWBgfBopmec0buJkFiRSMk6MTWPTktaiRqWn1YHDwyHt59mkz9+iVGnmaEsrshiu7ZuvpZ/lurBoWuhkpOxioNlEeOVzpY8LhKwWTciQeXl9VgQeuwWhmJLKW0o/9kq5anU3EimOpw+OYLiC5mTF+OPn3oqd37gKO79xFf7lT86b8d3v1Ay5WysQEimLs5VpJHrRA4UvjiIWFk/KZ0ewU1y5jqiGvNk1ckD5gx4dy/9+9X0kACV7IinzjHzzfOg92gU+470wPWLaTH/WRHkgHfz64KaFmgeT672KcuzjY9M4PVXa9KK5QAQ850ofF7S7lTmXlXQ+FHh0Gnl2Z0YjOXdRK9pcErbuHcJwMGb4xUKymOBzSvA5c98VicyUZZ0u2K1m+JxW7FE98tn8u4lKzq4CLQ8kNYEhkeK1D3Yyxq5mjB1gjB1mjN1sxDGzEcG/o2eRIZfMJhwT7zeHlNDhTveRANKtbpd2Fm96JLzeLo9t1vPxl3e7YbOYcurkwhM8p8uNi3rb4JTMOXO9nZIFbS4Jr56YQErmsy4HlYqYVD/3HrmE8XBMS+sst7ITSGetcM6145RbFFUKYiL84/uHMR6Oz4p8UwjRlEs4ON0euzbPczb/bqLCuVAAWbLopZXKPfKqozOMMTOAfwFwFYABAK8wxn7POd9b7bH1CI98JBiDx27J6GDXrFgtJi0QuCCnIU/3kWhxWrWmQMtK8MiFNDEXF0Sr2YQ1eToiDgVjSn63U8JXrl6FN0ZCeWWhHp8D248paYz1ciF3atLK3HrkV67uxj8/cRh/99B+AJVJK267BYkURywpYygQVTszzo5h27K6S5s8P9epw1evnYepSAJrFygTmLq8NhwYCsJsYrM69/ddGxZgcjqBDTmmLQnS9TEpXRvb2njkFwI4zDk/wjmPA/glgPcYcNwMJN1Vqh4CXXOB0M863LacBQXZ/VaOjITR7pK0hjyFEIZwrj7Lvh4lrzY74DkciGkdAfv8Prz33PwjrXp86ZLrevkOaNLKHFV1CjYu9OFDFyzUhhBXJK3oepIPBZTujUZW+Oq5dHmndg4bneJYjFaXhL+6bJlWJyDkozaXVPUAjUJ47Vb89eXnFKyUFe25Y0lZ67VSqxL9HgD6SbkD6mOGYlIr84DyC0EaFXGLlc/7zC7VPjIaymisVQhhCOfKs13v9yEcT2kxDkE5ucv6tdaLIRetbAv1WZktvnL1Kk2GqihrRdeT3OiqzmxcNgvefI4yR9TooqNyEe/TqKrOatDac6e4rtdKHWetMMZuZIxtZ4xtHxkZqegYmmGrk5N4thEeTL6CHZETK4zjkZFwSYFOQGlK5bFbDJkGUwridXZlFQaVY0DE373dJWXMmawlonHWXPVZ0dPqkvC1a9bAamY5pbdiCK36+TfGlDujWfaUr9vYA5dkhr+ttueveN+zlUNeDiKtOp6U08HOGkkrgwD0Le786mMZcM7v5Jxv4pxv6uzsrOiFxNWrXvTR2aaYR97qktDnb8Hj+4YwNZ1Q5j+W6JG7bRa88rUtuKaEQbpGsKzTBYfVPKO1rZieUgo9c6jrl0qtNHLB9ef70X/r2ysKIL6ptx0XLmnD3z+yH4OTkVnXrq87twfbv35VzeNbmkdeo7+ZnoxgZ43TD18BsJwxtpQxJgH4EIDfG3DcGYg32Ow55AKRO19ISrpyVTdeOzmJl48pxT364RPFsFvNc1bEYjGblBFwOkMeTaQwFUmUbsh9c6vrl4K4Myh15NxsrqFcGGP49nXrEIomEYol50TyqIc7qa468si1PPJUqrbph5zzJIBPAXgEwD4Av+Kc76n2uLkQ5az1dCLPJqVISVvWdIFz4D+fPQIAJXvktWC9vwV7TgU0LXBYy10u7YQSw4rr6e8vskWy+5w3CivnefAXlywFMDuph/WIcBza6uBvliv9sGbDlznnDwF4yIhjFUJcverp1no2KSatAMCa+V4saLHjxSPjMJuYZuzqkT5/C3763DG8MRLGynmesifSeB0WfOXqVbhydXlDkmeT957bUxd9X6rhM1cuBwfKHj7dqCxoseMLV63QOiTWknSvFX3TrCaeEAQoVy+71VQX0ea5QCrBI2eMYcuabvzsheNY1ObUrvD1iL7Cc+U8T9kzIhljuGnzstlaXkWsnu/F6vneWi+jKlw2C/7mnatrvYw5gzGW0R2xluiDnakapx/OGVazCQt8jjltTlRLrGYGr90CT5HgkGgV2ttRv7IKoKzPJZm1wqB802II4mxBxMEypJUKNPKG8sjnt9gLDkptNua12LFmQXFv7+LeNnS4Ja3Xcr2ijIBr0Qz5sNr+tdmnPRFEPtLdD5Vgp4mVN7NU0FCG/J8/fF6tlzCn3PqutdrtViFsFjMe/8JmOCsoCplr+vwt+NkLx5FIKWXhXR7bWXOHRRDZ6AuCErJc8bzUhjLk9ZC6NJeUc/dRSll+PbDe70MseRSHhkJl5ZATRDMiZQyW4LBW2DKgoTRyovERFZ5//YtXsfPkJOnjxFmNkFZiSWVC0FnhkRONz5J2J264aBFOTkSwsM2JD5y/sPhOBNGkMMZgNTO1spNXlHoIkCEn5hjGGL773vW1XgZB1A2S2YRESkYqxStKPQRIWiEIgqgpksWk9VqppGEWQIacIAiipghDnkzxinLIATLkBEEQNcVqNilZK1WkH5IhJwiCqCGSRTHkCfLICYIgGhPJLKQVueLma5S1QhAEUUNsqkYuc15xsJMMOUEQRA2xqh45AFgrTD8kQ04QBFFDJIsJsaQME6tszBtAGjlBEERNkSxKQVAixSlrhSAIohHRgp2yXHHWCkkrBEEQNUQUBAEmMuQEQRCNiGRWNHJGGjlBEERjIjTypFx5+iEZcoIgiBoiKjuT1P2QIAiiMdEHOyvtR06GnCAIooZY9d0PyZATBEE0HpLZhKTMEU/JJK0QBEE0ImIA83Q8Rd0PCYIgGhGbashTco0qOxljH2CM7WGMyYyxTdUciyAI4mxEnzteq2DnbgDvA/B0lcchCII4KxHSCoCKNfKqKjs55/sAZTI6QRAEUT6SziOnrBWCIIgGRO+RVyqtFPXIGWNbAczL8dTXOOe/K/WFGGM3ArgRABYtWlTyAgmCIJoZvUY+a9IK53xLRUeeeZw7AdwJAJs2beJGHJMgCKLRsVlIWiEIgmhojAh2Vpt++F7G2ACANwH4A2PskWqORxAEcbYhGeCRV5u18hsAv6nmGARBEGczUh3kkRMEQRBVYESwkww5QRBEDTEi/ZAMOUEQRA2x1TrYSRAEQVSHEcFOMuQEQRA1hDRygiCIBoc8coIgiAaH0g8JgiAaHL3xJmmFIAiiAWGMaV45SSsEQRANitDJrbUY9UYQBEFUjzDkNHyZIAiiQdGkFdLICYIgGhOrRfHESSMnCIJoUCjYSRAE0eBIFjMAwErSCkEQRGOiBTvJIycIgmhMbGZKPyQIgmhotGAnpR8SBEE0JiLYaSZDThAE0ZhIFhMsJgbGyJATBEE0JJLFXHGgEyBDThAEUXOsZlZx6iFAhpwgCKLm2Kr0yC0GroUgCIKogA9u8mNdj7fi/cmQEwRB1JhzF7Xi3EWtFe9P0gpBEESDU5UhZ4x9nzG2nzHWzxj7DWPMZ9C6CIIgiBKp1iN/DMA6znkfgIMAvlr9kgiCIIhyqMqQc84f5Zwn1V9fBOCvfkkEQRBEORipkX8cwMMGHo8gCIIogaJZK4yxrQDm5Xjqa5zz36nbfA1AEsA9BY5zI4AbAWDRokUVLZYgCIKYSVFDzjnfUuh5xtjHAFwL4ErOOS9wnDsB3AkAmzZtyrsdQRAEUR5V5ZEzxq4G8GUAl3HOp41ZEkEQBFEOrIATXXxnxg4DsAEYUx96kXP+VyXsNwLgeI6nWgBMFdm9A8BokW2KHaeU1zHiGMXWasQ6StmG1jo766C1lr/N2bZWI+yVfpvFnPPOGc9yzuvmH4A7S9hme7XHKfF1jDhGwbUasQ5aa03XQWultRY7RtX2qpRt6q2y84E5Ok4pr2PEMap9DaO2obXOzjporeVvQ2udhdepSlqpBYyx7ZzzTbVeRynQWmcHWuvsQGs1nrlaZ7155KVwZ60XUAa01tmB1jo70FqNZ07W2XAeOUEQBJFJI3rkBEEQhI6aG3LG2F2MsWHG2G7dYxsYYy8wxnYxxh5gjHnVx62MsbvVx/cxxr6adSwzY+w1xtiD9bxWxthnGWO7GWN7GGOfq4O1Soyxn6qPv84Y25zjeL/XH6se18oY+z9qJ849jLHvzdJaFzLGnmSM7VVf57Pq422MsccYY4fU/1vVxxlj7EeMscPq2s7LOp6XMTbAGPtxPa+VMfY99Tu7mzH2f+pgravU70eMMfalHMebFVtg5DoNtQPF0l5m+x+AtwI4D8Bu3WOvQCkyApQeLt9Wf/4TAL9Uf3YCOAZgiW6/LwD4BYAH63WtANYB2K0+ZgGwFcA5NV7rXwP4qfpzF4AdAEy6/d6nfq67jV6nUWsF0A7gBIBO9bm7oVQbG73W+QDOU3/2QOn6uQbA3wO4WX38ZgDfU39+J5QeRAzAxQBeyjreP6mf7Y/rda0AroHS6dQCwKX+bbw1XmsXgAsAfBfAl3Icb1ZsgVHrhMF2oOYeOef8aQDjWQ+vAPC0+vNjAN4vNgfgYoxZADgAxAEEAIAx5ofyhftJna91NZQTZJornSOfgmIoa7nWNQCeUPcbBjAJYBMAMMbcUE6K7xi9RoPX2gvgEOd8RN1uq24fI9d6mnP+qvpzEMA+AD0A3gPl4gH1/+vUn98D4Gdc4UUAPsbYfABgjJ0PoBvAo0av0+C1rgHwNOc8yTkPA+gHcHUt18o5H+acvwIgkX2s2bQFBq7TUDtQc0Oehz1QPhgA+ACAherPvwYQBnAaivf1A865MAA/hNIuQJ67ZQIof627AVzKGGtnjDmheEELMTfkW+vrAN7NGLMwxpYCOF/33LcB/AOAuW7BUO5aDwNYyRhbol48r8Msf66MsSUAzgXwEoBuzvlp9akzUAw0oJzkJ3W7DQDoYYyZoHyuM2SBelsrlM/8asaYkzHWAeByzOJnW+JaC/FDzIEtqHKdhtqBejXkHwfwScbYDii3L3H18QsBpAAsALAUwBcZY72MsWsBDHPOd9T7Wjnn+wB8D4oX9kcAO9XtarnWu6CctNuhnATPA0gxxjYCWMY5/80crU9PWWvlnE8AuAnAfQCegSJlzdrnqt6p3A/gc5zzgP45rtw7F0sH+ySAhzjnA7O0RI1q18o5fxTAQ1A+63sBvIBZ+myrXetc2QIDPlND7UBdDl/mnO8H8DYAYIytgHKbBCi68x855wkAw4yx56DcVp8LxUt7JwA7AC9j7Oec84/U4VqPcM7/E8B/qvv8LRTDNOvkW6t6a/d5sR1j7Hko2t9lADYxxo5B+a50Mca2cc431+FawTl/AGoFHFPaJs+WsbFCOYnv4Zz/r/rwEGNsPuf8tCpHDKuPDyLT0/Krj70Jikf2SQBuABJjLMQ5v7kO1wrO+Xeh6LxgjP0C6mdew7Xm4y2YZVtg0DphpB2oS4+cMdal/m8C8HUA/64+dQLAFepzLigBmf2c869yzv2c8yUAPgTgibkw4pWsNWufRUgHEmu2VvWW2aX+fBWAJOd8L+f83zjnC9TP9RIAB+fCiFey1qx9WqF4vIZrpIwxBuXk28c5/0fdU78H8Gfqz38G4He6xz/KFC4GMKXqrDdwzhepn+2XoGjTRhtxQ9aqZoC0q8fsA9AHg3X9Ctaak9m2BUatUz2WcXag0iipUf+g3KqdhhIMGADwFwA+C+WKfxDA7UgXLrkB/A8U/XQvgP+X43ibMXtZK4asFcqt/14o2qPhmRUVrHUJgANQAjdboXRYyz7eEsxe1ooha1WPs1f996FZWuslUG6b+6HcDu+Eom+2A3gcwCF1XW3q9gzAvwB4A8AuAJtyHPNjmJ2sFUPWCsWzFZ/riwA21sFa56nflQCUgPcAsjJpMAu2wMh1wkA7QJWdBEEQDU5dSisEQRBE6ZAhJwiCaHDIkBMEQTQ4ZMgJgiAaHDLkBEEQDQ4ZcqLpYYylGGM71S5zrzPGvqjmpxfaZwlj7E/mao0EUQ1kyImzgQjnfCPnfC2AqwC8A8CtRfZZAqU6lyDqHsojJ5oetfTdrfu9F0or1g4AiwH8N5T2rADwKc7584yxF6F0qDsKpZvdj6AUJm0GYAPwL5zz/5izN0EQBSBDTjQ92YZcfWwSwEoAQQAy5zzKGFsO4F7O+SamDKz4Euf8WnX7GwF0cc6/wxizAXgOwAc450fn8K0QRE7qsmkWQcwhVgA/Vjs9pqD0Qc/F2wD0McauV39vAbAcisdOEDWFDDlx1qFKKykoHepuBTAEYAOUmFE0324APs05f2ROFkkQZUDBTuKsgjHWCaWT4o+5oiu2ADjNOZcB/CkAs7ppEEofdMEjAG5SW5iCMbZCdGEkiFpDHjlxNuBgjO2EIqMkoQQ3RQvSfwVwP2Pso1Aa/IfVx/uhDNd4HcB/QZmtuQTAq2or0xGkR6QRRE2hYCdBEESDQ9IKQRBEg0OGnCAIosEhQ04QBNHgkCEnCIJocMiQEwRBNDhkyAmCIBocMuQEQRANDhlygiCIBuf/Bw7y3zYQKzs2AAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -152,7 +153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -161,22 +162,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8b5e9e488>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -200,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -209,22 +210,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c4d0f0358>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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aoqC00uv2YRCasYwuGZEpghq5UbtHLIF7Me8MefhNHi8FUS/QtCcwIJM5dofTy4TKwqQNSjze3dZEfT9e7HASlFbCaq14fDKhjR50T+JAP9NtZ5RGbtU9clfIkBfbTDg9/j4Dt61HM+SVhdakZkINrdo1bk1xsUdjR+ic6bjug2FnUxcfDTJIn07C75vwB9xQ8drGgynPfncd7mJ8ZSHlBZZgsFNbnGZECBGMC2X6waQnaJhNUdJKvhnylm4XRoP2JcTTMnXPcG+ShlxPPRxfVUBFoTVljVxKyc2PreI7/16b0nHSRd+slcQ3l9AHWiKGXM9asZkNOL3+4DZvBRYjRTYtvS/aK2/v9VCqe+RJzIT2BWYIg5HRwjnY4cQUGFeZktR+9fpWbl6yKuM6bqQhH9rv4kB7Lzc8upKH369P6Tg7D3czoaqAErs5FOwMFHADTeaDzO/b6Y6xIAgYlg2Ys8yQuxldZgdiSyturz8YAd7TnJwh39mkBUwmVhZSUZCcQYlFa4+HTpeX5fWtrGtoS+lY6SBkyEMeMyQ2uPUB2N7rCUol0fS6Qyvp9PO4PL7gNm8FgawViKyA6Pb66fX4gh55l8ubkOfk9fmDD5ZUPfJDHU4mVBVoWTODvO4f7Ghm6SeHBt2GhtZemrvdNGU4NrOnpSfoLB1MwSNP5IG0vL4VgG2NXQO8Mz5ur5+9LT1MqNQMeadT207Q6fEHDXhox6rMeuSxqh/C8Eg+WWfI68ocmAwiprQSbmSSlVZ2HtYyVkaV2TWPPMWgV/iM4G/v7UrpWOnA5YnOWkl8387w98TzyvukH5q1rBi98mGh1URxDI9cfyCXOMxUFVoBaEpApz7Y4QzKQunwyEeW2rWsmUEYUikldzy9jh+/sGnQbdgfmF18crBz0MdIB3tbezlqZDEAjYPMXLnuH8v54j+WD/g+fUeo7U2DN+R7WrrxS5hQVRDcaLmj10OvxxeK1wR3rMqOYKc5rB455KW04qa8wEKJ3RzTI9czJISAva3JeuTdjK1wYDQIKgsttHS7U9ooWD//8RMqeGHdgYync4XyyHUvJXFpJdyQx+tHeN4uaA8Kr18GvW+HxRj0yMMzV4KGPCCtQGI6dUNYMDtVQ97Y4aK22EZFoWVQwc4tBzupb+6hoaUX/yDGjMvrCz68Mm7IW3qYMbIEu9k4aI3cZjYk5EjpHnn94e64C/wGYkcgY2VCZWHQkLf1enB5fKEMqjRr5Bv3t3Pff7cl/bm40kq+GfLmgCEvtptjph/2BjzycRUF7G/rTWpw7GzqYkJVAQAVBRb8kpQqpu1t0QzNnRdMxyclj35YP+hjpYOgITeGpI/w1/sjfKDtj5O54ozKWtENektgZlMYyFqByNRR3ZDr6YeQWOZIuCFvTcGQe3x+Dne5qC62UVlojfsQ2X6oi+2HYhvZlzccBDSP69AgHgThD8fNBzJnyDudHlq63YytcFBbYhu0tDKyxM7+tt5+5ZUOp4ctBzsYV+HA65fsTlIK1QmPbZXaNUegrccdKKkcKa2kyyP/+7J6fv361qRnb26fH7NRYDDkcR652+un0+kNGfIY+cjdgcDa1Joi/DK+0YnG6/Ozp6WH8ZWFAFQEDEqiGQwH2508+kF9hOFvaO2h1GFm5sgSzppRw5KP9qScJpcKfbNWIjdI7o/wgRbPI9f7Fq6RQ+g7dFhNFNtjeeTa30sjPPJEDHkPQkCR1URLCg/cw10upCTgkcfOmpFS8uVHVvCtJ2MX1Xplw4Fgv5OV9CAUtLWbjXzS2GcXxKTZsK99UN607nyMKXdQXWQdtEc+qsyO0+Pvd6a0ancrUsLlx4wBtAflYNjZ1EVloZVim5niQK2c9l5PMGsFwuJBadLIdUloa2NyD12P1x+UVSBP88j1gFZ5gYVimymOtKIZiGkjioDEb6qG1l48PhnyyAMGJdEn7s7DXdz13EbW72sPvra3tZe6QGD26uPH0dbj4e2tmdtUWg9qBvcLNCceaInwyONJK14fRoPoo/+1BIKHDnMoa6UjlkYe7pEnEHDc19pLTZGN6mIrrd2DL6KmP5hqS6xUxsmaWbG7lV2Hu9l6sLOPdLL9UBdbG7u4/JjRQPLZUgD727Q2nDS5km2NXSlJegA3L1nFdwZRyVG/X0aXpeaRjyrVxv2+fhypFfWtGA2CS+aPAmDHIHXynU3dwftWl1baAxp5KF6TPo/8UKeT+sDsYWuSMpjbF9r8HPJUWtGf7hUBjbwzhiHvCXjk02q1YE2ihlyfnk2o1AaEHnRLNHNFP9+WsGlxQ0sPo8scACwYV4bFaGDVntaEjjcUuLx+LEZDcFoXlFYSGNzhy/sPtoduzn8s2xWsa9Pr9gdvHAjdPC3dbhwWIwaDoNDaN2tFLztaYjdjMxsptJoSeoA2BB6U5QWWlDRyPcWuusgWN2vm3ysatD4GyhyH88oGbTepL500HiEG55HrM8dTp1bh8vqpb05t7UGXy8t725qS9qj1vPwx5Q5qi200dgyu9sxI3ZD3syhveX0LM0cWU11kY0SJbVAeuZSSHU1dwfu2NOCR64uZwlNhIT0a+cr60D38SZLZNu7APagTLq24vD6ueuijIVtLkHWGXJdWYnnkPYELNaGqAIsxsYALhLIkaktsQJi0kqBHXl5goabYyuaD2rTY75c0tPYyulwz5FaTkaNGFbNqdyYNuS/oJUNywU7dYxhb4YjIWnli+V6WfLSbXrdPyxIIM+T6uZq7Q+WAjQZBkdUUlbWiGXV9WlxZaEnII29o62FUmZ0yhyWl9EPd2NWW2KiMMRPrcXt5Yd1+JlVrslu0wXl5w0HmjSlldLmDEcW2QXnk+1p7qSqyMntUKZB6wNPt9eOX8MzqfUl9bk9LD8U2EyUOM9XFNtxef0SJ4UTRZ6LxPHK318+avW0cM64cgEnVhYMy5Ct3t9La42HheO04JRGG3B+x+A3Sk+a3vL4Vm9nA3NGlbEtSWtE08r7SisvrZ8uBTt7ddpgnVzak3MZYZI0h17XWikILxYFyqNHeQk9YqltdmZ2GlsQ0cn01WKlDu5FL7WYMIrlVftNHFAcDVU1dLqTPzaX7fwkbnwXg6DFlrNvXPmyF5KNxeUMDG/oO7g372uN6LCFDXhA05D1uL1sbO/H4JKv3tmpZAubwB0XIIy8Mq+seXQGxvddDgcUYHOCJLMby+vwcaHMm5ZHXH+6Oqe8f7HBiNgrKHRYqCvpKOy+vP0i328d3z5kGROqie5p72Li/g3OPqgVgdLkj6WwpgP3tvYwstTO5phCD0LJgUkG/Xk+tbEjKo97T0sOYCs35qC3WnJrByCsldjMFFmNcQ75+Xzsur59jxpUBMLGqkB1NXUln/Dy3Zj82s4GzZmrfv8looNCqya4u79B45Ct2tzB3dCkzRxbzSWPngN/v3pYeDgRmsW6vP8KZCk8/3LhfcwLf33540IvCnu3nwZ01hrwlcHOXOTRpxeOTfTSvYIEmi4m6ckfCHnlbjweTQVAQyLgwGATlBcktF59WW8z2Q514fH72NnfzU/NfmdzwFLz0HfD0cvTYMtxeP5sOpB7MGgwuT2jjZYgMdh7qdLL4D8v458d7Yn5WD3aOLXcEFwVt2NeBft99tLMlQpMMP74urehEZxy1B+qs6Ggeef/fe2OnC69fUlfmoLxA88j7G/xdLi+f/fP7MVfZNnY4qS6yYTAIKov6zsT+vbKBMeUOzpheTXWRlW1hnuN/tzQCcM5MbX/X0UmMuXD2tfUyqtSGzWxkXEUBnxwc/BiRUtubtbbYxrZDXazf25LwZ/e09DAmMIusLdG+i8EEPIUQjCy1x5VW9GDh/LEhj7zH7eNAEufy+Py8uP4AZ0yviXAUSuxm2nrd9LpjaOQpBju7XV427u/gmHHlTK0totPpHXD167V//5jvPrUeCEgrcTTyTQe0+Nr+dmfEBjeJ0tLt5pv/WhP379ljyLvdCKF5zXr2Q7S8onvkdouRMeX2xA15r4dShxkhQntEJzrF15k+ogiPT7KzqRv7R7/ls8Z36JpwHnQfglWPMn+s5n2sTEJe2dvSk7ZyutHSSniwc+3ednz9pIDpOvrYgBZ5oN0Z3HhgVKmdj3e1RGzzph1f+1nf5k2nr0fupiQwEwICmSP9f+8Ngeuqe+Qen4y5F6jO397bxeEuN8vrW/rMiBo7nFQXa0YrWlrZ29LDBzub+ez8OoQQTK4pjDDkH+9qYVSpPejFjil30NjhSsrzk1Kyv62XkSWaHDG1tiglaUVf2bx47khONm9i6j+Ogo8fHPBzfr+koSUkB1YXaR55Kpkr+9v7GnKfX/LKxoOMryygKvDgjCdb9cd72w/T0u1m8dxREa+XOsy093hwekPph0EJI8FgZ4/by+4YcYo1e9vw+SULxpUzuVpLqPikH3llR1MXO5q62Rxw3jzR0kqYRr5xf0cwSLxs++GE2hnO0i2H6G9CkxZDLoQ4RwjxiRBiuxDi9kQ+I6Vka2NnMBjW3O2mzGHBaBBBDy56dWePx4fZKLCYDIwJeI/tCWh87T2ePtt8aXU/kvPIAbo/fpSZW+7jad9JmK54FMacAMt+S43DwKhSe8IBT79fcvlfPuCOp9cn3Ib+cEV5A6Fgpy9olA/EuPEg5JGPCxisA21O1jS0UVdm56yZNaza00qn0xtTIweCMx1Aq4DoivbIQ4a+stBKa48br88PUoJPM9Af7WwOFiDTp+x1ZQ7KAg+BeLnkLd1uHnxnJ1VFVpwef59yCfpiIP3cEJJWXlqvBTIvPlozFpOri9gemE5LKVle3xLUZ4GgN9uQROXN1oCeqwcIp9YWsbulJ24phIHQr9VIay+/s/4Zo98JL30bVj0aelNvK3gjv69DnS7cPn8wQF+jSyvtg3MkRsXxyB98dyer97Rx06kTg6/FM+R+v+Sb/1oT07A9t3ofJXYzp0ypini91GHmcGAxn74gyGDQbEIiHnm3y8vlf/mQc377bh/78vGuFgwCjh5TypQarc396eT/3azN2Jo6XbT3ePpmrRhDs+ItBzo5c0YNo0rtLNuefMDzv1saqQ48GGORsiEXQhiBPwDnAjOAzwkhZgz0uW2HujjrN+/wykbtZmrt0RYDAcGl3tGLgnpcXhwWzSjoN1UimmVrjztoEHQqCuIvDonFhKoCbjS/xNGr7mBbwdHca70Fm8UEJ38bOvbB2n8yb0wpqxP0yDfsb2d/u5MPdjSnnI4GukYenlUS5pEHjFu8HHFdR9e/0/YD2xm761/cZ/wtX9v1VR4UP+H6A3dzac8TsH81+P0Renm4Rx69b2csaUVKaNu/FR6+EH41hfbl/+Sqv33ENwJTR91Qjiy1BcdEH53c1Qmv3UXbX85jtmcNf/j80QB9tgFsbHcGjVZ01swHO5uZWFVAXcC4Ta4ppNvtC05/D3e5Iwz56HLNGCcT8NQzVkaW2sHvZ76jifPEh+zZsoqtjZ088M6OpIy6NuOQnLz1fynxtXGZ6/9xqPpEeP5/4JXvwd/Pg5+Ph/sXwJ6Pgp/bc7iTAnqD19hiMlBRYBl8CmKZndaeyNo8G/a186vXPuGcmbVcOr8u+HpFgYVSh7mPIV+xu5WnV+3jqagAYI/by2ubGjlvVm2EYQRNWtFLC4Q7FrYEdgny+vzc8vgq1u9rp9fjCxriUHtamFZbTJHNTEWhlq7a3+zpjU2HgnVrtjd19slaMRkNGIQWd+n1+Jg5spgTJ1Xw/o7DSd3zLq+Pd7Ye5lPTa+K+Jx27Dy8EtkspdwIIIZ4AFgP9FqaYXF1IbbGNt7c2cfkxY2juChly/cb3HtgIdQvBpD2Jut2+oPenTxH3tvRw1KiSfhvY2d3L3IJm8PvBoAfdLBR21YOzA2ktYtuhLqbUFEH3YTjQV2s1b3uN242P8bF9EfcX30atLzCIJp4OI4+G937NBeNuo71zD83r3FREPTii2bqqgUWG/WxzjWLT/g5mBTa8cHl9HGhzMq6yANr2gsEExSOCn9vX1ssnBzs4bWo1on0vHNaWEk/t2owE2K4NcqtfssiwDnu7kbV7tc/ub3dCT4vWPxka9CMP7+UsUwOjVnzA65anmfzmPs4HugzVWMqnUnT4AKM4zMT2j+CBf4CjgpEjFvIlYzlL/fMosIwOHiu6JnkfQ15g5irja5Q//C8wmqBsLCUv3sjvxDH8s+F0tr7fhH1PExcWtGOttzGmtZtFho2IHU5wlwYO2gBL/xfZ1UihLGGJZTks38QXKubStakexmixAKfHz1zPKuZ7m2D7ATi0hYdMTzF79RbkvsmcdGAc1gnHw3bNk5vv6WSRYTOH1zhp7fawyLCL0zxt8J/lsOsdju7Yx0arxPxvM5hMYDCC2QEj58HYE6ByMiDA74NDm2DvR0xoWMd7VifVr9rh+TYWuTtZZAGevo8PfDNY6zuD/fs+xd2fO7Xf8aLj9vq51Pg24xtfx3/6D+heOZ9re2bw4lgQH/4BqqbDSd+ADU8h/34Oh2Z+CX9vOzN3v856axvupQvg8GKons459k1UNG6H7YFiYAWVMGJO6GRSQuNGqJoKxrAZbWcjE2yaUd7f1suk6iKczl7uf/wpyuyj+enFsyJkTCEE0yot2PYuC50L2Ph+PYsMh7Ds3gZhXuranS1UeTr49Jxj+vS/xG7B1XmYRYadjO/ogO1anaOTjeupbW8C//TgPR6O9Pt58PEn8G7bzUMnjeO5Nfup/2gvFE0GwOuX2Pes5JwpVbBdcxouKdmKs2ELxJgxdDq92Pas4lsTK/hgRzOdGzqZ0tPC/pK5Ee+zmAys3tMGwIyRxVhMBv61ooGN+9uZXVcKXU1wMLAmwGCE0ceB2RY6QHczW1e8zTzPJ1xW1sXP+rQk8B2nWlZTCPFZ4Bwp5fWB368CjpVS3hL1vhuAGwDGjBkzf/fu3Xz33+t4Y8NuPrpxErc8+iHUzOLPVy9g16F2Xr/vJm4wvQjVM+GSv0LNDG5espKtjV288c1T6HR6mHX3a9x2zlRuPnVS/AYeWMfWB65mitylHeuU70BBFQ3P3k1d23KkvYw1467ni6sn8doJm6le/yB4Ygcj3i27iNu6PofRZGb+2DJ+d8U87Q+fvAL/vHxQ359fCvZVHMvo074M0y/kF2/s4m/LdrH20l6s//kq+Nww+3I48VaomsID7+zgHy+9x1vHfoxl3T9BDjyd3CcrWGecRY23gXnGnQgZx3MxmPlITmeZYQEv9kznp1++hIUTKjjz12+z7VAXVx5l555ZTbBzKb769zG278YjjSwb+UVOvf5nYDTzi1e28Jd3drD9m1MQez7kx8+uoGzuhdxy8RmwbxXdz36DgqY11Jcex7gvPkSroZy//fJbfM3wJGaSSIUbOY+nar/B99738/EpGylZ+XvwDuxd7jPW8Yn1KBYWNWM9uAqzSEDvthTBuBORFZP5x7KdzKgt4NhxpZrBdrbD3o+hPUYguXwiO82TWbW/hwtn1WAtKMU/Yg5Xv9DFfP8GrrO+SYlrPwA9thoc86+AM38Uuw1+H2x9Bee792Pb9z5N5QuouuU1Xt54iJuWrOK+z87g0xOA8vEAOLta2fDgV1jQ/iqd0s5b/jnsM4zghtrtGBr7kfOmXQDn/BR8Hnj5Ntj+Bkw+Cy5/THOoDm2Ghy/E63ZyffeNXHvNDZw6UnLoocuobltDV+k0Cj/1HZjxGe1B7fPAmsdpe+UeSj2JV4/0YUAcfRWG0++EwmrtRSl58dFfccKO31Am4ujtE06Fz/w5wvnxu7rZ8OdrmN36esLnHywb7Mdw1DeeB4vmaM6++1U6nF7MRsHGH55De6+HY37yRshuPbIYdr4VOsBp34dTbgv9vuRS2PZa8Ffxw46VUsoF0ecdNkMezoIFC+SK1/5F74PnYe05iEFobdhvm8TIM7+Ge/3TWOrfYmftOUzoXKXdLMfdxBtrd1DjrGdWlQGsxby9x4WtqJxjZ0wCexkUVAUuuoDOA5o3sfLvNPkK2TDqck5zvQnNmgfba63g912f4n8mNmLf8zZ+KbR2zFgMx1wPxig9ylrIg1ts/OTlLQgBXz11Et8+e2ro7wfX4+nt4qqHPuKsmbWMrXDwyAe7mTWqhG+dNQVByENp7HRy02Or+OLxo2nZ8Brn+5ZS6TuELKjmIeepuFxOvmp6HuqO0bz9VY+AtxeMFtwGG8LdjcloRBxznXbDCAPffHItVYVW7jh3WvA8V/71A6YZ9zPfu4pTLFvZ6qlk/HGLKZl2Cpjswff95Z0dfLCzhX/cdjUXPriO9fvaMQjY8MOzcVhM3Pnseh77cA9XHDOan10yG9DiF2ff/U9uMz/BRcZlMGIu1B3Dvm2rsbV+QoWImpJWTILmHcjCapYUX89du2bwt2sWsnpPK/e9uZ03vzyJ91et5ZnV+3BYjBw9poxvnDGFbreXqx76mGtOGMviOYHAl9EMI+Zw7+vb+dPbO9jxv+dBZyMfrlzBL17dyv9edBTTaotZv6+dHzy/kR9+egazRpVCyShueO4g9c3dXDSvjvtfWc2y60ZQagtNTK97+GMWjC1nXUM7E6oKuO2CuZoDYNTec+av32ZCVQF/uSrqXmpvgPaw9LCKiVBQyU9e3MQjH+xmy4/PCXqpTZ0uHBYjBWaBt/59ljzzPBM7PuQksQ6++AqMPT7y2K5O+L+rYOdSPIWj+EXrKcy96Jucv2Ayfr/k/N+/h9Pj4/VvnIzJaKCxw8lXHl3Jmr1tfP94O/NnTUcarVQX2bSZbNse6DjAn97ezvL6Fv52zULtPPXvwjv3aj9Ln3YPzFgMax7TjPlp34clnwVhxGOvxHhoI1smXMWMw2/g6mrhCdOFXFOyFg5vBWHQZisIcHdyqPgo7jp8Jv971aeoKLCwYncL//vSFs6cUcPrmxr5yUVHMb22GInkS/9YzjWlazml7RltnI45Dmwl2ne890OW+6fwO+8l3HjGLE6aVAnA159YzfGOfVzR/lftgXP6XVA1Fa/RxsElNzKydzvv1l3PyWd/FoFg88EOvv/MBm49YzInTark2/9eh9Pt43dXzA0GLF/b1Mif397Bn648mpoiW8Qluff1T9i8v4MHr17Abf9eR4nDTFnbBm52/Q3DmGPh8/8HthIW3PMGh7tczBxZzItfWwTAOb99h8pCK49dPQt+PhZmXQpHX6M9OAG+8nbgunchfzGeV+QJrKm5mDvOnYYYe1xMQ54OaWUfMDrs97rAa/1TUI1xwiLuW+tj8rRZvL95L7c63oL/fA2z0cJ3PDcwevINfO3YUnj+Flj2W04QDhpMY6FwNLg6mWBswtaxE1a8A54YuqUw4Jt1OWd8fBrXTzya0077KWx+HpwdfGA+lT8+vgHfiAls2P4cZxpW4pl5KV++7LK4TZ7WoS3BlzK0KCJI7SzMgK9Ocs+6VvwSRpXO5O3tvYyfV8clYZrhS8t2sUp28asTTuWvvqncu+Yi1nzBTMfb93P9vn+BCXaOvpgJ1/xZG5Sn3AZrn4DuJpqamnl+UxsnXHY7c2YeFTzmWnqZVlQMY44OvrbJ3MF73ZN53HI6f7jsaL749+U8OeP44EINne1WG5+YD4OtmNoSG+v3tTOlpigYj1g4voLHPtzTJ9h5gAq+4fkqBbMXc1b9L2HtDqz2cbzum895516IZ+RCLn5gOb+avZ8FnpUw9VzEybdxiaGAx//0Pl97YjUAZ8+sYcLEqRRUjeWHa97E45TMGjkRxkzDISXrDe1sMU9g8ZhpEe3WixQBUFTDlGPOZNUr8N/u8UwbM4kdzftYJT3YJ54IgYBbZVErK3e38sHOZkZWV1E65cSIY/bWSJbs72FfRyn/7+QZMGJ8xN/HlDvYE2v9Qkmd9i+K/W1ORpXaI6SGqrCglWnCIs6+/hgu+u1rvMwtlL7zC7jqmdABug5pxvPgBjj/V2wbcTEP3v8Bf7ZpWRUGg+AbZ0zmhkdX8uC7u2jpdvHEx3vxScmfrzyac44KeaZBSsdA6RictWW8uXEbnlHHaMZrzLHa7O+tn2qS3mnfg6JaqJsPL3wDtr0OhTVw7YsYikbw0j2XcMGuR5Alo7nc+0NmzTkJPj0Dtr4M+1Zp96TXCZPPoqP0RJbet4xvfWjlb9ccwyPL1rDTbuTCC07i5xuX8qFnEtPHjGd7Yydv9jRz9rmLYfx34e2fQ/N2aNkB0s/yo+7ishVTkRi4pnYBjNF04x12N52F87niimvg6evhxW9q3y9QLO38Z+Zv+PSl1wavw9Q6ScPrgiX7S2mtGMHTTc2aER8fypKpkC2sesvIBsM0qkdXs6Opm5GlNkwGA//Y08YFs+diGDsbzygrr9S3YjCOpWBkHV9suAf+egZMPY/zhIH/MpYZI0Jj48RJlTz64W7cu5Zh8bnhqIu17376hfDmj6HzoPa973ob4XPziPtEFs87DcaM6Xst9XEU9y+JsxyYLIQYj2bArwA+P+CnrIVYLn2Q9w6/z1P7nez1TWHSCbfwxboDCFsxL/5xH5/v9UBhFXzuCeht5dIHN1BbYuehL2ja2WMvb+Zv7+1i053nYPa7NH27+5BmaYtGQGE1LT1+2j9+Q6vTYDDCzIsAKAtkl/x9WT115cfxtO0kCjpNfLmfJuuZKxDS6KM5Y3oN2w518d1zpnHp/Do+/+BH3P38Ro6bWBFMP3pjcyOTqgsZX1nAcRMqWPLRHjbYF/BExY9Zs3cFdfIAo6sW8/8CsQEKKuEEbYLTtr+dn697jz/LCsLUTC3YGRUY0gvvzxpVQl3g3LEKjYVH20cGVr/OHV0a/PuxgYBfePqhxWhAiICMOvocuOTLIATvrzvA7f9czfxJJwOwW+5l/4xzYc5dwc/agb9cNZ8L73+Pth4Pt5ym6ZQ1xTYunDOSp1ftCwYghRCUOSzBmi4R7Y4KLpUXWJhaU8SHO5v50knjeXLlXsxGwYiSkDdVWWChpcfNivoWPju/r+GdXFPIB4GAaXigU2d0uYOPdrUgpYwwzvHY19bLqOiHfhS1JTa+sGg6f/rvedyx45/QsALqFkDzDnjsYs2Yf+4JmHIW7kAGksUUOveZM2o4alQxP39lC0aD4PxZI/japyYxKZBC1995Qcto0ccmpaPhM3+MfOOC60AYYeU/4OIHoHISRuBnBd9hX+VW5p1wBmse2cpXp1Rp+vS087V/YUwC7rpwBnc9u4Ffv76V1zc1ctHRo6grc1BVZA3WMfpwl5aDfuz4Cqgs0GTVMJo3HECuWAUQWTLCZNTSQisnwfX/1WJHnQe4c8lSDGOP5UeXRbbHYBCce1QtTyzfy+YDnUyrLeLC2SMj2xz4/p5a1cD9S7ezek8bFpOBabVFdLm8weDj5OpCnluznyKbic0TTofTZsF/fwgf/IEf+T38yAZt9ePh1fPh5G8ztbYIt9eP85M3sRgtMCYwA5tytmbIt70OR18FW1/FbSxghX8qv5tW3e+1TDlrRUrpBW4BXgU2A/+SUm5M9POnTKkKVmUrL7TCuBOhdlZkTXIhwFFOj8ePIyxDYlqtltu963A3mO3aIBw1X7sJSkaB0RysvlcSFXzUU9HcXj9fOWUC02sHXslVVWQNfk5P44rmhpMnsPquM/ncwjGYjAbuvXQOfin5zpNrcXp8tPd6+GhnC2cEBsGxEzRj8e62Jl5af4CpM+ZyqOZkPolTUlU/f1OUYdMMuTHiNT2LZe7oUkYEbtRYmSvhBrE2kO8cbshrim3c85mjuOTokOETQgQfHAUWo3YDC0FZoLDRnpaeiIJZ0Ywud/DIdQv52cWzgoFegBtPmUh5gYU5o0OvlQeMb592R6V7ARw3oZwV9a18+ZEVvL+jmZ9cNCsiq6ayyIqU2ibex02o6HPMyQHPvdBqYvqI4j5/H13uoMvlTXhpe3gOeX/MGV3Ko74z8VjL4O1fwL6V8NBZ4OyAa/4DU87S+hy1wS9o1+Lnl8zm1jMm885tp3Hf5+YNaMQhbHVnIrX0518DNywNBHQ1RpU5eMM7hzd3+zAZBMdP7Pt9hnPlsWO4YPYI7l+6nV6Pj88EcsRnjSphQ8CQf7SzmdpiG2MrYt9fJfbQfRy90jiY328wQvU0mHgaz/tPxFA2Luaxzps1ApdXq4z6nbOnBusUhc5lZkSJjZfWH6Sp08Wd50/n6uPG0uP2MbLEFpR19PTKTqdXG4+Tz4Ab34Xv7efmwt/yY8+ViJI6+PBP8N8fBxfQWfa8A6OPBYu2foOao6B4FGx7FaTEteVVlnpnMn9CNdXFkdJONOnwyJFSvgS8NJjPnjK1il+9vhUgmLUCBJfph9Pt8kbkLAeLWR3s1DJOYqDfcKUx8sgBaoqtfGbeKLpdPv5vxV6aulzBxRKxmD6iiGXbXYwojf2eaC9tTIWDuy6Ywe1Pr2f23a9RV27H65ecOUMz5NVFNiZVF/Lgu7to7/WweO4oXt5wgDe3xA4M6d9RdA68y+Pr45Hrv88ZXarVC7eaYu4AFL68f3xgUZC+wEnnyuPG9vmc1WTE6fFHGMpjxpVTXWTlL+/s5CsnTwBiG3KA2XWlWuQ+jCk1Ray668yI18oclph55NFlQwGOm1DBwx/s5r3th/nFJbO5dMHoiL/rD0IIzTTC0Q3g/LFlwdSycPStCPe09FBW0H9mksvr41CnK5hD3h+zR5XSg42VIz7Pcdv+ALve0WajVz6jeZkBorcT05k5soSZI/vP3opGXyi1tbGzz/VOhFFldj7c0Uy3q4n5Y8siVmDGQgjBzy6Zzab9HXj8fhYEznnUqBLe+uQQ3S4vH+5s4cRJFXFnO3oFRKCP1Nfa0zeI7/HJkPwWxYJx5dQW26grs3N6HI/3JxcdRVuPhwvnjAyOtTuj3hP+0IwYjyYLe62TeclXza1XnwWvfB3WPkHRmJspowPr4Y0w6/vhXxBMPhPW/5uNy5cys+cg6+2Xcf/nj2YgMr6y86iRJVQEbohwQx5rl6Bed+TqwolVhZgMgi39LIsPGnJHpDFxWEycNKmS286ehtVkZFptYCXXAKvuzps1gnNnjehjQPrj8mNG88h1C7n2xHGU2M0sHFce4fEeN6Gc9l4PZQ4zJ02uZGptMYe73DGXspuNBm1RRLQhj6q1AiFDrp9rRKkt5qKgcI/8rBk1vHrryUyO82AMR/eICiymsNeM3HzqRD7e1cIrgQ0Z4hnyRCkvTNwjP2FSJfPHlvGrS+f0MeJAcKxNrSkKFk8LZ2ptESaD4MRJsb1LfZVnIusXGgOLbUbGeeiHU+IwM76ygMc5WwvaV06GL70RYcQhtJFvdL8Hw5SaIqaPKObHL2wKesTJMKrUzoEOJ5sOdHBy1MKdeBRaTTx3y4k8deMJQQ949qgS/BJeXH+Aw10uTVaJQ/hYiuuRhxFrjOgYDYInbzyeB69eEPfBcfq0Gi4+uq7f+31shSP4sIg+l8VkYGyFQyvxvPDL4OlmzJ5nON6wCYHUsmzCmXw2uLvoelF7XFx77ZcjnI94pMUjTwWDQXDylCqeWb0vWNQIoNhuCtZxBm0laLfbG2E0LCYDE6oK+jW+bb26R97Xe3rs+mODP08NM+SLJscflJ9bOIbPLYwfdIiFEFof4w324yZowcTzZ2sPiPCHSuWkvhexstAaUYJXShlbWjEZqSqyBjXi2hJ7TI88vEaEwSCC38VA6OcrsEae94qFY/jjWzv49yptoUf0bChZyuN55L6+HnmJ3cxTN50Q91h6vZXjJvT1xkFzJl782qLgzCSa0WUOhIAN+zq4IEpTjUZfoToqAY8cYHZdibag6daPwVoUmbsdwO3VpD9LEo5EPMxGA//44jFc/Mf3ufbvy/n3jcdzsMPJfzc3Mnd0GefPjhEoDWNUqR1diYxegdkfRTZzsHY9EJTWHnpXywk/Ns61gX48crOhT20mn1/i88sIGSqaeLGuZDAbDYyrKGDboS6sUdflcwvHhIqFjZwHdQsZsXUJJxnG4TUXYho5L/JgE07Ba7BwrH8jruo5VNYmZmsy7pEDXHncGM6bVRsRzY+WVlyB0p2OKKMxtba432py+q4+JY7+jYm2ksuaka24Tp5SxSlTqrjm+HFA6KGyOc5MI7rwlL5sO1paOW9WbaCOtuYtjCyxxZZWfH4spviDPR66RxQ9pda9cv0mL07RkJcVWGjr9fRZDRcd7EyE0WUOzppRE9Nb15laWxTXiyuwmvjUtGqeXLF3wBLBXr+fCVUFAwY7debUldLY4aLR64hpxCF0rdPhkYMW/3j4umPw+Pyc9qu3uOKBD3nw3V384PmNA64+1PtVUWBhRox4QjJtqCqy8kljJ5WF1mD98VjYzcbgNY+UVox9ytjqW0GaTQMHpVNlcmBJf7Rj8dn5dVx2TNhYW3gDto5dXGJ8l+bKhcG01iCWAuoLNeNumX5OwufPCkM+f2w5f/zC/AhNMromeXegaFK4Rw5awHNfW2/EasJw2ns9GAJbhg3E9BFFadmKK1mKbWYevm5hUM6oHGB5cEWURx7crzPq5r72xPHceEqo5kVtiY3DXa4+haUGYxC182k3kiPGd3vFwjFUF1kpsppias3JUO4wI2XfImpun8ScpEGzmAw8cPWCAVcD98e1J4ynudvNf9Ye6Pd9iyZX8ea3TmVsRXzDFI4e4NVr48QieoPfdDCpuoi/f/EYLl8wmj9+4WjuvXQOh7tcfLCj/5oguvZ/0uTKPoHCZJkVuB7HTSjvNxtICBF0ymxRJSlcUdKKawi+q3hMqtIM+YAP2BmL8TmqsAovByqOi/mWD81aXr+Yem7C588KQx6LYruZLpc3OC3p0feMtER55AHjF29/vbZAwaxEBtrUmqK0bMWVDqbWFsWtvFZVaKUpzCPX99MMr7USi5El2lQ4uuKdy+vro68nQtAjt/Q15DazkZ9ePIsbAgHPVCgL1luJjAt4vP4+U9nh4MRJFUyuLuTvy3YNurZ0LGaMKMFoEMHaOLGIF+xMlaPHlPGzS2Zz3qwRXDB7BEVWE8+t6X85yOgyB8eOLw9ug5cKuiE/NkYmUTS6Tm4L+w5sZmOfolmeNM9e+mNSwA4NeC6TBefsqwHYW3ZszLcs8XyKn434nSbFJEjWGvISu+aF6SVRe8JqkYejyxDx5BWthG3/2QXhx0rHVlzpYGpNMVsbO2M+VCoKLHQ6vcGpva7FjizpP6im5w1HyyvuQRrEkEce+wHyqek1/M+nJsf8WzKECmdFe+T+YZk2RyOE4NoTx7Fxfwcr0rgrlN1iZGpNEesa4gce3d70BTvjYTMbOfuoWl7ZcLDfkr0Wk4H/+8rxnDCxMuVzLppcic1s4JR+4lM6pXYzZqPAFDZmrSYDHp+MuF+GYvYSDz1tNZHr4j/pm1zmuotGS1/9W0rJ7jYXrpF968z0R9Ya8mKbvv+jdvPqGy9HG426MjuFVlNcGaKtx51w1oSezpjqVlzpYFptEU6PP2bN9dAGCZq8olfjGzNA4EbPnojOXIkuiJ8oVpMBi8mQVAbPYNArV0ZXQPT4BicJpYOL5o2ixG7mH8vq03rcOaNLWNfQHtfTT7dGHo/Fc0fS6fLy1ieJ10dJhQXjytn4w3OCWUH9UeowB0vY6ugyS3jcYjg98qk1RXzn7KnB9SH9YbM7+FhOD86kw2nudtPj9g14L0eTvYbcHtoxG0IbL0d75EIIptQUxvXI2wObSiRCurbiSgehLJq+mn1FMJc80pDXxVmkpKMv9unjkfeTotUfNrMxIq9/qNA98ui9O90x8siHC4fFxBXHjOaVjQd5ZnVyW671x+y6Utp7PXE3AdE3lhjqB9jxEyqoLLTy7Or9Q3qecBKNpZQ5LH0cOl1mCc9c0T3y4RgjBoPgq6dNCpZM7g+z0YDZKOiNMdvR7+V4Cw7jnj+pdw8jwc0ldEOue+QxDMe0EcV8cjD2qsy2Hk/C6W/p2IorXUypKULEeajoHrmeubKnpYfqImuf+EE0hVYTRTZTn5V80XsNJkp1sTWhxS6pEs8j16SVzA3hG0+ZyNFjSvnG/63la0+siblheLLMCSyQiqeTu4LGaWglJZPRwAWzR/DmJ4fS0q908pVTJgaLt+lYY3jkriGKJ6QDm9kY05DrM/BEZibhZF8PAwQ3l3DqhlzrdExDXltEe68n5v56bT3uhDVySH0rrnRht+gPlb5tqQou0w8Z8kTzYUeU2PrUW4neXShRbjt7Go99KXbAJp3YLUbsZmOfXPLBavvpoqzAwhM3HM+3z5rCS+sPcNEflwWn84NlSk0hNrOBtXtj6+R6hlEidV5S5TPzRuH2+nl148EhP1cyTKou5LSpkSsxQxswh77/oLSSwTESD3ucBUz6pip9ivINQMYXBMVDTzHSvQFdIy+Ikep24qRKfvjpmRErvUBbENDh9Ca1svBzC8dwqDM9+2imytc/NTlm2/XyAiFppTdmgadYjCixR+wKk8iiiXjYLcYBZwHporzAElMjz5S0omM0CG45fTInTa5id3N3yu0xGQ3cef6MuCUnBhvPGAxz6kr4ztlTg0vpsxldMw83jkOV4ZMO7BZjTI18b0sPlYXWYOXRRMlaQ67rwIcCXraukcfyyCdWFTIxkMcZji7LJKqRAwkvNR4OPjNvVMzXHRYTDosxmBO+v703KY984/6QdJTNgz2cAqsxOCvTGU6jNhBzR5dGlF1IhVh1bXTcPt+w9VkITffNBcI3G9cJxhOyZIyEY+9HWtG3FEyG7OthAJvZSHWRNTjVCGatJPGkahuEIc8V9M2j97f1IuXAGSs6I0rsEYuCcsWQayv3ovOEZcY98uFmsIu3jnRieuSBujTZOEbslr6OCWg1fJINdEIWG3LQlgA3tGnif69bq+6XzCpBfXl+Mhp5rlBZaOVwlzsUHEnQkNcUR+rrunEcTLBzOLGYDMHUO53BZtvkMh6fzLs+J0Isj3w488iTJZZG7vX52d/mTDr1ELLckNeVOSI88lj6eH/EK2F7JFBRYOVwlytpQ64XK9JLHmRzZD8ca9Qu6VLKgHc6/AuCMkk2yUnZhL44zRXhkevSSvaNkVjSyoF2Jz6/PLKkFdAit/vbevH7JT0uX0x9vD/aeo9cj7yqyMLhLjd7W3qwmAxUFw1c6hJCC6p0Qx6v4Fa2Ee2Re/3Zq38OJa4M5s5nM/r4DR8jsTbhyBZsMYKdwRzyI88jt+PxSQ51uvqUsE2EI90jb+l2Ud/cTV2ZPeGiRfp3qOtz2Tz9DCfaIx/OxR7ZRD7KSYkQ8shjpB9m4felSSuRUuGeQS4Ggqw35FqHGlp76InaVCIRdEOeahnVbKSy0IJfwvqG9qQ0NX1WE/TI9cqJgyiaNZxEBzuDJUrzzZB7fRnNnc9WdGMdSyMf6sVTg8FuNgYXOersbe3BZIjcYzZRsnpE6AX5G1p76XH7+mxgMBDtvR6KbamXUc1G9NWd+9uTC47ocQY9C8iVxdPPcCwmQ0T53VzJtkk3SiOPjTVoyHMjj9xh6auR72npZWSpPaIYWKJkXw/D0Fc3NbT20O3yJp0kn+yqzlwifDelpAx50COPklaycLCHYzUZIr2tLF61N5SorJXY6DNKd4wxko2zNltAWvGHVWvc29IzqIwVyHJDbjMbqSy0hjzypIOdiRfMyjWqikIPqGSCI/omEPq0Lp17QA4lyiPX0AqFHXkzzFTRH+g5k35o0WvDhNrb0Dq4xUCQ5YYcNK98X5tmyGPtRNMf+qYSRyLhHnkywRG7OY5HnoWDPZzorbz0VXvZ6G0NJe5Bbst3pGMyamtMouMoJoNIefeioUC/D3V5xenxcbjLnfD+rtGkdBcIIX4phNgihFgnhHhGCFGayvFiUVdmD3jkXhwD7IATTXsSm0rkGiV2M6bAAE3mKW40iIhAiytngp1a+qE+Fc3mjIShRK3sjE+szKZsHR/RhlwvJ6LvhpUsqfbydeAoKeVsYCtwR4rH68OoMjv7WgfrkbuPyNRD0OoflxdYKC+wROxInggFViPdgfTD4dzXMBUsUXnCw1XONdsYbKXKfCBWHCVbvytbQFrpDThUepXX4iTvZZ2UeimlfE1KqefQfAjUpXK8WNSVOYI3bzIaud8vk9pUIhepLLQyOslyl6DVq+mJTj/M0gGvY41KL8tfj9yX9dcqU1hNxog4SjZUx4yHri70urX2tvdq9+NgU6XTWf3wOuD/4v1RCHEDcAPAmDF996qLR3hd3mQ8crfPz8VH1wU3dT0SufWMyYMaqA5LyCPPlaBhcOVedLGvLL1RhwqVtRIfq9nQZ2OJbB0ferAzKK0EPfLBmeQBPyWEeAOojfGn70spnwu85/uAF1gS7zhSygeABwAWLFiQ8L5Y4R5nMhq5zWzk3kvnJPz+XOSsmbEuy8AUWE1hWSu5Ysgjd4DJ2wVBPpW1Eg+L0dAnIJ6t49oWRyMfMo9cSnlGf38XQlwLXAB8SqZr48IwRpWGMjKSXRCkiI3DYqTTGQh2enLDs42ubpcrM4l0ksomIPmA5pGHBzt9WTuug8FOt+6RB6SVTGjkQohzgNuAT0spY+8WmyJ2i5HKwI44yS4IUsSmwGKKyCM3GsSgVpMNJ/oNGZRW8tAjz8eHVzJEa+RZnbUSlFYCwc6AR140SGkl1V7eDxQBrwsh1ggh/pzi8WKi51Yqjzw9OKzGiDzybPVawonnkedT4E8Z8v7RslbC88hl1spQ9qhgZ4fTg9VkCEouyZKSiyulHJZ9oOrKHKxtaFceeZqI8Miz2GsJR5cT3MGslfxbEOTKkVW4mcJiMtDlChWiyuax3TeP3JtScb/s7GUUeuZKsvXIFbFxROWR54JXG/LII4Od2XqjDgX6w0tVP4xNnwVBWZx+qEsrzrCslcFmrECOGPKptUVYTIYjdpXmcFNgMeH2+vH4/FnttYQTL/0wW6fOQ0Gwz1m44002EF3q2J3FTorZKDAaRCjY2etJySPPCa3iM3NHceyEiiO2bspwo89setw+XFm8+i2c6HrTuZI2mU6yecebbMAaXVgti8e2ECJiu7cOpzelVejZ2csoDAYx6GIyir4UhFVAzJlgZ1QeedA7NWR/29OFCnb2T3T6YTav7AQtl1zfqaszRY88e3upGDIcYTXJs3n6GY4lSlrJ5sp2Q0WulBzOFBajsU8Z22x2UuwWQ35p5Ir0Etq304vL6wt6u9lMdK2VXNH200muFDjLFNFL9D0+P+YsHiN2s7YBs5QyP7JWFOnFYY30yHPBIEYHO7N92jwU6FkruXC9MoHVZMDjk8FSx9lcawXAbjHR6/Hh8vpx+/yDXtUJypDnJeEeeTYHhMKJFezMhXank3wtFJYo+sxSD4R7fNktG9rNBno9vrA6K0paUSSBvkK22+3Leh1RJ7iVl0cPdsqcaHc6UcHO/gk+7D0h+S2bZ226tJJqLXJQhjwv0VfI9ri8OSOtCCG0BR9h3lYutDudqGBn/4TiKD68Pj9+md3fld2ipR+mWosclCHPS3Rppdvty5mVnaDdlJHeVv5krIDyyAciPCCeCyUcbH08ciWtKJJAD3bmkkcOgep2+eyRK428X6zm0M70ufDQs5uNOCM0cuWRK5LAbDRgMRlCGnkWD/ZwwmtpZHMdjaHCrbJW+iVcWsmFlb+OgLSSai1yUIY8bymwGLU88hzybMPLlGZ7IGsoUB55/4RnNgUNeRbLb/oS/VRrkYMy5HmLw2KiKyCt5MKCINBu1PCNJXJF208XuSAXZBJrWNZKLnxXNosRKaGp05VSLXJQhjxvKbAaae/RPIFcMYiaR56/C4L03ZyMeVSWIBnC88hzYU9XvSZ5Y4eTohRkFVCGPG9xWEy09riB3Jmqh2/llSv57+kkH/ucDCGP3JcTMlS4IU9lMRAoQ563FFiNtAU88myefoZjCdPIPT6Z1XU0hoJcCkxnApu5r0aezWNE31yiscOVUqATlCHPWyI88iwe7OGESyv56J26fTJnrlUm0Ou0h6cfZvNuSrpHfqjTmVLqIShDnrcUWIy09eaYRm6ODHZa8mynnHx8eCWDvh2gO1fyyAMeuccnU1oMBMqQ5y0OqwmppSVn9WAPx2KMDHbmm1HLx0JhyRCeR55LwU5IbTEQpMmQCyG+JYSQQojKdBxPMfQUhG1knSsGMTrYmc036VDg9vpy5lplgtAuUrnhkYenG2ZcIxdCjAbOAvakeizF8KEXzoLsHuzhRAY78887VcHO/gmvfujOBY/cEu6RZ15a+Q1wGyDTcCzFMKGXsoXcMeR6sNPvl1rWShbfpEOBklb6x2gQmAxCW6KvBzuz+PuyZ4tHLoRYDOyTUq5N4L03CCFWCCFWNDU1pXJaRRoI98hzZWWnHuz0+LN/2jwUePKwBnuyWAOrf3Oh1ko6NfIB/XkhxBtAbYw/fR/4HpqsMiBSygeABwAWLFigvPcME+6RZ7PXEo7FaMTrlzjd2b/YYyhw+fyUWFK74Y90rGZtA2aPN8eklRSzVgb8tJTyjFivCyFmAeOBtUIIgDpglRBioZTyYEqtUgw5uaiR6+llXW6tWlw+1iPPt4dXsuiF1XLBI7eaDAgBUg6DRx4PKeV6oFr/XQhRDyyQUh5OqUWKYaEg3JDniHHQ29kVKPtpyRFJKF24vb6cmT1lCksgjhLaWCJ7H/ZCCOxmIz1uX+azVhS5iSNcWjHnxjAIeuQubSFTNt+kQ4EKdg6MrpG7cqDWCoR08lSzVlL7dBhSynHpOpZi6MlFj1wPynYGPfLcaHe6UNLKwFhNxmAeucVoICD7Zi16LrnyyBWDwmHJvfRDvZ1droAhzzOjphUKy27DlGl0jVwrc5z935XdYsSSYi1yUIY8bymw5mCw0xStkedGu9OF5mXmV1wgWfQNunNl8ZTDYkzZGwdlyPMWRw4u0ddvTF1ayebUsqEgV4xTJrEGg525UcLBZjamrI9DGjVyRW5hNRmCu81ku46oo3vkna78M+RSShXsTAC9Hk+uPPTqyuwUWpUhVwwSIYTmlefQ0iw92JmP0oqeF63SD/vHag7lkefC+PjpxbOCVUhTQRnyPKbAYgqW+8wFghp5IP0wVyShdJALW5dlA7q0kisZPukqj5H9PVUMGQ6rMSe8Fh3dkHe7tAqIudT2VMmFBS7ZgL4gKFc88nShPPI8psBiwufPHW1F9146nPm3IChUX1tlrfSHrpHnSrAzXeRPTxV9cFiMOaW59skjz6G2p0oubJSQDQRrreSItJIulEeex9SW2DAacser7ZNHnkc3qtuXf3LSYLCajHh8EqfHT3lB/pi3/Ompog8/WnwU/hySVvLZI8+V2iGZxhKMo3ipKbZluDXDhzLkeUxJiqUzh5tojzyfNNBc2PEmG9C/nw6nN6++q6wx5B6Ph4aGBpxOZ6abcsRgs9moq6vDbM4tgx0Pk1FbxBSqR54/N2ooayV/+jwYwitk5lMwPGsMeUNDA0VFRYwbNy5nVhpmM1JKmpubaWhoYPz48ZluTtqwGA30ejS9OJ9uVBXsTAw9s8npya/0w6zpqdPppKKiQhnxNCGEoKKi4oib4egeVy6UKE0nKtiZGOFySj7NXrKqp/l0Yw4HR+L3qQf78s2gqZWdiRE+LvJpjORPTxVHBLpHnk+yCoRlreSRcRoMVmXIFYWFhQO+591332XmzJnMnTuXzZs3c9RRR/X7/vr6eh5//PHg7ytWrOBrX/taym3NV3QNNJ9uUlBZK4kSXrskn2Yv+dPTNLFkyRLuuOMO1qxZg91uH/D90YZ8wYIF3HfffUPZxCMa/ebMJ/0TVNZKooTvP5tPhjxrslbC+eF/NrJpf0dajzljZDE/uHBmQu996623uPvuu6msrGTDhg3Mnz+fxx57jIceeoh//etfvPrqq7z88sv85Cc/CX6mvr6eq666iu7ubgDuv/9+TjjhBG6//XY2b97M3Llzueaaa5g3bx733nsvL7zwAi0tLVx33XXs3LkTh8PBAw88wOzZs7n77rvZs2cPO3fuZM+ePdx6663Kiw8QDHbmmWfq9qpgZyKEG29zHn1XWWnIs4HVq1ezceNGRo4cyYknnsiyZcu4/vrree+997jgggv47Gc/S319ffD91dXVvP7669hsNrZt28bnPvc5VqxYwc9+9rOg4QbtIaHzgx/8gHnz5vHss8/y5ptvcvXVV7NmzRoAtmzZwtKlS+ns7GTq1KncdNNNR0w+eCoEg5155G1BqB65MuT9Y1MeefaQqOc8lCxcuJC6ujoA5s6dS319PSeddFLc93s8Hm655RbWrFmD0Whk69atA57jvffe46mnngLg9NNPp7m5mY4ObSZy/vnnY7VasVqtVFdX09jYGGxPPmMNbFKbbxKDylpJjAiNPI8eeikbciHE/wBfBXzAi1LK21JuVRZgtVqDPxuNRrxeb7/v/81vfkNNTQ1r167F7/djs6VW5yHZ8+cLerAvn25SCBnyfMvWSZaIrJU8euil1FMhxGnAYmCOlHImcG9aWpWDtLe3M2LECAwGA48++ii+wAKOoqIiOjs7Y35m0aJFLFmyBNAkl8rKSoqLi4etzbmIbsDzzaC5AhslHIlrA9JJvnrkqfb0JuBnUkoXgJTyUOpNyk1uvvlmHn74YebMmcOWLVsoKCgAYPbs2RiNRubMmcNvfvObiM/cfffdrFy5ktmzZ3P77bfz8MMPZ6LpOUXII8+vDRY8XplXHuZgseTpyk4hU9j5UwixBngOOAdwAt+WUi6P894bgBsAxowZM3/37t0Rf9+8eTPTp08fdFsUsTnSvtc7nl7HPz/eyxnTq/nrNcdkujnDxvvbD7PlYCfXnXTk1M0ZCnx+ycTvvQTAg1cv4MwZNRluUXoRQqyUUi6Ifn1AjVwI8QZQG+NP3w98vhw4DjgG+JcQYoKM8XSQUj4APACwYMGC3CmCrcgq8nVB0AmTKjlhUmWmm5H1GA0Cs1Hg8cm8GiMDGnIp5Rnx/iaEuAl4OmC4PxZC+IFKoCl9TVQoQliDGnn+3KSK5NB2CfLmVRwl1bvhWeA0ACHEFMACHE7xmApFXHQvS+nFinjoYySfyhmkmn74N+BvQogNgBu4JpasolCki6BHnkc3qSI58nHWlpIhl1K6gSvT1BaFYkCCGnke3aSK5MjHtQb501PFEYElD29SRXLk48M+f3qaAAcPHuSKK65g4sSJzJ8/n/POOy+hpfbRjBs3jsOH+w8VvPXWW5SUlDB37lymTZvGt7/97QGP++yzz7Jp06ak23MkYc3TBUGKxAnVrM8f85Y/PR0AKSUXXXQRp556Kjt27GDlypX89Kc/pbGxccjOuWjRItasWcPq1at54YUXWLZsWb/vV4Y8PNiZXwuCFImje+Iq2JlpXr4dDq5P7zFrZ8G5P4v756VLl2I2m7nxxhuDr82ZMweArq4uFi9eTGtrKx6Ph3vuuYfFixfT3d3NZZddRkNDAz6fj7vuuovLL78cgN///vf85z//wePx8OSTTzJt2rS457bb7cydO5d9+/YB8OCDD/LAAw/gdruZNGkSjz76KGvWrOH555/n7bff5p577gkW2/rqV79KU1MTDoeDBx98sN/zHAno02azSXnkitgojzyP0euOx8Jms/HMM8+watUqli5dyre+9S2klLzyyiuMHDmStWvXsmHDBs4555zgZyorK1m1ahU33XQT997bfwma1tZWtm3bxsknnwzAxRdfzPLly1m7di3Tp0/noYce4oQTTuDTn/40v/zlL1mzZg0TJ07khhtu4Pe//z0rV67k3nvv5eabb07fF5KlWFX6oWIA8nHRWHZ65P14zplASsn3vvc93nnnHQwGA/v27aOxsZFZs2bxrW99i+9+97tccMEFLFq0KPiZiy++GID58+fz9NNPxzzuu+++y5w5c9i2bRu33nortbXaAtoNGzZw55130tbWRldXF2effXafz3Z1dfH+++9z6aWXBl9zuVzp7HZWooKdioFQWSt5zMyZM1m5cmXMvy1ZsoSmpiZWrlzJmjVrqKmpwel0MmXKFFatWsWsWbO48847+dGPfhT8jF6Gtr8StIsWLWLt2rVs3LiRhx56KLipxLXXXsv999/P+vXr+cEPfoDT6ezzWb/fT2lpKWvWrAn+27x5c4rfQvajPHLFQOgG3GTIH/lN3Q0BTj/9dFwuFw888EDwtXXr1vHuu+/S3t5OdXU1ZrOZpUuXohf82r9/Pw6HgyuvvJLvfOc7rFq1alDnHj9+PLfffjs///nPAejs7GTEiBF4PJ5gmVuILIlbXFzM+PHjefLJJwFt1rB27dpBnT+XsOThYg9FclhNhrwr+avuhgBCCJ555hneeOMNJk6cyMyZM7njjjuora3lC1/4AitWrGDWrFk88sgjwYDi+vXrWbhwIXPnzuWHP/whd95556DPf+ONN/LOO+9QX1/Pj3/8Y4499lhOPPHEiODlFVdcwS9/+UvmzZvHjh07WLJkCQ899BBz5sxh5syZPPfccyl/D9lOKNiphq4iNlaTMe9mbCmVsR0sCxYskCtWrIh47Ugrt5otHGnfq9fn597XtvKVkydQVmDJdHMUWcjavW2sa2jjquPHZbopaWfQZWwVimzCZDRw+7lHdoqlIjXmjC5lzujSTDdjWMmv+YdCoVAcgWSVIVeFE9OL+j4Vivwgawy5zWajublZGZ80IaWkubkZm82W6aYoFIohJms08rq6OhoaGmhqUpsLpQubzUZdXV2mm6FQKIaYrDHkZrOZ8ePVxrIKhUKRLFkjrSgUCoVicChDrlAoFDmOMuQKhUKR42RkZacQoh3YluDbS4D2NL4v2fdWAv1v95P8cYeqrYm+dyj6NFTvzfdrBYn3K9NtzfS1Sua9uXqtxkopq/q8Q0o57P+AB9L93qE4ZuC9K3KorYmeP+19ypLresRdq2T6lem2ZvpaZcl1zci1ypS08p8heO9QHDNZMt3WoehXptuqrlXmz58r1yqZ9x5R1yoj0kouIYRYIWMUqclljsQ+gepXLnEk9gky1y8V7ByYBwZ+S85xJPYJVL9yiSOxT5ChfimPXKFQKHIc5ZErFApFjqMMuUKhUOQ4eWfIhRB/E0IcEkJsCHttjhDiAyHEeiHEf4QQxYHXLUKIvwdeXyuEODXG8Z4PP1amSFe/hBCXCyHWCSE2CiF+Pvw9CSGEGC2EWCqE2BRoz9cDr5cLIV4XQmwL/F8WeF0IIe4TQmwP9OHoqOMVCyEahBD3Z6I/Ye1IW7+EED8XQmwI/Ls8h/o0LTA2XUKIb8c4nlEIsVoI8cJw9yWqHWnrlxDi64HrtFEIcWtaG5poLuOR8g84GTga2BD22nLglMDP1wE/Dvz8VeDvgZ+rgZWAIexzFwOPhx8rl/sFVAB7gKrA3x4GPpXBPo0Ajg78XARsBWYAvwBuD7x+O/DzwM/nAS8DAjgO+CjqeL8LXK/7M3yt0tIv4HzgdbTidwWB612cI32qBo4BfgJ8O8bxvhm4Vi/k2LWK2S/gKGAD4AhcrzeASelqZ9555FLKd4CWqJenAO8Efn4duCTw8wzgzcDnDgFtwAIAIUQh2mC7Z2hbnBhp6tcEYJuUUq8l/EbYZ4YdKeUBKeWqwM+dwGZgFLAY7SFD4P/PBH5eDDwiNT4ESoUQIwCEEPOBGuC14etBbNLYrxnAO1JKr5SyG1gHnDN8PQmRbJ+klIeklMsBT/SxhBB1aA+pvw59y/snjf2ajvYA7pFSeoG30RzBtJB3hjwOG9EuDMClwOjAz2uBTwshTEKI8cD8sL/9GPgV0DOcDU2SZPu1HZgqhBgnhDChDc7RZAFCiHHAPOAjoEZKeSDwp4NoBhq0G2xv2McagFFCCAPateozhc80qfQL7TqeI4RwCCEqgdPIguuVYJ/647fAbYB/KNo3WFLs1wZgkRCiQgjhQJtlpe1aKUOucR1wsxBiJdr0yR14/W9oN80KtMH1PuATQswFJkopnxn+piZFUv2SUrYCNwH/B7wL1AO+4W1yXwKzn6eAW6WUHeF/k9q8daAc2puBl6SUDUPUxEGRar+klK8BL6Fdv38CH5Dh65Vqn4QQFwCHpJQrh66VyZOGa7UZ+DnajPAVYA1pvFZZs7FEJpFSbgHOAhBCTEGb1hGYAn1Df58Q4n00jewUYIEQoh7tO6wWQrwlpTx1eFveP4PoF1LK/xBYEiyEuIHMGwYz2g20REr5dODlRiHECCnlgYDEcCjw+j4ivZy6wGvHo3lDNwOFgEUI0SWlvH14etGXNPULKeVP0PRYhBCPE7iOmSDJPsXjRLTZ4nmADSgWQjwmpbxy6FreP2nqF1LKh4CHAsf8XzRnKi0ojxwQQlQH/jcAdwJ/DvzuEEIUBH4+E/BKKTdJKf8kpRwppRwHnARszTYjDsn3K+ozZWiebMZ0SiGEQBv4m6WUvw770/PANYGfrwGeC3v96kCWx3FAe0Dj/IKUckzgen0bTW/OpBFPS78CmR0VgWPOBmaToRjAIPoUEynlHVLKusC1ugJ4M8NGPC39ChxLv7fGEEqUSA/piprmyj+0KegBtGBEA/Al4OtonsxW4GeEVryOAz5BC3C8gVZCMvp448iOrJW09CtwnE2Bf1dkuE8noU1Z16FNRdegaYsVwH/RSiG/AZQH3i+APwA7gPXAghjHvJbMZ62kpV9oHqt+rT4E5uZQn2oD47QDLdjeQFTGDXAqmc9aSVu/0OTKTWixjbRmg6kl+gqFQpHjKGlFoVAochxlyBUKhSLHUYZcoVAochxlyBUKhSLHUYZcoVAochxlyBVHPEIInxBiTaDq3FohxLcCufX9fWacEOLzw9VGhSIVlCFX5AO9Usq5UsqZwJnAucAPBvjMOEAZckVOoPLIFUc8geX4hWG/T0Ar+VoJjAUeRSsDC3CLlPJ9IcSHaBXrdqFVt7sPbVHVqYAV+IOU8i/D1gmFoh+UIVcc8UQb8sBrbcBUoBPwSymdQojJwD+llAuEttnGt6WUFwTefwNQLaW8RwhhBZYBl0opdw1jVxSKmKiiWYp8xwzcH6ho6UOr4R6Ls4DZQojPBn4vASajeewKRUZRhlyRdwSkFR9axbofAI3AHLSYkTPex4D/kVK+OiyNVCiSQAU7FXmFEKIKrQrk/VLTFUuAA1JKP3AVYAy8tROthrvOq8BNgZKmCCGm6BUkFYpMozxyRT5gF0KsQZNRvGjBTb0k6R+Bp4QQV6MV/O8OvL4ObRORtcA/0Pb7HAesCpQ2bSK0FZtCkVFUsFOhUChyHCWtKBQKRY6jDLlCoVDkOMqQKxQKRY6jDLlCoVDkOMqQKxQKRY6jDLlCoVDkOMqQKxQKRY7z/wFXCEzwI2pfOgAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -248,16 +249,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.10431055915017552"
+       "0.02442223237001552"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -275,7 +276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -284,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -364,7 +365,7 @@
        "1991-03-31  -1.083333   0.000000            -0.500"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -375,16 +376,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.10431055915017552"
+       "0.02442223237001552"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -395,16 +396,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "-0.131542947831303"
+       "-0.03506348228551407"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -413,24 +414,32 @@
     "combined.corr().loc[('Cash Rate', \"Lagged Inflation\")]  # Question - what does negative correlation mean?"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- negative correlation means that as cash rate decreases, the lagged inflation incresese. In other words, the inflation increases a certain period of time after a decrease in the cash rate"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f0c4904ce10>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -467,24 +476,34 @@
     "Note that when you pull down your data, your time period will differ to the one used to create this image. Therefore, your results will vary, slightly, from mine. If you are unsure of your solution, rerun the provided solution for an \"up to date\" plot."
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- The spike in the -1 to -3 range above means when we lag the inflation by -1 to -3 days (or inflation leads by 1 to 3 days), that produces the most significant correlation\n",
+    "- This may be an indication to use a lag in the range -1 to -3 for modelling\n",
+    "- Similarly could be said for around 6 - 8"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c8b6849e48>]"
+       "[<matplotlib.lines.Line2D at 0x20049303880>]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -496,16 +515,28 @@
     }
    ],
    "source": [
-    "lags = np.linspace(-10,10,21)\n",
+    "lags = np.linspace(-15,15,31)\n",
     "lagged_corrs = []\n",
     "\n",
     "for lag in lags:\n",
-    "    corrr =  combined[[\"Cash Rate\"]].corrwith(combined['Inflation'].shift(int(lag)))\n",
+    "    combined = inflation.join(interest_rates).dropna()\n",
+    "    combined['Lagged Inflation'] = combined['Inflation'].shift(int(lag))\n",
+    "    corrr = combined.corr().loc[('Cash Rate', \"Lagged Inflation\")]    \n",
     "    lagged_corrs.append(corrr)\n",
     "\n",
     "plt.plot(lags.tolist(), lagged_corrs, \"-\")"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- This plot above is an updated version using new data\n",
+    "- It shows best correlation when lag is around 1-2\n",
+    "- Compared to the plot provided above on the old data, this shows that the lag/leading relationship between cash rate and inflation may have flipped over the past few years"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -522,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -531,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -540,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -549,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -586,33 +617,33 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>1993-03-31</td>\n",
-       "      <td>0.8</td>\n",
-       "      <td>5.25</td>\n",
+       "      <th>1993-12-31</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.75</td>\n",
        "      <td>1.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1993-06-30</td>\n",
+       "      <th>1994-03-31</th>\n",
        "      <td>0.5</td>\n",
-       "      <td>5.25</td>\n",
+       "      <td>4.75</td>\n",
        "      <td>1.8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1993-09-30</td>\n",
-       "      <td>0.5</td>\n",
+       "      <th>1994-06-30</th>\n",
+       "      <td>0.8</td>\n",
        "      <td>4.75</td>\n",
        "      <td>0.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1993-12-31</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.75</td>\n",
+       "      <th>1994-09-30</th>\n",
+       "      <td>0.5</td>\n",
+       "      <td>5.50</td>\n",
        "      <td>2.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1994-03-31</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>4.75</td>\n",
+       "      <th>1994-12-31</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>7.50</td>\n",
        "      <td>-0.2</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -622,14 +653,14 @@
       "text/plain": [
        "            Inflation  Cash Rate  Lagged Inflation\n",
        "Date                                              \n",
-       "1993-03-31        0.8       5.25               1.6\n",
-       "1993-06-30        0.5       5.25               1.8\n",
-       "1993-09-30        0.5       4.75               0.9\n",
-       "1993-12-31        0.0       4.75               2.4\n",
-       "1994-03-31        0.5       4.75              -0.2"
+       "1993-12-31        0.0       4.75               1.6\n",
+       "1994-03-31        0.5       4.75               1.8\n",
+       "1994-06-30        0.8       4.75               0.9\n",
+       "1994-09-30        0.5       5.50               2.4\n",
+       "1994-12-31        1.0       7.50              -0.2"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -640,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -649,22 +680,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f0c488b1da0>]"
+       "[<matplotlib.lines.Line2D at 0x200374bde40>]"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -688,7 +719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -697,13 +728,13 @@
        "(-1.0, 1.0)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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2NNx8ZqbFXzj9dKv33x8OOMD0IwnO1FWcF1+0/6833hhbWRRcibxyZfwFQaJzEUA1FAS7d8M559jsd8ECy1c9ZYp5sTlOUqIKmzbBihU2Uq1YYaqbFSts8F++HH75Ze9rWrQwHf4xx0D37la6dbM6kSNOBXnkEZNVsf7/GSoI+vSJ7bOK4zOCBHDttUVGp4MPtuBTxx8PkybZbNhx4krQZWTNGli1yr7eV6+27WBZudKym4fSoAF07AidO9tg36mTDfxduth2w4YJeJnoMn++JWp58MHY6+07drR65crYPiccLgjizJQptoLwiiuKjE7//S+ccQaMHWsfUaedlsgeOinF1q3mbrlunS2wCtZr19rAHyzFB3kRaN0a2rc3o+ypp9p2x45FpWnT+Fs148wjj0C9erb2LNY0bw7p6S4IIkJEhgAPAjWBSao6vtjxq4ExwG5gI3Chqq4IHCsEFgROXamqpxMDVC2Q4bHHwt//XtRevz5Mm2btZ58N778fG/c0J0UoKIANG+Cnn/Yu69cX1cFSXF0D5lPftq3FRO7Tx/T0bdtaad++6Fjt2vF/tyRi82ZLQXneefGJ0S9i6qFECoJE5SKAKAgCEakJTAAGA6uBeSIyQ1UXhZz2BZClqvkicglwD3BO4FiBqvaOtB9l99MMT9u27ft/rGFDC187YACccorNHHxmUA1QNVeRTZusbNxYcvnpJxMAwfg4xcnIsNgHrVrZ6qRWray0bm2lTRsrGRkp/yUfKapw223mMRTPDGKJFAT16pnTVqKIxoygH7BEVZcBiMjzwFDgV0Ggqh+EnD8b+EMUnlthatYsOcJgy5a2fP2MM8xd+uKLLf5IvXrx7aNTSQoL7TMyN9dKTk5RHSy5uTbg5+QUDf7bt4e/X61api9o0cK8bI44wuqWLa2tRQvbDpb09Pi+b4pSWGiZxB5/3P4P9uoVv2cH1xLEm0SvKoboCIK2wKqQ/dXA4aWcPxoI/edOF5FsTG00XlVfDXeRiIwFxgJ0KCvrRCXp0sWMUzffbEJg8WJ4772YPMoJx65dsGWL/c/YvHnfunjJzS2qt2wp+b4iplNv2tQG9/btbVFU8+a2EjYz00rz5kXbjRv7l3uc2bULfvc7U9Vef31s1w2Eo0MHM+Ps2BHfTGGpIgjKjYj8AcgCjglp7qiqa0SkC/C+iCxQ1aXFr1XVicBEgKysLC1+PFrUqQP33mvTtL/9zcaXROruqgw7d1ogmJ9/tn+0YF18O1jy8vat8/NLf0bt2rboo0kTG9RbtTL/9+Ag37SpDexNmljdrJm1ZWRADc/Kmux8+KEJgb/8BW66Kf7PD3oOrV5tDljxIlUEwRqgfch+u0DbXojICcBNwDGquiPYrqprAvUyEfkQOBTYRxDEm0GD7Itk7tzoxT5PKlTN8Ll1qxk1f/ll7+1w5eefi+rgdnCg37Gj7GempZlUzciwunHjIr1548Y2gBffzsgo2q5Xz7/SU5jgIufhwxPz/NC1BPEWBC1bxu954YiGIJgHdBeRzpgAGAH8LvQEETkUeBwYoqobQtqbAPmqukNEmgMDMENywjn8cBtzPvsswYJA1fTY27aFL1u37lsX3y6pFBaWrw/p6WZcadjQSqNG5t3SsKEN2sG6USMrxbeD+65Hd0ohqN1L1Aw8VBDEk7w8WzSXSCIWBKq6W0QuB97G3Ecnq+pCEbkDyFbVGcC9QAPgJbEvuqCb6AHA4yKyB6iB2QgWhX1QnGnUyBacff55KScFB+n8fPu6zs/fd7uksm3bvtuhA3xo+5495e94zZrmE1u/vg3QwbplS/vMadjQFiM1aLDvdnA/dMBv0KDauzI68SHRgqBdO6sTIQhSQTWEqs4EZhZruzVk+4QSrvsMiFKuoXIwZ46t1CwosAG8oKDU8mxeAbmLCtAjCpBge3CgD5bKUKdO0WBdr57VdesWqUqCbaEl2Nagwb7HgoN5/fp2b1efOFWQLVvsmyNRE8f0dPteiqcgSIZcBFDNVhZzxx0wc2b4Y2lpNhiHlFY167K5sC7bajSiQedWRcfq1dt7O7gfrIMDd/HtYJ2gSI6Ok8xs2WKT0ER+x8R7LcG2baahdUEQTx58EMaPt8E4Pb1oMK9TJ+zgvHkxHN0DJl4AF12UgP46TjUiGTz0OnSIb4KaZAgvAaaXrz5062Yx1rt1M4Vgs2b2lV7CF3q3buZa/tlnce6n41RDkkEQdOxoQV01Zg7qe+OCoAogAkceWYbB2HGcqJAMgqBDBzP95eTE53kuCKoI/ftbMqdNm0o/L15fEOXh228tv7jjVCWSRRBA/OwEyZCUBlwQlMmRR1o9e3b44ytWWIC62rVN09StG9x/f/z6F2TVKguL0acPHHigRVC46KLSIy84TjJRHQWBzwiqCIcdZiaEd97Zu33PHnj4YVtr8OGHFihrxAizKVx7LXz9dWz7tWuXJe64/36LmtqhA1xzjcVKe+AB68PkyZZytnjfHScZSQZB0D4QI2HVqtLPixaJXjsRpHp5DVWCevUsT8FDDxUNtjk5MHKkRSo86SSLlBiMU5KbCz16mGD4+OPou8Jt3w6jR8OrrxaF5unVC+66C846yzIRBhk+HC64AIYMgXHj4JZbPOSOk5zs2WORShI9IDZvbh9T69fH53nB/8P168fneSXhgqAcPPWU+fpee61FJH3rLftDeeQRC5UbOtg3bWoeqhddBP/+N/whigG3d++26IyvvAJ//KMl0+nfv+grpjj9+sG8eXDJJRbf/fPP4bnnLHSP4yQTW7eanS3RgqBGDVtUtm5dfJ4XFAR168bneSXhgqAcpKVZtqTGjWHiREsJ+9lnln8kHBdeCP/6lwmO3/wmOn/cqjb4v/KKqX6uvLJ819WrZ4JswAC4/HK7x4svRt4fx4kmyaIiAQtqG88ZQXp64mfqrigoJzVrmgro9dctz3FJQgDsR50wwZJanXSSGZTLy549lgNhzhxTQW3eDM88Y2lrJ0829U55hUAQEcvJfNtt8NJLplZynGSiugqCbdsSrxYCFwQVQsQG5PKoVrKybND99lvz4Jk+vexrfv4Zhg2zaKdHHGH6yqZNzR7x1Vdw551w++2V7/+115o94dJLi7wVHCcZqK6CID8/ObIgumoohpx5pgmBs8+2Af6888zFs3nzfc9dvBiGDrX6/vvNDXXJEgv5P2SICZZIp4+1a8MTT5jt4NprTX3lOMlAsgmCDRvMLhjrsGAuCKoJXbuaPeGOO+Cee+CNN+DWW82ts21bWLTIjMqvv26Rn99915LixIq+fc3z6Z57bJHcLbfY2gPHSSTJJggKC00126JFbJ+VLKohFwRxoE4dc+8891zT1RfX8bdsaeqaq64qckONJXfeaV8hDzxg9oIRI0wYJdpg5VRfkkkQtG5t9bp1sRcEPiOohhx8MHz6KSxdanlR16yxP7RjjzXf5XiRlmbrCq66ytJx3nMPHHecR1h1EkcyCYJWraxev95sarEkPz853tkFQZwRMf1/t26J7on9AY4fb+Ezrr8ezjjDwmQ4TrzZssX08cnwdRwqCGJNfn7RDCSRuDKgmiNirq5btsANNyS6N051JRheIhmS6wUTycdDECSLjSAqgkBEhojI9yKyRESuD3O8joi8EDg+R0Q6hRy7IdD+vYicFI3+OBXj4IPNbjFpkq1fcJx4kwxxhoIEM7/Ga0aQDLOgiAWBiNQEJgAnAwcC54rIgcVOGw1sVtVuwD+AuwPXHgiMAA4ChgCPBO7nxJlx42xK/Mc/ws6die6NU91IJkEApq5xQVAx+gFLVHWZqu4EngeGFjtnKPB0YHsacLyISKD9eVXdoarLgSWB+zlxplEji5301VdmQK4M//d/sN9+FhXVcSpCMgScC6VVq/jEG8rPTx3VUFsgNGjr6kBb2HNUdTewBWhWzmsBEJGxIpItItkbN26MQred4gwbZkHy7rrLwmiUl8JCuPlmW0D3ww8wbVrMuuikKMk2I4jH6uKdOy2QZDLMCKqM15CqTgQmAmRlZSVRPrDU4p//hFmzYNQoyM62NRDF2bXL1h98/rnFU/r2W/jiCwuP/cUX8Mknce+2U8VJRkEQ6zwewcijySAIojEjWAOEBkJuF2gLe46I1AIaAznlvNaJI02amNH4m2/MpTSUrVvN3bRLFwub8dhj5nqanm7hKv71L1uPMG+e5U1wnPKSjIJgyxbLXxwrUk0QzAO6i0hnEUnDjL8zip0zAxgV2B4OvK+qGmgfEfAq6gx0B+ZGoU9OBJxyClxxha08njLF2nJzbZC/4QazA8yYYXGQli61EBpjxpjr38CBNuWdNy+hr+BUIVST00YANuONFdu2WZ0MNoKIVUOqultELgfeBmoCk1V1oYjcAWSr6gzgCWCqiCwBcjFhQeC8F4FFwG7gMlUtjLRPTuT8/e+wYIGFxMjMhBtvtLhIr75qwfFKYsAAqz/5xISC45TFtm1mZ0omQRBc5LV+veUfiQXJNCOIio1AVWcCM4u13RqyvR04q4Rr7wLuikY/nOhRu7YlsMnKshlCerrNAk4qY6VHs2Zw4IHwn//Ep5+RMn++Bf8LfgE68ScYXqJRo8T2I5Tg30MsPYeSSRD4ymKnRJo3tzwKRx0FM2eWLQSCDBxoMZUKk3xu9+abcPjhlv9hx45E96Z8fPKJzdJOPBEOOMDWfQRVDGBpVH//e4ucWVVIpjhDQeIRZiJZ8hWDCwKnDHr1ssHn2GPLf81RR5nOd8GC2PUrUrKz4ayzLN/zN99ElvAnXkydanaal16yxEKdO5uBPisL5s6Fyy6Dk0+2tKrjxiW6t+UnGQVBZqbZvGIpCIIC3GcETkoStA0ko3pI1dZInHqqzXg++8xyTN99tw2myciePeatdd55cPTR8OOP1teZMy1/xebNNrN55BG4+mqLIvvYY7BwYaJ7Xj6SURDUqmXCIB4zAhcETkrSsaN9aSfTeoIffoDf/MYG/759bS3Em2+aUfD++81OMGpUbN0FK8K2bXDxxXDooRb35oYbLJ/Fm2/uPWAef7ytBr/8clv/cd99tjK8YUMTCloFVtwkoyCA2C8qc0HgpDwDB5ogSIaBSBXOP9/6M2yYqVMWLDAdO9gA9MQT8N13lpshGbjlFnj8cRuMLr7YEgc984zlkihOy5bw0EOmNgITduPG2YKoN9+Mb78rQ7IKgtat42MsdhuBk7IMGmT/iZJB3fLss7YK+h//sAF/zBibAYQyeLDZDO69Nz4xZkpj7lx48EG45BIbyO+/H373u4plkLv0UujRw9aDbNoUu75Gg2QVBLGeEbiNwEl5RoywVcrjxye2H1u3wp//bAbVUaNKP/dvf7PFcIk0tO7aZYKqdWvrT2VJS7MV4qtXm7dXXl7Uuhh1tmwxIdegQaJ7sjdBQRCrWW1wRpCeHpv7VwQXBE5MaNgQ/ud/bAHaokWJ68ff/gZr11oMpbK+qLt2Nc+bJ54wT6JEEFzIN2FC5F/IAwdaRNgFC2wtyNdf20rxq6829ViyzBS2bLE1BMmQlCaUVq1MMG/eHJv7B0NQJ0WucFWtcqVv377qJD+bNqnWr686cmR8n7t+verUqaqjRqmmpan+4Q/lv3bTJtWMDNWTT45Z90pk507Vhg1Vhw6N7n1fflm1Zk1V+7ZVrV3b6po1VY8/XnX8eNU5c1R37Yruc8vLyJGqHTsm5tml8dxz9u/0zTexuf+ll6o2bx6be5cEFu1hnzE1GWSRk6I0a2aLn559FpYvj+2z8vLgySfhhBNMrTJyJLz+uoXGvu++8t+nWTO46SbTzcc7W9u8eRa/aeTI6N73jDPgo49sprNggXlGffEFXHed2UOuv97cTw85ZN+Fdc8+G3s7T7IFnAvSpo3Va9fG5v7btiWHfQBcNeTEmD/9yaa+994b/Xvn5Jir5MCB5ilz4YXmY3/LLRY6YsMGG8hatKjYff/4R1NtTZgQ/T6XxqxZph4ZNCj69x4wwP59Dj7YksT37m15JxYuNGEwfrx5Tb33XtE1q1bZKuWjjjIPpiB5ebZyfN268Przn3+2+5VmaH3/fVvUB8krCNq1s3pNjOIhJ0t2MsBVQ07sGTvW1BFLlkTvnlu3qvbpY1P3Pn1Ub7pJdfZs1T17onP/yy83tdKGDdG5X3kYNEj10EPj97xQduxQbdxY9fzzi9ruvdf+fQcOtPrcc1V/8xv7dwmqmRo3trb//teu+eEH1QMOsGOjR4d/Vl6eqcBatVLdvFm1d2/V006L8QtWgoICe48774zN/U89VTXeQxmuGnISxbhxFsSueH6DylJYaO6UX35p6p/58+EvfzH1RrQMjpdeah5EkyZF535lUVBgq5yDawHiTVqaRZV99dWinNXPPQeHHQYffGAL2p57zlRKl19uMaj++U9b5Pbpp9Cnj6mgDjvMZmKDB1tIjHBqlUmTTAX200+mhkvWGUF6uqkKYzUjSCbVUMK/7itTKj0jKCgwi5wTd267zb6u/vOfyO911VV2r4cfjvxepXHccaodOqju3h3b56iqvveevdPMmbF/VknMmGF9eOst1e+/t+377is6vnmzamHhvtdt3qx6ww2qdeuq9uqluny56tKlqjVqqP75z3ufu3Onavv2qscco3rFFaoiNlu89NKYvVZE9OxpM55Y0K+f6kknxebeJUEJM4Iqk6oyKgwfDm+8YUrS9HSoW9dK6HZJpV69vetwbfXqFZXQ9po1E/3mCeeaa2DiRHNd/Pxza1uyxIyTtWpB06a2QrYsHn7YEuZcdZW5esaSyy4zY/Prr8Ppp9vX7XffWb/XrjX9eY8e0XnWrFn273DUUdG5X2UYPNhsI9OmWYgQETjnnKLjGRnhr8vIMFvNddfZn3zt2tZ+1lkW8+jGG4u++KdNM9vDhAlwzDG2v3Ztcs4IwBYextJGELRDJBrRcNaeJCcrK0uzg5amivDSS/D99zYPLyiwfIrB7fKU4Jy5otSpUyQg6tffd7ukttASbGvQYN9jVUTQPPmkGSz797e1BaGLnETgzjtt0Aiqd7ZvN0NzMKzCq6+a+uH00+Hll2P/2rt3W4TPYJLx3Ny9j2dmWtC3Xr0if9YRR9j7fPpp5PeKhN//Ht5+24zvrVrBhx9W/l7z59tCvnvugWuvNavCYYfZIr9Fi+y3nTataEX3NddE7TWixtixpgaLRaayrl3t/8LUqdG/d0mIyHxVzSreXr1mBGeFzY1TfgoLi4RCfv7e28H90HrbtpK38/NNUbphg+0H27Zts1GnIqSnFwmI0Lp4adgw/HZwP9jWsKF9nkaZ886zmDmrV9vkrH9/W0i0a5f9Z7v5ZtP7jxtnro5PPGGD4/nn27nnnQf9+pknUDxkX61a5lnz+ONw0EHQs6cl3ene3X6qE06w8NzvvGMDXmXZssVcR2+6KXp9ryzDh9u/b06OzboioW9fC4p33332p19YaMLhsceKFlGdeSY8/3zFwpzHk7Zt7b/ozp3h4zxFQjLZCKrXjKCqsHNnkVAoq2zdWlSH2/7ll73PKy/p6TZKBwVEcDu0DpaGDW1uH9wPbjdubH/p5bDgqtqAcd11Fna5Vi04+2wbPF5+2WRj166mVsrMjODfNoosX27G3dxcM6j26VO5+7z2ms1yPvggNq6jFaGgwP59t28398/mzSO735w5NtgH1SutWsGyZaYxrQpMmmRhvX/80aLqRpNGjWD0aIuBFS9iMiMQkabAC0An4EfgbFXdXOyc3sCjQCOgELhLVV8IHHsKOAYIhJ3ifFX9MpI+pQRpaVZKUspWlj17TMBs3WoC4pdf9t4Otx9a1q0z1dovv5izeHliNtesubeAyMiwOlgyMiAjA8nI4JpOGRw/PoN5izP4zcgMWh9gx9Y/UIuXXrIw0skiBMDURh9/bHr9k082r5+uXSt+n/ffN7l7xBHR72NFqVvXbCM//xy5EADz5Fq92r5tNm4sMq9VFYLBCdesia4gULX/iskQeRQinBGIyD1ArqqOF5HrgSaqel2xc3oAqqqLRaQNMB84QFXzAoLgdVWdVpHnpvyMoKqwe7eNGFu2FAmHLVusDm4H94PbwZKXZ+Xnn01AlUaDBhbBLiPD6qZNrQ7dbtp039K4cVwC2Hz3nQmDjAzT8ZfH6B1E1YTHfvtVjZDR1Y2vvzYb0IsvRq5ZDmXnTjMd3nWX2cXiRaxsBEOBQYHtp4EPgb0Egar+ELK9VkQ2AJlAXoTPdhJN0N2nadPK30PVZiF5eUUCYvPmvevgdrAsXWp1bm5RCMdw1KxpfWvWbO/SvHlRHSyZmVZnZFQ4Ctj++5sz2nHH2azls8+KTCx79tj6iWHDzM5RnLlzTcV0660VeqQTJ4JePatXR/e+yRSCGiIXBC1VNRi9fT1Q6reQiPQD0oClIc13icitwCzgelUNm0ZcRMYCYwE6dOgQYbedpEGkyA7Rvn3Fr9+xo0goBEtOzt7bwfLjj2at3LSp5Gz1NWsWCYcWLUxAtGixb2nZ0uqGDUGEww834/a558JTT1koaTCPkHvvhRkzLKJpcRv888+bFvC3v634qzuxp0kTU9tF24U0mbKTQTkEgYi8B7QKc2gvHwdVVREpUc8kIq2BqcAoVQ3qAm7ABEgaMBGbTdwR7npVnRg4h6ysrKpn4XZiQ506ZoFsFe5PtARU7ZMsJ8cU1zk5Jhw2bty3fPmluY2UFNA/Pf3X55/TsiV1WrZi6dWt2ZFvbS9d25qeTVqz6PtWTJ1amwsuKLq0sBBeeMFCRCerH311RyQ2awmSKTsZlEMQqOoJJR0TkZ9EpLWqrgsM9BtKOK8R8AZwk6rODrl3cDaxQ0SeBJLQk9hJOUSK3GbLawHcudMEwsaNVv/0U1FZvx5++glZtozTdnxK7V82wZV22euBy/cg5IzJZM+DbajRtg20acOqXW05dV1bRu7XFr5uZ3qIJk2SLzB/NScWgiDVVEMzgFHA+EA9vfgJIpIGvAJMKW4UDhEiAgwDEpQOxHHKIC3NBuoyloLWBoYP3cWCWRtosWc9Q3qu5aYL1rLsk3XM+vc6Ttq9hk7r18H8+XT4aQP/QuFurIC51LRrZ2qy9u2hQ4eiukMHE1zJMnpUE9q1g9mzyz6vIlQ51VAZjAdeFJHRwArgbAARyQIuVtUxgbajgWYicn7guqCb6L9FJBMQ4Evg4gj74zgJ5y931+bgg9uyTNryxJS+0AO6joULV8Jtiy3Uc48e0L7VLoYPWMfDN6yxT87Vqy3+QrCeNcviLxT3qmreHDp1KiqdO1vp0sUERTLkPkwhgjMC1ehN1qqcaqg0VDUHOD5MezYwJrD9DPBMCdcnKNai48SO/feHhx4yu3MwFpGIxegfNMhyArRsCT/l1ubEMR3gyFKcH3bvNmGwYoWVlSvN6L1ihfk2vvba3obvoFK7SxfzS+3WzUr37lY3bBjLV09J2ra1f+KcnOisrYDUmxE4jhOGSy7Zt61/f3MVfeMNC2SXl2eJ5UulVq0itdDAgfse37PHbBTLl1tZtszca5cuhbfeskWAobRqZdKpRw9bvLDffia5OneOSViRVCB0UVm0BEGq2Qgcx6kAbdtaILOxY6N0wxo1LKdimzaWhqw427aZUFi82MoPP1iZPt0M30HS0mzWcNBBFlDpoINs6tKtW7UXEKGCIBoBBsFnBI7jxJP69S1aXs+e+x7LzbWQId99B99+ayU726L0BiMOpKXBAQfY9YccYjkue/WqeP7PKkwsFpWllI3AcZwqTNOmcOSRVkLJzzfhsHChZbtfsMACIoXGS27d2qLsHXqohRnt29dGzBR0fW3Vyl4rmi6kPiNwHCe5qVfPBvni4VRzcuCrr6x88YWVt96ylXFgs4TDDrNY4YcfbnWTJvHvf5SpXduM+9EUBNu2mXBJFgcvFwSO45SPZs0soFJoYuWCAvNeys62pArz5sHMmUWqpf33Nyv5gAEWma979yo5a4j2orL8/HJHaI8LLggcx6k8deva1//hhxe1/fyzCYbZsy2BxKuvwuTJdqxFCzj6aMtTOWiQGaWTZTQshbZtzSkrWgQFQbLggsBxnOjSqNHeM4c9e8wo/Z//wCefwEcfWY5KMMFw7LGWMHnwYHOTTULatbPcE9EimbKTgQsCx3FiTY0a5nl0wAGW7gtsUdyHH9rq6VmzLPoe2LqGk0+2SHxHH21BBZOAzp2LoqFHw+yRbDOCigVedxzHiQadOlky6qlTTfn+zTeWs7FTJ3j0UTjxRLNJnHkmPP20RYdNIMEV4osXR+d+yZSdDFwQOI6TaETMVnDVVeaFlJtry69HjjQ7w/nnmw/n8cfDI49YxNc407271T/8UPp55cVnBI7jOKVRr56phh591FZxzZsH111nM4fLLrNV1IMHWyagLVvKvl8U6NLFNFzRmhEkm43ABYHjOMmLCGRlWXLfb7+1xW033WQ2hjFjbKYwYoQlfA6uZ4gBdepYYFdXDTmO4yQSEYt/dMcdpqOZO9eEwXvv2QyiUycYN84itMaA7t1dNeQ4jpM8iNgq5ocesjDd06aZkLjzTnPxOeMMC4uh0ctq2727zQiicUsXBI7jONEkLc28i95801Z9XXedOf0ff7wFyJsyxVKNRkiPHrZWbkPYhLwVw20EjuM4saJjR/jrX83IPHmyfb6PGmVJeh580EJiVJKg51CkdgJVtxE4juPEnvR0uOACi4M0c6a5/Vx1ldX/+Ads317hW0ZLEOzcaYutU2ZGICJNReRdEVkcqMOuuRORQhH5MlBmhLR3FpE5IrJERF4IJLp3HMeJDiK2Uvmjj2wl84EHwtVXm57nqacq5GnUqZPl6InUYJxs2ckg8hnB9cAsVe0OzArsh6NAVXsHyukh7XcD/1DVbsBmYHSE/XEcxwnPMcdYOIv33ze30wsusEQ7779frstr1bIJRaQzgmRLSgORC4KhwNOB7aeBYeW9UEQEOA6YVpnrHcdxKsWxx8KcOfDii7B1qxmVzzqrXG6nQc+hSEi2pDQQuSBoqarB7NjrgZYlnJcuItkiMltEhgXamgF5qro7sL8aaFvSg0RkbOAe2RtDc606juNUFBEb/BctsnUJb7xhaqOHHipVXdSjhwmCPXsq/+hffrG6Ss0IROQ9EfkmTBkaep6qKlCSh21HVc0Cfgc8ICJdK9pRVZ2oqlmqmpWZmVnRyx3Hcfalbl245RYTCEcdBVdcAQMHWtjsMHTvbo5Ha9dW/pE5OVY3b175e0SbMgWBqp6gqgeHKdOBn0SkNUCgDuthq6prAvUy4EPgUCAHyBCRYCjsdkAUcwA5juOUk06dbB3C1KkmBPr0gX/9a5/VY8EopJEYjIOCoFmzyt8j2kSqGpoBjApsjwKmFz9BRJqISJ3AdnNgALAoMIP4ABhe2vWO4zhxQQT+8AeLZ9S/P4wdawvVNm/+9ZRouJAGI2qnkiAYDwwWkcXACYF9RCRLRCYFzjkAyBaRr7CBf7yqLgocuw64WkSWYDaDJyLsj+M4TmS0aQNvvw1//zu8/rqFsliwALBMZenpkQmC4IwgGgluokVEGcpUNQc4Pkx7NjAmsP0ZcEgJ1y8D+kXSB8dxnKhTowb86U9w5JEwfDgccQRMnkyNc86hW7fIVUMZGeaOmiz4ymLHcZyS6N8f5s+HQw+1cNd3303nTsqPP1b+ljk5yWUoBhcEjuM4pdO6tS1EO/dcuP56/rTyClYuL6x0FNKcnOSyD4Anr3ccxymbOnXgmWegTRuOue8+HmcDeRufoUmL2hW+1aZNtrA5mfAZgeM4TnmoUQP+/ncWjLyHc3jRPIx27y77umIk44zABYHjOE4F2HnltVzDvTR590UYObLCwsBtBI7jOFWcTp3gPq7hk9Puhuefh4svLnfasp07LbyRzwgcx3GqME2bQoMG8HLXP8NNN8ETT8D48eW6NhlXFYMbix3HcSqEiCVC+/FH4JU7LT3mjTdaruQRI0q9NhlXFYPPCBzHcSpMp04BQSBiKTEHDrSUmHPnlnpdss4IXBA4juNUkE6dYMWKwE6dOvDKK7be4JxzIC+vxOuSMfIouCBwHMepMB072nj/65jfrJkZjlevhjFjSjQe+4zAcRwnRejUyepfZwVg8Yj+9jd4+WV49NGw17mNwHEcJ0UIKwgArr4aTjnF6jDJbXJyLEVlenrMu1ghXBA4juNUkI4drd4n+FyNGuZOWrdu2PUFybiqGFwQOI7jVJjMTBvrw0YhbdUK7r4bPvwQnnpqr0PJuKoYXBA4juNUGJFinkPFGTMGBgyAa66BjRt/bfYZgeM4Tgrx66KycNSoARMnwi+/mDAIsGmTCwLHcZyU4ddFZSVx4IHwv/8LU6f+muoyJWcEItJURN4VkcWBep8snCJyrIh8GVK2i8iwwLGnRGR5yLHekfTHcRwnXnTqBLm59tFfItddBw0bwi23UFgImzenoCAArgdmqWp3YFZgfy9U9QNV7a2qvYHjgHzgnZBTrg0eV9UvI+yP4zhOXCjRhTSUpk3h2mth+nS2vj8X1dQ0Fg8Fng5sPw0MK+P84cCbqpof4XMdx3ESSokupMW58kpo3pxat90EpOaMoKWqrgtsrwdalnH+COC5Ym13icjXIvIPEakTYX8cx3HiQnBGUKYgaNgQbryR+p+9xyA+qJqCQETeE5FvwpShoeepqgIlZmcQkdbAIcDbIc03APsDhwFNgetKuX6siGSLSPbGEHcsx3GcRNCyJTRpAl98UY6TL7mE7U1b82fuqZqCQFVPUNWDw5TpwE+BAT440G8o5VZnA6+o6q6Qe69TYwfwJNCvlH5MVNUsVc3KzMws7/s5juPEBBE4+mhbN1Ym6el8e/TFnMxbtPx5cay7VmEiVQ3NAEYFtkcB00s591yKqYVChIhg9oVvIuyP4zhO3Bg0CJYtg1Wryj53Ts+L2EUtWv7fIzHvV0WJVBCMBwaLyGLghMA+IpIlIpOCJ4lIJ6A98FGx6/8tIguABUBz4C8R9sdxHCduDBpk9UfFR7YwrNjZmpdlOGn/fhK2bYtpvypKRIJAVXNU9XhV7R5QIeUG2rNVdUzIeT+qaltV3VPs+uNU9ZCAqukPqro1kv44juPEk549zU5QHvVQTg48m3EZsmUL/PvfMe9bRfCVxY7jOJWkRg3LUlleQbC01QDo1QsefrjE5DWJwAWB4zhOBAwaBEuXWnKy0sjJgWbNBS67zEJOfPppXPpXHlwQOI7jREB57QS/hqA+91zLTPPCC7HuWrlxQeA4jhMBPXtCRkbZ6qFfI482aABDhlhKyz17Sr8oTrggcBzHiYCaNcu2E+zaZWkJWgZjLwwfDuvWwezZ8ehimbggcBzHiZBBg2DJElizJvzxlSuhsBC6dg00nHYapKXBtGnx6mKpuCBwHMeJkIEDrf7ss/DHly2zukuXQEPjxjB4sKmHksB7yAWB4zhOhBxyCNSqVXLcoaVLrf51RgCmHlq5ErKzY96/snBB4DiOEyHp6XDQQfDf/4Y/vmwZ1KkDbdqENJ5+ukmPJFAPuSBwHMeJAn37miAIp+lZuhQ6d7YFaL/StCkcd1xSqIdcEDiO40SBPn3MMyjcwrKlS0PsA6H89rd28PvvY96/0nBB4DiOEwX69LG6uHpI1VRDe9kHghx7rNWffBLTvpWFCwLHcZwo0KuXqX6KC4JNmyzBfVhB0KMHtGgBH38clz6WhAsCx3GcKFCvHhxwwL6CIOgxFFY1FMxu4zMCx3Gc1KBPn30FQXANQdgZAZggWLHCXEkThAsCx3GcKNGnD6xdC+vXF7UFZwSdO5dwUXA1WgJnBS4IHMdxokQ4g/GyZbZ+oG7dEi465BBbaZxAO4ELAsdxnCjRu7fVoYJg6dJS1EJgUeuOOsoFgeM4TirQqJE5AhUXBGENxaEMHAjffQcbNsS0fyURkSAQkbNEZKGI7BGRrFLOGyIi34vIEhG5PqS9s4jMCbS/ICJpkfTHcRwn0YQajAsKzGZQ6owAzGAM8J//xLRvJRHpjOAb4AygxDmNiNQEJgAnAwcC54rIgYHDdwP/UNVuwGZgdIT9cRzHSShHHWVOQDNnwvLl1lbmjKBvXzMiJEg9FJEgUNVvVbWstdH9gCWqukxVdwLPA0NFRIDjgGDEpaeBYZH0x3EcJ9GMGQMHHgiXXAJff21tZc4I0tLgyCMT5jlUKw7PaAusCtlfDRwONAPyVHV3SHvbkm4iImOBsYHdrSJS2eAczYFNlbw22Unld4PUfj9/t6pLie937rlWH3lkBe4mEnmPSqZjuMYyBYGIvAe0CnPoJlWdHmmvyouqTgQmRnofEclW1RLtGVWZVH43SO3383eruqTC+5UpCFT1hAifsQZoH7LfLtCWA2SISK3ArCDY7jiO48SReLiPzgO6BzyE0oARwAxVVeADYHjgvFFA3GYYjuM4jhGp++hvRWQ1cCTwhoi8HWhvIyIzAQJf+5cDbwPfAi+q6sLALa4DrhaRJZjN4IlI+lNOIlYvJTGp/G6Q2u/n71Z1qfLvJ5oEiZMdx3GcxOErix3Hcao5Lggcx3GqOdVKEJQU6qIqIiLtReQDEVkUCPNxZaC9qYi8KyKLA3WTRPe1sohITRH5QkReD+ynREgSEckQkWki8p2IfCsiR6bY7/a/gb/Jb0TkORFJr6q/nYhMFpENIvJNSFvY30qMfwbe8WsR6ZO4nleMaiMIygh1URXZDfxJVQ8EjgAuC7zP9cAsVe0OzArsV1WuxBwMgqRKSJIHgbdUdX+gF/aOKfG7iUhb4AogS1UPBmpinoJV9bd7ChhSrK2k3+pkoHugjAUejVMfI6baCAJKCHWR4D5VGlVdp6r/DWz/gg0mbbF3ejpwWpUN2yEi7YBTgUmB/ZQISSIijYGjCXjIqepOVc0jRX63ALWAuiJSC6gHrKOK/naq+jGQW6y5pN9qKDBFjdnYOqnWcelohFQnQRAu1EWJIS2qEiLSCTgUmAO0VNV1gUPrgZaJ6leEPAD8GdgT2K9QSJIkpjOwEXgyoPaaJCL1SZHfTVXXAH8HVmICYAswn9T47YKU9FtV2TGmOgmClEREGgAvA1ep6s+hxwKL9qqcf7CInAZsUNX5ie5LDKgF9AEeVdVDgW0UUwNV1d8NIKAvH4oJvDZAffZVraQMVfm3CqU6CYKSQl1UWUSkNiYE/q2q/xdo/ik4HQ3Uicl0ERkDgNNF5EdMhXccplfPCKgboOr+fquB1ao6J7A/DRMMqfC7AZwALFfVjaq6C/g/7PdMhd8uSEm/VZUdY6qTIAgb6iLBfao0AZ35E8C3qnp/yKEZWLgOqKJhO1T1BlVtp6qdsN/pfVX9PSkQkkRV1wOrRGS/QNPxwCJS4HcLsBI4QkTqBf5Gg+9X5X+7EEr6rWYA5wW8h44AtoSokJIbVa02BTgF+AFYikVPTXifIniXo7Ap6dfAl4FyCqZLnwUsBt4Dmia6rxG+5yDg9cB2F2AusAR4CaiT6P5V8p16A9mB3+5VoEkq/W7A7cB3WOKqqUCdqvrbAc9hto5d2GxudEm/FSCYZ+JSYAHmOZXwdyhP8RATjuM41ZzqpBpyHMdxwuCCwHEcp5rjgsBxHKea44LAcRynmuOCwHEcp5rjgsBxHKea44LAcRynmvP//L78vRQu7uMAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -735,16 +766,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([19, 63])"
+       "array([  0,   1,   2,   3,   4,  88,  89, 110], dtype=int64)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -755,16 +786,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([40])"
+       "array([ 56,  58,  59, 100, 101, 102, 103, 104], dtype=int64)"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -798,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -812,22 +843,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c8b7d6fd08>]"
+       "[<matplotlib.lines.Line2D at 0x200375a0f10>]"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -844,30 +875,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:541: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.\n",
-      "  warnings.warn(msg, FutureWarning)\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c8b7f13508>]"
+       "[<matplotlib.lines.Line2D at 0x200375e37f0>]"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -896,7 +919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -910,22 +933,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c8b7da1708>]"
+       "[<matplotlib.lines.Line2D at 0x2004ab17ac0>]"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -942,30 +965,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:541: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.\n",
-      "  warnings.warn(msg, FutureWarning)\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c8b623fcc8>]"
+       "[<matplotlib.lines.Line2D at 0x2004ab7e0e0>]"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -993,7 +1008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1002,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1011,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1020,22 +1035,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c8b7fab948>]"
+       "[<matplotlib.lines.Line2D at 0x2004acb69b0>]"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1063,36 +1078,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
-      "  % freq, ValueWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:541: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.\n",
-      "  warnings.warn(msg, FutureWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
-      "  % freq, ValueWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:541: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.\n",
-      "  warnings.warn(msg, FutureWarning)\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1c8b80b8988>]"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
+      "  self._init_dates(dates, freq)\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1104,19 +1129,24 @@
     }
    ],
    "source": [
-    "#ExponentialSmoothing?\n",
-    "\n",
     "euro = quandl.get(\"BUNDESBANK/BBEX3_M_AUD_EUR_CM_AC_A01\")  # Euros in AUD\n",
     "\n",
     "euros = euro['Value']\n",
     "euros.name = \"EURO\"\n",
     "euros.head()\n",
     "\n",
-    "usd = quandl.get(\"BUNDESBANK/BBEX3_M_AUD_USD_CM_AC_A01\")['Value']\n",
+    "usd = quandl.get(\"BUNDESBANK/BBEX3_M_AUD_USD_CM_AC_A01\")\n",
+    "usd = usd['Value']\n",
     "usd.name = \"USD\"\n",
     "usd.head()\n",
     "\n",
-    "#ExponentialSmoothing?\n",
+    "plt.plot(ts.acf(euros))\n",
+    "plt.title(\"Autocorrelation of EUR\")\n",
+    "plt.show()\n",
+    "\n",
+    "plt.plot(ts.acf(usd))\n",
+    "plt.title(\"Autocorrelation of USD\")\n",
+    "plt.show()\n",
     "\n",
     "euro_model = SimpleExpSmoothing(euros)\n",
     "euro_fit = euro_model.fit()\n",
@@ -1124,7 +1154,16 @@
     "\n",
     "usd_model = SimpleExpSmoothing(usd)\n",
     "usd_fit = usd_model.fit()\n",
-    "plt.plot(ts.acf(usd_fit.fittedvalues, nlags=100))\n"
+    "plt.plot(ts.acf(usd_fit.fittedvalues, nlags=100))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- After holtwinter model, we can see that there is still strong auto correlation"
    ]
   },
   {
@@ -1145,7 +1184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1154,7 +1193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1163,22 +1202,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8b800a148>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1202,22 +1241,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1c8b80a8788>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1241,7 +1280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -1269,14 +1308,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ADF Statistic: -10.18\n",
+      "ADF Statistic: -8.41\n",
       "p-value: 0.00\n"
      ]
     }
@@ -1305,6 +1344,67 @@
     "3. Another method to create stationary data is transform the data using a log function. Compute the log of the currencies and recompute the adfuller statistic."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "EUR stationary check\n",
+      "ADF Statistic: -2.33\n",
+      "p-value: 0.16\n",
+      "USD stationary check\n",
+      "ADF Statistic: -3.16\n",
+      "p-value: 0.02\n",
+      "\n",
+      "\n",
+      "EUR transformed stationary check\n",
+      "p-value: 0.00\n",
+      "USD transformed stationary check\n",
+      "p-value: 0.00\n",
+      "\n",
+      "\n",
+      "EUR logged stationary check\n",
+      "p-value: 0.14\n",
+      "USD logged stationary check\n",
+      "p-value: 0.08\n"
+     ]
+    }
+   ],
+   "source": [
+    "# exercise 1\n",
+    "# EUR seems non-stationary\n",
+    "print(\"EUR stationary check\")\n",
+    "result = adfuller(euros)\n",
+    "print('ADF Statistic: {:.2f}'.format(result[0]))\n",
+    "print('p-value: {:.2f}'.format(result[1]))\n",
+    "\n",
+    "# USD seems stationary\n",
+    "print(\"USD stationary check\")\n",
+    "result = adfuller(usd)\n",
+    "print('ADF Statistic: {:.2f}'.format(result[0]))\n",
+    "print('p-value: {:.2f}'.format(result[1]))\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "# exercise 2\n",
+    "# transformed verions are stationary\n",
+    "print(\"EUR transformed stationary check\")\n",
+    "print('p-value: {:.2f}'.format(adfuller(euros.diff().dropna())[1]))\n",
+    "print(\"USD transformed stationary check\")\n",
+    "print('p-value: {:.2f}'.format(adfuller(usd.diff().dropna())[1]))\n",
+    "print(\"\\n\")\n",
+    "\n",
+    "# exercise 3\n",
+    "# logged doesnt change the value much\n",
+    "print(\"EUR logged stationary check\")\n",
+    "print('p-value: {:.2f}'.format(adfuller(np.log10(euros))[1]))\n",
+    "print(\"USD logged stationary check\")\n",
+    "print('p-value: {:.2f}'.format(adfuller(np.log10(usd))[1]))\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1353,54 +1453,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 82,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Market Variance: 0.7265414157716941\n",
+      "Market (SP500) Variance: 0.7265414157716945\n",
       "Stock Covariance: 0.4684920443273424\n",
-      "Beta: 0.6448249668323929\n",
-      "Return on Market: 0.0772352134654195\n",
-      "CAPM: 0.058682569790322096\n"
+      "Beta: 0.6448249668323925\n",
+      "\n",
+      "Return on Market (Annualised): 7.7235213465419505%\n",
+      "CAPM: 5.868256979032208%\n"
      ]
     }
    ],
    "source": [
+    "# these answers were already here so I just added comments to explan the code\n",
+    "# get spx data (stock market benchmark)\n",
     "sp500 = quandl.get(\"CHRIS/CME_SP1\")\n",
-    "\n",
     "sp500_sample = sp500.loc[\"2016\":\"2018\"].dropna()\n",
-    "\n",
     "market = sp500_sample['Last'].pct_change().dropna() * 100\n",
     "\n",
+    "# get coca cola data\n",
     "cola = quandl.get(\"WIKI/KO\")\n",
-    "\n",
     "cola_sample = cola['2016':\"2018\"]\n",
-    "\n",
     "coca = cola_sample['Close'].pct_change().dropna() * 100\n",
     "\n",
+    "# compute variance of stock market\n",
     "market_variance = market[\"2016\":\"2018\"].var()\n",
-    "print(f\"Market Variance: {market_variance}\")\n",
+    "print(f\"Market (SP500) Variance: {market_variance}\")\n",
     "\n",
+    "# compute covariance between stock market and cocacola\n",
     "stock_cov = market.corr(coca)\n",
-    "\n",
     "print(f\"Stock Covariance: {stock_cov}\")\n",
     "\n",
+    "# compute beta value of cocacola relative to the overall marke\n",
     "beta = stock_cov / market_variance\n",
+    "print(f\"Beta: {beta}\\n\")\n",
     "\n",
-    "print(f\"Beta: {beta}\")\n",
-    "\n",
+    "# compute return of market over period\n",
     "e = ending_balance = sp500_sample.iloc[-1][\"Last\"]\n",
     "s = starting_balance = sp500_sample.iloc[0][\"Last\"]\n",
     "n = number_years = 3  # 2016, 2017, 2018\n",
-    "return_on_market = (e / s) ** (1 / n) - 1\n",
-    "\n",
-    "print(f\"Return on Market: {return_on_market}\")\n",
+    "return_on_market = (e / s) ** (1 / n) - 1 # annualized over 3 years\n",
+    "print(f\"Return on Market (Annualised): {return_on_market*100}%\")\n",
     "\n",
+    "# compute CAPM with given equation (2.5% may b the return of govt bonds)\n",
     "capm = .025  + beta * (return_on_market - .025)\n",
-    "print(f\"CAPM: {capm}\")\n"
+    "print(f\"CAPM: {capm*100}%\")\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- The above shows an expected return of around 6% for the stock given a risk-free rate of 2.5%.\n",
+    "- We have a approx 5% market risk premium.\n",
+    "- A beta of 0.6 shows that it is less risky when compared to the overall market\n",
+    "- However, this also results in a lower expected return as reflected by the model"
    ]
   },
   {
@@ -1413,7 +1526,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1427,7 +1540,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.3.1 - Ordinary Least Squares.ipynb b/notebooks/Module 1.3.1 - Ordinary Least Squares.ipynb
index e5a8a81..0dd329d 100644
--- a/notebooks/Module 1.3.1 - Ordinary Least Squares.ipynb	
+++ b/notebooks/Module 1.3.1 - Ordinary Least Squares.ipynb	
@@ -59,7 +59,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +68,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -98,27 +98,27 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>2007-03-31</td>\n",
+       "      <th>2007-03-31</th>\n",
        "      <td>517447.312500</td>\n",
        "      <td>6.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2007-06-30</td>\n",
+       "      <th>2007-06-30</th>\n",
        "      <td>528379.733333</td>\n",
        "      <td>6.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2007-09-30</td>\n",
+       "      <th>2007-09-30</th>\n",
        "      <td>511257.021277</td>\n",
        "      <td>6.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2007-12-31</td>\n",
+       "      <th>2007-12-31</th>\n",
        "      <td>547164.071090</td>\n",
        "      <td>6.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2008-03-31</td>\n",
+       "      <th>2008-03-31</th>\n",
        "      <td>567444.245132</td>\n",
        "      <td>7.25</td>\n",
        "    </tr>\n",
@@ -135,7 +135,7 @@
        "2008-03-31  567444.245132      7.25"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -156,20 +156,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-a135bc764dae4c6dbc745ee6bec0b5d7\"></div>\n",
+       "<div id=\"altair-viz-0a1752f8506b4befa0673312e3d2e739\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
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-       "      outputDiv = document.getElementById(\"altair-viz-a135bc764dae4c6dbc745ee6bec0b5d7\");\n",
+       "    if (outputDiv.id !== \"altair-viz-0a1752f8506b4befa0673312e3d2e739\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-0a1752f8506b4befa0673312e3d2e739\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -222,7 +222,7 @@
        "alt.Chart(...)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -248,20 +248,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-5caff6f7bde349d2bb66c0a0624e6e3e\"></div>\n",
+       "<div id=\"altair-viz-f5f3ff7a8c2f45b78a14cc5d3287bef7\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-5caff6f7bde349d2bb66c0a0624e6e3e\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-5caff6f7bde349d2bb66c0a0624e6e3e\");\n",
+       "    if (outputDiv.id !== \"altair-viz-f5f3ff7a8c2f45b78a14cc5d3287bef7\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-f5f3ff7a8c2f45b78a14cc5d3287bef7\");\n",
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        "    const paths = {\n",
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@@ -314,7 +314,7 @@
        "alt.LayerChart(...)"
       ]
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-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -405,60 +405,70 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Exercises\n",
     "\n",
     "1. Why wouldn't you use the actual error, that is `np.sum(predicted_prices - prices['MedianPrice'])`?\n",
+    "\n",
+    "Answer:\n",
+    "    - You need to square the results to remove any negative values\n",
+    "    - You should only look at the absolute value of the errors when summing to get an idea of the overall error\n",
+    "    - Including negative values would cancel out positive errors which would result in an incorrect low error\n",
+    "\n",
     "2. Try three different models (change the values of m and c in the equation before). What is your best performing model?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "---------- Model is m = -100 c = 1100\n",
       "The error is 4935.24\n",
       "The squared error is 689865.92\n",
+      "---------- Model is m = -110 c = 1000\n",
       "The error is 5181.09\n",
       "The squared error is 906373.12\n",
+      "---------- Model is m = -90 c = 1000\n",
       "The error is 4083.08\n",
-      "The squared error is 468521.42\n"
+      "The squared error is 468521.42\n",
+      "---------- Model is m = -80 c = 900\n",
+      "The error is 5226.59\n",
+      "The squared error is 777826.92\n",
+      "---------- Model is m = -70 c = 1000\n",
+      "The error is 4770.45\n",
+      "The squared error is 593269.72\n"
      ]
     }
    ],
    "source": [
-    "m = -100\n",
-    "c = 1100\n",
-    "predicted_prices = prices['CashRate'] * m + c  # Same values as before - this is our \"model\"\n",
-    "error = np.sum(np.abs(predicted_prices - prices['MedianPrice']))  # The \"absolute error\"\n",
-    "print(\"The error is {:.2f}\".format(error))\n",
-    "\n",
-    "squared_error = np.sum((predicted_prices - prices['MedianPrice']) ** 2)\n",
-    "print(\"The squared error is {:.2f}\".format(squared_error))\n",
-    "\n",
-    "m = -110\n",
-    "c = 1000\n",
-    "predicted_prices = prices['CashRate'] * m + c  # Same values as before - this is our \"model\"\n",
-    "error = np.sum(np.abs(predicted_prices - prices['MedianPrice']))  # The \"absolute error\"\n",
-    "print(\"The error is {:.2f}\".format(error))\n",
+    "m = [-100, -110, -90, -80, -70]\n",
+    "c = [1100, 1000, 1000, 900, 1000]\n",
     "\n",
-    "squared_error = np.sum((predicted_prices - prices['MedianPrice']) ** 2)\n",
-    "print(\"The squared error is {:.2f}\".format(squared_error))\n",
+    "for i in range(len(m)):\n",
     "\n",
-    "m = -90\n",
-    "c = 1000\n",
-    "predicted_prices = prices['CashRate'] * m + c  # Same values as before - this is our \"model\"\n",
-    "error = np.sum(np.abs(predicted_prices - prices['MedianPrice']))  # The \"absolute error\"\n",
-    "print(\"The error is {:.2f}\".format(error))\n",
+    "    print(\"-\" * 10 + \" Model is m = \" + str(m[i]) + \" c = \" + str(c[i]))\n",
+    "    predicted_prices = prices['CashRate'] * m[i] + c[i]  # Same values as before - this is our \"model\"\n",
+    "    error = np.sum(np.abs(predicted_prices - prices['MedianPrice']))  # The \"absolute error\"\n",
+    "    print(\"The error is {:.2f}\".format(error))\n",
     "\n",
-    "squared_error = np.sum((predicted_prices - prices['MedianPrice']) ** 2)\n",
-    "print(\"The squared error is {:.2f}\".format(squared_error))"
+    "    squared_error = np.sum((predicted_prices - prices['MedianPrice']) ** 2)\n",
+    "    print(\"The squared error is {:.2f}\".format(squared_error))\n"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Best performing model is m = -90, c = 1000 with the lowest absolute and squared error"
    ]
   },
   {
@@ -510,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -520,7 +530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -529,7 +539,7 @@
        "(53,)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -540,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
@@ -549,7 +559,7 @@
        "(53,)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -561,7 +571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -570,7 +580,7 @@
        "(53, 2)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -582,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
@@ -591,7 +601,7 @@
        "(53, 2)"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -602,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -611,7 +621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -620,7 +630,7 @@
        "((53, 2), (53, 1))"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -631,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
@@ -641,7 +651,7 @@
        "       [-78.60228844]])"
       ]
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-     "execution_count": 26,
+     "execution_count": 43,
      "metadata": {},
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@@ -660,22 +670,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1f026caf408>]"
+       "[<matplotlib.lines.Line2D at 0x17bdb507c70>]"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -707,7 +717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -747,19 +757,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The squared error is 355102.81\n"
+      "The value of beta is\n",
+      "[[1072.42638382]\n",
+      " [-121.29960623]]\n",
+      "The squared error is 219662.82\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -771,35 +784,53 @@
     }
    ],
    "source": [
-    "keep = cash_rate < 6\n",
-    "cash_rate = cash_rate[keep]\n",
-    "house_prices = house_prices[keep]\n",
+    "# remove values greater than 6\n",
+    "house_prices2 = house_prices[cash_rate < 6]\n",
+    "cash_rate2 = cash_rate[cash_rate < 6]\n",
     "\n",
-    "plt.plot(cash_rate, house_prices, 'ro')\n",
+    "# re-run fitting\n",
+    "ones = np.ones((len(cash_rate2)))\n",
+    "X = np.vstack([ones, cash_rate2]).T\n",
+    "Y = house_prices2.reshape((len(house_prices2), 1))\n",
+    "beta = np.linalg.inv(X.T.dot(X)).dot(X.T.dot(Y))\n",
+    "print(\"The value of beta is\")\n",
+    "print(beta)\n",
+    "\n",
+    "# visualize raw data\n",
+    "plt.plot(cash_rate2, house_prices2, 'ro')\n",
     "plt.xlabel(\"Cash Rate\")\n",
     "plt.ylabel(\"House Prices\")\n",
     "\n",
-    "# Here we create x values for \"each hour of study\" so we can plot the results\n",
-    "x_model = np.arange(0, 8, 0.25)\n",
-    "y_model = x_model * beta[1] + beta[0]  # Use our learned model to fit the line of best fit.\n",
+    "# visualize line of best fit\n",
+    "predicted_prices = cash_rate2 * beta[1] + beta[0]\n",
+    "plt.plot(cash_rate2, predicted_prices, 'b-')\n",
     "\n",
-    "plt.plot(x_model, y_model, 'b-')\n",
-    "predicted_prices = cash_rate * beta[1] + beta[0]\n",
-    "squared_error = np.sum((predicted_prices - house_prices) ** 2)\n",
+    "# compute error\n",
+    "squared_error = np.sum((predicted_prices - house_prices2) ** 2)\n",
     "print(\"The squared error is {:.2f}\".format(squared_error))\n"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- The error actually reduced\n",
+    "- Values above 6 could potentially not fit a linear trend, which made our model worse before we removed it"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[[1079.63294817]\n",
-      " [-125.04884346]]\n"
+      "[[982.9739336 ]\n",
+      " [-81.51471006]]\n",
+      "The squared error is 440517.83\n"
      ]
     }
    ],
@@ -809,26 +840,37 @@
     "# Store parameter versions here\n",
     "models = []\n",
     "\n",
+    "# compute length of original data\n",
     "n = len(cash_rate)\n",
     "\n",
     "for i in range(100):\n",
     "    \n",
     "    # Choose random indexes\n",
-    "    # replace=True is better statistically, we will come back to it later on...\n",
+    "    # use replace = TRUE to allow multiple same values to be sampled\n",
+    "    # note that we are using raw values, not filtered like above \n",
     "    random_indexes = np.random.choice(np.arange(n), size=int(n/2), replace=True)\n",
     "    cash_rate_sample = cash_rate[random_indexes]\n",
     "    house_prices_sample = house_prices[random_indexes]\n",
     "    \n",
+    "    # re-run model fitting\n",
     "    ones_sample = np.ones((len(cash_rate_sample)))\n",
     "    X_sample = np.vstack([ones_sample, cash_rate_sample]).T\n",
-    "\n",
     "    Y_sample = house_prices_sample.reshape((len(house_prices_sample), 1))\n",
     "\n",
+    "    # compute new betas\n",
     "    beta_sample = np.linalg.inv(X_sample.T.dot(X_sample)).dot(X_sample.T.dot(Y_sample))\n",
+    "\n",
+    "    # store as 2 values per element in the list\n",
     "    models.append(beta_sample)\n",
-    "# Average all parameters\n",
+    "\n",
+    "# Average all parameters over the 1st axis\n",
     "final_model = np.mean(models, axis=0)\n",
-    "print(final_model)"
+    "print(final_model)\n",
+    "\n",
+    "# compute error for our final model\n",
+    "predicted_prices = cash_rate * final_model[1] + final_model[0]\n",
+    "squared_error = np.sum((predicted_prices - house_prices) ** 2)\n",
+    "print(\"The squared error is {:.2f}\".format(squared_error))"
    ]
   },
   {
@@ -841,7 +883,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -855,7 +897,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.3.2 - Multivariate OLS.ipynb b/notebooks/Module 1.3.2 - Multivariate OLS.ipynb
index 9a96f4f..a014b95 100644
--- a/notebooks/Module 1.3.2 - Multivariate OLS.ipynb	
+++ b/notebooks/Module 1.3.2 - Multivariate OLS.ipynb	
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -82,7 +82,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -140,7 +140,7 @@
        "(3,)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -151,7 +151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -160,7 +160,7 @@
        "1"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -171,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -180,7 +180,7 @@
        "(3, 2)"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -191,7 +191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -200,7 +200,7 @@
        "2"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,7 +220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -229,7 +229,7 @@
        "6"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,18 +254,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "A = np.array([[10,20,30],[40,50,60]])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 13,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[10 20 30]\n",
+      " [40 50 60]]\n",
+      "beta is a vector as it is 1 dimensional\n"
+     ]
+    }
+   ],
    "source": [
-    "Beta represents a one-dimensional array or vector."
+    "# qs 1\n",
+    "A = np.array([[10, 20, 30],\n",
+    "              [40, 50, 60]])\n",
+    "print(A)\n",
+    "\n",
+    "# qs 2\n",
+    "print(\"beta is a vector as it is 1 dimensional\")"
    ]
   },
   {
@@ -301,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +319,7 @@
        "5"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -334,7 +343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -345,7 +354,7 @@
        "       [10, 12]])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -356,7 +365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -367,7 +376,7 @@
        "       [8, 9]])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -378,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -389,7 +398,7 @@
        "       [0.5, 0.6]])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -400,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -411,7 +420,7 @@
        "       [-95, -94]])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -431,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -444,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -454,7 +463,7 @@
        "       [490, 640]])"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -465,7 +474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -475,7 +484,7 @@
        "       [490, 640]])"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -495,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -533,7 +542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -542,7 +551,7 @@
        "(1, 3)"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -554,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -563,7 +572,7 @@
        "(3, 2)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -574,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -583,7 +592,7 @@
        "array([[2200, 2800]])"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -594,7 +603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -603,7 +612,7 @@
        "array([[2200, 2800]])"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -614,7 +623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
@@ -625,7 +634,7 @@
        "       [30]])"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -647,21 +656,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The dot product is the sum of the rows of B when V2 is a column vector of ones. Transposing reverses the dimensions.\n"
+      "original matrix\n",
+      "[[10 20]\n",
+      " [30 40]\n",
+      " [50 60]]\n",
+      "Column vector which can compute the dot produce of B with\n",
+      "[[1]\n",
+      " [1]]\n",
+      "dot product when all values in v2 are ones is row-wise sum\n",
+      "[[ 30]\n",
+      " [ 70]\n",
+      " [110]]\n",
+      "Transpose flips the matrix on its side, or swaps the values symmetrically along the diagonal\n",
+      "[[10 30 50]\n",
+      " [20 40 60]]\n"
      ]
     }
    ],
    "source": [
+    "print(\"original matrix\")\n",
+    "print(B)\n",
+    "\n",
+    "# qs 1\n",
     "V2 = np.array([[1], [1]])\n",
-    "B.dot(V2)\n",
-    "print(\"The dot product is the sum of the rows of B when V2 is a column vector of ones. Transposing reverses the dimensions.\")"
+    "print(\"Column vector which can compute the dot produce of B with\")\n",
+    "print(V2)\n",
+    "\n",
+    "# qs 2\n",
+    "print(\"dot product when all values in v2 are ones is row-wise sum\")\n",
+    "print(B.dot(V2))\n",
+    "\n",
+    "# qs 3\n",
+    "print(\"Transpose flips the matrix on its side, or swaps the values symmetrically along the diagonal\")\n",
+    "print(B.T)"
    ]
   },
   {
@@ -703,27 +737,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "reshape matrix\n",
       "[[0 1 2]\n",
       " [3 4 5]\n",
       " [6 7 8]]\n",
-      "[[3 0 0 0]\n",
-      " [0 3 0 0]\n",
-      " [0 0 4 0]\n",
-      " [0 0 0 5]]\n",
+      "diagonal matrix\n",
+      "[[3 0 0]\n",
+      " [0 3 0]\n",
+      " [0 0 3]]\n",
+      "identity matrix\n",
       "[[1. 0. 0.]\n",
       " [0. 1. 0.]\n",
       " [0. 0. 1.]]\n",
+      "scalar matrix\n",
       "[[ 0  1  2]\n",
       " [ 3  4  5]\n",
       " [ 6  7  8]\n",
       " [ 9 10 11]]\n",
+      "4 x 5 matrix\n",
       "[[0. 0. 0. 0. 0.]\n",
       " [0. 0. 0. 0. 0.]\n",
       " [0. 0. 0. 0. 0.]\n",
@@ -732,19 +770,29 @@
     }
    ],
    "source": [
+    "# qs1\n",
     "SQ = np.arange(9).reshape([3,3])\n",
+    "print(\"reshape matrix\")\n",
     "print(SQ)\n",
     "\n",
-    "DG = np.diag([3,3,4,5])\n",
+    "# qs2\n",
+    "DG = np.diag([3,3,3])\n",
+    "print(\"diagonal matrix\")\n",
     "print(DG)\n",
     "\n",
+    "# qs3\n",
     "I = np.eye(3)\n",
+    "print(\"identity matrix\")\n",
     "print(I)\n",
     "\n",
-    "SC = I*9\n",
+    "# qs4\n",
+    "SC = I * 9\n",
+    "print(\"scalar matrix\")\n",
     "print(C)\n",
     "\n",
-    "Z = np.zeros([4,5])\n",
+    "# qs5\n",
+    "Z = np.empty([4,5])\n",
+    "print(\"4 x 5 matrix\")\n",
     "print(Z)"
    ]
   },
@@ -772,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -781,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -790,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -800,7 +848,7 @@
        "       [-0.13611111,  0.06111111]])"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -811,17 +859,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[1.00000000e+00, 3.55271368e-15],\n",
-       "       [0.00000000e+00, 1.00000000e+00]])"
+       "array([[ 1.00000000e+00, -1.66533454e-15],\n",
+       "       [-7.10542736e-15,  1.00000000e+00]])"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -832,17 +880,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ True, False],\n",
-       "       [ True,  True]])"
+       "array([[False, False],\n",
+       "       [False, False]])"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -854,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -863,7 +911,7 @@
        "True"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -890,7 +938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -899,22 +947,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ce31a1a1c8>]"
+       "[<matplotlib.lines.Line2D at 0x21997be7070>]"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -947,22 +995,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ce31a90f48>]"
+       "[<matplotlib.lines.Line2D at 0x21997cdd7b0>]"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1043,12 +1091,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [],
    "source": [
     "import quandl\n",
     "\n",
+    "quandl.ApiConfig.api_key = 'hPGPuToY_GBPFpx5zsyM'\n",
     "interest_rates = quandl.get(\"RBA/F13_FOOIRATCR\")\n",
     "interest_rates = interest_rates[interest_rates.columns[0]]  # Extract the first column, whatever it is called\n",
     "interest_rates.name = \"InterestRate\"  # Rename, as the original had a long name. Hint: don't use spaces or special chars"
@@ -1056,7 +1105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -1071,7 +1120,7 @@
        "Name: InterestRate, dtype: float64"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1082,7 +1131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1092,7 +1141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1101,7 +1150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -1116,7 +1165,7 @@
        "Name: Inflation, dtype: float64"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1127,7 +1176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1137,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -1152,7 +1201,7 @@
        "Name: AUDUSD, dtype: float64"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1172,7 +1221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1181,7 +1230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -1190,7 +1239,7 @@
        "125"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1202,7 +1251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
@@ -1239,31 +1288,31 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>1990-03-31</td>\n",
+       "      <th>1990-03-31</th>\n",
        "      <td>16.5</td>\n",
        "      <td>1.6</td>\n",
        "      <td>0.7542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1990-06-30</td>\n",
+       "      <th>1990-06-30</th>\n",
        "      <td>15.0</td>\n",
        "      <td>1.8</td>\n",
        "      <td>0.7890</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1990-09-30</td>\n",
+       "      <th>1990-09-30</th>\n",
        "      <td>14.0</td>\n",
        "      <td>0.9</td>\n",
        "      <td>0.8265</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1990-12-31</td>\n",
+       "      <th>1990-12-31</th>\n",
        "      <td>12.0</td>\n",
        "      <td>2.4</td>\n",
        "      <td>0.7733</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1991-03-31</td>\n",
+       "      <th>1991-03-31</th>\n",
        "      <td>12.0</td>\n",
        "      <td>-0.2</td>\n",
        "      <td>0.7752</td>\n",
@@ -1282,7 +1331,7 @@
        "1991-03-31          12.0       -0.2  0.7752"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1293,7 +1342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1304,18 +1353,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 57,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2389: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
-      "  return ptp(axis=axis, out=out, **kwargs)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import statsmodels.api as sm\n",
     "# Nice helper function from statsmodels for adding a bias/constant\n",
@@ -1324,7 +1364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -1361,31 +1401,31 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>1990-03-31</td>\n",
+       "      <th>1990-03-31</th>\n",
        "      <td>1.0</td>\n",
        "      <td>16.5</td>\n",
        "      <td>0.7542</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1990-06-30</td>\n",
+       "      <th>1990-06-30</th>\n",
        "      <td>1.0</td>\n",
        "      <td>15.0</td>\n",
        "      <td>0.7890</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1990-09-30</td>\n",
+       "      <th>1990-09-30</th>\n",
        "      <td>1.0</td>\n",
        "      <td>14.0</td>\n",
        "      <td>0.8265</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1990-12-31</td>\n",
+       "      <th>1990-12-31</th>\n",
        "      <td>1.0</td>\n",
        "      <td>12.0</td>\n",
        "      <td>0.7733</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1991-03-31</td>\n",
+       "      <th>1991-03-31</th>\n",
        "      <td>1.0</td>\n",
        "      <td>12.0</td>\n",
        "      <td>0.7752</td>\n",
@@ -1404,7 +1444,7 @@
        "1991-03-31    1.0          12.0  0.7752"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1415,7 +1455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
@@ -1433,10 +1473,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.929</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Tue, 15 Feb 2022</td> <th>  Prob (F-statistic):</th>  <td>0.00141</td>\n",
+       "  <th>Date:</th>             <td>Tue, 03 Jan 2023</td> <th>  Prob (F-statistic):</th>  <td>0.00141</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>15:22:00</td>     <th>  Log-Likelihood:    </th> <td> -100.11</td>\n",
+       "  <th>Time:</th>                 <td>15:11:28</td>     <th>  Log-Likelihood:    </th> <td> -100.11</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   125</td>      <th>  AIC:               </th> <td>   206.2</td>\n",
@@ -1478,7 +1518,7 @@
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td>12.136</td> <th>  Cond. No.          </th> <td>    56.6</td>\n",
        "</tr>\n",
-       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -1488,8 +1528,8 @@
        "Dep. Variable:              Inflation   R-squared:                       0.102\n",
        "Model:                            OLS   Adj. R-squared:                  0.087\n",
        "Method:                 Least Squares   F-statistic:                     6.929\n",
-       "Date:                Tue, 15 Feb 2022   Prob (F-statistic):            0.00141\n",
-       "Time:                        15:22:00   Log-Likelihood:                -100.11\n",
+       "Date:                Tue, 03 Jan 2023   Prob (F-statistic):            0.00141\n",
+       "Time:                        15:11:28   Log-Likelihood:                -100.11\n",
        "No. Observations:                 125   AIC:                             206.2\n",
        "Df Residuals:                     122   BIC:                             214.7\n",
        "Df Model:                           2                                         \n",
@@ -1507,12 +1547,12 @@
        "Kurtosis:                      12.136   Cond. No.                         56.6\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 110,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1533,7 +1573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1543,22 +1583,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce421e13c8>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1599,22 +1639,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce42ab0608>"
-      ]
-     },
-     "execution_count": 118,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mean squared error = 15.428164313719911\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1626,19 +1663,23 @@
     }
    ],
    "source": [
+    "# qs 1 - split data into random train/test\n",
     "from sklearn.model_selection import train_test_split\n",
+    "training_data, testing_data = train_test_split(data, test_size=1/3, random_state=25)\n",
     "\n",
-    "training_data, testing_data = train_test_split(data, test_size=0.3, random_state=25)\n",
-    "\n",
-    "# Fit an OLS model and get a summary\n",
+    "# qs 2 - fit model\n",
     "est_mod = sm.OLS(training_data['Inflation'], training_data[[\"InterestRate\", \"AUDUSD\"]]).fit()\n",
-    "\n",
     "est_mod.summary()\n",
     "\n",
+    "# qs 3 - use model to prediction\n",
     "test_pred = est_mod.predict(testing_data[[\"InterestRate\", \"AUDUSD\"]])\n",
     "test_pred.name = \"TestDataPredictedInflation\"\n",
     "\n",
-    "pd.concat([testing_data['Inflation'], test_pred], axis=1).plot()"
+    "pd.concat([testing_data['Inflation'], test_pred], axis=1).plot()\n",
+    "\n",
+    "# compute error\n",
+    "err = np.sum((testing_data['Inflation'] - test_pred)**2)\n",
+    "print(\"Mean squared error = \" + str(err))\n"
    ]
   },
   {
@@ -1661,7 +1702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1671,22 +1712,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce42b86f48>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1712,7 +1753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1723,22 +1764,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce42c42a08>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1768,22 +1809,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running model: Inflation ~ InterestRate\n",
+      "Mean squared error = 13.384712375689888\n",
+      "Running model: Inflation ~ InterestRate + AUDUSD\n",
+      "Mean squared error = 13.488703693811685\n",
+      "Running model: Inflation ~ InterestRate * AUDUSD\n",
+      "Mean squared error = 12.565607357834592\n",
+      "Running model: Inflation ~ InterestRate + AUDUSD + (InterestRate * AUDUSD)\n",
+      "Mean squared error = 12.565607357834592\n",
+      "Running model: Inflation ~ InterestRate + AUDUSD + (InterestRate * AUDUSD) + AUDUSD**2\n",
+      "Mean squared error = 12.565607357834592\n"
+     ]
+    },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1ce42d2c5c8>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1795,21 +1890,32 @@
     }
    ],
    "source": [
-    "\n",
-    "\n",
-    "training_data, testing_data = train_test_split(data, test_size=0.3, random_state=25)\n",
+    "# qs 1 - split data into random train/test\n",
+    "training_data, testing_data = train_test_split(data, test_size=1/3, random_state=25)\n",
     "\n",
     "# Fit an OLS model and get a summary\n",
-    "est_mod2 = smf.ols(formula='Inflation ~ InterestRate + AUDUSD + (InterestRate * AUDUSD)', \n",
-    "              data=data).fit() \n",
+    "formulas = ['Inflation ~ InterestRate',\n",
+    "            'Inflation ~ InterestRate + AUDUSD',\n",
+    "            'Inflation ~ InterestRate * AUDUSD',\n",
+    "            'Inflation ~ InterestRate + AUDUSD + (InterestRate * AUDUSD)',\n",
+    "            'Inflation ~ InterestRate + AUDUSD + (InterestRate * AUDUSD) + AUDUSD**2',\n",
+    "            ]\n",
     "\n",
-    "est_mod2.summary()\n",
+    "for f in formulas:\n",
     "\n",
-    "test_pred2 = est_mod2.predict(testing_data[[\"InterestRate\", \"AUDUSD\"]])\n",
-    "test_pred2.name = \"Test2DataPredictedInflation\"\n",
+    "    # fit model\n",
+    "    est_mod2 = smf.ols(formula = f, data=data).fit() \n",
     "\n",
+    "    # predict\n",
+    "    test_pred2 = est_mod2.predict(testing_data[[\"InterestRate\", \"AUDUSD\"]])\n",
+    "    test_pred2.name = \"Test2DataPredictedInflation\"\n",
     "\n",
-    "pd.concat([testing_data['Inflation'], test_pred2], axis=1).plot()"
+    "    pd.concat([testing_data['Inflation'], test_pred2], axis=1).plot()\n",
+    "\n",
+    "    # compute error\n",
+    "    err = np.sum((testing_data['Inflation'] - test_pred2)**2)\n",
+    "    print(\"Running model: \" + f)\n",
+    "    print(\"Mean squared error = \" + str(err))"
    ]
   },
   {
@@ -1822,7 +1928,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1836,7 +1942,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.3.3 - Regression Tests.ipynb b/notebooks/Module 1.3.3 - Regression Tests.ipynb
index 249919a..a9398a7 100644
--- a/notebooks/Module 1.3.3 - Regression Tests.ipynb	
+++ b/notebooks/Module 1.3.3 - Regression Tests.ipynb	
@@ -66,7 +66,7 @@
    "outputs": [],
    "source": [
     "# Let's load a dataset from the scikit learn repository\n",
-    "# scikit-learn is a machine learning library, and has a few sample datasets \n",
+    "# scikit-learn is a machine learning library, and has a few sample datasets\n",
     "from sklearn.datasets import load_boston"
    ]
   },
@@ -74,7 +74,48 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
+      "\n",
+      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
+      "    the documentation of this function for further details.\n",
+      "\n",
+      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
+      "    dataset unless the purpose of the code is to study and educate about\n",
+      "    ethical issues in data science and machine learning.\n",
+      "\n",
+      "    In this special case, you can fetch the dataset from the original\n",
+      "    source::\n",
+      "\n",
+      "        import pandas as pd\n",
+      "        import numpy as np\n",
+      "\n",
+      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
+      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
+      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
+      "        target = raw_df.values[1::2, 2]\n",
+      "\n",
+      "    Alternative datasets include the California housing dataset (i.e.\n",
+      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
+      "    dataset. You can load the datasets as follows::\n",
+      "\n",
+      "        from sklearn.datasets import fetch_california_housing\n",
+      "        housing = fetch_california_housing()\n",
+      "\n",
+      "    for the California housing dataset and::\n",
+      "\n",
+      "        from sklearn.datasets import fetch_openml\n",
+      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
+      "\n",
+      "    for the Ames housing dataset.\n",
+      "  warnings.warn(msg, category=FutureWarning)\n"
+     ]
+    }
+   ],
    "source": [
     "boston_data = load_boston()"
    ]
@@ -87,7 +128,7 @@
     {
      "data": {
       "text/plain": [
-       "sklearn.utils.Bunch"
+       "sklearn.utils._bunch.Bunch"
       ]
      },
      "execution_count": 4,
@@ -131,7 +172,7 @@
       "        - RAD      index of accessibility to radial highways\n",
       "        - TAX      full-value property-tax rate per $10,000\n",
       "        - PTRATIO  pupil-teacher ratio by town\n",
-      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n",
+      "        - B        1000(Bk - 0.63)^2 where Bk is the proportion of black people by town\n",
       "        - LSTAT    % lower status of the population\n",
       "        - MEDV     Median value of owner-occupied homes in $1000's\n",
       "\n",
@@ -233,7 +274,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>0.00632</td>\n",
        "      <td>18.0</td>\n",
        "      <td>2.31</td>\n",
@@ -250,7 +291,7 @@
        "      <td>24.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>0.02731</td>\n",
        "      <td>0.0</td>\n",
        "      <td>7.07</td>\n",
@@ -267,7 +308,7 @@
        "      <td>21.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <th>2</th>\n",
        "      <td>0.02729</td>\n",
        "      <td>0.0</td>\n",
        "      <td>7.07</td>\n",
@@ -284,7 +325,7 @@
        "      <td>34.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
+       "      <th>3</th>\n",
        "      <td>0.03237</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.18</td>\n",
@@ -301,7 +342,7 @@
        "      <td>33.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
+       "      <th>4</th>\n",
        "      <td>0.06905</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.18</td>\n",
@@ -377,10 +418,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   108.1</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Tue, 15 Feb 2022</td> <th>  Prob (F-statistic):</th> <td>6.72e-135</td>\n",
+       "  <th>Date:</th>             <td>Tue, 03 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>6.72e-135</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>16:03:00</td>     <th>  Log-Likelihood:    </th> <td> -1498.8</td> \n",
+       "  <th>Time:</th>                 <td>15:33:56</td>     <th>  Log-Likelihood:    </th> <td> -1498.8</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3026.</td> \n",
@@ -455,7 +496,7 @@
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 8.281</td>  <th>  Cond. No.          </th> <td>1.51e+04</td> \n",
        "</tr>\n",
-       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.51e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.51e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -465,8 +506,8 @@
        "Dep. Variable:                 target   R-squared:                       0.741\n",
        "Model:                            OLS   Adj. R-squared:                  0.734\n",
        "Method:                 Least Squares   F-statistic:                     108.1\n",
-       "Date:                Tue, 15 Feb 2022   Prob (F-statistic):          6.72e-135\n",
-       "Time:                        16:03:00   Log-Likelihood:                -1498.8\n",
+       "Date:                Tue, 03 Jan 2023   Prob (F-statistic):          6.72e-135\n",
+       "Time:                        15:33:56   Log-Likelihood:                -1498.8\n",
        "No. Observations:                 506   AIC:                             3026.\n",
        "Df Residuals:                     492   BIC:                             3085.\n",
        "Df Model:                          13                                         \n",
@@ -495,7 +536,7 @@
        "Kurtosis:                       8.281   Cond. No.                     1.51e+04\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "[2] The condition number is large, 1.51e+04. This might indicate that there are\n",
        "strong multicollinearity or other numerical problems.\n",
@@ -522,7 +563,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -533,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -544,7 +585,7 @@
        "dtype: float64"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -597,7 +638,7 @@
     {
      "data": {
       "text/plain": [
-       "0.7406077428649428"
+       "0.7406426641094095"
       ]
      },
      "execution_count": 13,
@@ -622,14 +663,19 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "The adjusted R-squared is a modified version of R-squared that accounts for predictors that are not significant in a regression model. In other words, the adjusted R-squared shows whether adding additional predictors improve a regression model or not.\n",
-    "\n",
-    " - from https://corporatefinanceinstitute.com/resources/knowledge/other/adjusted-r-squared/\n",
+    "# Exercise 1\n",
+    "# This output produces a range of outputs including the cov, f-statistic, likelihood estimates, errors,\n",
+    "#   statistical significance, residuals, R squared (and adjusted), t-statistic\n",
+    "# For example, R^2 adjusted tests different indepedent variables against R^2 value. This can tell us which terms/variables sinigicanctly contributes\n",
+    "#   as a predictor\n",
     " \n",
-    "In general I would prefer the adjusted R-squared unless there is a very simple multiple with a small number of predictors which are uncorrelated."
+    "# Exercise 2\n",
+    "# R^2 adjusted should be used in multivariate regression as the R^2 will always increase if we add more variables."
    ]
   },
   {
@@ -648,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -657,7 +703,7 @@
        "108.07666617432622"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -668,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -677,7 +723,7 @@
        "6.722174750114365e-135"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -744,7 +790,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -758,7 +804,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.3.4 - Multicolinearity.ipynb b/notebooks/Module 1.3.4 - Multicolinearity.ipynb
index 25e1eac..48d2dfa 100644
--- a/notebooks/Module 1.3.4 - Multicolinearity.ipynb	
+++ b/notebooks/Module 1.3.4 - Multicolinearity.ipynb	
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,9 +59,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
+      "\n",
+      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
+      "    the documentation of this function for further details.\n",
+      "\n",
+      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
+      "    dataset unless the purpose of the code is to study and educate about\n",
+      "    ethical issues in data science and machine learning.\n",
+      "\n",
+      "    In this special case, you can fetch the dataset from the original\n",
+      "    source::\n",
+      "\n",
+      "        import pandas as pd\n",
+      "        import numpy as np\n",
+      "\n",
+      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
+      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
+      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
+      "        target = raw_df.values[1::2, 2]\n",
+      "\n",
+      "    Alternative datasets include the California housing dataset (i.e.\n",
+      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
+      "    dataset. You can load the datasets as follows::\n",
+      "\n",
+      "        from sklearn.datasets import fetch_california_housing\n",
+      "        housing = fetch_california_housing()\n",
+      "\n",
+      "    for the California housing dataset and::\n",
+      "\n",
+      "        from sklearn.datasets import fetch_openml\n",
+      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
+      "\n",
+      "    for the Ames housing dataset.\n",
+      "  warnings.warn(msg, category=FutureWarning)\n"
+     ]
+    }
+   ],
    "source": [
     "boston_data = load_boston()"
    ]
@@ -133,7 +174,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
+       "      <th>0</th>\n",
        "      <td>0.00632</td>\n",
        "      <td>18.0</td>\n",
        "      <td>2.31</td>\n",
@@ -150,7 +191,7 @@
        "      <td>24.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
+       "      <th>1</th>\n",
        "      <td>0.02731</td>\n",
        "      <td>0.0</td>\n",
        "      <td>7.07</td>\n",
@@ -167,7 +208,7 @@
        "      <td>21.6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
+       "      <th>2</th>\n",
        "      <td>0.02729</td>\n",
        "      <td>0.0</td>\n",
        "      <td>7.07</td>\n",
@@ -184,7 +225,7 @@
        "      <td>34.7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
+       "      <th>3</th>\n",
        "      <td>0.03237</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.18</td>\n",
@@ -201,7 +242,7 @@
        "      <td>33.4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
+       "      <th>4</th>\n",
        "      <td>0.06905</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.18</td>\n",
@@ -275,642 +316,278 @@
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-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col5 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col6 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col7 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col8 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col9 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col8 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col9 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col10 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col11 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col12 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col13 {\n",
-       "            background-color:  red;\n",
-       "        }</style><table id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >CRIM</th>        <th class=\"col_heading level0 col1\" >ZN</th>        <th class=\"col_heading level0 col2\" >INDUS</th>        <th class=\"col_heading level0 col3\" >CHAS</th>        <th class=\"col_heading level0 col4\" >NOX</th>        <th class=\"col_heading level0 col5\" >RM</th>        <th class=\"col_heading level0 col6\" >AGE</th>        <th class=\"col_heading level0 col7\" >DIS</th>        <th class=\"col_heading level0 col8\" >RAD</th>        <th class=\"col_heading level0 col9\" >TAX</th>        <th class=\"col_heading level0 col10\" >PTRATIO</th>        <th class=\"col_heading level0 col11\" >B</th>        <th class=\"col_heading level0 col12\" >LSTAT</th>        <th class=\"col_heading level0 col13\" >target</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row0\" class=\"row_heading level0 row0\" >CRIM</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col0\" class=\"data row0 col0\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col1\" class=\"data row0 col1\" >-0.200469</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col2\" class=\"data row0 col2\" >0.406583</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col3\" class=\"data row0 col3\" >-0.0558916</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col4\" class=\"data row0 col4\" >0.420972</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col5\" class=\"data row0 col5\" >-0.219247</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col6\" class=\"data row0 col6\" >0.352734</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col7\" class=\"data row0 col7\" >-0.37967</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col8\" class=\"data row0 col8\" >0.625505</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col9\" class=\"data row0 col9\" >0.582764</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col10\" class=\"data row0 col10\" >0.289946</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col11\" class=\"data row0 col11\" >-0.385064</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col12\" class=\"data row0 col12\" >0.455621</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row0_col13\" class=\"data row0 col13\" >-0.388305</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row1\" class=\"row_heading level0 row1\" >ZN</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col0\" class=\"data row1 col0\" >-0.200469</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col1\" class=\"data row1 col1\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col2\" class=\"data row1 col2\" >-0.533828</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col3\" class=\"data row1 col3\" >-0.0426967</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col4\" class=\"data row1 col4\" >-0.516604</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col5\" class=\"data row1 col5\" >0.311991</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col6\" class=\"data row1 col6\" >-0.569537</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col7\" class=\"data row1 col7\" >0.664408</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col8\" class=\"data row1 col8\" >-0.311948</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col9\" class=\"data row1 col9\" >-0.314563</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col10\" class=\"data row1 col10\" >-0.391679</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col11\" class=\"data row1 col11\" >0.17552</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col12\" class=\"data row1 col12\" >-0.412995</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row1_col13\" class=\"data row1 col13\" >0.360445</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row2\" class=\"row_heading level0 row2\" >INDUS</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col0\" class=\"data row2 col0\" >0.406583</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col1\" class=\"data row2 col1\" >-0.533828</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col2\" class=\"data row2 col2\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col3\" class=\"data row2 col3\" >0.062938</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col4\" class=\"data row2 col4\" >0.763651</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col5\" class=\"data row2 col5\" >-0.391676</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col6\" class=\"data row2 col6\" >0.644779</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col7\" class=\"data row2 col7\" >-0.708027</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col8\" class=\"data row2 col8\" >0.595129</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col9\" class=\"data row2 col9\" >0.72076</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col10\" class=\"data row2 col10\" >0.383248</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col11\" class=\"data row2 col11\" >-0.356977</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col12\" class=\"data row2 col12\" >0.6038</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row2_col13\" class=\"data row2 col13\" >-0.483725</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row3\" class=\"row_heading level0 row3\" >CHAS</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col0\" class=\"data row3 col0\" >-0.0558916</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col1\" class=\"data row3 col1\" >-0.0426967</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col2\" class=\"data row3 col2\" >0.062938</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col3\" class=\"data row3 col3\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col4\" class=\"data row3 col4\" >0.0912028</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col5\" class=\"data row3 col5\" >0.0912512</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col6\" class=\"data row3 col6\" >0.0865178</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col7\" class=\"data row3 col7\" >-0.0991758</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col8\" class=\"data row3 col8\" >-0.00736824</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col9\" class=\"data row3 col9\" >-0.0355865</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col10\" class=\"data row3 col10\" >-0.121515</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col11\" class=\"data row3 col11\" >0.0487885</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col12\" class=\"data row3 col12\" >-0.0539293</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row3_col13\" class=\"data row3 col13\" >0.17526</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row4\" class=\"row_heading level0 row4\" >NOX</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col0\" class=\"data row4 col0\" >0.420972</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col1\" class=\"data row4 col1\" >-0.516604</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col2\" class=\"data row4 col2\" >0.763651</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col3\" class=\"data row4 col3\" >0.0912028</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col4\" class=\"data row4 col4\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col5\" class=\"data row4 col5\" >-0.302188</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col6\" class=\"data row4 col6\" >0.73147</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col7\" class=\"data row4 col7\" >-0.76923</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col8\" class=\"data row4 col8\" >0.611441</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col9\" class=\"data row4 col9\" >0.668023</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col10\" class=\"data row4 col10\" >0.188933</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col11\" class=\"data row4 col11\" >-0.380051</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col12\" class=\"data row4 col12\" >0.590879</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row4_col13\" class=\"data row4 col13\" >-0.427321</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row5\" class=\"row_heading level0 row5\" >RM</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col0\" class=\"data row5 col0\" >-0.219247</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col1\" class=\"data row5 col1\" >0.311991</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col2\" class=\"data row5 col2\" >-0.391676</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col3\" class=\"data row5 col3\" >0.0912512</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col4\" class=\"data row5 col4\" >-0.302188</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col5\" class=\"data row5 col5\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col6\" class=\"data row5 col6\" >-0.240265</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col7\" class=\"data row5 col7\" >0.205246</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col8\" class=\"data row5 col8\" >-0.209847</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col9\" class=\"data row5 col9\" >-0.292048</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col10\" class=\"data row5 col10\" >-0.355501</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col11\" class=\"data row5 col11\" >0.128069</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col12\" class=\"data row5 col12\" >-0.613808</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row5_col13\" class=\"data row5 col13\" >0.69536</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row6\" class=\"row_heading level0 row6\" >AGE</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col0\" class=\"data row6 col0\" >0.352734</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col1\" class=\"data row6 col1\" >-0.569537</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col2\" class=\"data row6 col2\" >0.644779</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col3\" class=\"data row6 col3\" >0.0865178</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col4\" class=\"data row6 col4\" >0.73147</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col5\" class=\"data row6 col5\" >-0.240265</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col6\" class=\"data row6 col6\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col7\" class=\"data row6 col7\" >-0.747881</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col8\" class=\"data row6 col8\" >0.456022</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col9\" class=\"data row6 col9\" >0.506456</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col10\" class=\"data row6 col10\" >0.261515</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col11\" class=\"data row6 col11\" >-0.273534</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col12\" class=\"data row6 col12\" >0.602339</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row6_col13\" class=\"data row6 col13\" >-0.376955</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row7\" class=\"row_heading level0 row7\" >DIS</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col0\" class=\"data row7 col0\" >-0.37967</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col1\" class=\"data row7 col1\" >0.664408</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col2\" class=\"data row7 col2\" >-0.708027</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col3\" class=\"data row7 col3\" >-0.0991758</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col4\" class=\"data row7 col4\" >-0.76923</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col5\" class=\"data row7 col5\" >0.205246</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col6\" class=\"data row7 col6\" >-0.747881</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col7\" class=\"data row7 col7\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col8\" class=\"data row7 col8\" >-0.494588</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col9\" class=\"data row7 col9\" >-0.534432</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col10\" class=\"data row7 col10\" >-0.232471</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col11\" class=\"data row7 col11\" >0.291512</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col12\" class=\"data row7 col12\" >-0.496996</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row7_col13\" class=\"data row7 col13\" >0.249929</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row8\" class=\"row_heading level0 row8\" >RAD</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col0\" class=\"data row8 col0\" >0.625505</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col1\" class=\"data row8 col1\" >-0.311948</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col2\" class=\"data row8 col2\" >0.595129</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col3\" class=\"data row8 col3\" >-0.00736824</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col4\" class=\"data row8 col4\" >0.611441</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col5\" class=\"data row8 col5\" >-0.209847</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col6\" class=\"data row8 col6\" >0.456022</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col7\" class=\"data row8 col7\" >-0.494588</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col8\" class=\"data row8 col8\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col9\" class=\"data row8 col9\" >0.910228</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col10\" class=\"data row8 col10\" >0.464741</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col11\" class=\"data row8 col11\" >-0.444413</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col12\" class=\"data row8 col12\" >0.488676</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row8_col13\" class=\"data row8 col13\" >-0.381626</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row9\" class=\"row_heading level0 row9\" >TAX</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col0\" class=\"data row9 col0\" >0.582764</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col1\" class=\"data row9 col1\" >-0.314563</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col2\" class=\"data row9 col2\" >0.72076</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col3\" class=\"data row9 col3\" >-0.0355865</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col4\" class=\"data row9 col4\" >0.668023</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col5\" class=\"data row9 col5\" >-0.292048</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col6\" class=\"data row9 col6\" >0.506456</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col7\" class=\"data row9 col7\" >-0.534432</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col8\" class=\"data row9 col8\" >0.910228</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col9\" class=\"data row9 col9\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col10\" class=\"data row9 col10\" >0.460853</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col11\" class=\"data row9 col11\" >-0.441808</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col12\" class=\"data row9 col12\" >0.543993</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row9_col13\" class=\"data row9 col13\" >-0.468536</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row10\" class=\"row_heading level0 row10\" >PTRATIO</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col0\" class=\"data row10 col0\" >0.289946</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col1\" class=\"data row10 col1\" >-0.391679</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col2\" class=\"data row10 col2\" >0.383248</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col3\" class=\"data row10 col3\" >-0.121515</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col4\" class=\"data row10 col4\" >0.188933</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col5\" class=\"data row10 col5\" >-0.355501</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col6\" class=\"data row10 col6\" >0.261515</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col7\" class=\"data row10 col7\" >-0.232471</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col8\" class=\"data row10 col8\" >0.464741</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col9\" class=\"data row10 col9\" >0.460853</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col10\" class=\"data row10 col10\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col11\" class=\"data row10 col11\" >-0.177383</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col12\" class=\"data row10 col12\" >0.374044</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row10_col13\" class=\"data row10 col13\" >-0.507787</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row11\" class=\"row_heading level0 row11\" >B</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col0\" class=\"data row11 col0\" >-0.385064</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col1\" class=\"data row11 col1\" >0.17552</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col2\" class=\"data row11 col2\" >-0.356977</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col3\" class=\"data row11 col3\" >0.0487885</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col4\" class=\"data row11 col4\" >-0.380051</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col5\" class=\"data row11 col5\" >0.128069</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col6\" class=\"data row11 col6\" >-0.273534</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col7\" class=\"data row11 col7\" >0.291512</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col8\" class=\"data row11 col8\" >-0.444413</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col9\" class=\"data row11 col9\" >-0.441808</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col10\" class=\"data row11 col10\" >-0.177383</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col11\" class=\"data row11 col11\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col12\" class=\"data row11 col12\" >-0.366087</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row11_col13\" class=\"data row11 col13\" >0.333461</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row12\" class=\"row_heading level0 row12\" >LSTAT</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col0\" class=\"data row12 col0\" >0.455621</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col1\" class=\"data row12 col1\" >-0.412995</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col2\" class=\"data row12 col2\" >0.6038</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col3\" class=\"data row12 col3\" >-0.0539293</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col4\" class=\"data row12 col4\" >0.590879</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col5\" class=\"data row12 col5\" >-0.613808</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col6\" class=\"data row12 col6\" >0.602339</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col7\" class=\"data row12 col7\" >-0.496996</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col8\" class=\"data row12 col8\" >0.488676</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col9\" class=\"data row12 col9\" >0.543993</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col10\" class=\"data row12 col10\" >0.374044</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col11\" class=\"data row12 col11\" >-0.366087</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col12\" class=\"data row12 col12\" >1</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row12_col13\" class=\"data row12 col13\" >-0.737663</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0level0_row13\" class=\"row_heading level0 row13\" >target</th>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col0\" class=\"data row13 col0\" >-0.388305</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col1\" class=\"data row13 col1\" >0.360445</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col2\" class=\"data row13 col2\" >-0.483725</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col3\" class=\"data row13 col3\" >0.17526</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col4\" class=\"data row13 col4\" >-0.427321</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col5\" class=\"data row13 col5\" >0.69536</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col6\" class=\"data row13 col6\" >-0.376955</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col7\" class=\"data row13 col7\" >0.249929</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col8\" class=\"data row13 col8\" >-0.381626</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col9\" class=\"data row13 col9\" >-0.468536</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col10\" class=\"data row13 col10\" >-0.507787</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col11\" class=\"data row13 col11\" >0.333461</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col12\" class=\"data row13 col12\" >-0.737663</td>\n",
-       "                        <td id=\"T_0fc3e19c_8e34_11ec_b30c_a0a4c53b5af0row13_col13\" class=\"data row13 col13\" >1</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
+       "<style type=\"text/css\">\n",
+       "#T_bb875_row0_col0, #T_bb875_row1_col1, #T_bb875_row2_col2, #T_bb875_row3_col3, #T_bb875_row4_col4, #T_bb875_row5_col5, #T_bb875_row6_col6, #T_bb875_row7_col7, #T_bb875_row8_col8, #T_bb875_row8_col9, #T_bb875_row9_col8, #T_bb875_row9_col9, #T_bb875_row10_col10, #T_bb875_row11_col11, #T_bb875_row12_col12, #T_bb875_row13_col13 {\n",
+       "  background-color: red;\n",
+       "}\n",
+       "#T_bb875_row0_col1, #T_bb875_row0_col2, #T_bb875_row0_col3, #T_bb875_row0_col4, #T_bb875_row0_col5, #T_bb875_row0_col6, #T_bb875_row0_col7, #T_bb875_row0_col8, #T_bb875_row0_col9, #T_bb875_row0_col10, #T_bb875_row0_col11, #T_bb875_row0_col12, #T_bb875_row0_col13, #T_bb875_row1_col0, #T_bb875_row1_col2, #T_bb875_row1_col3, #T_bb875_row1_col4, #T_bb875_row1_col5, #T_bb875_row1_col6, #T_bb875_row1_col7, #T_bb875_row1_col8, #T_bb875_row1_col9, #T_bb875_row1_col10, #T_bb875_row1_col11, #T_bb875_row1_col12, #T_bb875_row1_col13, #T_bb875_row2_col0, #T_bb875_row2_col1, #T_bb875_row2_col3, #T_bb875_row2_col4, #T_bb875_row2_col5, #T_bb875_row2_col6, #T_bb875_row2_col7, #T_bb875_row2_col8, #T_bb875_row2_col9, #T_bb875_row2_col10, #T_bb875_row2_col11, #T_bb875_row2_col12, #T_bb875_row2_col13, #T_bb875_row3_col0, #T_bb875_row3_col1, #T_bb875_row3_col2, #T_bb875_row3_col4, #T_bb875_row3_col5, #T_bb875_row3_col6, #T_bb875_row3_col7, #T_bb875_row3_col8, #T_bb875_row3_col9, #T_bb875_row3_col10, #T_bb875_row3_col11, #T_bb875_row3_col12, #T_bb875_row3_col13, #T_bb875_row4_col0, #T_bb875_row4_col1, #T_bb875_row4_col2, #T_bb875_row4_col3, #T_bb875_row4_col5, #T_bb875_row4_col6, #T_bb875_row4_col7, #T_bb875_row4_col8, #T_bb875_row4_col9, #T_bb875_row4_col10, #T_bb875_row4_col11, #T_bb875_row4_col12, #T_bb875_row4_col13, #T_bb875_row5_col0, #T_bb875_row5_col1, #T_bb875_row5_col2, #T_bb875_row5_col3, #T_bb875_row5_col4, #T_bb875_row5_col6, #T_bb875_row5_col7, #T_bb875_row5_col8, #T_bb875_row5_col9, #T_bb875_row5_col10, #T_bb875_row5_col11, #T_bb875_row5_col12, #T_bb875_row5_col13, #T_bb875_row6_col0, #T_bb875_row6_col1, #T_bb875_row6_col2, #T_bb875_row6_col3, #T_bb875_row6_col4, #T_bb875_row6_col5, #T_bb875_row6_col7, #T_bb875_row6_col8, #T_bb875_row6_col9, #T_bb875_row6_col10, #T_bb875_row6_col11, #T_bb875_row6_col12, #T_bb875_row6_col13, #T_bb875_row7_col0, #T_bb875_row7_col1, #T_bb875_row7_col2, #T_bb875_row7_col3, #T_bb875_row7_col4, #T_bb875_row7_col5, #T_bb875_row7_col6, #T_bb875_row7_col8, #T_bb875_row7_col9, #T_bb875_row7_col10, #T_bb875_row7_col11, #T_bb875_row7_col12, #T_bb875_row7_col13, #T_bb875_row8_col0, #T_bb875_row8_col1, #T_bb875_row8_col2, #T_bb875_row8_col3, #T_bb875_row8_col4, #T_bb875_row8_col5, #T_bb875_row8_col6, #T_bb875_row8_col7, #T_bb875_row8_col10, #T_bb875_row8_col11, #T_bb875_row8_col12, #T_bb875_row8_col13, #T_bb875_row9_col0, #T_bb875_row9_col1, #T_bb875_row9_col2, #T_bb875_row9_col3, #T_bb875_row9_col4, #T_bb875_row9_col5, #T_bb875_row9_col6, #T_bb875_row9_col7, #T_bb875_row9_col10, #T_bb875_row9_col11, #T_bb875_row9_col12, #T_bb875_row9_col13, #T_bb875_row10_col0, #T_bb875_row10_col1, #T_bb875_row10_col2, #T_bb875_row10_col3, #T_bb875_row10_col4, #T_bb875_row10_col5, #T_bb875_row10_col6, #T_bb875_row10_col7, #T_bb875_row10_col8, #T_bb875_row10_col9, #T_bb875_row10_col11, #T_bb875_row10_col12, #T_bb875_row10_col13, #T_bb875_row11_col0, #T_bb875_row11_col1, #T_bb875_row11_col2, #T_bb875_row11_col3, #T_bb875_row11_col4, #T_bb875_row11_col5, #T_bb875_row11_col6, #T_bb875_row11_col7, #T_bb875_row11_col8, #T_bb875_row11_col9, #T_bb875_row11_col10, #T_bb875_row11_col12, #T_bb875_row11_col13, #T_bb875_row12_col0, #T_bb875_row12_col1, #T_bb875_row12_col2, #T_bb875_row12_col3, #T_bb875_row12_col4, #T_bb875_row12_col5, #T_bb875_row12_col6, #T_bb875_row12_col7, #T_bb875_row12_col8, #T_bb875_row12_col9, #T_bb875_row12_col10, #T_bb875_row12_col11, #T_bb875_row12_col13, #T_bb875_row13_col0, #T_bb875_row13_col1, #T_bb875_row13_col2, #T_bb875_row13_col3, #T_bb875_row13_col4, #T_bb875_row13_col5, #T_bb875_row13_col6, #T_bb875_row13_col7, #T_bb875_row13_col8, #T_bb875_row13_col9, #T_bb875_row13_col10, #T_bb875_row13_col11, #T_bb875_row13_col12 {\n",
+       "  background-color: lightgrey;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_bb875\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_bb875_level0_col0\" class=\"col_heading level0 col0\" >CRIM</th>\n",
+       "      <th id=\"T_bb875_level0_col1\" class=\"col_heading level0 col1\" >ZN</th>\n",
+       "      <th id=\"T_bb875_level0_col2\" class=\"col_heading level0 col2\" >INDUS</th>\n",
+       "      <th id=\"T_bb875_level0_col3\" class=\"col_heading level0 col3\" >CHAS</th>\n",
+       "      <th id=\"T_bb875_level0_col4\" class=\"col_heading level0 col4\" >NOX</th>\n",
+       "      <th id=\"T_bb875_level0_col5\" class=\"col_heading level0 col5\" >RM</th>\n",
+       "      <th id=\"T_bb875_level0_col6\" class=\"col_heading level0 col6\" >AGE</th>\n",
+       "      <th id=\"T_bb875_level0_col7\" class=\"col_heading level0 col7\" >DIS</th>\n",
+       "      <th id=\"T_bb875_level0_col8\" class=\"col_heading level0 col8\" >RAD</th>\n",
+       "      <th id=\"T_bb875_level0_col9\" class=\"col_heading level0 col9\" >TAX</th>\n",
+       "      <th id=\"T_bb875_level0_col10\" class=\"col_heading level0 col10\" >PTRATIO</th>\n",
+       "      <th id=\"T_bb875_level0_col11\" class=\"col_heading level0 col11\" >B</th>\n",
+       "      <th id=\"T_bb875_level0_col12\" class=\"col_heading level0 col12\" >LSTAT</th>\n",
+       "      <th id=\"T_bb875_level0_col13\" class=\"col_heading level0 col13\" >target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row0\" class=\"row_heading level0 row0\" >CRIM</th>\n",
+       "      <td id=\"T_bb875_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row0_col1\" class=\"data row0 col1\" >-0.200469</td>\n",
+       "      <td id=\"T_bb875_row0_col2\" class=\"data row0 col2\" >0.406583</td>\n",
+       "      <td id=\"T_bb875_row0_col3\" class=\"data row0 col3\" >-0.055892</td>\n",
+       "      <td id=\"T_bb875_row0_col4\" class=\"data row0 col4\" >0.420972</td>\n",
+       "      <td id=\"T_bb875_row0_col5\" class=\"data row0 col5\" >-0.219247</td>\n",
+       "      <td id=\"T_bb875_row0_col6\" class=\"data row0 col6\" >0.352734</td>\n",
+       "      <td id=\"T_bb875_row0_col7\" class=\"data row0 col7\" >-0.379670</td>\n",
+       "      <td id=\"T_bb875_row0_col8\" class=\"data row0 col8\" >0.625505</td>\n",
+       "      <td id=\"T_bb875_row0_col9\" class=\"data row0 col9\" >0.582764</td>\n",
+       "      <td id=\"T_bb875_row0_col10\" class=\"data row0 col10\" >0.289946</td>\n",
+       "      <td id=\"T_bb875_row0_col11\" class=\"data row0 col11\" >-0.385064</td>\n",
+       "      <td id=\"T_bb875_row0_col12\" class=\"data row0 col12\" >0.455621</td>\n",
+       "      <td id=\"T_bb875_row0_col13\" class=\"data row0 col13\" >-0.388305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row1\" class=\"row_heading level0 row1\" >ZN</th>\n",
+       "      <td id=\"T_bb875_row1_col0\" class=\"data row1 col0\" >-0.200469</td>\n",
+       "      <td id=\"T_bb875_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row1_col2\" class=\"data row1 col2\" >-0.533828</td>\n",
+       "      <td id=\"T_bb875_row1_col3\" class=\"data row1 col3\" >-0.042697</td>\n",
+       "      <td id=\"T_bb875_row1_col4\" class=\"data row1 col4\" >-0.516604</td>\n",
+       "      <td id=\"T_bb875_row1_col5\" class=\"data row1 col5\" >0.311991</td>\n",
+       "      <td id=\"T_bb875_row1_col6\" class=\"data row1 col6\" >-0.569537</td>\n",
+       "      <td id=\"T_bb875_row1_col7\" class=\"data row1 col7\" >0.664408</td>\n",
+       "      <td id=\"T_bb875_row1_col8\" class=\"data row1 col8\" >-0.311948</td>\n",
+       "      <td id=\"T_bb875_row1_col9\" class=\"data row1 col9\" >-0.314563</td>\n",
+       "      <td id=\"T_bb875_row1_col10\" class=\"data row1 col10\" >-0.391679</td>\n",
+       "      <td id=\"T_bb875_row1_col11\" class=\"data row1 col11\" >0.175520</td>\n",
+       "      <td id=\"T_bb875_row1_col12\" class=\"data row1 col12\" >-0.412995</td>\n",
+       "      <td id=\"T_bb875_row1_col13\" class=\"data row1 col13\" >0.360445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row2\" class=\"row_heading level0 row2\" >INDUS</th>\n",
+       "      <td id=\"T_bb875_row2_col0\" class=\"data row2 col0\" >0.406583</td>\n",
+       "      <td id=\"T_bb875_row2_col1\" class=\"data row2 col1\" >-0.533828</td>\n",
+       "      <td id=\"T_bb875_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row2_col3\" class=\"data row2 col3\" >0.062938</td>\n",
+       "      <td id=\"T_bb875_row2_col4\" class=\"data row2 col4\" >0.763651</td>\n",
+       "      <td id=\"T_bb875_row2_col5\" class=\"data row2 col5\" >-0.391676</td>\n",
+       "      <td id=\"T_bb875_row2_col6\" class=\"data row2 col6\" >0.644779</td>\n",
+       "      <td id=\"T_bb875_row2_col7\" class=\"data row2 col7\" >-0.708027</td>\n",
+       "      <td id=\"T_bb875_row2_col8\" class=\"data row2 col8\" >0.595129</td>\n",
+       "      <td id=\"T_bb875_row2_col9\" class=\"data row2 col9\" >0.720760</td>\n",
+       "      <td id=\"T_bb875_row2_col10\" class=\"data row2 col10\" >0.383248</td>\n",
+       "      <td id=\"T_bb875_row2_col11\" class=\"data row2 col11\" >-0.356977</td>\n",
+       "      <td id=\"T_bb875_row2_col12\" class=\"data row2 col12\" >0.603800</td>\n",
+       "      <td id=\"T_bb875_row2_col13\" class=\"data row2 col13\" >-0.483725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row3\" class=\"row_heading level0 row3\" >CHAS</th>\n",
+       "      <td id=\"T_bb875_row3_col0\" class=\"data row3 col0\" >-0.055892</td>\n",
+       "      <td id=\"T_bb875_row3_col1\" class=\"data row3 col1\" >-0.042697</td>\n",
+       "      <td id=\"T_bb875_row3_col2\" class=\"data row3 col2\" >0.062938</td>\n",
+       "      <td id=\"T_bb875_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row3_col4\" class=\"data row3 col4\" >0.091203</td>\n",
+       "      <td id=\"T_bb875_row3_col5\" class=\"data row3 col5\" >0.091251</td>\n",
+       "      <td id=\"T_bb875_row3_col6\" class=\"data row3 col6\" >0.086518</td>\n",
+       "      <td id=\"T_bb875_row3_col7\" class=\"data row3 col7\" >-0.099176</td>\n",
+       "      <td id=\"T_bb875_row3_col8\" class=\"data row3 col8\" >-0.007368</td>\n",
+       "      <td id=\"T_bb875_row3_col9\" class=\"data row3 col9\" >-0.035587</td>\n",
+       "      <td id=\"T_bb875_row3_col10\" class=\"data row3 col10\" >-0.121515</td>\n",
+       "      <td id=\"T_bb875_row3_col11\" class=\"data row3 col11\" >0.048788</td>\n",
+       "      <td id=\"T_bb875_row3_col12\" class=\"data row3 col12\" >-0.053929</td>\n",
+       "      <td id=\"T_bb875_row3_col13\" class=\"data row3 col13\" >0.175260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row4\" class=\"row_heading level0 row4\" >NOX</th>\n",
+       "      <td id=\"T_bb875_row4_col0\" class=\"data row4 col0\" >0.420972</td>\n",
+       "      <td id=\"T_bb875_row4_col1\" class=\"data row4 col1\" >-0.516604</td>\n",
+       "      <td id=\"T_bb875_row4_col2\" class=\"data row4 col2\" >0.763651</td>\n",
+       "      <td id=\"T_bb875_row4_col3\" class=\"data row4 col3\" >0.091203</td>\n",
+       "      <td id=\"T_bb875_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row4_col5\" class=\"data row4 col5\" >-0.302188</td>\n",
+       "      <td id=\"T_bb875_row4_col6\" class=\"data row4 col6\" >0.731470</td>\n",
+       "      <td id=\"T_bb875_row4_col7\" class=\"data row4 col7\" >-0.769230</td>\n",
+       "      <td id=\"T_bb875_row4_col8\" class=\"data row4 col8\" >0.611441</td>\n",
+       "      <td id=\"T_bb875_row4_col9\" class=\"data row4 col9\" >0.668023</td>\n",
+       "      <td id=\"T_bb875_row4_col10\" class=\"data row4 col10\" >0.188933</td>\n",
+       "      <td id=\"T_bb875_row4_col11\" class=\"data row4 col11\" >-0.380051</td>\n",
+       "      <td id=\"T_bb875_row4_col12\" class=\"data row4 col12\" >0.590879</td>\n",
+       "      <td id=\"T_bb875_row4_col13\" class=\"data row4 col13\" >-0.427321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row5\" class=\"row_heading level0 row5\" >RM</th>\n",
+       "      <td id=\"T_bb875_row5_col0\" class=\"data row5 col0\" >-0.219247</td>\n",
+       "      <td id=\"T_bb875_row5_col1\" class=\"data row5 col1\" >0.311991</td>\n",
+       "      <td id=\"T_bb875_row5_col2\" class=\"data row5 col2\" >-0.391676</td>\n",
+       "      <td id=\"T_bb875_row5_col3\" class=\"data row5 col3\" >0.091251</td>\n",
+       "      <td id=\"T_bb875_row5_col4\" class=\"data row5 col4\" >-0.302188</td>\n",
+       "      <td id=\"T_bb875_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row5_col6\" class=\"data row5 col6\" >-0.240265</td>\n",
+       "      <td id=\"T_bb875_row5_col7\" class=\"data row5 col7\" >0.205246</td>\n",
+       "      <td id=\"T_bb875_row5_col8\" class=\"data row5 col8\" >-0.209847</td>\n",
+       "      <td id=\"T_bb875_row5_col9\" class=\"data row5 col9\" >-0.292048</td>\n",
+       "      <td id=\"T_bb875_row5_col10\" class=\"data row5 col10\" >-0.355501</td>\n",
+       "      <td id=\"T_bb875_row5_col11\" class=\"data row5 col11\" >0.128069</td>\n",
+       "      <td id=\"T_bb875_row5_col12\" class=\"data row5 col12\" >-0.613808</td>\n",
+       "      <td id=\"T_bb875_row5_col13\" class=\"data row5 col13\" >0.695360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row6\" class=\"row_heading level0 row6\" >AGE</th>\n",
+       "      <td id=\"T_bb875_row6_col0\" class=\"data row6 col0\" >0.352734</td>\n",
+       "      <td id=\"T_bb875_row6_col1\" class=\"data row6 col1\" >-0.569537</td>\n",
+       "      <td id=\"T_bb875_row6_col2\" class=\"data row6 col2\" >0.644779</td>\n",
+       "      <td id=\"T_bb875_row6_col3\" class=\"data row6 col3\" >0.086518</td>\n",
+       "      <td id=\"T_bb875_row6_col4\" class=\"data row6 col4\" >0.731470</td>\n",
+       "      <td id=\"T_bb875_row6_col5\" class=\"data row6 col5\" >-0.240265</td>\n",
+       "      <td id=\"T_bb875_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row6_col7\" class=\"data row6 col7\" >-0.747881</td>\n",
+       "      <td id=\"T_bb875_row6_col8\" class=\"data row6 col8\" >0.456022</td>\n",
+       "      <td id=\"T_bb875_row6_col9\" class=\"data row6 col9\" >0.506456</td>\n",
+       "      <td id=\"T_bb875_row6_col10\" class=\"data row6 col10\" >0.261515</td>\n",
+       "      <td id=\"T_bb875_row6_col11\" class=\"data row6 col11\" >-0.273534</td>\n",
+       "      <td id=\"T_bb875_row6_col12\" class=\"data row6 col12\" >0.602339</td>\n",
+       "      <td id=\"T_bb875_row6_col13\" class=\"data row6 col13\" >-0.376955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row7\" class=\"row_heading level0 row7\" >DIS</th>\n",
+       "      <td id=\"T_bb875_row7_col0\" class=\"data row7 col0\" >-0.379670</td>\n",
+       "      <td id=\"T_bb875_row7_col1\" class=\"data row7 col1\" >0.664408</td>\n",
+       "      <td id=\"T_bb875_row7_col2\" class=\"data row7 col2\" >-0.708027</td>\n",
+       "      <td id=\"T_bb875_row7_col3\" class=\"data row7 col3\" >-0.099176</td>\n",
+       "      <td id=\"T_bb875_row7_col4\" class=\"data row7 col4\" >-0.769230</td>\n",
+       "      <td id=\"T_bb875_row7_col5\" class=\"data row7 col5\" >0.205246</td>\n",
+       "      <td id=\"T_bb875_row7_col6\" class=\"data row7 col6\" >-0.747881</td>\n",
+       "      <td id=\"T_bb875_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row7_col8\" class=\"data row7 col8\" >-0.494588</td>\n",
+       "      <td id=\"T_bb875_row7_col9\" class=\"data row7 col9\" >-0.534432</td>\n",
+       "      <td id=\"T_bb875_row7_col10\" class=\"data row7 col10\" >-0.232471</td>\n",
+       "      <td id=\"T_bb875_row7_col11\" class=\"data row7 col11\" >0.291512</td>\n",
+       "      <td id=\"T_bb875_row7_col12\" class=\"data row7 col12\" >-0.496996</td>\n",
+       "      <td id=\"T_bb875_row7_col13\" class=\"data row7 col13\" >0.249929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row8\" class=\"row_heading level0 row8\" >RAD</th>\n",
+       "      <td id=\"T_bb875_row8_col0\" class=\"data row8 col0\" >0.625505</td>\n",
+       "      <td id=\"T_bb875_row8_col1\" class=\"data row8 col1\" >-0.311948</td>\n",
+       "      <td id=\"T_bb875_row8_col2\" class=\"data row8 col2\" >0.595129</td>\n",
+       "      <td id=\"T_bb875_row8_col3\" class=\"data row8 col3\" >-0.007368</td>\n",
+       "      <td id=\"T_bb875_row8_col4\" class=\"data row8 col4\" >0.611441</td>\n",
+       "      <td id=\"T_bb875_row8_col5\" class=\"data row8 col5\" >-0.209847</td>\n",
+       "      <td id=\"T_bb875_row8_col6\" class=\"data row8 col6\" >0.456022</td>\n",
+       "      <td id=\"T_bb875_row8_col7\" class=\"data row8 col7\" >-0.494588</td>\n",
+       "      <td id=\"T_bb875_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row8_col9\" class=\"data row8 col9\" >0.910228</td>\n",
+       "      <td id=\"T_bb875_row8_col10\" class=\"data row8 col10\" >0.464741</td>\n",
+       "      <td id=\"T_bb875_row8_col11\" class=\"data row8 col11\" >-0.444413</td>\n",
+       "      <td id=\"T_bb875_row8_col12\" class=\"data row8 col12\" >0.488676</td>\n",
+       "      <td id=\"T_bb875_row8_col13\" class=\"data row8 col13\" >-0.381626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row9\" class=\"row_heading level0 row9\" >TAX</th>\n",
+       "      <td id=\"T_bb875_row9_col0\" class=\"data row9 col0\" >0.582764</td>\n",
+       "      <td id=\"T_bb875_row9_col1\" class=\"data row9 col1\" >-0.314563</td>\n",
+       "      <td id=\"T_bb875_row9_col2\" class=\"data row9 col2\" >0.720760</td>\n",
+       "      <td id=\"T_bb875_row9_col3\" class=\"data row9 col3\" >-0.035587</td>\n",
+       "      <td id=\"T_bb875_row9_col4\" class=\"data row9 col4\" >0.668023</td>\n",
+       "      <td id=\"T_bb875_row9_col5\" class=\"data row9 col5\" >-0.292048</td>\n",
+       "      <td id=\"T_bb875_row9_col6\" class=\"data row9 col6\" >0.506456</td>\n",
+       "      <td id=\"T_bb875_row9_col7\" class=\"data row9 col7\" >-0.534432</td>\n",
+       "      <td id=\"T_bb875_row9_col8\" class=\"data row9 col8\" >0.910228</td>\n",
+       "      <td id=\"T_bb875_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row9_col10\" class=\"data row9 col10\" >0.460853</td>\n",
+       "      <td id=\"T_bb875_row9_col11\" class=\"data row9 col11\" >-0.441808</td>\n",
+       "      <td id=\"T_bb875_row9_col12\" class=\"data row9 col12\" >0.543993</td>\n",
+       "      <td id=\"T_bb875_row9_col13\" class=\"data row9 col13\" >-0.468536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row10\" class=\"row_heading level0 row10\" >PTRATIO</th>\n",
+       "      <td id=\"T_bb875_row10_col0\" class=\"data row10 col0\" >0.289946</td>\n",
+       "      <td id=\"T_bb875_row10_col1\" class=\"data row10 col1\" >-0.391679</td>\n",
+       "      <td id=\"T_bb875_row10_col2\" class=\"data row10 col2\" >0.383248</td>\n",
+       "      <td id=\"T_bb875_row10_col3\" class=\"data row10 col3\" >-0.121515</td>\n",
+       "      <td id=\"T_bb875_row10_col4\" class=\"data row10 col4\" >0.188933</td>\n",
+       "      <td id=\"T_bb875_row10_col5\" class=\"data row10 col5\" >-0.355501</td>\n",
+       "      <td id=\"T_bb875_row10_col6\" class=\"data row10 col6\" >0.261515</td>\n",
+       "      <td id=\"T_bb875_row10_col7\" class=\"data row10 col7\" >-0.232471</td>\n",
+       "      <td id=\"T_bb875_row10_col8\" class=\"data row10 col8\" >0.464741</td>\n",
+       "      <td id=\"T_bb875_row10_col9\" class=\"data row10 col9\" >0.460853</td>\n",
+       "      <td id=\"T_bb875_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row10_col11\" class=\"data row10 col11\" >-0.177383</td>\n",
+       "      <td id=\"T_bb875_row10_col12\" class=\"data row10 col12\" >0.374044</td>\n",
+       "      <td id=\"T_bb875_row10_col13\" class=\"data row10 col13\" >-0.507787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row11\" class=\"row_heading level0 row11\" >B</th>\n",
+       "      <td id=\"T_bb875_row11_col0\" class=\"data row11 col0\" >-0.385064</td>\n",
+       "      <td id=\"T_bb875_row11_col1\" class=\"data row11 col1\" >0.175520</td>\n",
+       "      <td id=\"T_bb875_row11_col2\" class=\"data row11 col2\" >-0.356977</td>\n",
+       "      <td id=\"T_bb875_row11_col3\" class=\"data row11 col3\" >0.048788</td>\n",
+       "      <td id=\"T_bb875_row11_col4\" class=\"data row11 col4\" >-0.380051</td>\n",
+       "      <td id=\"T_bb875_row11_col5\" class=\"data row11 col5\" >0.128069</td>\n",
+       "      <td id=\"T_bb875_row11_col6\" class=\"data row11 col6\" >-0.273534</td>\n",
+       "      <td id=\"T_bb875_row11_col7\" class=\"data row11 col7\" >0.291512</td>\n",
+       "      <td id=\"T_bb875_row11_col8\" class=\"data row11 col8\" >-0.444413</td>\n",
+       "      <td id=\"T_bb875_row11_col9\" class=\"data row11 col9\" >-0.441808</td>\n",
+       "      <td id=\"T_bb875_row11_col10\" class=\"data row11 col10\" >-0.177383</td>\n",
+       "      <td id=\"T_bb875_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row11_col12\" class=\"data row11 col12\" >-0.366087</td>\n",
+       "      <td id=\"T_bb875_row11_col13\" class=\"data row11 col13\" >0.333461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row12\" class=\"row_heading level0 row12\" >LSTAT</th>\n",
+       "      <td id=\"T_bb875_row12_col0\" class=\"data row12 col0\" >0.455621</td>\n",
+       "      <td id=\"T_bb875_row12_col1\" class=\"data row12 col1\" >-0.412995</td>\n",
+       "      <td id=\"T_bb875_row12_col2\" class=\"data row12 col2\" >0.603800</td>\n",
+       "      <td id=\"T_bb875_row12_col3\" class=\"data row12 col3\" >-0.053929</td>\n",
+       "      <td id=\"T_bb875_row12_col4\" class=\"data row12 col4\" >0.590879</td>\n",
+       "      <td id=\"T_bb875_row12_col5\" class=\"data row12 col5\" >-0.613808</td>\n",
+       "      <td id=\"T_bb875_row12_col6\" class=\"data row12 col6\" >0.602339</td>\n",
+       "      <td id=\"T_bb875_row12_col7\" class=\"data row12 col7\" >-0.496996</td>\n",
+       "      <td id=\"T_bb875_row12_col8\" class=\"data row12 col8\" >0.488676</td>\n",
+       "      <td id=\"T_bb875_row12_col9\" class=\"data row12 col9\" >0.543993</td>\n",
+       "      <td id=\"T_bb875_row12_col10\" class=\"data row12 col10\" >0.374044</td>\n",
+       "      <td id=\"T_bb875_row12_col11\" class=\"data row12 col11\" >-0.366087</td>\n",
+       "      <td id=\"T_bb875_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
+       "      <td id=\"T_bb875_row12_col13\" class=\"data row12 col13\" >-0.737663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_bb875_level0_row13\" class=\"row_heading level0 row13\" >target</th>\n",
+       "      <td id=\"T_bb875_row13_col0\" class=\"data row13 col0\" >-0.388305</td>\n",
+       "      <td id=\"T_bb875_row13_col1\" class=\"data row13 col1\" >0.360445</td>\n",
+       "      <td id=\"T_bb875_row13_col2\" class=\"data row13 col2\" >-0.483725</td>\n",
+       "      <td id=\"T_bb875_row13_col3\" class=\"data row13 col3\" >0.175260</td>\n",
+       "      <td id=\"T_bb875_row13_col4\" class=\"data row13 col4\" >-0.427321</td>\n",
+       "      <td id=\"T_bb875_row13_col5\" class=\"data row13 col5\" >0.695360</td>\n",
+       "      <td id=\"T_bb875_row13_col6\" class=\"data row13 col6\" >-0.376955</td>\n",
+       "      <td id=\"T_bb875_row13_col7\" class=\"data row13 col7\" >0.249929</td>\n",
+       "      <td id=\"T_bb875_row13_col8\" class=\"data row13 col8\" >-0.381626</td>\n",
+       "      <td id=\"T_bb875_row13_col9\" class=\"data row13 col9\" >-0.468536</td>\n",
+       "      <td id=\"T_bb875_row13_col10\" class=\"data row13 col10\" >-0.507787</td>\n",
+       "      <td id=\"T_bb875_row13_col11\" class=\"data row13 col11\" >0.333461</td>\n",
+       "      <td id=\"T_bb875_row13_col12\" class=\"data row13 col12\" >-0.737663</td>\n",
+       "      <td id=\"T_bb875_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x238a8d09ec8>"
+       "<pandas.io.formats.style.Styler at 0x23595be3130>"
       ]
      },
      "execution_count": 9,
@@ -950,7 +627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -959,144 +636,137 @@
      "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
-      "Dep. Variable:                 target   R-squared:                       0.741\n",
-      "Model:                            OLS   Adj. R-squared:                  0.734\n",
-      "Method:                 Least Squares   F-statistic:                     108.1\n",
-      "Date:                Tue, 15 Feb 2022   Prob (F-statistic):          6.72e-135\n",
-      "Time:                        19:20:24   Log-Likelihood:                -1498.8\n",
-      "No. Observations:                 506   AIC:                             3026.\n",
-      "Df Residuals:                     492   BIC:                             3085.\n",
-      "Df Model:                          13                                         \n",
+      "Dep. Variable:                 target   R-squared:                       0.231\n",
+      "Model:                            OLS   Adj. R-squared:                  0.228\n",
+      "Method:                 Least Squares   F-statistic:                     75.66\n",
+      "Date:                Tue, 03 Jan 2023   Prob (F-statistic):           1.88e-29\n",
+      "Time:                        16:35:01   Log-Likelihood:                -1773.7\n",
+      "No. Observations:                 506   AIC:                             3553.\n",
+      "Df Residuals:                     503   BIC:                             3566.\n",
+      "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
-      "Intercept     36.4595      5.103      7.144      0.000      26.432      46.487\n",
-      "AGE            0.0007      0.013      0.052      0.958      -0.025       0.027\n",
-      "B              0.0093      0.003      3.467      0.001       0.004       0.015\n",
-      "CHAS           2.6867      0.862      3.118      0.002       0.994       4.380\n",
-      "CRIM          -0.1080      0.033     -3.287      0.001      -0.173      -0.043\n",
-      "DIS           -1.4756      0.199     -7.398      0.000      -1.867      -1.084\n",
-      "INDUS          0.0206      0.061      0.334      0.738      -0.100       0.141\n",
-      "LSTAT         -0.5248      0.051    -10.347      0.000      -0.624      -0.425\n",
-      "NOX          -17.7666      3.820     -4.651      0.000     -25.272     -10.262\n",
-      "PTRATIO       -0.9527      0.131     -7.283      0.000      -1.210      -0.696\n",
-      "RAD            0.3060      0.066      4.613      0.000       0.176       0.436\n",
-      "RM             3.8099      0.418      9.116      0.000       2.989       4.631\n",
-      "TAX           -0.0123      0.004     -3.280      0.001      -0.020      -0.005\n",
-      "ZN             0.0464      0.014      3.382      0.001       0.019       0.073\n",
+      "Intercept     35.6359      1.347     26.465      0.000      32.990      38.281\n",
+      "RAD            0.2762      0.100      2.770      0.006       0.080       0.472\n",
+      "TAX           -0.0386      0.005     -7.485      0.000      -0.049      -0.028\n",
       "==============================================================================\n",
-      "Omnibus:                      178.041   Durbin-Watson:                   1.078\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              783.126\n",
-      "Skew:                           1.521   Prob(JB):                    8.84e-171\n",
-      "Kurtosis:                       8.281   Cond. No.                     1.51e+04\n",
+      "Omnibus:                      170.143   Durbin-Watson:                   0.648\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              486.175\n",
+      "Skew:                           1.637   Prob(JB):                    2.68e-106\n",
+      "Kurtosis:                       6.513   Cond. No.                     1.66e+03\n",
       "==============================================================================\n",
       "\n",
-      "Warnings:\n",
+      "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-      "[2] The condition number is large, 1.51e+04. This might indicate that there are\n",
-      "strong multicollinearity or other numerical problems.\n",
+      "[2] The condition number is large, 1.66e+03. This might indicate that there are\n",
+      "strong multicollinearity or other numerical problems.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.formula.api as smf\n",
+    "\n",
+    "# both rad and tax\n",
+    "formula1 = \"target ~ RAD + TAX\"\n",
+    "model1 = smf.ols(formula=formula1, data=boston).fit()\n",
+    "print(model1.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
-      "Dep. Variable:                 target   R-squared:                       0.729\n",
-      "Model:                            OLS   Adj. R-squared:                  0.723\n",
-      "Method:                 Least Squares   F-statistic:                     110.8\n",
-      "Date:                Tue, 15 Feb 2022   Prob (F-statistic):          1.92e-131\n",
-      "Time:                        19:20:24   Log-Likelihood:                -1509.5\n",
-      "No. Observations:                 506   AIC:                             3045.\n",
-      "Df Residuals:                     493   BIC:                             3100.\n",
-      "Df Model:                          12                                         \n",
+      "Dep. Variable:                 target   R-squared:                       0.146\n",
+      "Model:                            OLS   Adj. R-squared:                  0.144\n",
+      "Method:                 Least Squares   F-statistic:                     85.91\n",
+      "Date:                Tue, 03 Jan 2023   Prob (F-statistic):           5.47e-19\n",
+      "Time:                        16:35:08   Log-Likelihood:                -1800.4\n",
+      "No. Observations:                 506   AIC:                             3605.\n",
+      "Df Residuals:                     504   BIC:                             3613.\n",
+      "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
-      "Intercept     29.7594      4.992      5.961      0.000      19.951      39.568\n",
-      "AGE           -0.0039      0.013     -0.292      0.770      -0.030       0.022\n",
-      "B              0.0084      0.003      3.079      0.002       0.003       0.014\n",
-      "CHAS           3.1144      0.874      3.563      0.000       1.397       4.832\n",
-      "CRIM          -0.0675      0.032     -2.090      0.037      -0.131      -0.004\n",
-      "DIS           -1.4902      0.203     -7.323      0.000      -1.890      -1.090\n",
-      "INDUS         -0.0584      0.060     -0.969      0.333      -0.177       0.060\n",
-      "LSTAT         -0.5164      0.052     -9.986      0.000      -0.618      -0.415\n",
-      "NOX          -15.2618      3.858     -3.956      0.000     -22.842      -7.682\n",
-      "PTRATIO       -0.8387      0.131     -6.398      0.000      -1.096      -0.581\n",
-      "RM             4.1146      0.421      9.772      0.000       3.287       4.942\n",
-      "TAX            0.0013      0.002      0.565      0.573      -0.003       0.006\n",
-      "ZN             0.0397      0.014      2.852      0.005       0.012       0.067\n",
+      "Intercept     26.3821      0.562     46.964      0.000      25.278      27.486\n",
+      "RAD           -0.4031      0.043     -9.269      0.000      -0.489      -0.318\n",
       "==============================================================================\n",
-      "Omnibus:                      189.296   Durbin-Watson:                   1.059\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              907.772\n",
-      "Skew:                           1.596   Prob(JB):                    7.58e-198\n",
-      "Kurtosis:                       8.733   Cond. No.                     1.46e+04\n",
+      "Omnibus:                      149.634   Durbin-Watson:                   0.632\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              359.231\n",
+      "Skew:                           1.515   Prob(JB):                     9.86e-79\n",
+      "Kurtosis:                       5.803   Cond. No.                         19.2\n",
       "==============================================================================\n",
       "\n",
-      "Warnings:\n",
-      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-      "[2] The condition number is large, 1.46e+04. This might indicate that there are\n",
-      "strong multicollinearity or other numerical problems.\n",
+      "Notes:\n",
+      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# only rad\n",
+    "formula2 = \"target ~ RAD\"\n",
+    "model2 = smf.ols(formula=formula2, data=boston).fit()\n",
+    "print(model2.summary())\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
-      "Dep. Variable:                 target   R-squared:                       0.735\n",
-      "Model:                            OLS   Adj. R-squared:                  0.729\n",
-      "Method:                 Least Squares   F-statistic:                     113.9\n",
-      "Date:                Tue, 15 Feb 2022   Prob (F-statistic):          1.21e-133\n",
-      "Time:                        19:20:24   Log-Likelihood:                -1504.3\n",
-      "No. Observations:                 506   AIC:                             3035.\n",
-      "Df Residuals:                     493   BIC:                             3089.\n",
-      "Df Model:                          12                                         \n",
+      "Dep. Variable:                 target   R-squared:                       0.220\n",
+      "Model:                            OLS   Adj. R-squared:                  0.218\n",
+      "Method:                 Least Squares   F-statistic:                     141.8\n",
+      "Date:                Tue, 03 Jan 2023   Prob (F-statistic):           5.64e-29\n",
+      "Time:                        16:35:10   Log-Likelihood:                -1777.5\n",
+      "No. Observations:                 506   AIC:                             3559.\n",
+      "Df Residuals:                     504   BIC:                             3568.\n",
+      "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
-      "Intercept     34.6286      5.123      6.760      0.000      24.563      44.694\n",
-      "AGE           -0.0006      0.013     -0.045      0.964      -0.027       0.026\n",
-      "B              0.0095      0.003      3.521      0.000       0.004       0.015\n",
-      "CHAS           3.0292      0.864      3.507      0.000       1.332       4.726\n",
-      "CRIM          -0.1067      0.033     -3.216      0.001      -0.172      -0.042\n",
-      "DIS           -1.4883      0.201     -7.390      0.000      -1.884      -1.093\n",
-      "INDUS         -0.0678      0.056     -1.214      0.225      -0.177       0.042\n",
-      "LSTAT         -0.5222      0.051    -10.198      0.000      -0.623      -0.422\n",
-      "NOX          -18.7012      3.847     -4.862      0.000     -26.259     -11.143\n",
-      "PTRATIO       -0.9851      0.132     -7.478      0.000      -1.244      -0.726\n",
-      "RAD            0.1346      0.041      3.262      0.001       0.054       0.216\n",
-      "RM             3.9117      0.421      9.294      0.000       3.085       4.739\n",
-      "ZN             0.0364      0.014      2.692      0.007       0.010       0.063\n",
+      "Intercept     32.9707      0.948     34.768      0.000      31.108      34.834\n",
+      "TAX           -0.0256      0.002    -11.906      0.000      -0.030      -0.021\n",
       "==============================================================================\n",
-      "Omnibus:                      168.699   Durbin-Watson:                   1.089\n",
-      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              702.173\n",
-      "Skew:                           1.452   Prob(JB):                    3.35e-153\n",
-      "Kurtosis:                       7.987   Cond. No.                     1.01e+04\n",
+      "Omnibus:                      172.501   Durbin-Watson:                   0.648\n",
+      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              494.072\n",
+      "Skew:                           1.661   Prob(JB):                    5.17e-108\n",
+      "Kurtosis:                       6.521   Cond. No.                     1.16e+03\n",
       "==============================================================================\n",
       "\n",
-      "Warnings:\n",
+      "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-      "[2] The condition number is large, 1.01e+04. This might indicate that there are\n",
+      "[2] The condition number is large, 1.16e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n"
      ]
     }
    ],
    "source": [
-    "import statsmodels.formula.api as smf\n",
-    "\n",
-    "columns1 = \"+\".join(boston.columns.difference([\"target\"]))\n",
-    "formula1 = \"target ~\" + columns1\n",
-    "all_model = smf.ols(formula=formula1, data=boston).fit()\n",
-    "print(all_model.summary())\n",
-    "\n",
-    "columns2 = \"+\".join(boston.columns.difference([\"target\"]))\n",
-    "formula2 = \"target ~\" + columns2 + \"-RAD\"\n",
-    "model2 = smf.ols(formula=formula2, data=boston).fit()\n",
-    "print(model2.summary())\n",
-    "\n",
-    "columns3 = \"+\".join(boston.columns.difference([\"target\"]))\n",
-    "formula3 = \"target ~\" + columns3 + \"-TAX\"\n",
+    "# only tax\n",
+    "formula3 = \"target ~ TAX\"\n",
     "model3 = smf.ols(formula=formula3, data=boston).fit()\n",
     "print(model3.summary())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1111,19 +781,19 @@
        "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   1.000</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>1.366e+32</td>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>2.361e+32</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Tue, 15 Feb 2022</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
+       "  <th>Date:</th>             <td>Tue, 03 Jan 2023</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>19:38:27</td>     <th>  Log-Likelihood:    </th>  <td>  33383.</td> \n",
+       "  <th>Time:</th>                 <td>16:51:45</td>     <th>  Log-Likelihood:    </th>  <td>  33648.</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>-6.676e+04</td>\n",
+       "  <th>No. Observations:</th>      <td>  1000</td>      <th>  AIC:               </th> <td>-6.729e+04</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th> <td>-6.675e+04</td>\n",
+       "  <th>Df Residuals:</th>          <td>   998</td>      <th>  BIC:               </th> <td>-6.728e+04</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>      <td> </td>    \n",
@@ -1137,29 +807,29 @@
        "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Intercept</th> <td>   -0.9677</td> <td>  1.1e-16</td> <td>-8.84e+15</td> <td> 0.000</td> <td>   -0.968</td> <td>   -0.968</td>\n",
+       "  <th>Intercept</th> <td>   -0.9677</td> <td> 8.31e-17</td> <td>-1.17e+16</td> <td> 0.000</td> <td>   -0.968</td> <td>   -0.968</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>X1</th>        <td>    0.1935</td> <td> 1.32e-17</td> <td> 1.46e+16</td> <td> 0.000</td> <td>    0.194</td> <td>    0.194</td>\n",
+       "  <th>X1</th>        <td>    0.1935</td> <td> 1.01e-17</td> <td> 1.92e+16</td> <td> 0.000</td> <td>    0.194</td> <td>    0.194</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>X2</th>        <td>    0.8387</td> <td> 9.34e-17</td> <td> 8.98e+15</td> <td> 0.000</td> <td>    0.839</td> <td>    0.839</td>\n",
+       "  <th>X2</th>        <td>    0.8387</td> <td> 7.08e-17</td> <td> 1.18e+16</td> <td> 0.000</td> <td>    0.839</td> <td>    0.839</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Omnibus:</th>       <td>41.789</td> <th>  Durbin-Watson:     </th> <td>   0.093</td>\n",
+       "  <th>Omnibus:</th>       <td>10.949</td> <th>  Durbin-Watson:     </th> <td>   0.043</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  18.512</td>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.004</td> <th>  Jarque-Bera (JB):  </th> <td>   7.297</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Skew:</th>          <td>-0.054</td> <th>  Prob(JB):          </th> <td>9.56e-05</td>\n",
+       "  <th>Skew:</th>          <td>-0.041</td> <th>  Prob(JB):          </th> <td>  0.0260</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Kurtosis:</th>      <td> 2.342</td> <th>  Cond. No.          </th> <td>8.91e+16</td>\n",
+       "  <th>Kurtosis:</th>      <td> 2.590</td> <th>  Cond. No.          </th> <td>8.83e+15</td>\n",
        "</tr>\n",
-       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 5.57e-30. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The smallest eigenvalue is 5.63e-28. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -1168,49 +838,56 @@
        "==============================================================================\n",
        "Dep. Variable:                      Y   R-squared:                       1.000\n",
        "Model:                            OLS   Adj. R-squared:                  1.000\n",
-       "Method:                 Least Squares   F-statistic:                 1.366e+32\n",
-       "Date:                Tue, 15 Feb 2022   Prob (F-statistic):               0.00\n",
-       "Time:                        19:38:27   Log-Likelihood:                 33383.\n",
-       "No. Observations:                1000   AIC:                        -6.676e+04\n",
-       "Df Residuals:                     998   BIC:                        -6.675e+04\n",
+       "Method:                 Least Squares   F-statistic:                 2.361e+32\n",
+       "Date:                Tue, 03 Jan 2023   Prob (F-statistic):               0.00\n",
+       "Time:                        16:51:45   Log-Likelihood:                 33648.\n",
+       "No. Observations:                1000   AIC:                        -6.729e+04\n",
+       "Df Residuals:                     998   BIC:                        -6.728e+04\n",
        "Df Model:                           1                                         \n",
        "Covariance Type:            nonrobust                                         \n",
        "==============================================================================\n",
        "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
        "------------------------------------------------------------------------------\n",
-       "Intercept     -0.9677    1.1e-16  -8.84e+15      0.000      -0.968      -0.968\n",
-       "X1             0.1935   1.32e-17   1.46e+16      0.000       0.194       0.194\n",
-       "X2             0.8387   9.34e-17   8.98e+15      0.000       0.839       0.839\n",
+       "Intercept     -0.9677   8.31e-17  -1.17e+16      0.000      -0.968      -0.968\n",
+       "X1             0.1935   1.01e-17   1.92e+16      0.000       0.194       0.194\n",
+       "X2             0.8387   7.08e-17   1.18e+16      0.000       0.839       0.839\n",
        "==============================================================================\n",
-       "Omnibus:                       41.789   Durbin-Watson:                   0.093\n",
-       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               18.512\n",
-       "Skew:                          -0.054   Prob(JB):                     9.56e-05\n",
-       "Kurtosis:                       2.342   Cond. No.                     8.91e+16\n",
+       "Omnibus:                       10.949   Durbin-Watson:                   0.043\n",
+       "Prob(Omnibus):                  0.004   Jarque-Bera (JB):                7.297\n",
+       "Skew:                          -0.041   Prob(JB):                       0.0260\n",
+       "Kurtosis:                       2.590   Cond. No.                     8.83e+15\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
-       "[2] The smallest eigenvalue is 5.57e-30. This might indicate that there are\n",
+       "[2] The smallest eigenvalue is 5.63e-28. This might indicate that there are\n",
        "strong multicollinearity problems or that the design matrix is singular.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 42,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "#just copied the solution here, interesting though with the beta values adding to 1, but seems somewhat intutive considering X1 and X2 are functions of Y\n",
+    "# create random distribution\n",
     "Y = np.random.random(1000)\n",
     "\n",
+    "# create 2 variables  which correlate with Y\n",
     "X1 = Y * 3 + 5\n",
     "X2 = Y / 2\n",
     "\n",
+    "# run regression\n",
     "data = pd.Series({\"X1\": X1, \"X2\": X2, \"Y\": Y})\n",
-    "\n",
     "est_corr = smf.ols(formula=\"Y ~ X1 + X2\", data=data).fit()\n",
-    "est_corr.summary()"
+    "est_corr.summary()\n",
+    "\n",
+    "# we can see the sum of beta sums to almost 1, meaning their combined contribution\n",
+    "# to Y shows that there is an interaction. It also shows that it results in a very \n",
+    "# good fit as seen from the R^2 and adjusted R^2 values. We can also seen that\n",
+    "# the variables have high significance as we already know these variables are\n",
+    "# directly related"
    ]
   },
   {
@@ -1235,651 +912,287 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
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-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col4 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col6 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col7 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col5 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col4 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col6 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col7 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col2 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col4 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col6 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col7 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col8 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col9 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col2 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col8 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col9 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col10 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col11 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col12 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col13 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col12 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col13 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col0 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col1 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col2 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col3 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col4 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col5 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col6 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col7 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col8 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col9 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col10 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col11 {\n",
-       "            background-color:  lightgrey;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col12 {\n",
-       "            background-color:  red;\n",
-       "        }    #T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col13 {\n",
-       "            background-color:  red;\n",
-       "        }</style><table id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >CRIM</th>        <th class=\"col_heading level0 col1\" >ZN</th>        <th class=\"col_heading level0 col2\" >INDUS</th>        <th class=\"col_heading level0 col3\" >CHAS</th>        <th class=\"col_heading level0 col4\" >NOX</th>        <th class=\"col_heading level0 col5\" >RM</th>        <th class=\"col_heading level0 col6\" >AGE</th>        <th class=\"col_heading level0 col7\" >DIS</th>        <th class=\"col_heading level0 col8\" >RAD</th>        <th class=\"col_heading level0 col9\" >TAX</th>        <th class=\"col_heading level0 col10\" >PTRATIO</th>        <th class=\"col_heading level0 col11\" >B</th>        <th class=\"col_heading level0 col12\" >LSTAT</th>        <th class=\"col_heading level0 col13\" >target</th>    </tr></thead><tbody>\n",
-       "                <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row0\" class=\"row_heading level0 row0\" >CRIM</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col0\" class=\"data row0 col0\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col1\" class=\"data row0 col1\" >-0.200469</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col2\" class=\"data row0 col2\" >0.406583</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col3\" class=\"data row0 col3\" >-0.0558916</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col4\" class=\"data row0 col4\" >0.420972</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col5\" class=\"data row0 col5\" >-0.219247</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col6\" class=\"data row0 col6\" >0.352734</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col7\" class=\"data row0 col7\" >-0.37967</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col8\" class=\"data row0 col8\" >0.625505</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col9\" class=\"data row0 col9\" >0.582764</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col10\" class=\"data row0 col10\" >0.289946</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col11\" class=\"data row0 col11\" >-0.385064</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col12\" class=\"data row0 col12\" >0.455621</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row0_col13\" class=\"data row0 col13\" >-0.388305</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row1\" class=\"row_heading level0 row1\" >ZN</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col0\" class=\"data row1 col0\" >-0.200469</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col1\" class=\"data row1 col1\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col2\" class=\"data row1 col2\" >-0.533828</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col3\" class=\"data row1 col3\" >-0.0426967</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col4\" class=\"data row1 col4\" >-0.516604</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col5\" class=\"data row1 col5\" >0.311991</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col6\" class=\"data row1 col6\" >-0.569537</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col7\" class=\"data row1 col7\" >0.664408</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col8\" class=\"data row1 col8\" >-0.311948</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col9\" class=\"data row1 col9\" >-0.314563</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col10\" class=\"data row1 col10\" >-0.391679</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col11\" class=\"data row1 col11\" >0.17552</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col12\" class=\"data row1 col12\" >-0.412995</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row1_col13\" class=\"data row1 col13\" >0.360445</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row2\" class=\"row_heading level0 row2\" >INDUS</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col0\" class=\"data row2 col0\" >0.406583</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col1\" class=\"data row2 col1\" >-0.533828</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col2\" class=\"data row2 col2\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col3\" class=\"data row2 col3\" >0.062938</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col4\" class=\"data row2 col4\" >0.763651</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col5\" class=\"data row2 col5\" >-0.391676</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col6\" class=\"data row2 col6\" >0.644779</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col7\" class=\"data row2 col7\" >-0.708027</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col8\" class=\"data row2 col8\" >0.595129</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col9\" class=\"data row2 col9\" >0.72076</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col10\" class=\"data row2 col10\" >0.383248</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col11\" class=\"data row2 col11\" >-0.356977</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col12\" class=\"data row2 col12\" >0.6038</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row2_col13\" class=\"data row2 col13\" >-0.483725</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row3\" class=\"row_heading level0 row3\" >CHAS</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col0\" class=\"data row3 col0\" >-0.0558916</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col1\" class=\"data row3 col1\" >-0.0426967</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col2\" class=\"data row3 col2\" >0.062938</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col3\" class=\"data row3 col3\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col4\" class=\"data row3 col4\" >0.0912028</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col5\" class=\"data row3 col5\" >0.0912512</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col6\" class=\"data row3 col6\" >0.0865178</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col7\" class=\"data row3 col7\" >-0.0991758</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col8\" class=\"data row3 col8\" >-0.00736824</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col9\" class=\"data row3 col9\" >-0.0355865</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col10\" class=\"data row3 col10\" >-0.121515</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col11\" class=\"data row3 col11\" >0.0487885</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col12\" class=\"data row3 col12\" >-0.0539293</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row3_col13\" class=\"data row3 col13\" >0.17526</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row4\" class=\"row_heading level0 row4\" >NOX</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col0\" class=\"data row4 col0\" >0.420972</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col1\" class=\"data row4 col1\" >-0.516604</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col2\" class=\"data row4 col2\" >0.763651</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col3\" class=\"data row4 col3\" >0.0912028</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col4\" class=\"data row4 col4\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col5\" class=\"data row4 col5\" >-0.302188</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col6\" class=\"data row4 col6\" >0.73147</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col7\" class=\"data row4 col7\" >-0.76923</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col8\" class=\"data row4 col8\" >0.611441</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col9\" class=\"data row4 col9\" >0.668023</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col10\" class=\"data row4 col10\" >0.188933</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col11\" class=\"data row4 col11\" >-0.380051</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col12\" class=\"data row4 col12\" >0.590879</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row4_col13\" class=\"data row4 col13\" >-0.427321</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row5\" class=\"row_heading level0 row5\" >RM</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col0\" class=\"data row5 col0\" >-0.219247</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col1\" class=\"data row5 col1\" >0.311991</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col2\" class=\"data row5 col2\" >-0.391676</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col3\" class=\"data row5 col3\" >0.0912512</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col4\" class=\"data row5 col4\" >-0.302188</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col5\" class=\"data row5 col5\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col6\" class=\"data row5 col6\" >-0.240265</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col7\" class=\"data row5 col7\" >0.205246</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col8\" class=\"data row5 col8\" >-0.209847</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col9\" class=\"data row5 col9\" >-0.292048</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col10\" class=\"data row5 col10\" >-0.355501</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col11\" class=\"data row5 col11\" >0.128069</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col12\" class=\"data row5 col12\" >-0.613808</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row5_col13\" class=\"data row5 col13\" >0.69536</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row6\" class=\"row_heading level0 row6\" >AGE</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col0\" class=\"data row6 col0\" >0.352734</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col1\" class=\"data row6 col1\" >-0.569537</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col2\" class=\"data row6 col2\" >0.644779</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col3\" class=\"data row6 col3\" >0.0865178</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col4\" class=\"data row6 col4\" >0.73147</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col5\" class=\"data row6 col5\" >-0.240265</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col6\" class=\"data row6 col6\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col7\" class=\"data row6 col7\" >-0.747881</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col8\" class=\"data row6 col8\" >0.456022</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col9\" class=\"data row6 col9\" >0.506456</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col10\" class=\"data row6 col10\" >0.261515</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col11\" class=\"data row6 col11\" >-0.273534</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col12\" class=\"data row6 col12\" >0.602339</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row6_col13\" class=\"data row6 col13\" >-0.376955</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row7\" class=\"row_heading level0 row7\" >DIS</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col0\" class=\"data row7 col0\" >-0.37967</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col1\" class=\"data row7 col1\" >0.664408</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col2\" class=\"data row7 col2\" >-0.708027</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col3\" class=\"data row7 col3\" >-0.0991758</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col4\" class=\"data row7 col4\" >-0.76923</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col5\" class=\"data row7 col5\" >0.205246</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col6\" class=\"data row7 col6\" >-0.747881</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col7\" class=\"data row7 col7\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col8\" class=\"data row7 col8\" >-0.494588</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col9\" class=\"data row7 col9\" >-0.534432</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col10\" class=\"data row7 col10\" >-0.232471</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col11\" class=\"data row7 col11\" >0.291512</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col12\" class=\"data row7 col12\" >-0.496996</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row7_col13\" class=\"data row7 col13\" >0.249929</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row8\" class=\"row_heading level0 row8\" >RAD</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col0\" class=\"data row8 col0\" >0.625505</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col1\" class=\"data row8 col1\" >-0.311948</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col2\" class=\"data row8 col2\" >0.595129</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col3\" class=\"data row8 col3\" >-0.00736824</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col4\" class=\"data row8 col4\" >0.611441</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col5\" class=\"data row8 col5\" >-0.209847</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col6\" class=\"data row8 col6\" >0.456022</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col7\" class=\"data row8 col7\" >-0.494588</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col8\" class=\"data row8 col8\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col9\" class=\"data row8 col9\" >0.910228</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col10\" class=\"data row8 col10\" >0.464741</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col11\" class=\"data row8 col11\" >-0.444413</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col12\" class=\"data row8 col12\" >0.488676</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row8_col13\" class=\"data row8 col13\" >-0.381626</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row9\" class=\"row_heading level0 row9\" >TAX</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col0\" class=\"data row9 col0\" >0.582764</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col1\" class=\"data row9 col1\" >-0.314563</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col2\" class=\"data row9 col2\" >0.72076</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col3\" class=\"data row9 col3\" >-0.0355865</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col4\" class=\"data row9 col4\" >0.668023</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col5\" class=\"data row9 col5\" >-0.292048</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col6\" class=\"data row9 col6\" >0.506456</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col7\" class=\"data row9 col7\" >-0.534432</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col8\" class=\"data row9 col8\" >0.910228</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col9\" class=\"data row9 col9\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col10\" class=\"data row9 col10\" >0.460853</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col11\" class=\"data row9 col11\" >-0.441808</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col12\" class=\"data row9 col12\" >0.543993</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row9_col13\" class=\"data row9 col13\" >-0.468536</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row10\" class=\"row_heading level0 row10\" >PTRATIO</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col0\" class=\"data row10 col0\" >0.289946</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col1\" class=\"data row10 col1\" >-0.391679</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col2\" class=\"data row10 col2\" >0.383248</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col3\" class=\"data row10 col3\" >-0.121515</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col4\" class=\"data row10 col4\" >0.188933</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col5\" class=\"data row10 col5\" >-0.355501</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col6\" class=\"data row10 col6\" >0.261515</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col7\" class=\"data row10 col7\" >-0.232471</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col8\" class=\"data row10 col8\" >0.464741</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col9\" class=\"data row10 col9\" >0.460853</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col10\" class=\"data row10 col10\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col11\" class=\"data row10 col11\" >-0.177383</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col12\" class=\"data row10 col12\" >0.374044</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row10_col13\" class=\"data row10 col13\" >-0.507787</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row11\" class=\"row_heading level0 row11\" >B</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col0\" class=\"data row11 col0\" >-0.385064</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col1\" class=\"data row11 col1\" >0.17552</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col2\" class=\"data row11 col2\" >-0.356977</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col3\" class=\"data row11 col3\" >0.0487885</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col4\" class=\"data row11 col4\" >-0.380051</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col5\" class=\"data row11 col5\" >0.128069</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col6\" class=\"data row11 col6\" >-0.273534</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col7\" class=\"data row11 col7\" >0.291512</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col8\" class=\"data row11 col8\" >-0.444413</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col9\" class=\"data row11 col9\" >-0.441808</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col10\" class=\"data row11 col10\" >-0.177383</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col11\" class=\"data row11 col11\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col12\" class=\"data row11 col12\" >-0.366087</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row11_col13\" class=\"data row11 col13\" >0.333461</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row12\" class=\"row_heading level0 row12\" >LSTAT</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col0\" class=\"data row12 col0\" >0.455621</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col1\" class=\"data row12 col1\" >-0.412995</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col2\" class=\"data row12 col2\" >0.6038</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col3\" class=\"data row12 col3\" >-0.0539293</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col4\" class=\"data row12 col4\" >0.590879</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col5\" class=\"data row12 col5\" >-0.613808</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col6\" class=\"data row12 col6\" >0.602339</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col7\" class=\"data row12 col7\" >-0.496996</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col8\" class=\"data row12 col8\" >0.488676</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col9\" class=\"data row12 col9\" >0.543993</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col10\" class=\"data row12 col10\" >0.374044</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col11\" class=\"data row12 col11\" >-0.366087</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col12\" class=\"data row12 col12\" >1</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row12_col13\" class=\"data row12 col13\" >-0.737663</td>\n",
-       "            </tr>\n",
-       "            <tr>\n",
-       "                        <th id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0level0_row13\" class=\"row_heading level0 row13\" >target</th>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col0\" class=\"data row13 col0\" >-0.388305</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col1\" class=\"data row13 col1\" >0.360445</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col2\" class=\"data row13 col2\" >-0.483725</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col3\" class=\"data row13 col3\" >0.17526</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col4\" class=\"data row13 col4\" >-0.427321</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col5\" class=\"data row13 col5\" >0.69536</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col6\" class=\"data row13 col6\" >-0.376955</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col7\" class=\"data row13 col7\" >0.249929</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col8\" class=\"data row13 col8\" >-0.381626</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col9\" class=\"data row13 col9\" >-0.468536</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col10\" class=\"data row13 col10\" >-0.507787</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col11\" class=\"data row13 col11\" >0.333461</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col12\" class=\"data row13 col12\" >-0.737663</td>\n",
-       "                        <td id=\"T_b1cb97c2_8e3a_11ec_bc3a_a0a4c53b5af0row13_col13\" class=\"data row13 col13\" >1</td>\n",
-       "            </tr>\n",
-       "    </tbody></table>"
+       "<style type=\"text/css\">\n",
+       "#T_43fc7_row0_col0, #T_43fc7_row1_col1, #T_43fc7_row2_col2, #T_43fc7_row2_col4, #T_43fc7_row2_col7, #T_43fc7_row2_col9, #T_43fc7_row3_col3, #T_43fc7_row4_col2, #T_43fc7_row4_col4, #T_43fc7_row4_col6, #T_43fc7_row4_col7, #T_43fc7_row5_col5, #T_43fc7_row6_col4, #T_43fc7_row6_col6, #T_43fc7_row6_col7, #T_43fc7_row7_col2, #T_43fc7_row7_col4, #T_43fc7_row7_col6, #T_43fc7_row7_col7, #T_43fc7_row8_col8, #T_43fc7_row8_col9, #T_43fc7_row9_col2, #T_43fc7_row9_col8, #T_43fc7_row9_col9, #T_43fc7_row10_col10, #T_43fc7_row11_col11, #T_43fc7_row12_col12, #T_43fc7_row12_col13, #T_43fc7_row13_col12, #T_43fc7_row13_col13 {\n",
+       "  background-color: red;\n",
+       "}\n",
+       "#T_43fc7_row0_col1, #T_43fc7_row0_col2, #T_43fc7_row0_col3, #T_43fc7_row0_col4, #T_43fc7_row0_col5, #T_43fc7_row0_col6, #T_43fc7_row0_col7, #T_43fc7_row0_col8, #T_43fc7_row0_col9, #T_43fc7_row0_col10, #T_43fc7_row0_col11, #T_43fc7_row0_col12, #T_43fc7_row0_col13, #T_43fc7_row1_col0, #T_43fc7_row1_col2, #T_43fc7_row1_col3, #T_43fc7_row1_col4, #T_43fc7_row1_col5, #T_43fc7_row1_col6, #T_43fc7_row1_col7, #T_43fc7_row1_col8, #T_43fc7_row1_col9, #T_43fc7_row1_col10, #T_43fc7_row1_col11, #T_43fc7_row1_col12, #T_43fc7_row1_col13, #T_43fc7_row2_col0, #T_43fc7_row2_col1, #T_43fc7_row2_col3, #T_43fc7_row2_col5, #T_43fc7_row2_col6, #T_43fc7_row2_col8, #T_43fc7_row2_col10, #T_43fc7_row2_col11, #T_43fc7_row2_col12, #T_43fc7_row2_col13, #T_43fc7_row3_col0, #T_43fc7_row3_col1, #T_43fc7_row3_col2, #T_43fc7_row3_col4, #T_43fc7_row3_col5, #T_43fc7_row3_col6, #T_43fc7_row3_col7, #T_43fc7_row3_col8, #T_43fc7_row3_col9, #T_43fc7_row3_col10, #T_43fc7_row3_col11, #T_43fc7_row3_col12, #T_43fc7_row3_col13, #T_43fc7_row4_col0, #T_43fc7_row4_col1, #T_43fc7_row4_col3, #T_43fc7_row4_col5, #T_43fc7_row4_col8, #T_43fc7_row4_col9, #T_43fc7_row4_col10, #T_43fc7_row4_col11, #T_43fc7_row4_col12, #T_43fc7_row4_col13, #T_43fc7_row5_col0, #T_43fc7_row5_col1, #T_43fc7_row5_col2, #T_43fc7_row5_col3, #T_43fc7_row5_col4, #T_43fc7_row5_col6, #T_43fc7_row5_col7, #T_43fc7_row5_col8, #T_43fc7_row5_col9, #T_43fc7_row5_col10, #T_43fc7_row5_col11, #T_43fc7_row5_col12, #T_43fc7_row5_col13, #T_43fc7_row6_col0, #T_43fc7_row6_col1, #T_43fc7_row6_col2, #T_43fc7_row6_col3, #T_43fc7_row6_col5, #T_43fc7_row6_col8, #T_43fc7_row6_col9, #T_43fc7_row6_col10, #T_43fc7_row6_col11, #T_43fc7_row6_col12, #T_43fc7_row6_col13, #T_43fc7_row7_col0, #T_43fc7_row7_col1, #T_43fc7_row7_col3, #T_43fc7_row7_col5, #T_43fc7_row7_col8, #T_43fc7_row7_col9, #T_43fc7_row7_col10, #T_43fc7_row7_col11, #T_43fc7_row7_col12, #T_43fc7_row7_col13, #T_43fc7_row8_col0, #T_43fc7_row8_col1, #T_43fc7_row8_col2, #T_43fc7_row8_col3, #T_43fc7_row8_col4, #T_43fc7_row8_col5, #T_43fc7_row8_col6, #T_43fc7_row8_col7, #T_43fc7_row8_col10, #T_43fc7_row8_col11, #T_43fc7_row8_col12, #T_43fc7_row8_col13, #T_43fc7_row9_col0, #T_43fc7_row9_col1, #T_43fc7_row9_col3, #T_43fc7_row9_col4, #T_43fc7_row9_col5, #T_43fc7_row9_col6, #T_43fc7_row9_col7, #T_43fc7_row9_col10, #T_43fc7_row9_col11, #T_43fc7_row9_col12, #T_43fc7_row9_col13, #T_43fc7_row10_col0, #T_43fc7_row10_col1, #T_43fc7_row10_col2, #T_43fc7_row10_col3, #T_43fc7_row10_col4, #T_43fc7_row10_col5, #T_43fc7_row10_col6, #T_43fc7_row10_col7, #T_43fc7_row10_col8, #T_43fc7_row10_col9, #T_43fc7_row10_col11, #T_43fc7_row10_col12, #T_43fc7_row10_col13, #T_43fc7_row11_col0, #T_43fc7_row11_col1, #T_43fc7_row11_col2, #T_43fc7_row11_col3, #T_43fc7_row11_col4, #T_43fc7_row11_col5, #T_43fc7_row11_col6, #T_43fc7_row11_col7, #T_43fc7_row11_col8, #T_43fc7_row11_col9, #T_43fc7_row11_col10, #T_43fc7_row11_col12, #T_43fc7_row11_col13, #T_43fc7_row12_col0, #T_43fc7_row12_col1, #T_43fc7_row12_col2, #T_43fc7_row12_col3, #T_43fc7_row12_col4, #T_43fc7_row12_col5, #T_43fc7_row12_col6, #T_43fc7_row12_col7, #T_43fc7_row12_col8, #T_43fc7_row12_col9, #T_43fc7_row12_col10, #T_43fc7_row12_col11, #T_43fc7_row13_col0, #T_43fc7_row13_col1, #T_43fc7_row13_col2, #T_43fc7_row13_col3, #T_43fc7_row13_col4, #T_43fc7_row13_col5, #T_43fc7_row13_col6, #T_43fc7_row13_col7, #T_43fc7_row13_col8, #T_43fc7_row13_col9, #T_43fc7_row13_col10, #T_43fc7_row13_col11 {\n",
+       "  background-color: lightgrey;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_43fc7\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_43fc7_level0_col0\" class=\"col_heading level0 col0\" >CRIM</th>\n",
+       "      <th id=\"T_43fc7_level0_col1\" class=\"col_heading level0 col1\" >ZN</th>\n",
+       "      <th id=\"T_43fc7_level0_col2\" class=\"col_heading level0 col2\" >INDUS</th>\n",
+       "      <th id=\"T_43fc7_level0_col3\" class=\"col_heading level0 col3\" >CHAS</th>\n",
+       "      <th id=\"T_43fc7_level0_col4\" class=\"col_heading level0 col4\" >NOX</th>\n",
+       "      <th id=\"T_43fc7_level0_col5\" class=\"col_heading level0 col5\" >RM</th>\n",
+       "      <th id=\"T_43fc7_level0_col6\" class=\"col_heading level0 col6\" >AGE</th>\n",
+       "      <th id=\"T_43fc7_level0_col7\" class=\"col_heading level0 col7\" >DIS</th>\n",
+       "      <th id=\"T_43fc7_level0_col8\" class=\"col_heading level0 col8\" >RAD</th>\n",
+       "      <th id=\"T_43fc7_level0_col9\" class=\"col_heading level0 col9\" >TAX</th>\n",
+       "      <th id=\"T_43fc7_level0_col10\" class=\"col_heading level0 col10\" >PTRATIO</th>\n",
+       "      <th id=\"T_43fc7_level0_col11\" class=\"col_heading level0 col11\" >B</th>\n",
+       "      <th id=\"T_43fc7_level0_col12\" class=\"col_heading level0 col12\" >LSTAT</th>\n",
+       "      <th id=\"T_43fc7_level0_col13\" class=\"col_heading level0 col13\" >target</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row0\" class=\"row_heading level0 row0\" >CRIM</th>\n",
+       "      <td id=\"T_43fc7_row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row0_col1\" class=\"data row0 col1\" >-0.200469</td>\n",
+       "      <td id=\"T_43fc7_row0_col2\" class=\"data row0 col2\" >0.406583</td>\n",
+       "      <td id=\"T_43fc7_row0_col3\" class=\"data row0 col3\" >-0.055892</td>\n",
+       "      <td id=\"T_43fc7_row0_col4\" class=\"data row0 col4\" >0.420972</td>\n",
+       "      <td id=\"T_43fc7_row0_col5\" class=\"data row0 col5\" >-0.219247</td>\n",
+       "      <td id=\"T_43fc7_row0_col6\" class=\"data row0 col6\" >0.352734</td>\n",
+       "      <td id=\"T_43fc7_row0_col7\" class=\"data row0 col7\" >-0.379670</td>\n",
+       "      <td id=\"T_43fc7_row0_col8\" class=\"data row0 col8\" >0.625505</td>\n",
+       "      <td id=\"T_43fc7_row0_col9\" class=\"data row0 col9\" >0.582764</td>\n",
+       "      <td id=\"T_43fc7_row0_col10\" class=\"data row0 col10\" >0.289946</td>\n",
+       "      <td id=\"T_43fc7_row0_col11\" class=\"data row0 col11\" >-0.385064</td>\n",
+       "      <td id=\"T_43fc7_row0_col12\" class=\"data row0 col12\" >0.455621</td>\n",
+       "      <td id=\"T_43fc7_row0_col13\" class=\"data row0 col13\" >-0.388305</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row1\" class=\"row_heading level0 row1\" >ZN</th>\n",
+       "      <td id=\"T_43fc7_row1_col0\" class=\"data row1 col0\" >-0.200469</td>\n",
+       "      <td id=\"T_43fc7_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row1_col2\" class=\"data row1 col2\" >-0.533828</td>\n",
+       "      <td id=\"T_43fc7_row1_col3\" class=\"data row1 col3\" >-0.042697</td>\n",
+       "      <td id=\"T_43fc7_row1_col4\" class=\"data row1 col4\" >-0.516604</td>\n",
+       "      <td id=\"T_43fc7_row1_col5\" class=\"data row1 col5\" >0.311991</td>\n",
+       "      <td id=\"T_43fc7_row1_col6\" class=\"data row1 col6\" >-0.569537</td>\n",
+       "      <td id=\"T_43fc7_row1_col7\" class=\"data row1 col7\" >0.664408</td>\n",
+       "      <td id=\"T_43fc7_row1_col8\" class=\"data row1 col8\" >-0.311948</td>\n",
+       "      <td id=\"T_43fc7_row1_col9\" class=\"data row1 col9\" >-0.314563</td>\n",
+       "      <td id=\"T_43fc7_row1_col10\" class=\"data row1 col10\" >-0.391679</td>\n",
+       "      <td id=\"T_43fc7_row1_col11\" class=\"data row1 col11\" >0.175520</td>\n",
+       "      <td id=\"T_43fc7_row1_col12\" class=\"data row1 col12\" >-0.412995</td>\n",
+       "      <td id=\"T_43fc7_row1_col13\" class=\"data row1 col13\" >0.360445</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row2\" class=\"row_heading level0 row2\" >INDUS</th>\n",
+       "      <td id=\"T_43fc7_row2_col0\" class=\"data row2 col0\" >0.406583</td>\n",
+       "      <td id=\"T_43fc7_row2_col1\" class=\"data row2 col1\" >-0.533828</td>\n",
+       "      <td id=\"T_43fc7_row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row2_col3\" class=\"data row2 col3\" >0.062938</td>\n",
+       "      <td id=\"T_43fc7_row2_col4\" class=\"data row2 col4\" >0.763651</td>\n",
+       "      <td id=\"T_43fc7_row2_col5\" class=\"data row2 col5\" >-0.391676</td>\n",
+       "      <td id=\"T_43fc7_row2_col6\" class=\"data row2 col6\" >0.644779</td>\n",
+       "      <td id=\"T_43fc7_row2_col7\" class=\"data row2 col7\" >-0.708027</td>\n",
+       "      <td id=\"T_43fc7_row2_col8\" class=\"data row2 col8\" >0.595129</td>\n",
+       "      <td id=\"T_43fc7_row2_col9\" class=\"data row2 col9\" >0.720760</td>\n",
+       "      <td id=\"T_43fc7_row2_col10\" class=\"data row2 col10\" >0.383248</td>\n",
+       "      <td id=\"T_43fc7_row2_col11\" class=\"data row2 col11\" >-0.356977</td>\n",
+       "      <td id=\"T_43fc7_row2_col12\" class=\"data row2 col12\" >0.603800</td>\n",
+       "      <td id=\"T_43fc7_row2_col13\" class=\"data row2 col13\" >-0.483725</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row3\" class=\"row_heading level0 row3\" >CHAS</th>\n",
+       "      <td id=\"T_43fc7_row3_col0\" class=\"data row3 col0\" >-0.055892</td>\n",
+       "      <td id=\"T_43fc7_row3_col1\" class=\"data row3 col1\" >-0.042697</td>\n",
+       "      <td id=\"T_43fc7_row3_col2\" class=\"data row3 col2\" >0.062938</td>\n",
+       "      <td id=\"T_43fc7_row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row3_col4\" class=\"data row3 col4\" >0.091203</td>\n",
+       "      <td id=\"T_43fc7_row3_col5\" class=\"data row3 col5\" >0.091251</td>\n",
+       "      <td id=\"T_43fc7_row3_col6\" class=\"data row3 col6\" >0.086518</td>\n",
+       "      <td id=\"T_43fc7_row3_col7\" class=\"data row3 col7\" >-0.099176</td>\n",
+       "      <td id=\"T_43fc7_row3_col8\" class=\"data row3 col8\" >-0.007368</td>\n",
+       "      <td id=\"T_43fc7_row3_col9\" class=\"data row3 col9\" >-0.035587</td>\n",
+       "      <td id=\"T_43fc7_row3_col10\" class=\"data row3 col10\" >-0.121515</td>\n",
+       "      <td id=\"T_43fc7_row3_col11\" class=\"data row3 col11\" >0.048788</td>\n",
+       "      <td id=\"T_43fc7_row3_col12\" class=\"data row3 col12\" >-0.053929</td>\n",
+       "      <td id=\"T_43fc7_row3_col13\" class=\"data row3 col13\" >0.175260</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row4\" class=\"row_heading level0 row4\" >NOX</th>\n",
+       "      <td id=\"T_43fc7_row4_col0\" class=\"data row4 col0\" >0.420972</td>\n",
+       "      <td id=\"T_43fc7_row4_col1\" class=\"data row4 col1\" >-0.516604</td>\n",
+       "      <td id=\"T_43fc7_row4_col2\" class=\"data row4 col2\" >0.763651</td>\n",
+       "      <td id=\"T_43fc7_row4_col3\" class=\"data row4 col3\" >0.091203</td>\n",
+       "      <td id=\"T_43fc7_row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row4_col5\" class=\"data row4 col5\" >-0.302188</td>\n",
+       "      <td id=\"T_43fc7_row4_col6\" class=\"data row4 col6\" >0.731470</td>\n",
+       "      <td id=\"T_43fc7_row4_col7\" class=\"data row4 col7\" >-0.769230</td>\n",
+       "      <td id=\"T_43fc7_row4_col8\" class=\"data row4 col8\" >0.611441</td>\n",
+       "      <td id=\"T_43fc7_row4_col9\" class=\"data row4 col9\" >0.668023</td>\n",
+       "      <td id=\"T_43fc7_row4_col10\" class=\"data row4 col10\" >0.188933</td>\n",
+       "      <td id=\"T_43fc7_row4_col11\" class=\"data row4 col11\" >-0.380051</td>\n",
+       "      <td id=\"T_43fc7_row4_col12\" class=\"data row4 col12\" >0.590879</td>\n",
+       "      <td id=\"T_43fc7_row4_col13\" class=\"data row4 col13\" >-0.427321</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row5\" class=\"row_heading level0 row5\" >RM</th>\n",
+       "      <td id=\"T_43fc7_row5_col0\" class=\"data row5 col0\" >-0.219247</td>\n",
+       "      <td id=\"T_43fc7_row5_col1\" class=\"data row5 col1\" >0.311991</td>\n",
+       "      <td id=\"T_43fc7_row5_col2\" class=\"data row5 col2\" >-0.391676</td>\n",
+       "      <td id=\"T_43fc7_row5_col3\" class=\"data row5 col3\" >0.091251</td>\n",
+       "      <td id=\"T_43fc7_row5_col4\" class=\"data row5 col4\" >-0.302188</td>\n",
+       "      <td id=\"T_43fc7_row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row5_col6\" class=\"data row5 col6\" >-0.240265</td>\n",
+       "      <td id=\"T_43fc7_row5_col7\" class=\"data row5 col7\" >0.205246</td>\n",
+       "      <td id=\"T_43fc7_row5_col8\" class=\"data row5 col8\" >-0.209847</td>\n",
+       "      <td id=\"T_43fc7_row5_col9\" class=\"data row5 col9\" >-0.292048</td>\n",
+       "      <td id=\"T_43fc7_row5_col10\" class=\"data row5 col10\" >-0.355501</td>\n",
+       "      <td id=\"T_43fc7_row5_col11\" class=\"data row5 col11\" >0.128069</td>\n",
+       "      <td id=\"T_43fc7_row5_col12\" class=\"data row5 col12\" >-0.613808</td>\n",
+       "      <td id=\"T_43fc7_row5_col13\" class=\"data row5 col13\" >0.695360</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row6\" class=\"row_heading level0 row6\" >AGE</th>\n",
+       "      <td id=\"T_43fc7_row6_col0\" class=\"data row6 col0\" >0.352734</td>\n",
+       "      <td id=\"T_43fc7_row6_col1\" class=\"data row6 col1\" >-0.569537</td>\n",
+       "      <td id=\"T_43fc7_row6_col2\" class=\"data row6 col2\" >0.644779</td>\n",
+       "      <td id=\"T_43fc7_row6_col3\" class=\"data row6 col3\" >0.086518</td>\n",
+       "      <td id=\"T_43fc7_row6_col4\" class=\"data row6 col4\" >0.731470</td>\n",
+       "      <td id=\"T_43fc7_row6_col5\" class=\"data row6 col5\" >-0.240265</td>\n",
+       "      <td id=\"T_43fc7_row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row6_col7\" class=\"data row6 col7\" >-0.747881</td>\n",
+       "      <td id=\"T_43fc7_row6_col8\" class=\"data row6 col8\" >0.456022</td>\n",
+       "      <td id=\"T_43fc7_row6_col9\" class=\"data row6 col9\" >0.506456</td>\n",
+       "      <td id=\"T_43fc7_row6_col10\" class=\"data row6 col10\" >0.261515</td>\n",
+       "      <td id=\"T_43fc7_row6_col11\" class=\"data row6 col11\" >-0.273534</td>\n",
+       "      <td id=\"T_43fc7_row6_col12\" class=\"data row6 col12\" >0.602339</td>\n",
+       "      <td id=\"T_43fc7_row6_col13\" class=\"data row6 col13\" >-0.376955</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row7\" class=\"row_heading level0 row7\" >DIS</th>\n",
+       "      <td id=\"T_43fc7_row7_col0\" class=\"data row7 col0\" >-0.379670</td>\n",
+       "      <td id=\"T_43fc7_row7_col1\" class=\"data row7 col1\" >0.664408</td>\n",
+       "      <td id=\"T_43fc7_row7_col2\" class=\"data row7 col2\" >-0.708027</td>\n",
+       "      <td id=\"T_43fc7_row7_col3\" class=\"data row7 col3\" >-0.099176</td>\n",
+       "      <td id=\"T_43fc7_row7_col4\" class=\"data row7 col4\" >-0.769230</td>\n",
+       "      <td id=\"T_43fc7_row7_col5\" class=\"data row7 col5\" >0.205246</td>\n",
+       "      <td id=\"T_43fc7_row7_col6\" class=\"data row7 col6\" >-0.747881</td>\n",
+       "      <td id=\"T_43fc7_row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row7_col8\" class=\"data row7 col8\" >-0.494588</td>\n",
+       "      <td id=\"T_43fc7_row7_col9\" class=\"data row7 col9\" >-0.534432</td>\n",
+       "      <td id=\"T_43fc7_row7_col10\" class=\"data row7 col10\" >-0.232471</td>\n",
+       "      <td id=\"T_43fc7_row7_col11\" class=\"data row7 col11\" >0.291512</td>\n",
+       "      <td id=\"T_43fc7_row7_col12\" class=\"data row7 col12\" >-0.496996</td>\n",
+       "      <td id=\"T_43fc7_row7_col13\" class=\"data row7 col13\" >0.249929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row8\" class=\"row_heading level0 row8\" >RAD</th>\n",
+       "      <td id=\"T_43fc7_row8_col0\" class=\"data row8 col0\" >0.625505</td>\n",
+       "      <td id=\"T_43fc7_row8_col1\" class=\"data row8 col1\" >-0.311948</td>\n",
+       "      <td id=\"T_43fc7_row8_col2\" class=\"data row8 col2\" >0.595129</td>\n",
+       "      <td id=\"T_43fc7_row8_col3\" class=\"data row8 col3\" >-0.007368</td>\n",
+       "      <td id=\"T_43fc7_row8_col4\" class=\"data row8 col4\" >0.611441</td>\n",
+       "      <td id=\"T_43fc7_row8_col5\" class=\"data row8 col5\" >-0.209847</td>\n",
+       "      <td id=\"T_43fc7_row8_col6\" class=\"data row8 col6\" >0.456022</td>\n",
+       "      <td id=\"T_43fc7_row8_col7\" class=\"data row8 col7\" >-0.494588</td>\n",
+       "      <td id=\"T_43fc7_row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row8_col9\" class=\"data row8 col9\" >0.910228</td>\n",
+       "      <td id=\"T_43fc7_row8_col10\" class=\"data row8 col10\" >0.464741</td>\n",
+       "      <td id=\"T_43fc7_row8_col11\" class=\"data row8 col11\" >-0.444413</td>\n",
+       "      <td id=\"T_43fc7_row8_col12\" class=\"data row8 col12\" >0.488676</td>\n",
+       "      <td id=\"T_43fc7_row8_col13\" class=\"data row8 col13\" >-0.381626</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row9\" class=\"row_heading level0 row9\" >TAX</th>\n",
+       "      <td id=\"T_43fc7_row9_col0\" class=\"data row9 col0\" >0.582764</td>\n",
+       "      <td id=\"T_43fc7_row9_col1\" class=\"data row9 col1\" >-0.314563</td>\n",
+       "      <td id=\"T_43fc7_row9_col2\" class=\"data row9 col2\" >0.720760</td>\n",
+       "      <td id=\"T_43fc7_row9_col3\" class=\"data row9 col3\" >-0.035587</td>\n",
+       "      <td id=\"T_43fc7_row9_col4\" class=\"data row9 col4\" >0.668023</td>\n",
+       "      <td id=\"T_43fc7_row9_col5\" class=\"data row9 col5\" >-0.292048</td>\n",
+       "      <td id=\"T_43fc7_row9_col6\" class=\"data row9 col6\" >0.506456</td>\n",
+       "      <td id=\"T_43fc7_row9_col7\" class=\"data row9 col7\" >-0.534432</td>\n",
+       "      <td id=\"T_43fc7_row9_col8\" class=\"data row9 col8\" >0.910228</td>\n",
+       "      <td id=\"T_43fc7_row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row9_col10\" class=\"data row9 col10\" >0.460853</td>\n",
+       "      <td id=\"T_43fc7_row9_col11\" class=\"data row9 col11\" >-0.441808</td>\n",
+       "      <td id=\"T_43fc7_row9_col12\" class=\"data row9 col12\" >0.543993</td>\n",
+       "      <td id=\"T_43fc7_row9_col13\" class=\"data row9 col13\" >-0.468536</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row10\" class=\"row_heading level0 row10\" >PTRATIO</th>\n",
+       "      <td id=\"T_43fc7_row10_col0\" class=\"data row10 col0\" >0.289946</td>\n",
+       "      <td id=\"T_43fc7_row10_col1\" class=\"data row10 col1\" >-0.391679</td>\n",
+       "      <td id=\"T_43fc7_row10_col2\" class=\"data row10 col2\" >0.383248</td>\n",
+       "      <td id=\"T_43fc7_row10_col3\" class=\"data row10 col3\" >-0.121515</td>\n",
+       "      <td id=\"T_43fc7_row10_col4\" class=\"data row10 col4\" >0.188933</td>\n",
+       "      <td id=\"T_43fc7_row10_col5\" class=\"data row10 col5\" >-0.355501</td>\n",
+       "      <td id=\"T_43fc7_row10_col6\" class=\"data row10 col6\" >0.261515</td>\n",
+       "      <td id=\"T_43fc7_row10_col7\" class=\"data row10 col7\" >-0.232471</td>\n",
+       "      <td id=\"T_43fc7_row10_col8\" class=\"data row10 col8\" >0.464741</td>\n",
+       "      <td id=\"T_43fc7_row10_col9\" class=\"data row10 col9\" >0.460853</td>\n",
+       "      <td id=\"T_43fc7_row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row10_col11\" class=\"data row10 col11\" >-0.177383</td>\n",
+       "      <td id=\"T_43fc7_row10_col12\" class=\"data row10 col12\" >0.374044</td>\n",
+       "      <td id=\"T_43fc7_row10_col13\" class=\"data row10 col13\" >-0.507787</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row11\" class=\"row_heading level0 row11\" >B</th>\n",
+       "      <td id=\"T_43fc7_row11_col0\" class=\"data row11 col0\" >-0.385064</td>\n",
+       "      <td id=\"T_43fc7_row11_col1\" class=\"data row11 col1\" >0.175520</td>\n",
+       "      <td id=\"T_43fc7_row11_col2\" class=\"data row11 col2\" >-0.356977</td>\n",
+       "      <td id=\"T_43fc7_row11_col3\" class=\"data row11 col3\" >0.048788</td>\n",
+       "      <td id=\"T_43fc7_row11_col4\" class=\"data row11 col4\" >-0.380051</td>\n",
+       "      <td id=\"T_43fc7_row11_col5\" class=\"data row11 col5\" >0.128069</td>\n",
+       "      <td id=\"T_43fc7_row11_col6\" class=\"data row11 col6\" >-0.273534</td>\n",
+       "      <td id=\"T_43fc7_row11_col7\" class=\"data row11 col7\" >0.291512</td>\n",
+       "      <td id=\"T_43fc7_row11_col8\" class=\"data row11 col8\" >-0.444413</td>\n",
+       "      <td id=\"T_43fc7_row11_col9\" class=\"data row11 col9\" >-0.441808</td>\n",
+       "      <td id=\"T_43fc7_row11_col10\" class=\"data row11 col10\" >-0.177383</td>\n",
+       "      <td id=\"T_43fc7_row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row11_col12\" class=\"data row11 col12\" >-0.366087</td>\n",
+       "      <td id=\"T_43fc7_row11_col13\" class=\"data row11 col13\" >0.333461</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row12\" class=\"row_heading level0 row12\" >LSTAT</th>\n",
+       "      <td id=\"T_43fc7_row12_col0\" class=\"data row12 col0\" >0.455621</td>\n",
+       "      <td id=\"T_43fc7_row12_col1\" class=\"data row12 col1\" >-0.412995</td>\n",
+       "      <td id=\"T_43fc7_row12_col2\" class=\"data row12 col2\" >0.603800</td>\n",
+       "      <td id=\"T_43fc7_row12_col3\" class=\"data row12 col3\" >-0.053929</td>\n",
+       "      <td id=\"T_43fc7_row12_col4\" class=\"data row12 col4\" >0.590879</td>\n",
+       "      <td id=\"T_43fc7_row12_col5\" class=\"data row12 col5\" >-0.613808</td>\n",
+       "      <td id=\"T_43fc7_row12_col6\" class=\"data row12 col6\" >0.602339</td>\n",
+       "      <td id=\"T_43fc7_row12_col7\" class=\"data row12 col7\" >-0.496996</td>\n",
+       "      <td id=\"T_43fc7_row12_col8\" class=\"data row12 col8\" >0.488676</td>\n",
+       "      <td id=\"T_43fc7_row12_col9\" class=\"data row12 col9\" >0.543993</td>\n",
+       "      <td id=\"T_43fc7_row12_col10\" class=\"data row12 col10\" >0.374044</td>\n",
+       "      <td id=\"T_43fc7_row12_col11\" class=\"data row12 col11\" >-0.366087</td>\n",
+       "      <td id=\"T_43fc7_row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
+       "      <td id=\"T_43fc7_row12_col13\" class=\"data row12 col13\" >-0.737663</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_43fc7_level0_row13\" class=\"row_heading level0 row13\" >target</th>\n",
+       "      <td id=\"T_43fc7_row13_col0\" class=\"data row13 col0\" >-0.388305</td>\n",
+       "      <td id=\"T_43fc7_row13_col1\" class=\"data row13 col1\" >0.360445</td>\n",
+       "      <td id=\"T_43fc7_row13_col2\" class=\"data row13 col2\" >-0.483725</td>\n",
+       "      <td id=\"T_43fc7_row13_col3\" class=\"data row13 col3\" >0.175260</td>\n",
+       "      <td id=\"T_43fc7_row13_col4\" class=\"data row13 col4\" >-0.427321</td>\n",
+       "      <td id=\"T_43fc7_row13_col5\" class=\"data row13 col5\" >0.695360</td>\n",
+       "      <td id=\"T_43fc7_row13_col6\" class=\"data row13 col6\" >-0.376955</td>\n",
+       "      <td id=\"T_43fc7_row13_col7\" class=\"data row13 col7\" >0.249929</td>\n",
+       "      <td id=\"T_43fc7_row13_col8\" class=\"data row13 col8\" >-0.381626</td>\n",
+       "      <td id=\"T_43fc7_row13_col9\" class=\"data row13 col9\" >-0.468536</td>\n",
+       "      <td id=\"T_43fc7_row13_col10\" class=\"data row13 col10\" >-0.507787</td>\n",
+       "      <td id=\"T_43fc7_row13_col11\" class=\"data row13 col11\" >0.333461</td>\n",
+       "      <td id=\"T_43fc7_row13_col12\" class=\"data row13 col12\" >-0.737663</td>\n",
+       "      <td id=\"T_43fc7_row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
       ],
       "text/plain": [
-       "<pandas.io.formats.style.Styler at 0x238b8c7a9c8>"
+       "<pandas.io.formats.style.Styler at 0x235b810d240>"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1902,7 +1215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1913,7 +1226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1931,10 +1244,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   108.1</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Tue, 15 Feb 2022</td> <th>  Prob (F-statistic):</th> <td>6.72e-135</td>\n",
+       "  <th>Date:</th>             <td>Tue, 03 Jan 2023</td> <th>  Prob (F-statistic):</th> <td>6.72e-135</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>19:39:08</td>     <th>  Log-Likelihood:    </th> <td> -1498.8</td> \n",
+       "  <th>Time:</th>                 <td>16:51:57</td>     <th>  Log-Likelihood:    </th> <td> -1498.8</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3026.</td> \n",
@@ -2009,7 +1322,7 @@
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td> 8.281</td>  <th>  Cond. No.          </th> <td>1.51e+04</td> \n",
        "</tr>\n",
-       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.51e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.51e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -2019,8 +1332,8 @@
        "Dep. Variable:                 target   R-squared:                       0.741\n",
        "Model:                            OLS   Adj. R-squared:                  0.734\n",
        "Method:                 Least Squares   F-statistic:                     108.1\n",
-       "Date:                Tue, 15 Feb 2022   Prob (F-statistic):          6.72e-135\n",
-       "Time:                        19:39:08   Log-Likelihood:                -1498.8\n",
+       "Date:                Tue, 03 Jan 2023   Prob (F-statistic):          6.72e-135\n",
+       "Time:                        16:51:57   Log-Likelihood:                -1498.8\n",
        "No. Observations:                 506   AIC:                             3026.\n",
        "Df Residuals:                     492   BIC:                             3085.\n",
        "Df Model:                          13                                         \n",
@@ -2049,14 +1362,14 @@
        "Kurtosis:                       8.281   Cond. No.                     1.51e+04\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "[2] The condition number is large, 1.51e+04. This might indicate that there are\n",
        "strong multicollinearity or other numerical problems.\n",
        "\"\"\""
       ]
      },
-     "execution_count": 45,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2074,16 +1387,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "15113.517599135064"
+       "15113.517599134999"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2102,14 +1415,37 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Exercises\n",
     "\n",
-    "1. Research the Gauss-Markov theorem for BLUE. What are the key assumptions behind this finding? You'll see the word \"homoscedastic\", which we will investigate further in later modules.\n",
-    "\n",
-    "\n",
+    "1. Research the Gauss-Markov theorem for BLUE. What are the key assumptions behind this finding? You'll see the word \"homoscedastic\", which we will investigate further in later modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Exercise 1\n",
+    "# BLUE = best linear unbiased estimator\n",
+    "#   for a given Y = x*beta + e\n",
+    "#   - x has a full rank, and x^Tx is invertible\n",
+    "#   - linearity as the parameters we are estimating are linear\n",
+    "#   - data must be randomly sampled from the population\n",
+    "#   - non-collinearity meaning the independent variables should not be perfectly correlated with eachother\n",
+    "#   - independent variables arent connected to the error term\n",
+    "#   - error of the variance is constant regardless of the values in our independent variables"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "#### Extended exercise\n",
     "Another way to check multicollinearity is to see how stable coefficients for your input variables are when you change the sample of data. On this front, write code that performs the following test:\n",
     "\n",
@@ -2125,76 +1461,126 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Intercept    23.236584\n",
-      "CRIM         -0.129848\n",
-      "ZN            0.012949\n",
-      "INDUS        -0.029862\n",
-      "CHAS         -2.184735\n",
-      "NOX          -7.427196\n",
-      "RM            5.040470\n",
-      "AGE          -0.034978\n",
-      "DIS          -0.814954\n",
-      "RAD           0.270592\n",
-      "TAX          -0.012100\n",
-      "PTRATIO      -1.048337\n",
-      "B             0.008887\n",
-      "LSTAT        -0.288276\n",
+      "Intercept    36.371617\n",
+      "CRIM         -0.028666\n",
+      "ZN            0.032561\n",
+      "INDUS        -0.096924\n",
+      "CHAS          0.750348\n",
+      "NOX         -20.988534\n",
+      "RM            4.746670\n",
+      "AGE          -0.000714\n",
+      "DIS          -1.379658\n",
+      "RAD           0.171416\n",
+      "TAX          -0.011105\n",
+      "PTRATIO      -1.421754\n",
+      "B             0.014274\n",
+      "LSTAT        -0.181193\n",
       "dtype: float64\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x1080 with 15 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
+    "# solution was already here so I just added comments\n",
     "# Extended exercise (copied off solutions)\n",
-    "\n",
     "def run_experiment(data, target_variable_name='target', sample_size=100):\n",
-    "    \"\"\"Fits an OLS to the data, predicting the target from all other variables\n",
+    "    \n",
+    "    \"\"\"\n",
+    "    Fits an OLS to the data, predicting the target from all other variables\n",
     "    \"\"\"\n",
+    "\n",
+    "    # sample data\n",
     "    sample = data.sample(sample_size)\n",
-    "    \n",
     "    column_names = [col for col in data.columns if col != target_variable_name]\n",
+    "\n",
     "    # Create formula, predicting target from the sum of all others\n",
     "    formula = \"{} ~ {}\".format(target_variable_name, str.join(\" + \", column_names))\n",
+    "\n",
+    "    # file model\n",
     "    model = smf.ols(formula=formula, data=sample).fit()\n",
     "    \n",
+    "    # return parameters\n",
     "    return model.params\n",
     "\n",
-    "\n",
     "# example: \n",
-    "print(run_experiment(boston))\n",
-    "\n",
-    "# Run many times, join results in a dataframe\n",
+    "print(run_experiment(boston))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                  0          1          2          3          4          5   \\\n",
+      "Intercept  30.954893  29.482438  63.669066  47.937148  67.411532  55.298316   \n",
+      "CRIM       -0.423893  -0.116215  -0.140651  -0.112825  -0.108579  -0.131324   \n",
+      "ZN          0.056272   0.035793   0.031952   0.039153   0.056150   0.027036   \n",
+      "INDUS      -0.114741  -0.140088   0.075940   0.258160  -0.073999  -0.338520   \n",
+      "CHAS        1.776149  -2.361702   1.727301   3.999287  -0.725574   4.393273   \n",
+      "\n",
+      "                  6          7          8          9   ...         90  \\\n",
+      "Intercept  56.054093  36.350839  15.943197  11.052787  ...  36.087031   \n",
+      "CRIM       -0.121410  -0.076621   0.144527   0.065873  ...  -0.042352   \n",
+      "ZN          0.063205   0.001910   0.033304   0.036226  ...   0.036209   \n",
+      "INDUS      -0.085492  -0.194690  -0.005662  -0.180609  ...  -0.096581   \n",
+      "CHAS        5.249656   0.953616   8.765477   4.858239  ...   1.628504   \n",
+      "\n",
+      "                  91         92         93         94         95         96  \\\n",
+      "Intercept  43.346224  16.095004  61.455237  49.993994  50.472227  11.074460   \n",
+      "CRIM       -0.156501  -0.007188  -0.158715  -0.056281  -0.060648  -0.142484   \n",
+      "ZN          0.049933   0.024346   0.010920  -0.008770  -0.004610   0.005185   \n",
+      "INDUS      -0.002879   0.203502   0.066096   0.115108  -0.054104   0.129790   \n",
+      "CHAS       -0.616311   1.438113   3.782876   1.380794   2.705159   4.882245   \n",
+      "\n",
+      "                  97         98         99  \n",
+      "Intercept  57.851000  48.151587  51.476203  \n",
+      "CRIM        0.014961  -0.175709  -0.142935  \n",
+      "ZN          0.078409   0.108368  -0.018502  \n",
+      "INDUS       0.045358   0.159122  -0.043249  \n",
+      "CHAS        7.653074   2.138669  -1.729407  \n",
+      "\n",
+      "[5 rows x 100 columns]\n",
+      "Number of columns is 14\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1080x1080 with 15 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Run experiment many times and concatenate parameter outputs into one data frame\n",
     "results = pd.concat([run_experiment(boston) for i in range(100)], axis=1)\n",
+    "print(results.head())\n",
     "\n",
+    "# retrieve columns\n",
     "columns = results.index.values\n",
     "n_columns = len(columns)\n",
-    "\n",
-    "n_columns\n",
-    "\n",
+    "print(\"Number of columns is \" + str(n_columns))\n",
     "n_rows, n_cols = 5, 3\n",
     "\n",
     "# Output histogram plot for each varaible\n",
-    "# A plot with n_rows rows, n_cols cols, and sized so each is 5 by 3 (inches)\n",
     "fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols, figsize=(n_cols * 5, n_rows * 3))\n",
     "\n",
+    "# we can plot all values of each variable for all 100 simulations (row-wise)\n",
     "for row in range(n_rows):\n",
     "    for col in range(n_cols):\n",
     "        plot_number = (row * n_cols) + col\n",
@@ -2207,6 +1593,14 @@
     "        ax.hist(results.loc[column_name], bins=30)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- we can see that some variables have a narrow range, while a few (INDUS, RAD, DIS) have a wide range"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2235,7 +1629,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -2249,7 +1643,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.4"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.4.1 - PCA Introduction.ipynb b/notebooks/Module 1.4.1 - PCA Introduction.ipynb
index ca2f8f6..47a1ce1 100644
--- a/notebooks/Module 1.4.1 - PCA Introduction.ipynb	
+++ b/notebooks/Module 1.4.1 - PCA Introduction.ipynb	
@@ -57,14 +57,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -87,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -96,7 +96,7 @@
        "(200, 2)"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -116,17 +116,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
-       "    svd_solver='auto', tol=0.0, whiten=False)"
+       "PCA(n_components=2)"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -139,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +147,7 @@
        "array([0.97634101, 0.02365899])"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -168,7 +167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -178,7 +177,7 @@
        "       [-0.32862557,  0.94446029]])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -198,12 +197,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -236,7 +235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -245,7 +244,7 @@
        "(2, 2)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -256,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -286,14 +285,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-0.61045903 -0.22815074]\n"
+      "[array([-0.61045903]) array([-0.22815074])]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<__array_function__ internals>:180: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n"
      ]
     }
    ],
@@ -312,22 +318,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.patches.FancyArrow at 0x28028d61508>"
+       "<matplotlib.patches.FancyArrow at 0x1f93539beb0>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -387,26 +393,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([[ 0.70710678,  0.70710678],\n",
-       "       [ 0.70710678, -0.70710678]])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(200, 2)\n",
+      "Principal Component Analysis(nobs: 200, nvar: 2, transformation: Standardize (Correlation), normalization: True, number of components: 2, SVD)\n",
+      "[[ 0.70710678  0.70710678]\n",
+      " [ 0.70710678 -0.70710678]]\n"
+     ]
     }
    ],
    "source": [
     "from statsmodels.multivariate.pca import PCA\n",
+    "\n",
+    "# find shape of input data\n",
+    "print(X.shape)\n",
+    "\n",
+    "# compute PCA\n",
     "model = PCA(X)\n",
-    "components = model.eigenvecs\n",
-    "components"
+    "print(model)\n",
+    "\n",
+    "# retrieve eigen vectors\n",
+    "print(model.eigenvecs)\n"
    ]
   },
   {
@@ -427,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -439,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -452,7 +464,7 @@
        "       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -463,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -472,7 +484,7 @@
        "(1797, 64)"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -483,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -492,7 +504,7 @@
        "0"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -503,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -515,7 +527,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1296x144 with 1 Axes>"
       ]
@@ -541,7 +553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -553,7 +565,7 @@
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1296x144 with 1 Axes>"
       ]
@@ -572,7 +584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -581,7 +593,7 @@
        "0.0"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -602,12 +614,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 950.4x172.8 with 16 Axes>"
       ]
@@ -637,17 +649,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=None,\n",
-       "    svd_solver='auto', tol=0.0, whiten=False)"
+       "PCA()"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -660,7 +671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -669,33 +680,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\IPython\\html.py:14: ShimWarning: The `IPython.html` package has been deprecated since IPython 4.0. You should import from `notebook` instead. `IPython.html.widgets` has moved to `ipywidgets`.\n",
-      "  \"`IPython.html.widgets` has moved to `ipywidgets`.\", ShimWarning)\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "34866f27c3a447a3a89c90f04438b48e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(IntSlider(value=0, description='i', max=1796), Output()), _dom_classes=('widget-interact…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "%matplotlib inline\n",
     "from custom import figures\n",
     "# Move the slider to view a different sample from the database. i is the index of the data point.\n",
     "figures.plot_pca_interactive(X)"
@@ -725,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -754,22 +743,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x280387f7608>"
+       "<matplotlib.collections.PathCollection at 0x1f935eb7280>"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -795,22 +784,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PathCollection at 0x2803a185c48>"
+       "<matplotlib.collections.PathCollection at 0x1f9367fe500>"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -837,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -846,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
@@ -855,7 +844,7 @@
        "(0.5529411764705883, 0.8274509803921568, 0.7803921568627451, 1.0)"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -866,7 +855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -875,7 +864,7 @@
        "(0.7450980392156863, 0.7294117647058823, 0.8549019607843137, 1.0)"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -886,22 +875,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x2803a2847c8>"
+       "<matplotlib.legend.Legend at 0x1f9367ffcd0>"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 864x864 with 1 Axes>"
       ]
@@ -939,22 +928,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1797, 64)\n"
+     ]
+    },
     {
      "data": {
+      "image/png": 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KR4FHusd1c1T/q4GHu/ofA27r+n8Q+BywBHwceN60ax3z57gGuHeeau/q/Hz32H/qv9V5OXe6Wi8H9nXnzyeAl8xq/d76L0mNmNclF0nSCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNeL/ANf6oUqVmwerAAAAAElFTkSuQmCC",
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x28039efc7c8>]"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1000, 50)\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -966,12 +971,34 @@
     }
    ],
    "source": [
-    "# Fit a PCA model to the data\n",
-    "full_pca = PCA(n_components=64)\n",
-    "full_pca.fit(X)\n",
+    "# qs 1\n",
+    "print(X.shape)\n",
     "\n",
-    "# Plot the cumulative sum\n",
-    "plt.plot(np.cumsum(full_pca.explained_variance_ratio_))"
+    "# perform pca on x\n",
+    "pca = PCA(n_components = 64)\n",
+    "pca.fit(X)\n",
+    "\n",
+    "# show cumulative variance with number of features\n",
+    "plt.bar([i for i in range(X.shape[1])],\n",
+    "        height = np.cumsum(pca.explained_variance_ratio_))\n",
+    "plt.show()\n",
+    "\n",
+    "# qs 2\n",
+    "# generate a 1000 x 50 datasset\n",
+    "X2 = np.array([np.random.normal(0, 1, 1000) for _ in range(50)]).T\n",
+    "print(X2.shape)\n",
+    "\n",
+    "# perform pca on x\n",
+    "pca = PCA(n_components = 50)\n",
+    "pca.fit(X2)\n",
+    "\n",
+    "# show cumulative variance with number of features\n",
+    "plt.bar([i for i in range(X2.shape[1])],\n",
+    "        height = np.cumsum(pca.explained_variance_ratio_))\n",
+    "plt.show()\n",
+    "\n",
+    "# for random dataset, it seems that PCA doesnt explain too much\n",
+    "# for real dataset, it would give a similar shape as the first barplot"
    ]
   },
   {
@@ -984,7 +1011,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -998,7 +1025,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.4.2 - PCA Interpretation.ipynb b/notebooks/Module 1.4.2 - PCA Interpretation.ipynb
index 1302bb1..5f76f03 100644
--- a/notebooks/Module 1.4.2 - PCA Interpretation.ipynb	
+++ b/notebooks/Module 1.4.2 - PCA Interpretation.ipynb	
@@ -44,6 +44,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -64,10 +65,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1797, 50)\n",
+      "(64, 50)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# exercise\n",
+    "# Whiten ensures uncorrelated outputs with unit component-wise variances\n",
+    "# Whitening will remove some information from the transformed signal (the relative variance scales of the components) \n",
+    "# but can sometime improve the predictive accuracy of the downstream estimators by making their data respect some hard-wired assumptions.\n",
+    "\n",
+    "# extended exercise\n",
+    "from sklearn.datasets import load_digits\n",
+    "digits = load_digits()\n",
+    "X = digits.data\n",
+    "\n",
+    "def PCA(X, n_components = 10):\n",
+    "\n",
+    "    # normalize data to 0 mean\n",
+    "    mu = np.mean(X, axis=0)\n",
+    "    X = X - mu\n",
+    "\n",
+    "    # compute covariance\n",
+    "    temp = np.matmul(X.T, X)\n",
+    "    \n",
+    "    # eigen decomp\n",
+    "    eig_vals, eig_vecs = np.linalg.eig(temp)\n",
+    "    \n",
+    "    # sort by eigen value, largest eigen value shows most significant eigen vectors\n",
+    "    order = np.argsort(eig_vals)[::-1]\n",
+    "    components = eig_vecs[:, order[:n_components]]\n",
+    "    \n",
+    "    # use the most significant eigen vectors to transform X into the PC space\n",
+    "    Z = np.matmul(X, components)\n",
+    "    \n",
+    "    # return output\n",
+    "    return Z, components\n",
+    "\n",
+    "# try function with some random data\n",
+    "x_pc, vecs = PCA(X, 50)\n",
+    "print(x_pc.shape)\n",
+    "print(vecs.shape)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -138,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -154,14 +201,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Wall time: 5.54 s\n"
+      "CPU times: total: 3 s\n",
+      "Wall time: 4.73 s\n"
      ]
     }
    ],
@@ -169,6 +217,74 @@
     "%time my_function()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Sparse PCA Fraction explained variance: 0.9811575037906795\n"
+     ]
+    }
+   ],
+   "source": [
+    "# qs 1\n",
+    "from sklearn.decomposition import PCA, SparsePCA\n",
+    "\n",
+    "# PCA\n",
+    "digits_pca = PCA()\n",
+    "digits_pca.fit(X)\n",
+    "plt.bar([i for i in range(X.shape[1])],\n",
+    "        height = np.cumsum(digits_pca.explained_variance_ratio_))\n",
+    "plt.title(\"PCA\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Randomized PCA\n",
+    "digits_pca = PCA(svd_solver='randomized', whiten=True)\n",
+    "digits_pca.fit(X)\n",
+    "plt.bar([i for i in range(X.shape[1])],\n",
+    "        height = np.cumsum(digits_pca.explained_variance_ratio_))\n",
+    "plt.title(\"Randomized PCA\")\n",
+    "plt.show()\n",
+    "\n",
+    "# Sparse PCA\n",
+    "digits_pca = SparsePCA()\n",
+    "digits_pca.fit(X)\n",
+    "P = digits_pca.components_.T  # loadings\n",
+    "T = digits_pca.transform(X)  # score\n",
+    "Xc = X - X.mean(axis=0)  # center data\n",
+    "explained_variance = np.trace(P @ T.T @ T @ P.T)\n",
+    "total_variance = np.trace(Xc.T @ Xc)\n",
+    "print('Sparse PCA Fraction explained variance:', explained_variance / total_variance)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -182,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,22 +308,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f1ab6a8efd0>]"
+       "[<matplotlib.lines.Line2D at 0x1f5dfd285e0>]"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -232,7 +348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -241,7 +357,7 @@
        "(200, 2)"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -255,17 +371,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "PCA(copy=True, iterated_power='auto', n_components=2, random_state=None,\n",
-       "  svd_solver='auto', tol=0.0, whiten=False)"
+       "PCA(n_components=2)"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -278,17 +393,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([[ 0.25040349,  0.96814157],\n",
-       "       [ 0.96814157, -0.25040349]])"
+       "array([[ 0.26000816,  0.96560642],\n",
+       "       [ 0.96560642, -0.26000816]])"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -299,12 +414,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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zOUZTzGZxOkYQGsYKFbYuT+Patm6np647XXNX6gps6E7TOXGo/nrxhjtoSbik4y5lP6SrOc7dq9oYnCjyyuksXgCdTS4fvmOp3kxV15UGd6Wu0Mt7z6+333bP/4adijJRqj55uqmnGYCY6/AzGxfr7ktq1mhwV+oy9WWy/OvzR/jJs+crWt9613s5WXSYKPms70rRmozWSxB8+I6ls9hbtdBpcFfqMnx3/wBffPIImf1PEwbVWXqyezVlJ8lnfqa6GceuA4MMjBfpaYnrMoyadRrclbqEvkyWLz5xBAQmjrxYb29fv40DZybYuecYj97bq0sw6oaiwV2pS9h1YBA/NLQmXQZf/2m9PdG7lWyxwtHhHJ/4+xfpaI6Rjjts7E6zY1OXztzVrNLgrtQlDIwXaU26nD0zQHHoJABiuwSd6xjNe0wUPCbLAZMln2TUJuHa7NxT4NF7ezXAq1mjee5KXUJPS5y4a3Fs//kbqfFlN1MmQhCGFP0Q1xFsS5go+hwZzpOOu7rrkppVGtyVuoQdm7oYnKhQOfFKvS3RuwVboOSFlP0QExoMhogjnJ0oabkBNes0uCt1CRu60yxdFCXbcDO199a7MUBoIOpYINX9Ur2gunmHlhtQs02Du1KXoaU0SHlyDIBkcwsdK27CtQQBjAkJQ0NoDLmyT1siouUG1KzTG6pKXQY5s79+vGzTnZwcK+FYgusIQQh+UN1xybJg28pF/Oo9K/RmqppVlwzuIrIM+Dugi+pm2DuNMX8hIq3A14GVVDfI/pAxZkxEBPgL4CGgAPyaMebFC322Ujeyvky2/mDSd773g3q7vexWEhGblkQM17YYK1QoVAIitsUDGzr5o1/cPIu9VqrqcpZlfOAzxpiNwN3AJ0VkI/BZ4AljzFrgidprgPcDa2tfjwJfmvZeKzXDpnZcyhY92uPCkVfP57d3rL+DuGtxLlfBC0J6WuIsXxQn6tp89J4Vs9hrpc67ZHA3xmSmZt7GmEmgD+gBHga+UrvsK8DP144fBv7OVO0FWkSke7o7rtRMmtpxKR13OXHwJfxKGYBEx1KaO7pxbcGEIcdH8rwxlKPohbxnTZsuxagbxhXdUBWRlcBW4DmgyxiTqZ06S3XZBqqB/1TD207X2t76WY+KyD4R2Tc8PHyl/VZqRk3tuARw+MVn6u2L1t7OknSMwckKlmXRHHOwRSh6Ae9e0zZb3VXqbS47uItICvhn4LeNMRON54wxhup6/GUzxuw0xmwzxmzr6Oi4krcqNeN6WuJM1sr4Hn7hJ/X2FbfczUjBY3FzjIhrYYyAwG3LWzg8mJ+t7ir1NpcV3EXEpRrY/8EY8y+15sGp5Zba96Fa+wCwrOHtS2ttSs0ZOzZ1kS16ZAaHGDhyEACxbG654x5yJZ+WhMuyRQl6FsVZ3ZFieVtSH1pSN5RLBvda9suXgT5jzOcbTj0GPFI7fgT4dkP7x6TqbiDbsHyj1JywoTvNo/f2Mvj6Pqr/MIX1t2whnkrj2kLZCyh5ge6Rqm5YlzNzfzfwUeB+EXm59vUQ8MfAdhF5A3iw9hrge8BRoB/4a+AT099tpWbehu40kcHX6q8/9PAHePTeXm5e0sxYsbpko3ukqhvVJfPcjTFPA3KR0w9c4HoDfPIa+6XUrGjMbV+SjvG9Xefz27dv386G7jT//Rc3v+k63SNV3Yj0CVWlaqZy29Nxl+50jGNHj3DmdDXxK5pI8s+nYvx09+F6rXYN5upGprVllKppzG0fyZV55sdP1s8tuel2lrY1kS167NxzjL5MdhZ7qtSlaXBXqmYqt/3w2Ql+8NogR19+tn4uumILo/lyPfhrrXZ1o9PgrlRNT0ucE+fyPH98DM/zmDz2cv1catVW+oeqeexaq13NBRrclarZsamLw4M5yl5A9mQfYbkAgNPUxkS0k+HJEqC12tXcoMFdKc5nyWRLHmU/IHf0pfq5ZO9W/MAwNFniu/sz7D0ywrqu5Cz2VqlL0+CuFrzGCpDLFsWJOBaFhiWZRO8WQqBYCclXPBanozzeN6w3VdUNTYO7WvAas2TWdKZwwwqlM6/Xz7vLtxCPWHQ1x+hsinF2okwYhnpTVd3QNM9dLXgD40W60zEAOppipMcOQxgAEOtaRSLdSsyxaUtFiLk2AJlsCdexZ63PSl2KztzVgtdYAXJ4ssTxV/fWzyVWbaXih5T9gNF8hULFJ+pYjOQrelNV3dA0uKsFb6oCZLbo0T+UY+TQvvq55MqtYKDkh+TLPgNjRYZzZVzb0loy6oamwV0teFMVINNxl5OnTpMfOgmAOC7p3luIuBZioOSFVIKQiaLPp+5freUH1A1Ng7tSVAP8p7evo7dytN6WXH4zTakEccciHrGJR2yaYy5bl7fwgc1v21xMqRuK3lBVC15jhcf9z/1bvb117e1UfINYFitbY8Rdm5F8hZuX6Ixd3fg0uKsFqy+T5avPnuDp/hEWJVw2LE5yYv/5m6mL1m7DAO2pCHHXZqLk61q7mjM0uKsFaerBpaPDORbFq/8Z7HnuJfLZUQDizYt437vuJDRwdqLEaN7DsYRPPaBr7Wpu0OCuFqSpB5e8wJCK2ogI2f4X6ueXb7qT33jfanYdGCTi2tyzOl6v467UXKDBXS1IUw8upWIOZS8gCA0Drz1XP7/61rt1Qw41p2m2jFqQph5cWtORJFuocHI4y8SxV+vnU6tv19oxak67ZHAXkb8RkSEROdDQ9l9FZOAtG2ZPnfs9EekXkUMi8rMz1XGlrsXUg0sRxyYZcyidOojxKwC0di9nw9pVWjtGzWmXM3P/W2DHBdq/YIzZUvv6HoCIbAQ+Atxce8//FBEtwKFuOI0PLuXKAVamcdZ+GwfPZHntjM7c1dx1yTV3Y8weEVl5mZ/3MPA1Y0wZOCYi/cCdwLPv/Dalrr/GNfX/83+eLzmw9Oa7mCj5TJR8+jLZGV13n8qxf+1MlomSTzrusLE7rTdv1TW7lhuqvyUiHwP2AZ8xxowBPcDehmtO19qUumHd1e0wfOIQAGLZpFdX67ev70qx68DgNQXZxgekelrenHEzlY4ZBCGnR4sgkC1USLg2O/cUePTeXg3w6qpd7Q3VLwGrgS1ABvjzK/0AEXlURPaJyL7h4eGr7IZS16Yvk+Wv/vExMAaApmXraWpOc/uKFpa3Ja9pr9TGTUC60zGyRY+de47Vb9ROpWOenSwTdS3ScZeYa3N2oqybcKtrdlUzd2NM/bdORP4a+E7t5QCwrOHSpbW2C33GTmAnwLZt28zV9EOpazEVfA88f77kQMf6O1jTmaQ9VQ3G11LWt3ETEKD+fepfA1PpmLmSTypqky/7jOYr5Cs+xhiaa9crdTWuauYuIt0NL38BmMqkeQz4iIhERaQXWAs8f21dVGr69WWy/OFjB3nl1BiHXnym3m4vu5UfvDbIU4eGODGSv6ZSAwPjRZpib54/NcWc+r8GptIxUzGHsUKFTLZExQ9JRqqlDk6PFTUdU121y0mF/EeqN0TXi8hpEfk48Kci8qqI7AfuAz4NYIx5DfgGcBDYBXzSGBPMWO+Vugp9mSx/9oPDvDE4ycDxoxRGq/8QtSJxEj3rqfjVX1lL5Jp+TuMmIFMmS379XwNT6ZhxRzg9VmSiWCFXrtavgfNr/kpdjUsGd2PMrxhjuo0xrjFmqTHmy8aYjxpjbjHGbDbG/JwxJtNw/R8ZY1YbY9YbY74/s91X6sr9/bMnOHYujwDjDSUH4ituwVgOK1qTvG99J8taE9cUXBs3AQmNqR9P/WtgQ3eaBzd0MDhZQUSIOBYRxyJXDljVnrjmNX+1sGn5AbXgvHQqSypqM1mC/LGX6+3RFVsRYOoGUOMSytWYyqWfypaJ2ELCtfjy08frmTOHB/PcvaqN5rhL2QuIuTYlL2Ak79HZMMtX6kppcFcLjsEgCGEQUjzxSr090buFJYtiBLXMmclpCK5TufTf3T/AF588gheEtCUjeH7Azj0FcmWPmxY3s6YjyYsnxwGI2MK5XJls0ePDdyy9pp+vFi4N7mrB2bqsheeOjlI8c5iwXADAaW5n0ZJeghCaY059CWU6gmtfJssXnzgCAm3JCGOFCkeG8yQiNoExJFyble0pblveQv9wntFchbZUVPPc1TXR4K4WnI/es4JMtsTxx1+qt7Wt28Zty9KcnaiQTlTTFz98x9JpCa67Dgzih4bWpEuhEjCa9xDAD0JsS+oz9uVtSSKOTbboaWBX10yDu1pwNnSn+Z0d6/nx58/Xk9m47d3curyV353Gx/6nnk7915cH8PwQ24LRfIWS5xMa8IKQRYkIUcfmpyfGKHgBG7vT0/ZHRS1sGtzVgrQ0ZXHq9fPr7V/73KN0dnZO2+dPPSCVjrt0NUU5NZrnyFCOwIAlIEAIlLyAzqYIJd+QirpaU0ZNGw3uakF66qmn8P1qDvr6jZv4h1fGGRjPvK3+y9VqfDq1LRnh9bOTiAgYgzEQAKmIRSLicC7nsbI9WS85oMFdTQfdrEMtSLt3764fp1bddtH6L1er8enUkYLH4uYYjm1hC0Rdi4gttZRLQ9ELWNOZvObUS6Ua6cxdLTh9mSxf/9fv1l8v3XTnReu/XOz9F6v0OKWnJU626FXrxZd8WhIu2aIH2HQ0xTg+kq89CSssb01MSy0bpRrpzF0tKH2ZLH/+rb0MnToKgOW45FrWci5Xql/zTjPoS1V6nNL4dGoqWq0VE3eryzC2JXQ3R0lEXZJRh1uWNr/t6VWlrpXO3NWCsuvAIMOv/7T+un3NrTjRGP1DedpTMeCdH156a6XHih9wdDjHZ76xn+0bu1jXleTwYJ6B8SJx18LzA5rjLhMln1uXtRCP2LxwYozByQpNUZtkxOZcrsLG7rhmyahppcFdLSgD40VOHXiu/nrjtneDgXO5MqExTJb8iz681JfJsvvgIKEJSccjtCVdjp4rELWF0IQcG87xLy+e5rbl1VrwkyWfEyN5lqSrfzQyE2UsDLZlcd/6jjddc26y9KayBBrk1bXSZRm1oHQ3R3n9hZ/UXzvLt9DVHKEtFSWTLZGOuxd8gGhqOca1hahtUfICnjs2hjEGREjHI5ydLJOMOpydKGOJUPEDTo4UOHBmgg3dzWzsbma84LO+K8XK9tTbrpnOG7pK6cxdLRh9mSxPPP08xYlRAJxEGi+9jKPnCnz2/ev5wOaL7wg5tRxz85JmXjw5TtQRQmMYy1doTUXZ1NPMU4eGGS9UOBIYTo0VSLg2qahNJQixREjHXfzQkMmWWNmeAqB/OP+2a6Z+ns7e1bXQ4K7mvb5Mlq8+e4InXh/i6LN76u2xFbcyXvTZsqyFw4N5PvAOnzG1a5Ilbr0GjC2CFxpuX9HCaK7C8GQZgKhtUfZChifLdDdH6W5J1D+nNekykq/UX+dKPo4FzbHzuy5pSqSaDhrc1bw2tZxydDgHxlA4dr6eTMf6bUQdm0IluGQwbUxt7GiK0dEUY3FTlMNDOVzb5pXTWSK2RSUIiboWUUewRBicLPPedR31z1ncHGOitq5f8nxGcmXyZZ/e9iTnciXaU7FpqUaplK65q3ltajnFCwxepULx5IH6uUXrbscPQ0bylUsG0wttvHEmW8T3A7710mnOTpRwLFjeGifm2lQCQ1PUQkRwbbv+Htu2+NT9q6n4AT85Mkpz3KG9KYoXGvYdH+P4uZymRKppoTN3Ne80PmR08MwEty5rJhVzKJx6DeNXl0Tc1h7spg5CA65tXTKYTm288dVnT/B43wjZfIVsyaezOcq6zhQHzkyQKwe0NwlLF1WXYbJFjy6ruo4+9cDTHSurS0AvnRpnUcJlU08zxlAv9ZuZKPO5n9uo6+3qmunMXc0rb33IyLWF546OEbFg9PD5LfWSvVspeCFNMYdP3b/6soNp0Qu5q7cNLzT4YcjAWJETIwXaki4iwunxAmEYMpgtcqahBMHH37OSHZu6eLxvuLYkE5AZL/K/Xsnw6uksazuTvP+WxSxvTWhgV9PikjN3Efkb4IPAkDFmU62tFfg6sBI4DnzIGDMmIgL8BfAQUAB+zRjz4sx0Xam3e+tDRjcvaWbPoWEOZCYpHT+/3t605jbuXdvGpx5Ye9nBdOqzK37AeNHDEQjFMFbwsCwh6kDZN5waK+IFhrWdScLQ8N39GX5w4CyrOhJ0Nsep+AETRR8BbIGj53KcHCuwuDnG7SsWzcT/LGoBupyZ+98CO97S9lngCWPMWuCJ2muA9wNra1+PAl+anm4qdXkaC3YBdDTFSCddgnyWyTP9AIhl8+8efj9ru5ovO7BPPcC09+g5fnx4GMcSAmPwQwiNwRbwQmiOR3jgpk7evbqNfCWk7IdEHWE4V2Z33xD7jo3w6kCW1qRLJQgpeAF+aLBFGJwoMzhR1hx3NS0uOXM3xuwRkZVvaX4YeF/t+CvAU8Dv1tr/zhhjgL0i0iIi3caYzLT1WKl30JjVMqXsGVIjfVDbG3XFTZvpaFt0wQyZCxUFA+oPMGEsBisBYgx+UH2PbVU31Q5CuKWnmWePjlDyQopegGOBF0DEqZb7PXIujx8aWmIOtoAlAkDMtYm6FstaE5rjrqbF1d5Q7WoI2GeBqbtRPcCphutO19o0uKvrYsemLnbuOQZU88UnSz6OJUwcOb86uO62d3HiXJ6zk2X+0z+98rYgno679adF/3TXIc5NlhkrVih5ASUvxBiD69h4YXVHJaSa9riyLc7idIwXT44jAgnXZqLk4YeG0FiIQBAaHEuYLFffG3ctlrclsUSIubbmuKtpc803VGuzdHOl7xORR0Vkn4jsGx4evtZuKAWcz2pJx916OYHfun/Vm+rJjKbX8/jrQ2TzFVyb+iP/f//sifp6fWNpgBOjBQrlAGMES8C2hEIlQBBWdyTZ2N3MstYEd61qpS8zSVdzFMey8AJDaAQBCpWARMQhHXdIRmyCsPo5tmVhiVD2Q9Z0JjXHXU2bq525D04tt4hINzBUax8AljVct7TW9jbGmJ3AToBt27Zd8R8HpS5mQ3e6vqzRl8ny1R88x+S5swBY0QR0rmVpKkYsYvPSySy3r2ghHXd5/tgoD2yobrU3PFniqcPDFMsBhYpPKloNyo4lOLbQmhByFZ+Sb4hHYOvyNK5tM1bwePeaVsbzHs8fr9aesaS6r54lwuJ0vPbdYIzh+EgBOP/+ixUtU+pKXW1wfwx4BPjj2vdvN7T/loh8DbgLyOp6u5otU2mRL/34yXpbasVmohGXlkSkuu0d0D+Up7c9wdBkie+9miHu2uTLPsVKgGNTn3nblhCxLQqVgK6mGGu6mvjMz6yrr9F3Nrm8d00brmOzbnGcRckIr5zOcnIkT0SgPRWpz9JvX9GCa9vcuqyFjqZY/f1a9ldNl8tJhfxHqjdP20XkNPCHVIP6N0Tk48AJ4EO1y79HNQ2yn2oq5K/PQJ+Vuiy7DgwSBCF9LzxTb2tefRsj+QrJqENLIkLUsRicKHJussyihIstFplsiSAMsS3BC2BRMkLZC6n4IaEBx6pmyJwZL7HrwOCbSvRO/UEBaEtFuWNlK51NUeKOxYHM5Jtm+dmid8EKlEpNh8vJlvmVi5x64ALXGuCT19oppabDwHiRM2M5Rt84n9/uLN+MF4ScnSgTc20KZY/hyTKC0NueZFlrnHP5agEwEViUcHFsYTSs4AXQHHMIQvBDw+0rm+vr9VNB+q1PsgrC1mVpfvWeFQBvmuXrLF3NJC0/oOatnpY4P97zDH4pD4Db3I7buhTLFiqez3ihwnCugmNZLFsUw7KEo+cK1eqPQCU0bF1WrQBZrIREXYNlCS21sgFTOzeN5sr84WMHWd6aoKclzrquZP1J1qmMnak/AJ/evm4W/xdRC4kGdzVvretKMvDa+SyZRO9WjAhC9aaoFxq2b+gkM1FmLF9mvFimUKnmpkcdh7ZUdROPiGPT0xLn0Xt7+fLTx2ulf6vr9cOTJQ4P5vDCkLt6W8kWPb74xBHWL05d0abbSk03De5qXurLZHm8b5jg9Cv1tsjyLcQdC8uyaEm4lP2Q5W1JKn7I65kJIrZFzBGyRZ98OcBgeKJviJWt1ZujX376OCdHC3h+8KbNNhCIuzbPHRslV/IZnCxhW9SvAa3Rrq4/De5q3unLZPnP/7Sfk4MjDB05X+I32XsrRS8kFbOYLPlYUt0Me6Tgsbg5Rq7iM1n0MMDidIyOVJTFzTGeOz5KS8xFrOpDSCdHqss8y9uSjOYqeEGAJYJtCYKh4gccOjuJJcLmZWmt0a5mhQZ3Na/0ZbL86a5DnBgpUD6xH8JqjQC3cxUm3lJ9utQSKkGIseDUaIHRXIVFCYd4xKbshSxJRUjHXSbLPoVzeYrlAAysaEtQ9kMK5YCjw3lcx6Y1FSFbqBBzbYLQcHaijGNZ+FZ1O71KEHLT4iYsy9L8dXVdaXBX88JUTZgfHjzLaK6CiGHo9X318/GVWwDwDXihoas5Rsy16WqOMlmu7ojUnoqSjjuk49UlG1uEI+fyhEGIF1ZrxSQi1bV4P4Q/++Vb6ctk+Y2vvkjUsRjNVxDAsS2WplyypYAgRGu0q1mh9dzVnNdYwx0DhUr1AaR8w5Z6sZVbAXAEJkseZyequexDk9XAu3lpC11NUUpeyOtnJzl2Ls9YvowJDVJ7uvTMeIlCpVqq19QqbmzoTvPeNW2UvOqOTrmyhy3g2DarO1Jao13NGp25qzmvsYZ7U9wlHDUEuRH80dPVC2yX6NKNAIRAGELEtjg1ViSTLXN0OMeDGzr44pNHsKS6M1MQhuQrARFHKFYMyaiFLTA0USIVdVnXleILuw8zMF4kX/KYKHmkog62Va0OmcmWWN2htWLU7NHgrua8gfEi3ekYw5Ml8mWfkhcy0X++CmRs6c3YbrT6wlTLCWSLHsYYoq7F73/rAMsXJVjflWJle4rhyRI/PDhIqRIQhCGOBSUvQKjWbPeCkNfPGiJOtaLjnsFJbBFSCZvhnEfUEVoTUQbGS0RdR9fa1azQ4K7mvJ6WOMeGcxweylWDsS0Uj79cPx9buQVLIDDV8qWOVHdAChC8wCCEnB4vUglCUjGHjqYYi5tjnB4r4joWS9IxMuNFsuWAlphDd0sMP4RDgzlSMQcvMLSlIsQjDnf2ttE/nGey6FEJQi0voGaNBnc1563rSvL3e0/gBWH1ywsonXi5fj6+cguBAau2dt7ZFGGs4GHCkKC2k1LSshAR+ofytKdiGKrtccciGXVwHZtkCN0tcUIDzTGbsh/SP5QnFXMoV3wmSh4dTTE6mmL1DUM0sKvZojdU1ZzWl8nyzRcG8IKQYsWn6IWUho8TFqpb1VnxZlI9q2mKOjTFHCyBfDmoFwEDwEBoqksv53JlQmOYKHpgDGU/5Oi5PJUgZGlrjMCYajD3Q6KOxUTJY01Hklw5IGJbhMaQLXpki159AxClZoMGdzWnffXZE5wcKRB3barbYkDx2Mv187EVt+KHQsy1EISu5mg1x51qYTDHFhzbojsdw7WFtlSUvswEhUpAW1OUdV1N9LRUa7AXvZDmmMuajiRlP2Si5NMUdYg4NsvbEmxa0lzfIESXY9Rs02UZNWf1ZbJ879UMubJPEJ7fDqx0/M0pkKGBiaJXDeRWNavG8wMmy9UHnJakY8Qci7Giz+d+biO7DgyScG0OD+XqM/SmqM1IrsLmnjRtqSjrOlMcHsyRTlQ/73d2rNdgrm4oGtzVnDT1JGqu5BNyPrAbv0L59Gv16+K9W4Dq+vm9aztwbZtnjoyQiDp0tyQQgVzJ5+xEmWTUZteBQQ5msty0uJlUzKF/OE+u5NPZHKOrOcbK9hQD40V6O1L85n2rNaCrG5YGdzUn7TowyGi+gmNbVPyw9mARlE4fxPgVANzWHpzmTiK2sL6riXWLzwfiw0M5tq1speT5/PTYGMmow7quJE8dGuLYuTwnzxXY1ruIe1a1AdRvkGrJXjVX6Jq7mpMGxotMlDwM5k0z91JDCmSydyuOJSxJx9i87HxgX9GeZOmiOOm4yyuns6RiDjctTnF8pFq1saspwmCuzLNHRhmaLOoNUjUnaXBXc1LEFsby1XIDjb/Ejevt6TW3s7Itwa3LWuoba0C1EuTNS9J8evs6li6K4wchPzo0zLFzOTLZIhHXpiVeza555dSE3iBVc5Iuy6g5SYBkxCZX9kDABir5LJXBo9Xzls03PvfvaWpO1+vOTO2KlC16fPiOpfRlsrxxdpLRgoep/ZHIFn2KlZAVbQnuXddBJlvSpRg1J+nMXc1J5cBw7/p2UlEXW6ppjcHp/Uwt0HSv3cSd65fX9zRNx923pSnuOjBIxLFrtdhBLMESKHo+QxMlvv/qWU6OFujLZGd3sEpdhWsK7iJyXEReFZGXRWRfra1VRHaLyBu174ump6tKndfTEifqOKxb3MTGJWluX9GKe/b8xhyb7njvJT9jYLyIbQk9LTHiEZsgMISmenO25IfYFnQ3R9m555gGeDXnTMfM/T5jzBZjzLba688CTxhj1gJP1F4rNS36Mlm+sPswBzNZ9h4ZIe4IZS9kvFB5U/32j/3SB+vXTy3LdKerZQGmgnVPS5yIY+HYFqs7mljb1YRTK0OQijlsW7mIle3VvVB3HRicrSErdVVmYlnmYeArteOvAD8/Az9DLUCNgfqmxc2sX5xicLJCOu5gTZ6lNFYNwMlUEx966H7gfDngih/w3LFRnj82ytHhHF999gQ7NnXRmoyQK/mUKtVt90SqhcLuW99Rvwmr+5+quehab6ga4IciYoD/xxizE+gyxmRq588CF8wfE5FHgUcBli9ffo3dUAvBVKAemijyw4NZ8mWfqGOxqj3BvcE5dtWue+D++3BdF6guvTgWvHwqS9SxSEVtxvJlvrM/w3CuTHc6RtyxODZaQBB62xKs6ki+LbtGa7KrueZag/t7jDEDItIJ7BaR1xtPGmNMLfC/Te0PwU6Abdu2XfAapRpVN8ao8MyRUVzbqmbLlHx+eHCIx77/jfp1Dz74YP24pyXOU4eGiDoWMdcmX/YZzntEbItswaOnJUE04vD5D93Khu70m/518NbsGqXmkmtaljHGDNS+DwHfAu4EBkWkG6D2fehaO6kUVAP1CyfHcW2LmGvhBSElPyQMfDJ959fbV916d/14x6auanlfYzDGMDxZBgOL09W9U6d2cJpaU3+n7Bql5pKrnrmLSBKwjDGTteOfAf4P4DHgEeCPa9+/PR0dVWrHpi7+3387SjJqY4whW/CphIZK5jBhuQBANN3BM8MRPlB7z9QepwfOTDBZ9gmMYemiGLZlkXRt4O1r6hu60xrM1Zx3LTP3LuBpEXkFeB74rjFmF9Wgvl1E3gAerL1W6ppt6E6zsj1JEEK+EuCFppq22FDit3Xd7bx8euJN7/vVe1awqiPFXb1trGpP4odQ9kPWdCYBXVNX89NVz9yNMUeBWy/QPgI8cC2dUupifu7WxfzF4/0UvbBeT6bQUHIgvnIrhjffwplaatl1YJDmuMtEyWd9V4rWZLReN0bX1NV8o+UH1JzRl8my9+gYMdem6IUAhOUC5TOH6tfYS29h67KWt723camlL5Nl14FBBsaL9LTE+fAdS3UZRs07GtzVnHG+zK+QiFRL/WZPvQphddMNt3MVYbSZo+fyfGH3YXZs6rpg0NY1dbUQaG0ZNWcMjBep+CFBaIi7NgYoN5T4jfduoSXhcHKkwHf3Z/j0117hu/sHZq2/Ss0mDe5qzpgqF2Bbgm1Vvxcb1ttb19yOwaLoBbQmXRD44pNHtC6MWpB0WUbNGTs2dbH/9DgjuQq5so8/MYI3choAsV1aV29GgETEplAJmCh6jOUrfPwr+7h5STMbu9MXXapRar7RmbuaMzZ0p/mdHeu5d107i5IulZMv18+1rd5M2Tjkyh7ZQoUjwzmyhQrU9kg9OVLg+LmcVnhUC4YGdzWnbOhO899/cTM/+k/38d5Ept6+5Oa7qAQhrm2BVDfELvohjghNMYeYa3N2oqwVHtWCocFdzUlhGPL444/XXz+4fTvvWtVKxLGp+AbHEhxL8MKQ1mSEqGMxUfK0wqNaMHTNXc1J+/fvZ3h4GICOjg6y8W4KpYCmmE3Fr6ZGRhyprcE7lLyA5pirT6OqBUNn7mpOapy13/Guezk9Vmai5NPZFGNxOoaI0BxziDg22aJHyQtY3Fx9InXHpgtWoVZqXtGZu5qT/vU7368fvyYrWFHxsSxhLAzJlwOCMGSsELKmM0U84pCOO6xsT2m2jFowNLirOeflY4Ps/ckz9dfJ3q2MFz1cC3wjiEBzzMW2hTWdTVqyVy1IGtzVnNGXyfLVZ0/wzf+1i8ArA5DsXEZLZzdlL6To+aSi1Rl6yQuIuXY9O0aDu1poNLirOaEvk+VPdx3i5EiB8cPnN+Zwlt2KUN3v0QtMdQMPL6Dsh2zqadbsGLVg6Q1VNSdMFQ2zLJg42ljidwsj+QqpqI1rCwYh5trcvqKF9lRMs2PUgqUzdzUnDIwXGZ4skTk7RPnskWqjWESW3YIfhGSLPjcvSdMUc1nWmqAp5mitdrWgaXBXc0K+5DGYLZM7/grUNuOILlmPRJNYlsWipMvnHr4ZQGu1K4UGdzVHnB4rYllQaijxG1u5BUtgZWuCHbd014O4BnOldM1dzRETZZ9ExKbUUOI32bsVCyj6oT6YpNRbzFhwF5EdInJIRPpF5LMz9XPUwtDVHKMyMoA/US05IJE4iZ71RFyL96xp09m6Um8xI8FdRGzgL4H3AxuBXxGRjTPxs9TC8MBN7QzuO/9UamLFZkKxWdWe5KP3rJjFnil1Y5qpmfudQL8x5qgxpgJ8DXh4hn6WWgAKFUPp4FP11/7kOezDT7I8ktdZu1IXMFPBvQc41fD6dK2tTkQeFZF9IrJvqrqfUhdzYmiM4sRI/XX57BHe+JfP86XffIi1a9fyiU98gm9961uMj4/PXieVuoHMWraMMWYnsBNg27ZtZrb6oeaGhJelpXMJ2eEMxrz516W/v5/+/n6+9KUvYVkWd955J9u3b2f79u3cfffduK47S71WavbMVHAfAJY1vF5aa1Pqqnz0Z++iGP8uTVGbwUP7OHboIIdf/AmDh16iVDpfXiAMQ/bu3cvevXv5b//tv5FKpXjf+97Hgw8+yPbt29mwYQMiMosjUer6kLfOgqblQ0Uc4DDwANWg/lPg3xljXrvQ9du2bTP79u270Cml6voy2Tc9oLRjUxerWmP85Cc/Yffu3ezevZsXXnjhbTP7Rj09PfVA/+CDD9LVpSmUau4SkReMMdsueG4mgnvthz4E/N+ADfyNMeaPLnatBnc1XUZGRnjyySfrwf748ePveP3mzZvrSzjvfe97SSQS16ejSk2DWQnuV0KDu5oJxhiOHDlSD/RPPvkk2Wz2otdHIhHe85731IP91q1bsSx9zk/duDS4KwX4vs++ffvqwf7ZZ5/F9/2LXt/W1sYDDzxQD/YrVmg+vbqxaHBX6gJyuRw//vGP68H+4MGD73j92rVr6+v1H/zgBzULR806De5KXYaBgQEef/xxdu/ezeOPP87g4OAFr2tqamJkZESDu5p17xTctSqkUjU9PT088sgjPPLIIxhjePXVV+uz+j179lAsVlMu77vvPg3s6oanwV2pCxARNm/ezObNm/nMZz5DuVyup1zedttts909pS5Jg7tSlyEajXLfffdx3333zXZXlLosmuellFLzkAZ3pZSahzS4K6XUPKTBXSml5iEN7kopNQ9pcFdKqXlIg7tSSs1DN0T5AREZBk5cpx/XDpy7Tj9rNsz38cH8H+N8Hx/M/zFer/GtMMZ0XOjEDRHcrycR2XexWgzzwXwfH8z/Mc738cH8H+ONMD5dllFKqXlIg7tSSs1DCzG475ztDsyw+T4+mP9jnO/jg/k/xlkf34Jbc1dKqYVgIc7clVJq3pu3wV1E/i8ReV1E9ovIt0SkpeHc74lIv4gcEpGfbWjfUWvrF5HPzkrHr4CI/LKIvCYioYhse8u5eTHGRnO5741E5G9EZEhEDjS0tYrIbhF5o/Z9Ua1dROR/1Ma8X0Ru+GLyIrJMRH4kIgdrv5//odY+n8YYE5HnReSV2hg/V2vvFZHnamP5uohEau3R2uv+2vmVM95JY8y8/AJ+BnBqx38C/EnteCPwChAFeoEjgF37OgKsAiK1azbO9jguMcYNwHrgKWBbQ/u8GWPDmOZs3y8wlnuB24ADDW1/Cny2dvzZht/Xh4DvAwLcDTw32/2/jPF1A7fVjpuAw7Xfyfk0RgFStWMXeK7W928AH6m1/xXwm7XjTwB/VTv+CPD1me7jvJ25G2N+aIyZ2tp+L7C0dvww8DVjTNkYcwzoB+6sffUbY44aYyrA12rX3rCMMX3GmEMXODVvxthgLvf9TYwxe4DRtzQ/DHyldvwV4Ocb2v/OVO0FWkSk+7p09CoZYzLGmBdrx5NAH9DD/BqjMcbkai/d2pcB7ge+WWt/6xinxv5N4AERkZns47wN7m/x76nODKD6S3aq4dzpWtvF2uei+TjGudz3y9FljMnUjs8CXbXjOT3u2vLDVqoz23k1RhGxReRlYAjYTfVfluMNk8rGcdTHWDufBdpmsn9zeps9EXkcWHyBU39gjPl27Zo/AHzgH65n36bL5YxRzS/GGCMicz6NTURSwD8Dv22MmWicqM6HMRpjAmBL7X7et4CbZrdHbzang7sx5sF3Oi8ivwZ8EHjA1Ba7gAFgWcNlS2ttvEP7rLnUGC9iTo3xMr3TmOaDQRHpNsZkaksSQ7X2OTluEXGpBvZ/MMb8S615Xo1xijFmXER+BNxDdUnJqc3OG8cxNcbTIuIAaWBkJvs1b5dlRGQH8DvAzxljCg2nHgM+Urt73QusBZ4Hfgqsrd3tjlC96fHY9e73NJmPY5zLfb8cjwGP1I4fAb7d0P6xWkbJ3UC2YWnjhlRbS/4y0GeM+XzDqfk0xo6pDDwRiQPbqd5b+BHwS7XL3jrGqbH/EvBkw4RzZsz2XeeZ+qJ6E/EU8HLt668azv0B1fWxQ8D7G9ofonpn/wjVZY9ZH8clxvgLVNf1ysAg8IP5Nsa3jHfO9v0t4/hHIAN4tf//Pk51/fUJ4A3gcaC1dq0Af1kb86s0ZEXdqF/Ae6jeXNzf8N/fQ/NsjJuBl2pjPAD8l1r7KqoTqX7gn4BorT1We91fO79qpvuoT6gqpdQ8NG+XZZRSaiHT4K6UUvOQBnellJqHNLgrpdQ8pMFdKaXmIQ3uSik1D2lwV0qpeUiDu1JKzUP/P2Ug9susq9nHAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -370,11 +485,113 @@
     "\n",
     "Solution is in `solutions/PCA Application Example Solution.ipynb`"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<AxesSubplot:xlabel='Date'>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import quandl\n",
+    "quandl.ApiConfig.api_key = 'hPGPuToY_GBPFpx5zsyM'\n",
+    "bonds = quandl.get(\"RBA/F02\")\n",
+    "bonds.head()\n",
+    "bonds.plot()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "number of PCs needed to explain 0.99 variance is 2\n",
+      "i = 0: R2 = 0.9981153462659237\n",
+      "i = 1: R2 = 0.987137256108799\n",
+      "i = 2: R2 = 0.9822917399083556\n",
+      "i = 3: R2 = 0.9770998172100794\n",
+      "i = 4: R2 = 0.961494752126558\n",
+      "i = 5: R2 = 0.9731953678315978\n",
+      "i = 6: R2 = 0.9035102148817923\n",
+      "i = 7: R2 = 0.8381543964513842\n",
+      "28.0760733951652\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels.api as sm\n",
+    "\n",
+    "# qs 1\n",
+    "train = bonds.iloc[:len(bonds)-1].copy()\n",
+    "test = bonds.iloc[len(bonds)-1:].copy()\n",
+    "\n",
+    "# qs 2\n",
+    "pca = PCA()\n",
+    "pca.fit(train)\n",
+    "vars = np.cumsum(pca.explained_variance_ratio_)\n",
+    "\n",
+    "# qs 3\n",
+    "print(\"number of PCs needed to explain 0.99 variance is \" + str(np.where(vars>0.99)[0][0] + 1))\n",
+    "\n",
+    "# qs 4 # qs 5\n",
+    "# Predictions for values. I'll just append here, and transpose at the end\n",
+    "estimators, Y_pred, Y_real = [],[],[]\n",
+    "\n",
+    "training_pc = pca.transform(train)\n",
+    "\n",
+    "for i in range(pca.n_components_):\n",
+    "\n",
+    "    # Here, we lag X compared to Y. X will be one time period behind Y\n",
+    "    X, Y = training_pc[:-1, i], training_pc[1:, i]\n",
+    "    \n",
+    "    X = sm.add_constant(X)\n",
+    "\n",
+    "    # Fit an OLS model and get a summary\n",
+    "    ols_fit = sm.OLS(Y, X).fit()\n",
+    "    print(\"i = \" + str(i) + \": R2 = \" + str(ols_fit.rsquared))\n",
+    "\n",
+    "    estimators.append(ols_fit)\n",
+    "    Y_real.append(Y)\n",
+    "    Y_pred.append(ols_fit.predict(X))\n",
+    "\n",
+    "# qs 6\n",
+    "Y_real_x = pca.inverse_transform(np.array(Y_real).transpose())\n",
+    "Y_pred_x = pca.inverse_transform(np.array(Y_pred).transpose())\n",
+    "\n",
+    "# qs 7\n",
+    "print(np.sum((Y_real_x - Y_pred_x)** 2))"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -388,7 +605,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.5.1 - Bayesian Statistics Introduction.ipynb b/notebooks/Module 1.5.1 - Bayesian Statistics Introduction.ipynb
index 3fd9042..5f9b790 100644
--- a/notebooks/Module 1.5.1 - Bayesian Statistics Introduction.ipynb	
+++ b/notebooks/Module 1.5.1 - Bayesian Statistics Introduction.ipynb	
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -252,7 +252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -260,9 +260,54 @@
      "n": "3"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 792x648 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "%run -i bayesian_updating_plot.py"
+    "# error in the original code so had to fix and rerun here\n",
+    "import scipy.stats as stats\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "dist = stats.beta\n",
+    "n_trials = [0, 1, 2, 3, 4, 8, 15, 50, 500, 5000]\n",
+    "data = stats.bernoulli.rvs(0.5, size=n_trials[-1]) \n",
+    "x = np.linspace(0, 1, 100)\n",
+    "\n",
+    "# For the already prepared, I'm using Binomial's conj. prior.\n",
+    "for k, N in enumerate(n_trials):\n",
+    "    sx = plt.subplot(int(len(n_trials)/2), 2, k+1)\n",
+    "    plt.xlabel(\"$p$, probability of heads\") \\\n",
+    "        if k in [0, len(n_trials)-1] else None\n",
+    "    plt.setp(sx.get_yticklabels(), visible=False)\n",
+    "    heads = data[:N].sum()\n",
+    "    y = dist.pdf(x, 1 + heads, 1 + N - heads)\n",
+    "    plt.plot(x, y, label=\"observe %d tosses,\\n %d heads\" % (N, heads))\n",
+    "    plt.fill_between(x, 0, y, color=\"#348ABD\", alpha=0.4)\n",
+    "    plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n",
+    "\n",
+    "    leg = plt.legend()\n",
+    "    leg.get_frame().set_alpha(0.4)\n",
+    "    plt.autoscale(tight=True)\n",
+    "\n",
+    "\n",
+    "plt.suptitle(\"Bayesian updating of posterior probabilities\",\n",
+    "             y=1.02,\n",
+    "         fontsize=14)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
    ]
   },
   {
@@ -277,6 +322,68 @@
     "2. Rerun the analysis with a biased coin (i.e. one with an 80% chance of heads). Observe how the Bayesian analysis probabilities change in the new plots."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 792x648 with 10 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import scipy.stats as stats\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "dist = stats.beta\n",
+    "n_trials = [0, 1, 2, 3, 4, 5, 15, 50, 500, 5000]\n",
+    "data = stats.bernoulli.rvs(0.8, size=n_trials[-1]) \n",
+    "x = np.linspace(0, 1, 100)\n",
+    "\n",
+    "# For the already prepared, I'm using Binomial's conj. prior.\n",
+    "for k, N in enumerate(n_trials):\n",
+    "    sx = plt.subplot(int(len(n_trials)/2), 2, k+1)\n",
+    "    plt.xlabel(\"$p$, probability of heads\") \\\n",
+    "        if k in [0, len(n_trials)-1] else None\n",
+    "    plt.setp(sx.get_yticklabels(), visible=False)\n",
+    "    heads = data[:N].sum()\n",
+    "    y = dist.pdf(x, 1 + heads, 1 + N - heads)\n",
+    "    plt.plot(x, y, label=\"observe %d tosses,\\n %d heads\" % (N, heads))\n",
+    "    plt.fill_between(x, 0, y, color=\"#348ABD\", alpha=0.4)\n",
+    "    plt.vlines(0.5, 0, 4, color=\"k\", linestyles=\"--\", lw=1)\n",
+    "\n",
+    "    leg = plt.legend()\n",
+    "    leg.get_frame().set_alpha(0.4)\n",
+    "    plt.autoscale(tight=True)\n",
+    "\n",
+    "\n",
+    "plt.suptitle(\"Bayesian updating of posterior probabilities\",\n",
+    "             y=1.02,\n",
+    "         fontsize=14)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Probabilities become skewed towards 0.8.\n",
+    "- More samples shows more precise distribution"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -361,7 +468,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -375,7 +482,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.5.2 - Bayesian Inference Basics.ipynb b/notebooks/Module 1.5.2 - Bayesian Inference Basics.ipynb
index eff653f..a275cae 100644
--- a/notebooks/Module 1.5.2 - Bayesian Inference Basics.ipynb	
+++ b/notebooks/Module 1.5.2 - Bayesian Inference Basics.ipynb	
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 23,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 24,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -98,7 +98,7 @@
        "0.9"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -120,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 25,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -143,7 +143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 26,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -163,7 +163,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 27,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 28,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -202,7 +202,7 @@
        "0.05000085"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 29,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -228,7 +228,7 @@
        "1.799969400520191e-05"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -239,7 +239,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -254,7 +254,7 @@
        "1.052632520776466e-07"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -265,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -280,7 +280,7 @@
        "0.9999820003059948"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,7 +291,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -306,7 +306,7 @@
        "0.999999894736748"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -316,18 +316,92 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Exercises\n",
     "\n",
     "1. Check that substituting $x$ above correctly leads to the sum of both model probabilities (given spots and given no spots) to be 1\n",
-    "2. We learn from public medical records that 80% of people with chickenpox have spots and that 1 in every 10,000 people have chickenpox. (As before, 1 in every 20 people have spots.)\n",
-    "3. Suppose $x$ (the probability of spots without chickenpox) were much higher, like among teenagers. How would this affect the posterior probability of chickenpox? First, consider this intuitively, then run the code again to check your understanding.\n",
+    "2. We learn from public medical records that 80% of people with chickenpox have spots and that 1 in every 10,000 people have chickenpox. (As before, 1 in every 20 people have spots.). Suppose $x$ (the probability of spots without chickenpox) were much higher, like among teenagers. How would this affect the posterior probability of chickenpox? First, consider this intuitively, then run the code again to check your understanding.\n",
     "\n",
     "Repeat the above steps for chickenpox."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n",
+      "1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# qs 1\n",
+    "print(posterior('smallpox', 'spots', normalize=True) + posterior('not smallpox', 'spots', normalize=True))\n",
+    "print(posterior('smallpox', 'no spots', normalize=True) + posterior('not smallpox', 'no spots', normalize=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0\n",
+      "1.0\n",
+      "0.00039988003598920324\n",
+      "0.00015999040057596546\n"
+     ]
+    }
+   ],
+   "source": [
+    "# qs 2\n",
+    "# p(chicken pox) = 1/10000, p(spots | chicken pox) = 0.8, p(spots) = 0.05\n",
+    "# x: p(spots | no chicken pox)\n",
+    "# p(chicken pox | spots) = p(chicken pox) * p(spots | chicken pox) / p(spots) = 1/100000 * 0.8 / 0.05\n",
+    "def prior(data):\n",
+    "    if data == \"chicken pox\":\n",
+    "        return(1e-4)\n",
+    "    else:\n",
+    "        return(1 - 1e-4) \n",
+    "\n",
+    "def likelihood(data, model):\n",
+    "    if data == 'spots':\n",
+    "        return {'chicken pox': 0.8, 'not chicken pox': 0.2}[model]\n",
+    "    else:\n",
+    "        return {'chicken pox': 0.2, 'not chicken pox': 0.8}[model]\n",
+    "    \n",
+    "def evidence(data):\n",
+    "\n",
+    "    return (posterior('chicken pox', data, normalize=False) +\n",
+    "            posterior('not chicken pox', data, normalize=False))\n",
+    "\n",
+    "print(posterior('chicken pox', 'spots', normalize=True) + posterior('not chicken pox', 'spots', normalize=True))\n",
+    "print(posterior('chicken pox', 'no spots', normalize=True) + posterior('not chicken pox', 'no spots', normalize=True))\n",
+    "\n",
+    "# if probability of p(spots | no chicken pox) is higher, then p(chicken pox | spots) would decrease \n",
+    "print(posterior('chicken pox', 'spots'))\n",
+    "\n",
+    "# re-define likelihood function with different probabilities\n",
+    "def likelihood(data, model):\n",
+    "    if data == 'spots':\n",
+    "        return {'chicken pox': 0.8, 'not chicken pox': 0.5}[model]\n",
+    "    else:\n",
+    "        return {'chicken pox': 0.2, 'not chicken pox': 0.5}[model]\n",
+    "\n",
+    "print(posterior('chicken pox', 'spots'))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -354,7 +428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -364,7 +438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 36,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -379,7 +453,7 @@
        "0.00039988003598920324"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -428,7 +502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 37,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -446,7 +520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 38,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -467,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 39,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -482,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 40,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -494,10 +568,10 @@
     {
      "data": {
       "text/plain": [
-       "0.20613038097752118"
+       "0.20613038097752093"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -515,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 41,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -531,16 +605,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.3999999999999999"
+       "0.4"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -551,7 +625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 43,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -567,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 44,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -578,7 +652,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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PQzjiUEtLi9IhEI0Lczd6mA2hXrxerx8209CzPVAI8za6DC6kwgJ6VMzd4bEHmoiIwpbQX0BzLmhSG71WQCM4hIMmhgU0ERGF7XQPNAtoUhchBEx6LXugaUJYQBMRUdhYQJOaGXUaLuVNE8ICeooEgzwwlTLSVE40OuauctS07xMMoVto+qJoNUIe+8pQ43436bXw+KPnw58a92EsmMh+ZwE9BSwWC+rq6uD1enlQRJiUEm1tbTCZTEqHokrMXWVIKeH1elFXVweLxaJ0OGOSoI+uHmiTyYS2tjbmbYSp9ZyboI+eaRiZu8qYaO5yFo4pkJubi9bWVpw8eRJ+f/T0zsQLk8mE3NxcpcNQJeaucnQ6Hex2O1JTU5UOZUyibQhHbm4uamtrOWuAAtR4zk1M0KOrz6d0GACYu0qaSO4qWkALIRwAHgcwH6Hlcm+XUu5UMqbJoNFokJ6ejvT0dKVDIQoLc5fGKtpm4dDr9SgqKlI6DFKJJLMerS6v0mEAYO6qldJDOB4F8JqUcjaAEgCHFI6HiIjGINp6oInCkWQ2oKM3OgpoUifFeqCFEHYAqwDcBgBSSi8AZjMRkQqY9aE/H71eDvUh9XGYDejoYclB46dkD3QRgBYATwgh9gkhHhdCnHX3jBDiTiFEmRCijOODSC2Yt6RWY83daBvCQRTOeTfJrEePNwAvF1OhcVKygNYBWALg11LKxQB6APzgs42klJuklKVSytK0tLRIx0g0LsxbUqux5q5Bp4FOI9DLxSgoSoRz3nVYDACATg7joHFSsoCuBVArpfyo//vnECqoiYhIBRIMWvZAkyolmfUAgI7e6JiJg9RHsQJaStkIoEYIMav/qUsBVCoVDxERhcfMAppUKskc6oHmjYQ0XkrPA/1NAFuEEAYAxwF8ReF4iIhojMwGHYdwkCo5+nugOYSDxkvRAlpKWQ6gVMkYiIhofBL0WvRxFg5SoYEe6PYeDuGg8VF6HmgiIlIpsyF6lkMmCgeHcNBEsYAmIqJxSWABTSqVYNDCpNdwCAeNGwtoIiIaF95ESGoWWo2QQzhofMIeAy2EMALIBpAAoEVKyVUiiIjiUOgmQo6BJnVymA3sgaZxG1MPtBDCJoS4WwjxLoAuAEcBHADQKIQ4JYR4TAixbCoDJSKi6MJ5oEnNksx69kDTuI1aQAsh7gVQDeB2AG8CWAdgEYBiAOcB+BFCPdlvCiFeE0LMnKJYiYgoipj1HANN6pVkNqCjhz3QND5jGcJxLoALpZQHhnl9F4DfCSG+DuAOABcCODJJ8RERUZRymPXo9Qbg9gVg0muVDocoLA6znrNw0LiNWkBLKa8fy4aklB4Av5pwRERxxOMPoNvtR6rVqHQoRGFLt5kAAC3dHuQlmxWOhig8KRYDOvt88AWC0Gs5pwKFZyxDOFKEEL8VQjQKIfxCiDYhxAdCiP8WQpwTiSCJYtXDrx7GRf+9TekwiMYlzRb64Nfi8igcCVH4sh0JkBJo7HIrHQqp0Fg+cm0GsBLABgA3A/gugCUIDdXYIYTYJoSYNnUhEsUue4IeLo8f/kBQ6VCIwjZYQHezgCb1yU0KXTWp7ehTOBJSo7GMgb4QwDmfHgMthHgUwI0AnADuA/CBEGKVlLJqasIkik2JCaFDsNvtR5LFoHA0ROFJ7y+gm1lAkwrlJCUAAOo6WUBT+MbSA10HIGWoF6SUrVLK7wD4KYBHJzMwonhgT9ADALr6OJUSqU+yxQAh2ANN6pTtCI3hr+3oVTgSUqOxFNCPAnhilHme/wzggskJiSh+JJpYQJN66bQapFiMaOnmGFJSH6NOi3SbEXUcwkHjMJZZODYKIbIAfCiE2AbgLwgV3vJTzW4BwBUJicJkN4cKaKebBTSpU5rNyB5oUq3cpASOgaZxGdO8LVLKHwJYjlCR/F8ILeNdKYSoEUJ0AvgBgO9MVZBEsYpDOEjt0m1GjoEm1cpNMnMMNI3LWG4iBABIKcsA3CiE0CG0EuEsAHYArQDellK2TkmERDGMQzhI7dJsRlQ1dSsdBtG45CQl4JX9DQgEJbQaoXQ4pCJjLqAHSCn9AMr6H0Q0AQM90M4+v8KREI1Pms2IVpcHwaCEhgUIqUxuUgL8QYnmbjey7AlKh0MqMpaFVIrGujERkjexkIjih0mvgUGrYQ80qVa6zQhfQKKTOUwqlOMIFc0cB03hGssY6J39KxGeN1wDIUSSEOJuAJUA1k1adEQxTgiBxAQdC2hSLS6mQmpWkGIBAJxo7VE4ElKbsQzhmA3ghwD+JoQIAtgDoB6AG0ASgLkA5gDYBeDbUsrXwwlACKFFaDhInZRybTg/SxQLEhP0nIWDVCvdFppLt7nbjVmZNoWjIQpPfrIZJr0Ghxs5jp/CM2oPtJSyU0r5XQA5AL4O4BAAB4AiAH4AfwCwWEq5Itziud+3+rdJFJfsCXo42QNNKpXbv5rbyTYuRkHqo9UIzEy3sYCmsIUzC0cfgOeEEH9FqFcaAA5LKcc9g74QIhfAlQA2ALh3vNshUrNEkx4dvV6lwyAalyy7CWaDFkebXUqHQjQuszNteOcwl7Kg8IxpHugBQojLAdQA2Nv/aBNCPC6ESB/n+z8C4HsAgiO8551CiDIhRFlLCxOc1CGcvGUPNEWTcM+5QghMT7PiWAsLaFLWeOuFWZk2tLo8aHVxHD+NXVgFNICNALYDmAkgA8CX+v+9N5zZOgBACLEWQLOUcs9I7aSUm6SUpVLK0rS0tDDDJVJGOHlrT9DzJkKKGuM5585It+IYe6BJYeOtF+ZkJQIAh3FQWMItoPMB3CelPC6lbJVSviilvBDAOwAeDXNbKwBcLYSoBvAMgEuEEJvD3AaR6iUm6OB0+yGlVDoUonGZnmZBfZcbPR7OZ07qM3Dz66EGp8KRkJqEW0DvR+hmws96CMAl4WxISnmflDJXSlkI4EaEVjO8Ocx4iFTPnqBHICjR4w0oHQrRuMxItwIAh3GQKqVajUi1GvEJe6ApDOEW0E8B2CiEmP6Z59MAdExOSETxhct5k9oNFNC8kZDUqiTXjr0nWcbQ2IW7lPfP+r8e6p+NYx9CRfgXAXxnvEFIKbcB2DbenydSs9PLefsGV8UiUpP8ZAu0GsEeaFKtZUXJ+PsnzWh1eZBqNSodDqlAuAV0GoBFn3rcAGAWAAHgASHEtQA+BvCxlPLlSYuSKIYNFNDsgSa1Mug0KEq14FADL4GTOi0rTAYAlFW3Y/X8LIWjITUIq4CWUrYB+Hv/AwAghDACmI/TRfUahKamc0xSjEQxLdlqAAA0cylkUrEl+Q68UdmEYFBCoxFKh0MUlgU5dhh1Guw60cECmsYk3B7os0gpPQgt7z3idHRENLSCZAsAoLq1R+FIiMavtCAZfyqrxfFWF2akc0lvUheDToNFeQ6UnWxXOhRSiXBvIiSiSZZg0CLbbmIBTaq2tDAJAFBWzRuxSJ2WFyXjYL0TXb0cTkejYwFNFAUKUy04zgKaVGxaqgXJFgPKOJMBqdSFs9IRCEpsq2pWOhRSgZgooA/UdeHKn7+Hfad44o5nm949hut/s1OVC5IUpVpQ3cYCmtRLCIEl+UnYwwKaVGpRngMpFgP+fogFNI0uJgpovVaDg/VO1Hb0KR0KKehwowu1Hb0QQn03MBWlWtDZ60NHj1fpUIjG7dxpyTjR2oOa9l6lQyEKm1YjcPHsdGw73AxfIKh0OBTlYqKAzkw0AQCanG6FIyElNXe7kd6fC2pTlBq6kZDDOEjNLpuTAQB4s7JJ4UiIxufS2elwuv3YXc2bCWlkMVFAJyboYNRpOA1YnGvsciMjUZ0T4BemciYOUr/CVAtmpltZQJNqrSpOg9mgxV8r6pUOhaJcTBTQQghkJJrQ2MUe6HjW5HQPXo1Qm7wkM7QagRMsoEnlLp+bgV3V7ejs5XAkUh+LUYfV8zPx8scNcPsCSodDUSwmCmggNIyDQzjiV583AKfbr9ohHAadBgUpZnzS6FQ6FKIJ+dy8TASCEq8fbFQ6FKJxuXZxLrrdfrx1iFdSaHgxU0CnJxo5hCOODXx4ylBpAQ0AywqSsetEOwJB9c0iQjSgJNeOGelWPLO7RulQiMblvOkpyLKb8CxzmEYQMwX0wBAONU5hRhM3UECrdQgHACyflgyn24/Djd1Kh0I0bkII3LgsD/tOdTKXSZW0GoEvnZOP94604kgTc5iGFkMFtBF9vgC6PX6lQyEFNA72QKvzJkIAWD4tBQDw0Yk2hSMhmphrl+TCoNXgqY9OKh0K0bh8aXk+DDoNnvigWulQKErFUAEd6nls5jjouNTsDA3fUesYaADIcSQgNykBHx3n9EmkbskWA64qycazZTVoc3FoHalPitWI9Yty8PzeWrRweCgNIeYK6MYuJno8anK6kaDXItGkUzqUCTl3Wgo+OtEGPyfxJ5W7+6Lp8PiD+O2OE0qHQjQuX79oOnwBid9sP6Z0KBSFYq6A5kwc8anRGZoDWo2rEH7aZXMy0NHrw/vHOIyD1G1GuhVXLMjCHz6oZg8eqVJRqgXrF+dg84cnOU0unSWGCujQ2NembiZ5PGp2elQ9fGPAxbPTYE/Q4y97a5UOhWjC7r28GB5/EP/zxmGlQyEal29dOhNSAv/9OnOYzhQzBbTZoIPNpOOnxDjVqOJFVD7NqNPiyoVZeP1gE3p4Qyyp3PQ0K247vxDPltWgoqZT6XCIwpaXbMYdFxRh695a7D3VoXQ4FEUUK6CFEHlCiHeEEJVCiINCiG9NdJsz0q2orOdCFPGmq9eHmo5eTE+zKh3KpLh2cQ76fAE8v69O6VCIJuyfLpuJdJsR332ugiu7kSr948UzkJFoxA//cgBeP+9PoRAle6D9AL4jpZwL4FwA9wgh5k5kg8sKk/FxbRdP0nFm76kOSAksK0pSOpRJsbQgCYvyHPjNtmPw8WZCUrlEkx4Pf2Ehqppc+Ckvg5MKWYw6PLhuPg41OPHrbbyhkEIUK6CllA1Syr39/+4GcAhAzkS2WVqQBG8giAN1XZMRIqlE2cl26DQCi/IcSocyKYQQ+NalM1HX2YfnORaaYsBFs9Jxy7kFeHzHCbx2oEHpcIjC9rl5mVi3KBu/ePsI9nEoByFKxkALIQoBLAbw0US2s7Qg1AO5u5rJHU92V3dgXo4dZoO6p7D7tItmpaEkz4H/fr0KXX0+pcMhmrD7187BojwHvvOnChysZycHqc9/XD0fmXYT/vGpfejq5Xk53ileQAshrAC2Avi2lPKsAcxCiDuFEGVCiLKWlpYRt5ViNWJ6mgVl1VyIIl54/AFU1HRiWUF0Dd8IJ2+H+XlsuGY+2ns8+K9XP5mCCImGNtHcHY5Rp8X/3bIU9gQ9bntiN0619U7atomAqcvdAXazHhu/tATN3W7849N7OV9/nFO0gBZC6BEqnrdIKZ8fqo2UcpOUslRKWZqWljbqNpcVJmNXdTs8fo6Djgd7T3bC4w+itDBZ6VDOEG7eDmV+jh13rCzC07tO8bI3Rcxk5O5wMhJN+MPt58AXCOKLj32ImnYW0TR5pjJ3ByzKc+Cha+bjvSOt+I+XKyGlnJL3oein5CwcAsBvARySUv5ssrZ7xYIsdLv9eONg02RtkqLYn/fUwGbUYVVxqtKhTIl/+fwslOQ58C9//hiHG7uVDodowmZm2LD5juVwefz4h9/sZF6T6tywLB93rpqGP+48iY1vH1U6HFKIkj3QKwDcAuASIUR5/+OKiW505YxU5CYl4JndpyYeIUW1rj4fXtnfgKsXZcfU+OdPM+q0+PVNS2A2aHHLbz/CybYepUMimrD5OXY8e9e5CEqJ637zAbZXTf7ldqKp9IPVs3Ht4hz8z5tV2PQuZ+aIR0rOwrFDSimklAullIv6H69MdLsajcANpXl4/2gbTrSy2Ihlf9lbC7cviC+ek690KFMq25GAzV9dDm8giH/4zU4cauBc56R+szMT8fw3zkeOIwFfeWIXNr59BMEgL4eTOmg0Aj+5biGuXJiF/3zlE2x8+wiHc8QZxW8inAo3LMtDgl6Ln7zGm69iVbfbh43vHENpQRLm59iVDmfKFWfY8Oyd50EjBP7hNzvx6n6OiSb1y00y4/lvnI+1C7Px0zeqcPNvP0JdZ5/SYRGNiU6rwaM3LML6xTn46RtV+PFfKxHgh8C4EZMFdHqiCd+4aDpePdCID462Kh0OTYFfvH0UrS4P/v2qCa29oyqzMm34yz3nY3q6FXdv2Yv7nt8PF5f7JpUzG3R49MZFePgLC1BR04nV//sunvzwJHujSRV0Wg3+5x9K8NWVRfj9B9W44w+7OfVonIjJAhoAvrZqGvKSE/Dd5z5Gm8ujdDg0id4/2orH3zuO60tzsTDXoXQ4EZVlT8Cf7joXd66ahmd2n8Jl/7MdL5bXsdggVRNC4IZl+Xjt26tQkufAv71wANf86n3sOckpSSn6aTQC96+diw3r52PHkVZcvXEH5zqPAzFbQJv0WvzqS0vR4vLgnqf2cnnvGHG8xYV/fGovpqdZ8e9XzVM6HEUYdVr86xVzsPXu85FqM+Bbz5Tj6l/uwBsHG1lIk6rlJZvx5B3n4NEbF6HJ6cYXfr0Tdz1ZhqomztRB0e+m5QV49q5z4fYFsP6XH+C3O07wnBzDYraABoAFuXb85AsL8dGJdtzxh9283K1yVU3duGHThxBC4P9uWQqrMTZn3hirJflJePGelfjZ9SXo6vPhzif34PL/3Y4/7qyG081LiKROQgisW5SDd/7lIvzzZcV4/2gbPv/Iu7hny14cqGOvHkW3pQXJePVbq3DBzFQ8+HIlvvT4h6jmhAYxKaYLaAC4ZnEOfnpdCXYea8M1v3wfR9iToUovf1yP9b98HwDwzJ3nYlqaVeGIooNWI3Dtkly8852L8MgNi2Ax6vDvLx7EORvewjef3oe3Kpu4qBCpktmgw7cum4n3vncx7r5wOrZXtWDtL3bgS499iDcONvJmLYpayRYDHr+1FP917QIcrHPi84+8i1/8/QivhMcYoaZpV0pLS2VZWdm4fvaDo6345tP70O324xsXT8edq6bF7NzBsaSmvRf/+cohvHqgEYvzHfjVTUuQZU8Y9/aEEHuklKWTGOKoJpK34ZJSYn9dF57dXYO/7W9AZ68PVqMOFxan4aJZaVhVnIaMRFNEYqHJFeu5O5quPh+e+ugU/rizGg1dbmTbTbh+WR7+oTQPOY7xnxNoaimRt0D05G5jlxv/8fJBvLK/EfnJZty3ZjZWz89EaC05imaj5W7cFNAA0NLtwY//ehAvf9yANJsRX7ugCDeek49Ek34So6TJUN3ag9/uOIFnd9dACOCfLp2JO1dNg147sYsm8VSEeP1BfHCsFa8fbMTfDzWjuTt0M+2MdCuWFyVjWWEyluQnIS85gSdzFYin3B2JPxDEm5VNeGrXKbx3pBVCAOdNS8G6RdlYPS8LdjPP59Ek3gvoAe8dacFDLx/C4aZuLMpz4DufK8bKGak890YxFtBD2F3djp+9UYWdx9uQoNfiyoVZWLcoG+dOS5lwgUbj19Xnw5uVTXhhXx12HG2FQavBtUty8E+XzkT2JPUwxWsRIqVEZYMT7x9txQfH2lBW3TF4T0CKxYAFuXbMz7ZjbnYiZmfaUJBigVbDE3s0idfcHUlNey+e31uHv+yrRXVbL/RagZUzUrF6fiYunZOBVKtR6RDjHgvo0/yBIJ7fW4f/fasKDV1uLMl34J6LZ+DiWenQ8HwbdVhAj2B/bRee2nUSL1c0oNvjR6JJhwuK07ByRiqWFyWjKNXCT4dTyOsP4kB9Fz483ob3qlqxu7od/qBEblICri/Nw43L8pA+ycMNWISEBIISnzQ6se9UJypqOrG/rgtHml2D40qNOg1mpFsxI92KaalWFKVZUJRiQUGqmVdsFMLcHZ6UEh/XduFv+xvwyv4G1Hb0QQhgcZ4Dl8xOx4XF6ZiXncgiRQEsoM/m8Qfwp901+M3246jr7MOMdCu+sqIQ6xfncGhpFGEBPQZuXwDbq1rwVmUT3j3SgiZn6FJ3klmPhbkOzM9JxOzMRMzKtKEgxQyjTjvpMcQ6p9uHY80uHG7sxqEGJ/bXdeFgvRMefxAAMDvThotmpeNz8zKwOM8xZR9cWIQMz+0L4EiTC4canTjc2I0jzS4cb3GhrrMPnz5NOMx65CWZkZuUgBxHAnKSEpBlT0C2w4TMRBNSrEb2Xk8B5u7YDFxtebOyCW9/0oyPa0MzdyRbDDh/egpWzkjFedNTkJ9sZgdJBLCAHp4vEMTLH9fj8fdO4GC9EzaTDl9YkosbluVhTlai0uHFPRbQYZJS4lhLD3adaMe+Ux1n9cxpBJCTlICCZAvykkMFRJY9AZl2EzISjUizmpCYoIurE3MgKNHe40VztxtNTjcauzxo6OpDbUcfTrX34mRbD1pd3sH2ZoMW87ITUZLrwOL8JJxTlIw0W2QutbIICZ/bF0B1Ww+qW0O/y1Ptvajp6ENtey/qOvsGPwQN0GoE0qxGpCcakW4zIs1mRJrViFSbESkWI1KsBqRYDEi2GOAwG1hsjxFzd3xauj1470gLdhxpxY6jrYP3AmQmmnBOUTKWFSZhaUEyZmXamItTgAX06KSUKDvZgc0fnsSr+xvhDQQxPycR1y7OxVUl2RH7+0hnYgE9Cdy+AI61uHC02YVjLT040dqDU209qO3oQ1uP96z2Bq0GyRYDkiwGJJn1cJj1sCfokWjSw2bSwWrUwWrSw2LQwmzUwWzQIkGvhUmvgVGnhXHgq04DvVYz6Sd1KSX8QQlfIAivPwiPPwiPLwi3P4A+bwB9vgB6vX70egPo8fjh8gTQ7fah2+1HV58v9Oj1oaPXi/YeLzp6vfjsjFIaEVo1LzcpAQUpZhSlhoYDzEy3Ij/ZrNilVBYhk0vK0Ien+k43Grr6Qh+g+j9Etbg8aHa60eryoK3Hi6FONUIA9gQ9kswGOMz9XxP0sPcfMwPHjT1Bj8QEPRITdLANHEcGXVxdkmfuTtxAB8nO423YdaIdu060DV5xtBi0WJjrwOJ8BxbmOlCSZ0dmoimuOkOmAgvo8HT0ePGXfXXYurcWB+ud0AjgvOkpWDM/C5+bl4F0G2dRihQW0FOszxvoLxjcaO52o6Xbg1aXF20uDzp6vejo9aGz14uuPj+cbh+8n+mtGwshAL1GA51WQKvpfwgBIQQ0IvS6gIAQgJSAhISUQFACQSkRlBKBQKho9geD8AXG9zu3GLShIsYU+lAw8CEh1WJAqm2gt9EU6o23GaGLwhsyWYQoY+AqRVuPB20uL9p6vGh3edDe60NH/4ewzl4fOvu86OjxwdnnQ/cYFj6yGgc+kOpO/9uog9mohdWog2Xge4MWFoMOCQYtzAYtzAZd/1dt/3O6wQ+x0VowMXcnn5QStR192HOyA3tPdaC8phOV9U74+3sEUq0GzM+xY152IuZm2TEnizfYhosF9PgdaerGSxX1+NvHDTje2gMhgEV5Dlw6Ox0XzQqN64/W81UsGC13OVp9ghIMWhSlWlCUahlTe48/gB5PAC63Hy6PH32+UE9vnzcAtz8Ity8Ajy8Ajz8IbyAIfyDUU+wLSPgDQfiDEoFgqCiWAIJBOVg0DxgopjWaUIGtEaGie6A3W9//b0N/D7dRr4FJp4Wpv4BI0IeKCstAAWIIFSj8o0HjpdWI0FCOMC5F+gPBwaseTrcPzv4PoQNXQ5xuP7rdPrjcfvR4Q1dKXG4fmrvdoWPM40ePxz9YDI3VQP4PFNQm/cC/+68S6bX9x4tm8DnTZ64cnfFvnRYGnQZGneYzXz/1vFYTV73p0UIIgbxkM/KSzbhmcQ6A0BXHygYnPq7pxIF6Jw7UdeG9I62Dw/gS9FoUZ9owO8OG4kwbijOsKM6wId1mZDFDk2pmhg3f+dws3Ht5MaqaXHjjYCPePNSEn75RhZ++UYVUqxErZ6RgRf+4/twks9IhxxUW0BEW+qOqRbLFoHQoRFFNp9WEhkFN8Fjx+APo9QTQ6wsNSer1hoYo9XkD6PEG4B743hdEn9ePPl9oGFOfN/SBts8XgLt/WFN7T2iok8cXes3tC33wnYxV8XQaAXuCHnv+7fIJb4vGz6TXYkl+EpbkJw0+9+kbbA81hG6yfetQE54tqxlsYzPpMD0tNFRtepoV09IsmJ5mQV4ybzyniRFCYFamDbMybfjmpTPR3O3Gu1WteO9IC9470ooXyusBALlJCTinKBmlBclYWpCEmelWfjCfQiygiSimDXxoTRq96bgN3E8wUFB7fAF4A6F7Czz+IDz+wOD9Bt6B7wNysJ23/3kNezCjkkmvxYJcOxbk2s94vtXlQVVTN440he6ROdrswvaqFjy3p3awzcCN54UpFhSmWFCQYkZB/9e8JDMSDCyuKTzpNhOuW5qL65bmIhiUqGruxs5jbfjoeDu2H27B83vrAISGuC3MtWNhrgML++f658JZk4cFNBHRBOm1oeFQFiNPqfEk1WpEqtWI86ennvG80+3Dif4bzo+3uHCirRfVrT14oaYO3e4zx/an2YzIS0oIDSXpnx4yN8ncPz2kCSY9C2wankYjMDszNNXuV1YUQUqJE6092HuqE+U1oXH9j793fHAom82kw5zMRMzOCvVoF2fYMDPdCoeZV8XDxbM9ERHRJEo06VGS50BJnuOM56WU6Oj1nZ4Osr0Xp/ofZdUd+GtF/VkzGqVajchxmJDtSEC2I1RUZztCU6dm2U1Is0bnDdukDCEEpqVZMS3NiuuW5gIIDUE63NiNygYnDtZ34VBDN57fWze4Gi0QumF2Wmpo6FFRqmXwKklBipmLuwyDe4WIiCgChBBI7p8DfXH+2YOKfIEgGrvcqOvsQ11H3+DX+q4+VDV1Y9vhFvT5Amf8jEaELuln2E3ITDQiI9H0qUfo+zSrEQ6znpfu45RJrz3rA52UEvVdbhxudA4OPzre0oM3KpvQ/pnpeVOtBuQkmZHbv3BWtt2ETHvow1yWPX4Xz1K0gBZCrAbwKAAtgMellP+lZDxERERK0Ws1g7OCDEVKia4+H+o73Wh09qGhKzSFamNXaP714y092HmsDU732VNAGrQapNlCCxylWY2Ds+KkWj/11WpEqs3AHsc4IIQIrSTrSMAlszPOeK2rL3SV5GRb7+CVktqOvtAKn4eazpqOV6sRSLEYBnMrdXDhLMPgdLfJZgOSzAbYzXrYjLExh79iR4kQQgvglwAuB1ALYLcQ4iUpZaVSMREREUUrIQQc5tAKnnOzh1/quc8bQHN3qLBu6g4taNTi8qDF6UFztwcn23pRdrLjrJ7GAWaDFilWA1KtodVDU62G/hVEQyuJDvSip1iMSLYYYNBxCEkssSfo+288dJz1mpQSbT1eNHa5Ud8ZWjyryekZXAejxeXBoYZutPV4hl1zQiMAmym0MJY9QQ+b8fQicwPT5352obkEg3ZwStGBqUIN2tC0ogatBnptaHpenVZAr4nMtKBKfsw8B8BRKeVxABBCPANgHQAW0EREROOUYND2j2EdeX0CXyCINpe3fwGwUPHT6vKgtTu06FGry4Pajl6U13Sio9c77HSNNqMO1y7JwY/XzZ+K/w5FESHE4M2z83Psw7aTUsLp9qO9x4v2Hg86ekKrFw+uZtz/6O6fz/9Uey9cntD6GL2e0OxEE6EROGPhudC6GAOLzwkMlNdCAD/9hxJcNCs97PdQsoDOAVDzqe9rASz/bCMhxJ0A7gSA/Pz8yERGNEHMW1Ir5m780Gs1yLSHVo8dTTAYGj7S1nN6pd3QiqKhr/NG6BGPFOZu9BAiNK+9PUE/5oXmPs3rD6LPG0Bv/2Jzg/Pu98/B/+npQb39C815/aHF5nyB0Nz8vkD/Ssz9i88FgxLBTy08N7AQ93iXR4/6gU5Syk0ANgGhpTkVDodoTJi3pFbMXRqKRiMGFzaakW5VOpwhMXdjh6F/xVY79EqHMiwlBy7VAcj71Pe5/c8REREREUUtJQvo3QBmCiGKhBAGADcCeEnBeIiIiIiIRqXYEA4ppV8I8Y8AXkdoGrvfSSkPKhUPEREREdFYKDoGWkr5CoBXlIyBiIiIiCgcQkr1jLMXQrQAODnES6kAWiMcTjTifjhtuH1RIKVMi2QgI+QtwN/ZAO6HkJH2A3M3OnE/hETNORdgvTAG3A+njSt3VVVAD0cIUSalLFU6DqVxP5ymln2hljinGvdDiJr2g5pinUrcDyFq2Q9qiXOqcT+cNt59weWDiIiIiIjCwAKaiIiIiCgMsVJAb1I6gCjB/XCaWvaFWuKcatwPIWraD2qKdSpxP4SoZT+oJc6pxv1w2rj2RUyMgSYiIiIiipRY6YEmIiIiIooIFtBERERERGFgAU1EREREFAYW0EREREREYWABTUREREQUBhbQRERERERhYAFNRERERBQGFtBERERERGFgAU1EREREFAYW0EREREREYWABTUREREQUBhbQRERERERhYAFNRERERBQGFtBERERERGFgAU1EREREFAYW0EREREREYWABTUREREQUBhbQRERERERhYAFNRERERBQGFtBERERERGFgAU1EREREFAYW0EREREREYWABTUREREQUBp3SAYQjNTVVFhYWKh0GqdiePXtapZRpkXxP5i1NBuYuqZESeQswd2niRstdVRXQhYWFKCsrUzoMUjEhxMlIvyfzliYDc5fUSIm8BZi7NHGj5S6HcBARERERhYEFNBERERFRGFhAExERERGFgQU0EREREVEYVHUToVoEg0G0trais7MTgUBA6XDijslkQm5uLvR6vdKhqA5zVzlarRYOhwOpqanQaNi3ES6fz4fa2lq43W6lQ4k7POdODHNXORPJXRbQU6C2thZCCBQWFkKv10MIoXRIcUNKiba2NtTW1qKoqEjpcFSHuasMKSV8Ph+amppQW1uL/Px8pUNSndraWthsNhQWFjJvI4jn3Ilj7ipjornLbo4p0NPTg5ycHBgMBh4MESaEQEpKCj/JjxNzVxlCCBgMBuTk5KCnp0fpcFTJ7XYjJSWFeRthPOdOHHNXGRPNXRbQU4SXYJXDk9DEMHeVw30/MTz2lcH9PnHch8qYyH7n2ZqIiIiIKAwsoImIiIiIwsACmqbMxo0bUVpaCqPRiNtuu23U9u3t7Vi/fj0sFgsKCgrw1FNPjek1osnG3CU1Yt6SWqkxdzkLB02Z7Oxs3H///Xj99dfR19c3avt77rkHBoMBTU1NKC8vx5VXXomSkhLMmzdvxNeIJhtzl9SIeUtqpcbcZQ90nPL7/XjwwQdRWFiIlJQUPPXUU/jJT36CDRs2TNp7XHvttbjmmmuQkpIyatuenh5s3boVDz74IKxWK1auXImrr74aTz755IivUfxh7pIaMW9JrZi7Q2MPdJy6//77UVZWhoqKCrz77rv43ve+B41Ggw8//PCstmvXrsWOHTuG3M7KlSvx8ssvTzieqqoq6HQ6FBcXDz5XUlKC7du3j/gaxR/mLqkR85bUirk7NBbQccjpdOKRRx5BZWUl7HY7li9fjk8++QQbNmyAzWY7q/1kJPxoXC4XEhMTz3jObreju7t7xNcovjB3SY2Yt6RWzN3hcQhHHHr77bdRXFyMadOmAQC8Xi/sdju++c1vKhaT1WqF0+k84zmn0wmbzTbiaxRfmLukRsxbUivm7vBYQMeh+vp6ZGdnD36/adMm5OTkDJtga9asgdVqHfKxZs2aSYmpuLgYfr8fR44cGXyuoqIC8+bNG/E1ii/MXVIj5i2pFXN3eIoO4RBC/DOArwKQAPYD+IqUkuuBTrHc3FyUl5ejoaEBp06dwpNPPgmXywWv1wuDwXBW+1dffXVc7+P3++H3+xEIBBAIBOB2u6HT6aDTnZ12FosF1157Lf793/8djz/+OMrLy/Hiiy/igw8+GPE1ii/MXVIj5i2pFXN3BFJKRR4AcgCcAJDQ//2fANw20s8sXbpUqkFlZaXSIYzI4/HIW265RdrtdllUVCT37t0rL7vsMrlixYpJfZ8HHnhAIvThaPDxwAMPDL6+evVquWHDhsHv29ra5Lp166TZbJZ5eXlyy5YtY3ptKMP9DgCUyQjnulryVkrm7gDmLnN3MjFvI19jMHcnB3N3+BwToTaRJ4TIAfAhgBIATgAvAPi5lPKN4X6mtLRUlpWVRSbACTh06BDmzJmjdBhxbbjfgRBij5SyNJKxqCVvAeZuNGDujg9zV1nRlLcAc5fGbry5q9gYaCllHYCfAjgFoAFA11DFsxDiTiFEmRCirKWlJdJhEo0L85bUirlLasXcpUhSrIAWQiQBWAegCEA2AIsQ4ubPtpNSbpJSlkopS9PS0iIdJtG4MG9JrZi7pFbMXYokJWfhuAzACSlli5TSB+B5AOcrGA8RERER0aiULKBPAThXCGEWQggAlwI4pGA8RERERESjUnIM9EcAngOwF6Ep7DQANikVDxERERHRWCg6D7SU8gEADygZAxERERFROLgSYRw7ceIE1qxZg6SkJOTk5OCJJ56Y1O23t7dj/fr1sFgsKCgowFNPPTVs24suuggmk2lwxaJZs2aNe1sU+6Y6dzdu3IjS0lIYjUbcdtttI7YdLTeZuzRgKvPW4/HgjjvuQEFBAWw2GxYtWjTiohbMWwrHVJ9zb775ZmRlZSExMRHFxcV4/PHHh20bLbnLAjqOXXfddbj88svR2tqKxx57DA899NCkbv+ee+6BwWBAU1MTtmzZgrvvvhsHDx4ctv3GjRvhcrngcrlw+PDhCW2LYttU5252djbuv/9+3H777aO2HS03mbs0YCrz1u/3Iy8vD9u3b0dXVxceeughXH/99aiurh6yPfOWwjHV59z77rsP1dXVcDqdeOmll3D//fdjz549Q7aNmtwdaZWVaHuoZWWhaF5VaEBFRYUsKCgY/P5vf/ubXL58+aRt3+VySb1eLw8fPjz43M033yy///3vD9n+wgsvlI899tikbEvK6FoVSy15KyVz97N++MMfyltvvXXY10fLTeZu5ER77kYybwcsWLBAPvfcc2c9H+t5K5m7kyrSufvJJ5/IzMxM+eyzz571WjTlLnug49T777+PlStXIhgMYs+ePbj33ntx9913D9l27dq1cDgcQz7Wrl075M9UVVVBp9OhuLh48LmSkpIRPwXed999SE1NxYoVK7Bt27YJbYti11TnbjhGy03mLg2IdN42NTWhqqoK8+bNO+s15i2FI1K5+41vfANmsxmzZ89GVlYWrrjiirPaRFPuKnoTISmnvLwcpaWluPjii/Huu+9i8eLFWL9+/ZBtX3755bC373K5kJiYeMZzdrsd3d3dQ7Z/+OGHMXfuXBgMBjzzzDO46qqrUF5ejunTp4e9LYptU5274RgtN5m7NCCSeevz+XDTTTfh1ltvxezZs896nXlL4YhU7v7qV7/CL37xC+zcuRPbtm2D0Wg8q0005S57oONUeXk5li1bhnfeeQdHjx5FcnIyvv/970/a9q1WK5xO5xnPOZ1O2Gy2IdsvX74cNpsNRqMRt956K1asWIFXXnllXNui2DbVuRuO0XKTuUsDIpW3wWAQt9xyCwwGAzZu3DhkG+YthSOS51ytVouVK1eitrYWv/71r896PZpylwV0HAoEAjh06BAWL14MjUaD6dOnY8WKFcO2X7NmzeDsGJ99rFmzZsifKS4uht/vx5EjRwafq6ioGPJy4lCEEAgNQZr4tih2RCJ3wzFabjJ3CYhc3kopcccdd6CpqQlbt26FXq8fsh3zlsZKqXOu3+/HsWPHzno+qnJ3pAHS0fZQy00B0X5DwMGDB6VWq5XPPfec9Pv9ct++fTInJ0fu3LlzUt/nhhtukDfeeKN0uVxyx44dMjExUR44cOCsdh0dHfK1116TfX190ufzyc2bN0uz2XzGTQBj3daAaLqhRS15KyVzd4DP55N9fX3yBz/4gbz55psHc3Moo+Umczcyojl3I5W3d911l1y+fLns7u4etW0s561k7k6aSORuU1OTfPrpp2V3d7f0+/3ytddek2azWb744otDto+W3FW8KA7noZYDIpoPBiml3Lx5s1ywYIEsKCiQVqtVlpSUyK1bt076+7S1tcl169ZJs9ks8/Ly5JYtWwZfW716tdywYYOUUsrm5mZZWloqrVartNvtcvny5fKNN94Y87aGEk0nc7XkrZTM3QEPPPCABHDG44EHHpBSnpm7Uo6em8zdyIjm3I1E3lZXV0sA0mg0SovFMvjYvHmzlDK+8lYydydNJHK3ublZrlq1Strtdmmz2eT8+fPlpk2bBl+P1twVoTbqUFpaKsvKypQOY1SHDh3CnDlzlA5jWN/97neRnJyM++67T+lQpsxwvwMhxB4pZWkkY1FL3gLM3WjA3B2faM5d5m1k8xZg7k4W5u7wucsx0HFo3759UXuwEo2EuUtqxLwltWLuDo8FdByqqKgYcmojomjH3CU1Yt6SWjF3h8d5oONQS0uL0iEQjQtzl9SIeUtqxdwdHnugiYiIiIjCwAKaiIiIiCgMLKCJiIiIiMLAAnqKBINBpUOIW2qamjEaMXeVw30/MTz2lcH9PnHch8qYyH5nAT0FLBYL6urq4PV6eVBEmJQSbW1tMJlMSoeiSsxdZUgp4fV6UVdXB4vFonQ4qmQymdDW1sa8jTCecyeOuauMieYuZ+GYArm5uWhtbcXJkyfh9/uVDifumEwm5ObmKh2GKjF3laPT6WC325Gamqp0KKqUm5uL2tpazhqgAJ5zJ4a5q5yJ5K6iBbQQwgHgcQDzEVou93Yp5U4lY5oMGo0G6enpSE9PVzoUorAwd0mt9Ho9ioqKlA6DKGzMXXVSugf6UQCvSSmvE0IYAJgVjoeIiIiIaESKFdBCCDuAVQBuAwAppReAV6l4iIiIiIjGQsmbCIsAtAB4QgixTwjxuBDirLtnhBB3CiHKhBBlHB9EasG8JbVi7pJaMXcpkpQsoHUAlgD4tZRyMYAeAD/4bCMp5SYpZamUsjQtLS3SMRKNC/OW1Iq5S2rF3KVIUrKArgVQK6X8qP/75xAqqImIiIiIopZiBbSUshFAjRBiVv9TlwKoVCoeIiIiIqKxUHoWjm8C2NI/A8dxAF9ROB4iIiIiohEpWkBLKcsBlCoZAxERERFROLiUNxERERFRGFhAExERERGFgQU0EREREVEYWEATEREREYWBBTQRERERURhYQBMRERERhYEFNBERERFRGFhAExERERGFgQU0xYyfvXEYl/x0m9JhEBERUYxjAU0xo8npQY/Xr3QYREREFONYQFPM6OrzIdGkVzoMIiIiinEsoClmON0+2BNYQBMREdHUYgFNMaOrjwU0ERERTT0W0BQzuvp8SGQBTURERFOMBTTFDCd7oImIiCgCWEBTTAgGJbo9fvZAExER0ZRjAU0xodvjh5RAokmndChEREQU41hAU0xw9vkAgEM4iIiIaMqxgKaY0NVfQHMIBxEREU01xQtoIYRWCLFPCPGy0rGQerEHmoiIiCJF8QIawLcAHFI6CFK3LhbQREREFCGKFtBCiFwAVwJ4XMk4SP04hIOIiIgiReke6EcAfA9AUOE4SOWcbvZAExERUWQoVkALIdYCaJZS7hml3Z1CiDIhRFlLS0uEoiO16erzQasRsBi0SocCgHlL6sXcJbVi7lIkKdkDvQLA1UKIagDPALhECLH5s42klJuklKVSytK0tLRIx0gq0dXnQ6JJByGE0qEAYN6SejF3Sa2YuxRJihXQUsr7pJS5UspCADcCeFtKebNS8ZC6Ofv8HL5BREREEaH0GGiiSdHV52MBTURERBERFeseSym3AdimcBikYl19Ps7AQURERBHBHmiKCU43C2giIiKKDBbQFBPaXF4kmVlAExER0dRjAU2q19HjRVefD4UpFqVDISIiojjAAppU70RbDwCgKJUFNBEREU09FtCketWtoQK6kAU0ERERRQALaFK9E6090GoE8pLMSodCREREcYAFNKne8dYe5CYlwKBjOhMREdHUY8VBqlfd2sPxz0RENKI+bwCtLo/SYVCMYAFNqialxAkW0ERENIpH/34El/7PdvyprAZSSqXDIZVjAU2q1tLtQa83wAKaiIhGdN3SHMxMt+J7z32MLz32EY63uJQOiVSMBTSpWlVT6AQ4LdWqcCRERBTNZqTb8Ke7zsOG9fNxoL4Lqx95D4+8VQWPP6B0aKRCLKBJ1XadaINGACV5dqVDISKiKKfRCNy0vAB//86F+Pz8TDzy1hGseeQ97DjSqnRopDIsoEnVPjzRjvk5dthMXMabiIjGJt1mwi++uBh/vP0cBKTEzb/9CP/41F40drmVDo1UggU0qZbbF0B5TSeWFyUrHQoREanQquI0vP7tVfj2ZTPxRmUTLv2fbdj07jH4AkGlQ6MoxwKaVKu8phNefxDLi1KUDoWIiFTKpNfi25cV481/XoVzp6XgP1/5BKsfeRfvHWlROjSKYiygSbV2HmuDEMAy9kATEdEEFaRY8NvbluG3t5bCH5S45be7cOcfy3CqrVfp0CgKsYAmVZJS4pX9DViSnwR7Asc/ExHR5Lh0Tgbe+OdV+O7nZ2HH0VZc9rPt+K9XP0G326d0aBRFWECTKh2sd+JIswvrF+coHQoREcUYo06Ley6egXf+5SKsLcnCb7Yfw8U/3Yand51CIMhFWIgFNKnUC/vqoNcKXLkgS+lQiIgoRmUkmvCz6xfhxXtWoDDFgvue348rHn0P26s4PjresYAm1XH7AnihvA4XzUpHksWgdDhERBTjSvIc+PPXz8OvblqCPl8At/5uF25+/CMcrO9SOjRSiGIFtBAiTwjxjhCiUghxUAjxLaViIXV5dncNWl1efGVFodKhEBFRnBBC4IoFWXjz3lX4t7VzcaC+C1f+fAe+/cw+1LTzRsN4o1Pwvf0AviOl3CuEsAHYI4R4U0pZqWBMFOU8/gB+s/0YlhUm4bxpnL6OKNKklAgEJXRaXsCk+GTUaXHHyiJctzQX/7f9GH73/gn8bX8DvnROPu65ZAbSbSalQ6QIUKyAllI2AGjo/3e3EOIQgBwALKBpWL/dcQINXW785LqFEEIoHQ5R3DnW0oO1v3gPC3LsWJTnQEmeA4vyHMhxJPCYpLhiT9Dje6tn48vnFeLRvx/B5o9O4U9ltbhtRSHuWjUNDjOHGMYyJXugBwkhCgEsBvDREK/dCeBOAMjPz49sYBRVTrb14NG3jmD1vExcMDNN6XBGxLwltRotd406Db50TgHKazrwh50n4X3vBAAg1WrEorzTRfXCXAenmKSIUuq8m2k34f9duwB3rpqGR96qwm+2H8PmnSdx+8oi3L6yiMdBjBJSKjsdixDCCmA7gA1SyudHaltaWirLysoiExhFFa8/iBs37URVkwtv3XshMu3ju0QmhNgjpSyd5PBGxLylyRCNuev1B3G4sRvlNR3YV9OJippOHGvpGXx9WpoFi/p7qEtyHZiTlQiDjkM/4okSeQsoe9493NiN/32zCq8dbESiSYc7Vk7DV1YWItHEQlpNRstdRXughRB6AFsBbBmteKb4JaXEQ3+rxN5Tnfjll5aMu3gmosll0GmwINeOBbl23HJe6LmuPh8+rg0V0+U1nXi3qgXP760bbD8vO/GMorogxcyhHxRTZmXa8JtbluJAXRce/fsR/O9bVXh8x3HcvqIIt68ogt3MQjoWKFZAi9AZ87cADkkpf6ZUHBT9fvnOUfxx50l87YIiXLmQ8z4TRTN7gh4XzEwbHGYlpURdZx/KazrxcW0X9p3qwNO7TuGJ96sBAElmPUr6i+lF+Q4synVwekqKCfNz7Hjsy6U4UNeFn//9CB79+xH8dscJ3HxuAe5YWYQ0m1HpEGkClOyBXgHgFgD7hRDl/c/9q5TyFeVComgipcSjfz+CR946gmsX5+C+NXOUDomIwiSEQG6SGblJZqxdmA0A8AeCONzUHSqqa7pQXtOJ7VVHMDCisCDFHCqo80JF9dysRJj0WgX/F0TjNz/Hjk1fLsWhBid++c5R/N+7x/DE+ydwfWke7lw1DXnJZqVDpHFQchaOHQB43Y6G5PYFcP8LB/Dcnlp8YUkuHv7CAmg0TBeiWKDTajAv24552XbctDz0nMvjx/7aUDFdXtOBXSfa8VJFPQBArxWYk5U4OOxjUb4DRSkWnhNIVeZkJWLjl5bg3hYXfrP9GJ7ZfQpP7TqFKxdk4c5V0zA/x650iBSGqJiFg+jTKuuduPdP5fiksRvfunQmvn3ZTI6RJIpxVqMO501PwXnTT8/v3tjl7i+oQ2Oqt+6pxR93ngQAJJp0p4d+9M/8wUvipAbT0qz4yXUluPfyWfjd+yew5cOTeKmiHudPT8HXLpiGC4vT+OFQBVhAU9To8fjxi7eP4rc7jsOeYMATX1mGi2elKx0WESkk027CansmVs/PBAAEghJHm10or+lAef/Qj19vP4ZAMDT2I8eRMHiD4qJ8B+Zn25Fg4NAPik6ZdhP+9Yo5uOfiGXh61yn8/v1qfOX3uzE9zYKvrCjCtUtyYDawTItW/M2Q4vq8ATy96xR+te0oWl1eXLc0Fz+8Yg5vJCKiM2g1ArMybZiVacMNy0LP9Xr9OFDnHJz1o7ymE3/b33C6fYYNJXkOLO7vpZ6RboWWvXsURewJenz9wum4fUURXtnfgN/uOIH7XziA/379MG5cloebzy3gOOkoxAKaFNPkdGPLR6ew5cOTaOvx4rxpKXjsy7OwOD9J6dCISCXMBh3OKUrGOUXJg8+1dHsGh31U1Hbi5Y/r8fSuUwAAi0GLhbmnV1BcnO9ARiKnxiTlGXQaXLM4B+sWZWPPyQ488X41Ht9xApveO45LZqXjlvMKsGomh3dECxbQFFG9Xj/+fqgZL+yrwzuHmyEBXDIrHXddOP2MP4BEROOVZjPi8rkZuHxuBgAgGJQ43toz2EtdUduJ3+44Dl8gNPQjM9E0OOyjJNeBhbl2WIz880jKEEKgtDAZpYXJaOjqw5YPT+GZ3afw9yeakZ9sxhfPycd1S3M55l9hPEPQlGt2urGtqgV/P9SE7VUtcPuCyEw04a4Lp+PGZXkoSLEoHSIRxTCNRmBGuhUz0q34wtJcAKGZfiobnCg/dbqofu1gY6i9AGam2wZvTizJs2NWhg06LVdRpMjKsifgXz4/C/906Uy8drARWz48iYdf+wT/88ZhXD43Azcsy8MFM9M4LEkBLKBp0jU73Sg7GZqGauexNhxu6gYQ6uX5h6V5uGJBFs4pSuYBT0SKMem1WJKfhCWfGjLW3uNFRW3nYFH9emUjni2r6W+vwYIc+2BRvSjPgRxHAmcIoogw6DS4uiQbV5dk42izC8/sOoWte2vx6oFGZNtNuHZJLq5bmovCVHZIRQoLaJqQVpcHhxqcOFDnxIG60F3xdZ19AIAEvRalhUm4ZnEOLixOw5wsG//YfMqP/3oQlfVOpcOgKDI3OxEPXDVP6TDiVrLFgItnpQ/O/iOlxKn2XpTXdGLfqVAv9R92noT3vRMAQkNFQtPo2bEoLwkL8+xINHGZZppaM9KtuH/tXHx39Sy8VdmMP5XV4FfbjmLjO0dRWpCE9UtysHZBNpcMn2IsoGlUwaBEg9ONEy09ON7qwrFmF462uHC40YVWl2ewXV5yaAqpr6woxJKCJMzPtsOg4yVPIlInIQQKUiwoSLFg3aIcAIDXH8ShBmeop7p/TPVbh5oGf2Z6muWMWT9mZybyPEhTwqjT4sqFWbhyYRYau9z4y746bN1bix/+5QB+/FIlLpqVhnWLcnDpnHSu5DkFWEATAkGJVpcH9Z19qO90o66zF3Udfajp6MOp9l6cau+F1x8cbG8xaDEjw4ZLZqehOMOGuVmJmJudCIeZ086Fgz2NROpj0Gn6x0U78OXzQs919fnwcf/Qj4raTrxb1YLn99YNtp+fnYhFeUkoybNjcV4S8pI59IMmV6bdhLsvmo6vXzgNB+qc+Mu+Ovz143q8UdkEi0GLy+ZmYO3CbFwwM5XF9CRhAR3Der1+tLm8aHV50DrwtduD5m4PmrvdaHJ60OR0o7nbM7gQwQCbUYe8ZDOmp1lw8aw0FKZaUJRqwbRUKzISjTz5ExH1syfoccHMNFwwMw1AaOhHbUff4HjqitpOPLXrJH73fqgjItliQEmufXAsdUmug/Pe06QQQmBBrh0Lcu344ZVz8OHxNvy1oh6vHWzEi+X1sBp1uHROOtbMz8Sq4jQu1DIB3HNRTkqJPl8AXX0+dLv96OrzoavXF/ra50Nnnw+dvV509vrQ0etFR68X7S4v2nu9cPuCQ27TnqBHus2ITLsJ09NSkWk3ItOegGy7CVn2BOQkJcCewLFTRETjIYRAXrIZeclmrF2YDQDwB4I43NQdGvbRX1Rvq2qB7O+7KEwxny6o8xyYm5XInkKaEK1GYMWMVKyYkYoHr5mPD4614ZWPG/BGZaiYNuk1WDkjDZ+bm4GLZ6dzWrwwsYCeAr5AEL3eAPq8AfR6/ej1BtDj6f/q9aPH44fLE3ou9O/+hzv0tdvtR7cnVDB3u/1n9Q5/mhBAokkPh1mPJLMBaVYjijNsSLEYkGwxIsViQIrVgFSrEWk2I1KsBhh1PCkTEUWSTqvBvGw75mXbcdPyAgBAt9uH/f03X5ef6sSHx9vwYnk9AECvFZiblXhGUV2UYuEiGjQueq0GFxan4cLiNGwIzMeuE+14o7IJbxxsxFuHmiAEUJLrwCWzQzfRzstOZK6NIuYLaCklfAEJjz8Arz8Iz+AjAI/v9L/dvtNf3b4A3L5A6DVfAG5/EH3e0HOf/nefL4De/n/3ev3o84aeG5icfywMOg1sRh0sRh1sptDXbIcJVqMViQl62Ew62Eyhr/YEPRJNetgTQg+HWQ+bSc/p4IiIVMhm0uP86ak4f3rq4HONXW6U13RgX/9Kis/tqcUfd54EACSadIMF9UBRnWplryGFR6fV4PwZqTh/RioeuGouDtY78fdDzXj7cDN+9mYVfvZmFVKtBqyamYYLikM92Ok2rtb5WaovoB9/7zheKK+D1x88/QiECuOBgnkihABMOi1Meg1Mei0SDNrB7xMMWiSZDUgwaJGg18Bs0CHBoIW5v53ZoIPFqEWCXguLUQezIfTVYtTBatDBbNRCz4n5iYioX6bdhNX2LKyenwUgdJP30WYXyms6UF4T6q3+1bZjg1cmc5MSziio52fbkWDgVUYaGyEE5ufYMT/Hjm9dNhOtLg/erWrBtsMt2FbVguf3hW6GLc6w4vzpqThvegqWFyVz0gDEQAFtNuiQbjPBoNVAr9PAqNPAoNPAoA39e/B7nQbG/sJ34N9GXagoNug0MOm0MOo1g8Wxsf97o07DG+aIiEgRWo3ArEwbZmXacMOy0HO9Xj8O1DlRXtOBipou7DvViZc/bjjdPsOGxfmOwen0pqdZeTmexiTVasS1S3Jx7ZJcBIMSlQ1OvHekFR8ca8Uzu0/h9x9UQwhgVoYNy4tCy40vLUhCtiNB6dAjTvUF9JeW5+NLy/OVDoOIiCgizAYdzilKxjlFyYPPNXe7UVHT1d9T3YmXyuux5aNTAACrURdaRTH/9PCPjERekqeRaTSne6fvvmg6PP4AKmq68OHxNuw60Y4/76nFH/qHF2XZTViSnxTKr3wH5mUnxvwMH7H9vyMiIooD6TYTLp9rwuVzMwCEFsA63tqD8v6x1OU1nXjs3ePw9w/9yEw0nbEs+cJcOyxGlgQ0PKNOe8YHN38giMoGJ8qqO7D3VOiD29/2h66EaERoxcT52XbMzQ6tFTE3K7bWi+DRQkREFGM0GoEZ6VbMSLfiuqW5AAC3L4CD9c4ziurXDjaG2gtgZrptsAexJNeB4gwrdLxPh4ah02qwMNeBhbkO3I4iAEBLtwcVNZ34uLYTB+qd2HG0dXAcNRD64FacacPMdCtmplsxPd2KaakWJFsMqhsuq2gBLYRYDeBRAFoAj0sp/0vJeIiIiGKVSa/F0oIkLC1IGnyuvcc7WEwPFNTPltUAABL02jOGfpTkOZBtN6mu0KHISbMZcdncDFzWfyUECA0vOtTQjU8anDjc2I3DTd346HjbGZM8JJp0KEq1oCDFgvxkM/KSE5CbZEa2IwFZdlNUzomuWAEthNAC+CWAywHUAtgthHhJSlmpVExERETxJNliwMWz03Hx7HQAoalfq9t6zyiqf/9+NbyBULGTZjOiJNeBxf1F9YJcOxJNXHiLhpduMyHdZsKFxWmDzwWCEnUdfTjW4sKxFhdOtPbgZFsv9tV04G/7G85a/yLJrEdGogkZiSak2YxItRr717swIMmihz3BEJrqN0GHRJM+IhNAKNkDfQ6Ao1LK4wAghHgGwDoALKCJiIgUIIRAUaoFRakWXLM4BwDg8QfwSUP34NLk5bWdeOtQU397YHqaFV9Ykou7L5quZOikIlqNQH6KGfkp5sEPbwN8gSAau9yo7ehDXWcfGjr70Oh0o8npRpPTg8ON3Wjr8Yy45oZWIwanFDbpT8/AptNooNMIaDUCQgACAt/5XDFKC5OH3dZwlCygcwDUfOr7WgDLP9tICHEngDsBID+fs22QOjBvSa2Yu/RZRp0WJf1DOL58Xui5rl4fKmpPj6UOyrEvIDZVmLuxQa/VIC/ZjLxk87BtpJRwuv1o7/Gio9eLrj4fnP2Pbo8fvZ4AXB7/4KJ3Hl9ojRBfIAh/QCIoJYJBQCKI8WZu1N9EKKXcBGATAJSWlip/hBKNAfOW1Iq5S2NhN+uxqjgNqz51WV5pzN34IYQYXJW5CBZFYlDy9to6AHmf+j63/zkiIiIioqilZAG9G8BMIUSREMIA4EYALykYDxERERHRqBQbwiGl9Ash/hHA6whNY/c7KeVBpeIhIiIiIhoLRcdASylfAfCKkjEQEREREYVDyCi4c3ashBAtAE4O8VIqgNYIhxONuB9OG25fFEgpI3rXywh5C/B3NoD7IWSk/cDcjU7cDyFRc84FWC+MAffDaePKXVUV0MMRQpRJKUuVjkNp3A+nqWVfqCXOqcb9EKKm/aCmWKcS90OIWvaDWuKcatwPp413X3CReyIiIiKiMLCAJiIiIiIKQ6wU0JuUDiBKcD+cppZ9oZY4pxr3Q4ia9oOaYp1K3A8hatkPaolzqnE/nDaufRETY6CJiIiIiCIlVnqgiYiIiIgiggU0EREREVEYWEATEREREYWBBTQRERERURhYQBMRERERhYEFNBERERFRGFhAExERERGFgQU0EREREVEYWEATEREREYWBBTQRERERURhYQBMRERERhYEFNBERERFRGFhAExERERGFgQU0EREREVEYWEATEREREYWBBTQRERERURhYQBMRERERhYEFNBERERFRGFhAExERERGFgQU0EREREVEYWEATEREREYWBBTQRERERURhYQBMRERERhUGndADhSE1NlYWFhUqHQSq2Z8+eVillWiTfk3lLk4G5S2qkRN4CzF2auNFyV1UFdGFhIcrKypQOg1RMCHEy0u/JvKXJwNwlNVIibwHmLk3caLnLIRxERERERGFgAU1EREREFAYW0EREREREYWABTUREREQUBlXdRDgcn8+H2tpauN1upUOJOyaTCbm5udDr9UqHQpMgGAyitbUVnZ2dCAQCSocTV7RaLRwOB1JTU6HRsG8jXPw7oBz+HZgY5q5yJpK7MVFA19bWwmazobCwEEIIpcOJG1JKtLW1oba2FkVFRUqHQ5OgtrYWQggUFhZCr9fzeIoQKSV8Ph+amppQW1uL/Px8pUNSHf4dUAb/Dkwcc1cZE83dmOjmcLvdSElJYeJFmBACKSkp/NQcQ3p6epCTkwODwcDjKYKEEDAYDMjJyUFPT4/S4agS/w4og38HJo65q4yJ5m5MFNAAmHgK4X6PPRw+oBzu+4nh+UgZ3O8Tx32ojInsd56tiYiIiIjCwAKaiIiIiCgMLKBjgMfjwR133IGCggLYbDYsWrQIr7766og/097ejvXr18NisaCgoABPPfXUmF4jinU8nkiNmLekVmrN3ZiYhSPe+f1+5OXlYfv27cjPz8crr7yC66+/Hvv370dhYeGQP3PPPffAYDCgqakJ5eXluPLKK1FSUoJ58+aN+BpRrOPxRGrEvCW1Um3uSilV81i6dKkcSmVl5ZDPRwufzyf/4z/+QxYUFMjk5GS5ZcsW+fDDD8uHHnpoyt5zwYIF8rnnnhvyNZfLJfV6vTx8+PDgczfffLP8/ve/P+Jrw4n2/f9pAMpklORtNFLD7zJejyfm7siiPXeZt8zd4TB3z6aG3GUPdATcf//9KCsrQ0VFBd59911873vfg0ajwYcffnhW27Vr12LHjh1DbmflypV4+eWXR32/pqYmVFVVDftpq6qqCjqdDsXFxYPPlZSUYPv27SO+RhQNeDyRGjFvSa2Yu0NjAT3FnE4nHnnkEVRWVsJut2P58uX45JNPsGHDBthstrPajyW5RuLz+XDTTTfh1ltvxezZs4ds43K5kJiYeMZzdrsd3d3dI75GpDQeT6RGzFtSK+bu8HgT4RR7++23UVxcjGnTpgEAvF4v7HY7vvnNb076ewWDQdxyyy0wGAzYuHHjsO2sViucTucZzzmdTthsthFfI1IajydSI+YtqRVzd3gsoKdYfX09srOzB7/ftGkTcnJyhv1lrlmzBlardcjHmjVrhn0fKSXuuOMONDU1YevWrSOu615cXAy/348jR44MPldRUYF58+aN+BqR0ng8kRoxb0mtmLsjGGmA9FQ/APwzgIMADgB4GoBppPZqvInwxRdflBkZGbK+vl5++OGHsrCwUKampkqPxzOp73PXXXfJ5cuXy+7u7jG1v+GGG+SNN94oXS6X3LFjh0xMTJQHDhwY9bWhRPP+/yzwRqwRRfvvMp6PJ+buyKI5d5m3vIlwJMxddeauksVzDoATABL6v/8TgNtG+hk1FtAej0fecsst0m63y6KiIrl371552WWXyRUrVkzae1RXV0sA0mg0SovFMvjYvHnzYJvVq1fLDRs2DH7f1tYm161bJ81ms8zLy5NbtmwZ02tDieb9/1ksQkYW7b/LeD6emLsji+bcZd6ygB4Jc1eduStCbSJPCJED4EMAJQCcAF4A8HMp5RvD/UxpaaksKys76/lDhw5hzpw5UxQpjUZN+18IsUdKWRrJ9xwub6ORmn6XsWq43wFzd2TMXWVFU94CzF0au/HmrmJjoKWUdQB+CuAUgAYAXUMVz0KIO4UQZUKIspaWlkiHSTQuzFtSK+YuqRVzlyJJsQJaCJEEYB2AIgDZACxCiJs/205KuUlKWSqlLE1LS4t0mETjwrwltWLukloxdymSlJyF4zIAJ6SULVJKH4DnAZyvYDxERERERKNSsoA+BeBcIYRZCCEAXArgkILxEBERERGNSskx0B8BeA7AXgD7+2PZpFQ8RERERERjoehS3lLKBwA8oGQMRERERETh4EqERERERERhYAEdISdOnMCaNWuQlJSEnJwcPPHEE5O6/fb2dqxfvx4WiwUFBQV46qmnhm170UUXwWQyDS6vOWvWrHFvi0gJU308bdy4EaWlpTAajbjttttGbDva8cLjiQZMZd56PB7ccccdKCgogM1mw6JFi/Dqq68O2555S+GY6nPuzTffjKysLCQmJqK4uBiPP/74sG2jJXdZQEfIddddh8svvxytra147LHH8NBDD03q9u+55x4YDAY0NTVhy5YtuPvuu3Hw4MFh22/cuBEulwsulwuHDx+e0Laixe/fP4GvPLFL6TAoAqb6eMrOzsb999+P22+/fdS2ox0vaj2eaPJNZd76/X7k5eVh+/bt6OrqwkMPPYTrr78e1dXVQ7Zn3lI4pvqce99996G6uhpOpxMvvfQS7r//fuzZs2fIttGSuyygI+Djjz9GW1sb7r33Xmi1WgDAZM5R2dPTg61bt+LBBx+E1WrFypUrcfXVV+PJJ59UdFuRdqTZhYraLqXDoCk21ccTAFx77bW45pprkJKSMmK70Y4XNR9PNLmmOm8tFgt+9KMfobCwEBqNBmvXrkVRUdGQRQjzlsIRiXPuvHnzYDQaAQBCCAghcOzYsbPaRVPusoCOgPfffx8rV65EMBjEnj17cO+99+Luu+8esu3atWvhcDiGfKxdu3bIn6mqqoJOp0NxcfHgcyUlJSN+4rrvvvuQmpqKFStWYNu2bRPaVrTo8waQoNcqHQZNsak+nsIx2vGi5uOJJlek87apqQlVVVWYN2/eWa8xbykckcrdb3zjGzCbzZg9ezaysrJwxRVXnNUmmnJX0Vk44kV5eTlKS0tx8cUX491338XixYuxfv36Idu+/PLLYW/f5XIhMTHxjOfsdju6u7uHbP/www9j7ty5MBgMeOaZZ3DVVVehvLwc06dPD3tb0aTXG4DZwAI61k318RSO0Y4XNR9PNLkimbc+nw833XQTbr31VsyePfus15m3FI5I5e6vfvUr/OIXv8DOnTuxbdu2wR7pT4um3GUPdASUl5dj2bJleOedd3D06FEkJyfj+9///qRt32q1wul0nvGc0+mEzWYbsv3y5cths9lgNBpx6623YsWKFXjllVfGta1o0utjAR0Ppvp4Csdox4uajyeaXJHK22AwiFtuuQUGgwEbN24csg3zlsIRyXOuVqvFypUrUVtbi1//+tdnvR5NucsCeooFAgEcOnQIixcvhkajwfTp07FixYph269Zs2ZwdozPPtasWTPkzxQXF8Pv9+PIkSODz1VUVAx56W4oQghIKSdlW0rq8/qRwAI6pkXieArHaMeLmo8nmjyRylspJe644w40NTVh69at0Ov1Q7Zj3tJYKXXO9fv9Q46BjqrclVKq5rF06VI5lMrKyiGfjwYHDx6UWq1WPvfcc9Lv98t9+/bJnJwcuXPnzkl9nxtuuEHeeOON0uVyyR07dsjExER54MCBs9p1dHTI1157Tfb19Umfzyc3b94szWazPHz4cNjbGhAt+/+KR9+VX3li14htAJTJKMnbaBQtv8vhROp48vl8sq+vT/7gBz+QN9988+DxMpTRjpfJOp6YuyOL5tyNVN7eddddcvny5bK7u3vUtrGct5K5O2kikbtNTU3y6aeflt3d3dLv98vXXntNms1m+eKLLw7ZPlpyV/GiOJyHGgvozZs3ywULFsiCggJptVplSUmJ3Lp166S/T1tbm1y3bp00m80yLy9PbtmyZfC11atXyw0bNkgppWxubpalpaXSarVKu90uly9fLt94440xb2so0bL/L/7vd+Q3tuwZsQ2LkJFFy+9yOJE6nh544AEJ4IzHAw88IKU883iScvTjZbKOJ+buyKI5dyORt9XV1RKANBqN0mKxDD42b94spYyvvJXM3UkTidxtbm6Wq1atkna7XdpsNjl//ny5adOmwdejNXdFqI06lJaWyrKysrOeP3ToEObMmaNARKP77ne/i+TkZNx3331KhzJlomX/n/uff8cFM1Px3/9QMmwbIcQeKWVpBMMaNm+jUbT8LocTz8cTc3dk0Zy7zNvI5i3A3J0szN3hc5djoKfYvn37ovbAiDW9Xj9vIoxxPJ5IjZi3pFbM3eGxgJ5iFRUVQ04jRJPP7QsiwcCZGWMZjydSI+YtqRVzd3isNqZYS0uL0iHEBX8gCG8gyB7oGMfjidSIeUtqxdwdHnugKSb0+gIAwAKaiIiIphwLaIoJfd5QAc15oImIiGiqxUwBrabZRGJJtOz3Xi97oCdLtPxO4xH3/cRw/ymD+33iuA+VMZH9HhMFtFarhc/nUzqMuOTz+aDTKT+UvtfrBwAk6JWPRc30ej36+vqUDiNu9fX1Dbt6HI2MfweUEy1/B9SKuaucieRuTBTQDocDTU1NCAaDSocSV4LBIJqammC325UOZXAIB3ugJyY9PR11dXXo7e1lj0gESSnR29uLuro6pKenKx2OKvHvgDKi6e+AWjF3lTHR3FX0I6MQwgHgcQDzEVrt63Yp5c5wt5Oamora2locPnx4kiOk0VgsFqSmpiodBodwTJLExEQAQH19PXtEIkyv1yMjI2Pwd0Dh4d8B5UTL3wG1Yu4qZyK5q/Q1l0cBvCalvE4IYQBgHs9GNBoN8vPzJzcyUpVe3kQ4aRITE1nEkerw7wCpFXNXnRQroIUQdgCrANwGAFJKLwCvUvGQuvX5QmOgzVxIhYiIiKaYkmOgiwC0AHhCCLFPCPG4EMKiYDykYhzCQURERJGiZAGtA7AEwK+llIsB9AD4wWcbCSHuFEKUCSHKuCIODSfa5oFm3pJaMXdJrZi7FElKFtC1AGqllB/1f/8cQgX1GaSUm6SUpVLK0rS0tIgGSOoxOAZaHx0FNPOW1Iq5S2rF3KVIUqyAllI2AqgRQszqf+pSAJVKxUPq1usNQK8V0GtjYmZGIiIiimJK33H1TQBb+mfgOA7gKwrHQyrV5/VHTe8zERERxTZFC2gpZTmAUiVjoNjQ6w1wBg4iIiKKCF7vppjQ5wtwBg4iIiKKCBbQFBP6vIGomYGDiIiIYhsLaIoJoSEcLKCJiIho6rGAppjQ6wsggWOgiYiIKAJYQFNM6PP6YeYsHERERBQBLKApJnAIBxEREUUKC2iKCd1uP6wmDuEgIiKiqccCmlTP4w+gq8+HNKtR6VCIiIgoDrCAJtVr6fYAANITWUATERHR1GMBTao3UECn2VhAExER0dRjAU2qN1hAW00KR0JERETxgAU0qV4zh3AQERFRBLGAJtVr6fZACCDFYlA6FCIiIooDLKBJ9Zq7PUixGKDTMp2JiIho6rHiINVr6fYglVPYERERUYSwgCbVa+l2Iz2RNxASERFRZLCAJtVr6fZwERUiIiKKGBbQpGpSSrS4PJyBg4iIiCKGBTSpWmevD76AZA80ERERRYziBbQQQiuE2CeEeFnpWEh9WlxchZCIiIgiS/ECGsC3ABxSOghSp7rOPgBAlp03ERIREVFkKFpACyFyAVwJ4HEl4yD1OtbsAgBMT7MqHAkRERHFC6V7oB8B8D0AQYXjIJU62uxCssWAJK5CSERERBGiWAEthFgLoFlKuWeUdncKIcqEEGUtLS0Rio7U4liLCzOisPeZeUtqxdwltWLuUiQp2QO9AsDVQohqAM8AuEQIsfmzjaSUm6SUpVLK0rS0tEjHSFHuaLML09MtSodxFuYtqRVzl9SKuUuRpFgBLaW8T0qZK6UsBHAjgLellDcrFQ+pT3uPFx29Po5/JiIioohSegw00bgd7b+BcEY6C2giIiKKHJ3SAQCAlHIbgG0Kh0Eqc6yFM3AQERFR5LEHmlSrqqkbJr0GOY4EpUMhIiKiOMICmlRr36lOLMxxQKMRSodCREREcYQFNKmS2xfAwfouLClIUjoUIiIiijMsoEmVKmo64QtIlLKAJiIioghjAU2qVHayAwCwlAU0ERERRRgLaFKlPSc7MD3NwiW8iYiIKOJYQJPq+AJBlFW3Y1lhstKhEBERURxiAU2qs/tEO5xuPy6ala50KERERBSHWECT6rxR2QSjToNVxalKh0JERERxiAU0qYqUEm9WNuGCmakwG6JiIU0iIiKKMyygSVUO1jtR19mHy+dmKB0KERERxSkW0KQqfy6rgUGrwefmZiodChEREcUpFtCkGm5fAH/ZV4fV8zM5fR0REREphgU0qcarBxrgdPtx4zl5SodCREREcYwFNKmClBKPv3cC01ItOG9aitLhEBERURxjAU2qsO1wCw7WO/H1i6ZDCKF0OERERBTHWEBT1JNS4udvH0GOIwHrF+coHQ4RERHFORbQFPVe/rgB+0514puXzIBey5QlIiIiZbEaoajW5w3g/71yCPOyE/EPpbx5kIiIiJTHApqi2k/fOIz6LjceuGoetBqOfSYiIiLlKVZACyHyhBDvCCEqhRAHhRDfUioWik4fHm/D794/gS+fV4BzipKVDoeIiIgIAKBT8L39AL4jpdwrhLAB2COEeFNKWalgTBQlWro9+NYz+1CQbMYP1sxWOhwiIiKiQYoV0FLKBgAN/f/uFkIcApADgAV0nPP4A7jnqb3o6vPh9185B2aDkp/ziKaO2xdAZYMTFTWdKK/pRKvLgy1fPVfpsIiIaBRRUZkIIQoBLAbw0RCv3QngTgDIz8+PbGAUccGgxHf//DF2nWjHozcuwpysRKVDGhfmLX1WMChxoq1nsFgur+nEoQYnfAEJAMhINGJxXhJ8gaCis80wd0mtmLsUSYoX0EIIK4CtAL4tpXR+9nUp5SYAmwCgtLRURjg8iqBgUOLfXjyAlyrq8d3Pz8K6Reqd85l5S60uD8pPhQrlitpOVNR0wun2AwAsBi0W5Npxx8ppWJRnx6K8JGTaTQpHHMLcJbVi7lIkKVpACyH0CBXPW6SUzysZCynLHwjih385gGfLavD1C6fjGxdNVzokojHr8wZwoL4LFTWd2FfTifJTnajr7AMAaDUCszJsuHJh9mCxPCPdyllliAhuXwAn23pR096LRqcbbS4vOvu86PMG4PUHEZQSWo0GRr0GFoMW9gQ9UqxGZCQake1IQF6SGRaj4n2hcUmxvS5C6zH/FsAhKeXPlIqDlNfj8eNbz5TjrUNN+OYlM3Dv5cVcrpuiViAocazFFepdrg0Vy4ebuhEIhjq8chwJWJTvwG3nF6Ikz4H5OYkcx09ECAYlDjU68dHxdpTXdOJAfReqW3sQ/ExfudWog9mghV6rgVYjEAhKePwB9HgC6PMFztpuRqIRM9NtmJ1pw7ycRJTkOlCUauHf0Smm5Fl9BYBbAOwXQpT3P/evUspXlAuJIq26tQdf37wHVU3d+I918/Dl8wqVDonoDI1d7sFhGOWnOrG/rgsuT2gohs2kQ0muA3dfOB0leQ6U5NmRbouOoRhEpDy3L4DtVS14/WAjth9uQVuPFwCQZTdhQY4dVy3MxrQ0C/KTzciyJyDFahjxHgiPP4A2lxeNTjfqOvpwqr0Xx1pcONLkwpMfnoTHHwQAOMx6lBYkYXlRCs6fkYI5mYnQ8KrXpFJyFo4dAPjbjFNSSrxYXo/7XzgArUbg9185B6uK05QOi+Kcy+PH/tquwWK5vKYTjU43AECvFZidmYj1i3OwKM+BkjwHpqVa+EeJiM5SXtOJZ3efwssVDej2+GFP0OOiWWm4sDgN505LQbYjYVzbNeq0yHYkINuRgCX5SWe85g8EcbTFhYqaTuw52YHd1R1461AzACDFYsAFM1NxyZwMXFicBnuCfsL/x3jH64oUcc3dbjzw4kG8eqARSwuS8PMvLkbOOE8mROPlDwRR1eQK9S73z4pxpLl78HJqQYoZ5xQloyTPgUV5DszLToRJr1U2aJpSvkAQTU43mpwetHR70N7jRUevF84+H7o9fvR5A+jzBuALBOEPSgxcedcKQKfVwKjTIEGvhdWkg82khyNBj2SLAan9Y1Yz7SbYTCxcYpU/EMQrBxrx2/eOo6K2Cwl6LdYsyMQ1i3Jw3vSUKZ9dR6fVYHZmImZnJuKGZaFZSBq73NhxtBXvHWnB9qoWvFBeD51G4LzpKVg9PxOfn5eJVKtxSuOKVSygKWL8gSCe2nUKP339MNz+IL63ehbuWjWdN1PRlJNSor7LjfJTZw7FGBhP6DDrUZLrwOfnZ2Jxf+9yssWgcNQ0FYJBidqOPlQ1deNoiwsnWnpwoq1n8CYuOcTcDQatBokJOiQYtDDptDDoNNBpBIQQkP3b9AWC8PiD6PMG4PL4B4f5fJbNpENekhkFKWYUplowPc2KmelWFGfYkGDgBzQ1CgQlXthXh1+8fQTVbb2YlmrBf6ybh/WLcxT/wJRpN+G6pbm4bmkuAkGJ8poOvFHZhNcPNOKHfzmAf3vhAFbMSMVVJdlYPT8TifyAN2YsoGnKSSnx9ifNePi1T1DV5MKKGSl4cN18TEuzKh0axSin24ePa0JDMfadOr1ICRAqhublJOKGZXlYnO9ASa4DBSlm3nATg6SUONXei339H5wO1HXhUEP3GcVtqtWIwhQzzpueglxHArIcCchMNCHNZkSK1YAks2FcVx4CQYmuPh/ae7xodXnQ5HSjscuN+s4+1HT04XBTN9461DQ4D7gQQFGqBfOz7ViYa8eiPAfm59h51SPKvVvVgg1/O4TDTd2Ym5WI39y8BJ+bmxmVQ7u0GoGlBclYWpCMH6yejU8au/Hyx/V4+eMGfO+5j/FvLxzA5XMz8IWluVg1M42dW6NgAU1TRkqJbYdb8PO3j2DfqU4UpVrwm5uX4vPzMlis0KTxBYL4pKEb5TUdKK/pQnlNB4619Ay+Pi3VglUzU7Gov1iek5UIg065hUpo6kgpUdXkwgfHWrHrRDt2V7ej1RW6aStBr8Xc7ERcuyQHc7ISUZxhw4x065SNBdVqBJItBiRbDJiRPnRngT8QxMn2Xhxp6sahhm5UNjhRVt2OlyrqAYTG3c/PseOcomScNy0FywqTOWVZlKjr7MOPXzqINyqbkJ9sxi+/tARr5kdn4TwUIQTmZCViTlYi/uVzs1Be04m/7KvDXytCBXVGohFfWJKLG5flIz/FrHS4UYlHIk06jz+Alysa8Nh7x/FJYzdyHAn4f9cuwHVLcxVdYY3Ub6BHMTRuOVQsH6h3wtt/53mKxYBFeQ6sW9R/o1+uA3YzL0nGsq5eH7YfacG2w81470grWrpDVxpyHAlYNTMNSwuTsDgvCcUZVuii7Pyj02owPc2K6WlWrJ6fNfh8c3douNHeU53YXd2O3+04gf/bfhx6rcDSgiRcWJyOS2anozjDys6ICAsGJTZ/dBL/9eonkBL43upZuGNlEYw69V4pEEJgcX4SFucn4YdXzsHbh5rx5z21+M32Y/jVtmNYMSMFXzqnAJfPzWDnw6ewgKZJc6qtF8/sPoU/ldWg1eXFzHQrfnLdQqxfnMPCmcals9c7uOx1RU0nKmq70N4/DZRJr8H8bDu+fG7BYO9yblICC4o4UN/Zh9cPNuKNg03YVd2OQFDCYdZj5YxUXDAzFedPT0Vesnp7zdJtJnxuXiY+Ny8TANDr9aOsugPvH23Fu0da8fBrn+Dh1z5BjiMBn5uXgc/Py8SywmRecp9ijV1ufOfP5Xj/aBsumJmK/1y/QNV5NhSjTos1C7KwZkEWGrvc+FNZDZ7dXYN7ntqLVKsRNyzLxZeWF/DGf7CApgnqdvvw2oFGPL+3DjuPt0EjgEtmp+PW8wuxckYqixkaM48/gMp65xkFc3VbL4DQ+NAZaVZcOjt9cFaM2Zm2qOtRpKnT3O3G3z5uwF8r6rH3VCcAYGa6FXetmoZL52RgUZ4jZgtIs0GHVcVpWFWchvsANDndePuTZrxV2YQtH53CE+9XI9VqxBULMnF1STaWFiTx3DvJ3v6kCd/5UwU8/iD+c/0CfPGcvJjfx5l2E/7p0pm45+IZeLeqBVs+OolfbzuGX287hkvnZODL5xXE9d95FtAUtm63D+8cbsErHzfgncPN8PiDKEgx497Li3Hd0txxz29J8SMYlKhu6zljCrnKBufgDVXpNiMW5ztw/bI8LMpzYGGuA1aO/Yw7bl8Ab1Q24bk9tdhxpAVBCczOtOFfPleMKxZkxe2NyBmJJnzxnHx88Zx89Hj8eOdwM/72cQOe3V2DP+48ibzkBKxfnIvrluRy/OoEBYISj7xVhV+8fRRzsxKx8UuL4y7vtBqBi2en4+LZ6ajt6MVTH53Cs7tr8GZlE6alWfDlcwvwhaW5is84Emn8i0RjUtvRi3c+acZbh5qx81gbvIEg0mxG3LgsD1cvysaSfPZ40PBaXZ7QEIyaTuyr6cTHtV3o6vMBAMwGLRbm2nH7yqLBKeSy7PwQFs8ON3bj6V2n8Jd9dejq8yHbbsLdF03HNYtyMDPDpnR4UcVi1GHtwmysXZgNl8eP1w804i/9U6r9/O9HcP70FNx4Tj4+Py9D1eN0ldDt9uHbz5Tj75804/rSXPzHuvlxPytKbpIZ31s9G9+6bCZe2d+A339wEj/6ayX++/XDuG5pLr58fiGmx8kHDBbQNCSXx49dJ9rw3pFWvFvVMjirQUGKGbecV4DPz8vE0oKkmL1kSuPn9gVwoK4L5f3FckVNJ2o7+gAAGgEUZ9hwxYJMlOQ6sCjfgZnpNuYRwRcI4tUDjXhyZzV2V3fAoNVg9fxM3LAsD+dNS1HN7AZKshp1+MLSXHxhaS7qO/uwdU8tni2rwT89vQ+pVgNuWJaHm5YX8CrhGNR39uH23+/GkWYXHlw3D7ecV6h0SFHFqNNi/eJcrF+ci/KaTvzxg2o8vasGf9h5EhfMTMVt5xfiolnpMX1uZwFNAICuPh/2nGzHRyfa8dHxduyv60IgKGHUaXBOUTK+eE4+Lp6djmmpFvY006BgUOJYiwv7PjVu+ZPGbgT6l/PLcSSgJM+OL59XgJJcBxbk2mE28LRDp3X2erHlo1P4485qNDk9yE8241+vmI3rluZxMZsJyHYk4Jv941ffO9qKJ3eGxq/+ZvtxrJ6XiTsuKDprKWgKqWrqxpd/uws9Hj9+/5VluGBmmtIhRbVFeQ4sumER/vXKOXj6o1PY/NFJ3PGHMuQnm3HLuQW4vjQvJmdD4l+yOBQIShxtdqGiphN7T3Vg76kOVDW5AITmHV2Y68DXL5yG86enYmlBUtxfsqLTmp3uM4rlj2u7BhelsJl0WJhrx9cvnIZFeUkoybMj3WZSOGKKVrUdvXj8vRN4dncN+nwBXDAzFf/v2gW4qDidvc2TSKMRuLA4DRcWp6GmvRdPfngSz+w6hb/tb0BpQRK+fuF0XDKb+3xAeU0nbv3dLhh0Gvzp6+dhTlai0iGpRqrViG9eOhNfv2g6Xj/YiD98UI0NrxzC/7x5GNcsysEt5xVgXrZd6TAnDQvoGOcPBHGitQcH653YX9eF/XVdOFjXhR5vaAnjRJMOi/OTsHZhNpYVJmNRnoPLyRIAoMfjx/7+oRgDN/o1dLkBADqNwOwsG65ZnI1FeUlYlOfAtFQL/wjTqI63uPCrbcfwwr46AMC6RTn42qoizM5koTLV8pLN+Ncr5uBbl87En8pq8Ph7J/DVP5ZhVoYN91wyA1cuyIrpS+6jKatux21P7EaSRY8td5zLGzDHSa/VDI7Lr6x34o87q/FCeR2e2V2DJfkO3HxuAa5YkKX6zjkhpVQ6hjErLS2VZWVlSocRtdpcHhxu6sbhxm580tCNQ41OHG7shqd/kQmjToO52YlYmGPHwv7xp0Up8VX0CCH2SClLI/measjbQFCiqqk7NIVc/7LHVU3d6B+Jgfxk8+D0cYvyHJiXnaj6k5/aqD13j7e48Iu3j+LF8joYdBp88Zx8fO2CaRyPqyBfIIi/VtTjV9uO4WizC9PTLPinS2di7cLsSSuklchbIPzcLatux62/24WMRBOe+tq5yLTz6tlk6ur14bm9tdj84UmcaO1BklkfWunwnPxhV+pU2mi5yx5olZFSoqHLjeMtPTja3I2jLS4caXLhaLMLbf0LTABAklmPOVmJuPncAszNSsT8HDump1k4by5BSon6Lvdgr3J5TScO1HWht/+qhD1Bj5I8Bz43L3NwVgyORaXxquvswyNvVmHr3loYdVp87YJp+NqqaUi1GpUOLe7ptRpcuyQX1yzKwWsHG/HoW0fwrWfK8ct3juLey2fh8/My4uKel4qaTtz2xG5kJJrwzJ3nIj2RxfNks5v1uGNlEW5fUYgPjrVhy0cn8fsPqvH4jhNYVpiEG5bl44oFmaq6R0Y9kcYRKSVaXV6cbOtBdVsvqlt7cKK1B8dbe1Dd2oM+X2Cwrc2kw8x0Ky6fm4GZGTYUZ1gxK8OGNJsxLk58NDqn24f9tf2zYvT3Lg8sd2zQajAnOxHXl+ahJM+ORXlJKEwxM3dowtp7vPjlO0fx5M6TgABuO78Id180HWk2Fs7RRqMRuGJBFlbPy8Tf9jfgf9+qwtc378GiPAd+sGY2zp2WonSIU+ZIUzdufWIXHGY9tnxtOYvnKSaEwIoZqVgxIxUt3R48t6cWfyqrwb/8uQIPvHgAVyzIwrVLcrG8KDnqr46zgFZIr9ePuo4+1HT0oqa9DzXtvTj1qcdAbyAQmsQ8NykB01ItOG9aCqalWTAtzYIZ6VakWVko02m+QBCfNHSjvPb0UIxjLS4MjNSalmrBBTNST6/ml2Xj3LA0qTz+AJ54vxq/fPsoerx+XLc0F9++rJhDNVRAoxG4qiQba+Zn4vm9dfjft6pw46YPcdmcdPxgzZyovdQ+XvWdffjy73ZBr9Vgy1eXc/75CEuzGXH3RdPx9QunoexkB/5cVoO/fdyAP++pRbbdhKsWZeOqhdmYl50YlXUOC+gpEAxKtPZ40NDpRkNXH+o63ajr6EN9Zx/q+h/tnxpuAYTGJ+cnm5GfbMZ501NQkGxGQaoFBclm5CWboefQC/oMKSVqO/pCs2L0F8sH6roGx7ynWAxYlOfA1SXZWJTnQEmuIyanEqLoIKXE6websOGVStS09+GS2en4wZrZKObCJ6qj02pwff8iWb97/wR+/c4xrH7kXdxyXgG+fWlxTJxHnG4fvvLEbrjcfjx713koSLEoHVLcEkJgWWEylhUm48dXz8cblaHFgB5/7wT+b/txFKaYsWZBFj4/LxMLc+xR0zPNAjpMHn8AzU4PmrvdaOzyoNHpRpPTjYYuNxq7+tDQFfp+YEniASa9BjmOBOQkmTE/x47cpIT+hxl5yQnsSaZRdfZ6UVHbNVgsV9R0Do57N+o0WJBjx83nFgze6JeblMCcoog40tSNH/31IN4/2obiDCuevOMczp0bA0x6Lb5x0QxcX5qHn71ZhT98UI0X9tXhe6tn4/rSPNXO2OELBHHPlr041uLCH24/B3OzOQNMtEgwaLFuUQ7WLcpBe48Xrx1oxKsHGvDYu8fx623HkG4z4uJZ6bh4dhrOn5GKRAWXD1e0gBZCrAbwKAAtgMellP+lRBxSSnR7/Gjt9qCl24MWlwfNztNfm7vdaOn2oMnpRkev76yfN+g0yEw0IctuwtKCJGTZE5DtMCHLnoAsuwnZjgQkmfUsZmjMPP4AKuudoeWv+8cvn2gNrQYpBDAjzYpLZqcPDsWYlWnjVQqKuB6PHz//+xH8dscJmA1a/PjqebhpeT5vVo4xqVYj/nP9Aty0PB8/fqkS9z2/H0/vOoWHrpmPhbkOpcML24MvV+K9I634yRcWYsWMVKXDoWEkWwz40vJ8fGl5Pjp7vXjncDPeqmzGK/sb8GxZDTQCWJDrwLnTknFOYTIW5ydF9IZ3xQpoIYQWwC8BXA6gFsBuIcRLUsrKydi+PxBER68PbT0etLu8aO3xos3lQZvLi1aXp/8R+ndLt2fwsven6bUCaVYj0mxG5CWbsbQgCRmJJmQkGpGeaEJm/8PB4pgmQEqJE609/b3KXdhX04lD9U54A6GcTLcZsSjPgX8ozcWi/tX8bAp+6iYCgDcrm/DAiwdQ3+XGDaV5+N7qWUjhzBoxbV62Hc/edS5eqqjHQ387hHW/fB9fPrcA//L5Wao5Jz296xT+uPMkvnZBEa5flqd0ODRGDrNhcOlwXyCIfac6seNICz441obf7QgN9QCAvOQELMixY3ZmIoozrChKtSI/2Twl61so2QN9DoCjUsrjACCEeAbAOgBhFdCvHWjA9qpWtPd40N7jRVuPF+09XnT1+TDUFNdajUCS2YBUqwFpNiOmpVqQajMi1WpAqtWIdJsJqTYD0m0mOBL0UTPWhmKLlBKPvHUEe091oKKmE053aDU/s0GLBTl2fGVFYWgoRr6DN7ZQVGlyuvHAiwfx2sFGzM604RdfWoylBclKh0URIoTAukU5uHh2Ov7n9cP444cn8frBJvx43Tx8fl6m0uGNaM/JDvz7iwewqjgNP1gzR+lwaJz0Wg3OKUrGOUXJuBeA2xfoX1m5Ex/XduJAnROv7G8842eSLQZkJJqQYjHAbtbDYtDCqNNCqxG48Zy8cS3kpGQBnQOg5lPf1wJY/tlGQog7AdwJAPn5+Wdt5OPaLrxZ2YgkswHJFgNmZ9qQbDEg2WJEiiVUFCdbDEjpL5BZFFMkjJa3Qgi8fjB0gF+xIGuwWJ6ZblPtuEKKDaPlblefD+8fbcX3V8/GVy8o4tChOJVo0uPH6+Zj/ZJc/GDrx9h5rE3xAnq03H3/aCuy7An4+Y2LeJ6NISa9FsunpWD5p6Zb7PH4cbylB8dbXahp70V9lxvNTjdaXV7Ud/WhzxuA2xdAIChx4ay0cRXQiq1EKIS4DsBqKeVX+7+/BcByKeU/DvczaljRjaJbNK3m5g8EOVaUxiyacrfb7VPNJXuaer5AEP6AHPIyebStROh0+xS98YzUI5pXIqwD8OkBSLn9zxHFBRbPpFYsnunT9FoN9CqZTp7FM00WJf+C7wYwUwhRJIQwALgRwEsKxkNERERENCrFeqCllH4hxD8CeB2haex+J6U8qFQ8RERERERjoeg80FLKVwC8omQMRERERETh4CBMIiIiIqIwKDYLx3gIIVoAnBzipVQArREOJxpxP5w23L4okFJGdI3hEfIW4O9sAPdDyEj7gbkbnbgfQqLmnAuwXhgD7ofTxpW7qiqghyOEKFNimpxow/1wmlr2hVrinGrcDyFq2g9qinUqcT+EqGU/qCXOqcb9cNp49wWHcBARERERhYEFNBERERFRGGKlgN6kdABRgvvhNLXsC7XEOdW4H0LUtB/UFOtU4n4IUct+UEucU4374bRx7YuYGANNRERERBQpsdIDTUREREQUESygiYiIiIjCwAKaiIiIiCgMLKCJiIiIiMLAApqIiP5/e3ceH1V59///dWXf9wVIgLCFXbYICLhVpYJbUVu14lZ721a72lXr99be1m7f/nrXn1Zb1NZWcWmlrdTdFpCigIAsshPCkoSQFbJPMsv1/SMRq4ZAIJkzZ/J+Ph5jyMzJzMcz7zPzmWuuc46IiPSAGmgRERERkR5QAy0iIiIi0gNqoEVEREREekANtIiIiIhID6iBFhERERHpATXQIiIiIiI9oAZaRERERKQH1ECLiIiIiPSAGmgRERERkR5QAy0iIiIi0gNqoEVEREREekANtIiIiIhID6iBFhERERHpATXQIiIiIiI9oAZaRERERKQH1ECLiIiIiPSAGmgRERERkR6IcrqAnsjKyrIFBQVOlyEutmHDhhprbXYwH1O5ld6g7IobOZFbUHbl9J0ou65qoAsKCli/fr3TZYiLGWMOBPsxlVvpDcquuJETuQVlV07fibKrKRwiIiIiIj2gBlpEREREpAfUQIuIiIiI9IAaaAkbOyoaWLaz0ukyREREJMy5aifC4/F6vZSVleHxeJwupd+Ji4sjPz+f6Ohop0vh+XWl/PW9Mrbc92mnS3GtQCBATU0NR48exe/3O11OvxIZGUlaWhpZWVlERGhso6f0PuCcUHofcCNl1zmnk92waKDLyspITk6moKAAY4zT5fQb1lpqa2spKytj2LBhTpeDx+snLjrS6TJcraysDGMMBQUFREdHa3sKEmstXq+XyspKysrKGDJkiNMluY7eB5wRau8DbqTsOuN0sxsWwxwej4fMzEwFL8iMMWRmZobMp+Y2X4DY6LCItGOam5vJy8sjJiZG21MQGWOIiYkhLy+P5uZmp8txJb0POCPU3gfcSNl1xulmN2y6DQXPGaG03j1eP3FRGoE+XZo+4Byt+9MTSq9H/YnW++nTOnTG6ax3vVpL2NAUDhEREQkGNdASNtp8AWKjFGkRERHpW+o2JGxoBFpERESCQQ10mFi4cCEDBw4kJSWFwsJCHn/88W6Xr6urY8GCBSQmJjJ06FCeeeaZk7otlLX5AsRpJ0LpBdqexI2UW3ErN2Y3LA5jJ3DXXXfxxBNPEBsby86dOznvvPOYMmUK06ZN63L5O+64g5iYGCorK9m0aROXXHIJkyZNYvz48d3eFso8Xj+x2olQeoG2J3Ej5Vbcyo3Z1XBdEPh8Pu6//34KCgrIzMzkmWee4Re/+AUPPPBArz3G+PHjiY2NBTr2KjXGsHfv3i6XbW5uZsmSJdx///0kJSUxZ84cLr/8cp566qlubwt1Hq8OY9cfaHsSN1Juxa2U3a5pBDoI7rnnHtavX8/mzZtZuXIl3/ve94iIiGDNmjWfWPbSSy9l1apVXd7PnDlzeOmll477OLfffjtPPvkkra2tTJkyhfnz53e53O7du4mKiqKwsPDYdZMmTeKtt97q9rZQ17EToUagw522J3Ej5VbcStntmhroPtbQ0MCvf/1rtm/fTmpqKjNmzGDnzp088MADJCcnf2L57sJ1Io888ggPPfQQq1evZsWKFcc+zX1cU1MTKSkpH7kuNTWVxsbGbm8LdW1ev+ZAhzltT+JGyq24lbJ7fOo2+tiyZcsoLCxk+PDhALS3t5OamsrXvva1Pnm8yMhI5syZQ1lZGY8++miXyyQlJdHQ0PCR6xoaGkhOTu72tlDn8ekoHOFO25O4kXIrbqXsHp+jDbQx5lvGmG3GmK3GmGeNMXFO1tMXDh06xKBBg479vmjRIvLy8o77ZM6bN4+kpKQuL/PmzTvpx/X5fMedP1RYWIjP52PPnj3Hrtu8eTPjx4/v9rZQ5g9YvH6r40CHOW1P4kbKrbiVstsNa60jFyAP2AfEd/7+Z+Dm7v5m2rRptivbt2/v8vpQ8OKLL9rc3Fx76NAhu2bNGltQUGCzsrJsW1tbrz1GZWWlffbZZ21jY6P1+Xz2tddeswkJCfbFF1887t9cc8019tprr7VNTU121apVNiUlxW7duvWEt3UlFNZ/c5vXDv3+S/bRFcXdLgest0HO+vFyG4pC4bnsTn/enpTd7oVydpXb4PcYym7vUHa76WO7u7EvL50NdCmQQcdc7JeAud39jRsb6La2NnvDDTfY1NRUO2zYMPvee+/ZCy+80M6ePbvXHqOqqsqec845NjU11SYnJ9sJEybYRYsWfWSZiy++2D7wwAPHfq+trbVXXHGFTUhIsIMHD7aLFy8+qdu6Egrrv66pzQ79/kv2D6tKul1OTUj3QuG57E5/3p6U3e6FcnaVWzXQ3VF23Zld07GMM4wx3wAeAFqBN6y113e3fFFRkV2/fv0nrt+xYwdjx47tmyLlhEJh/VfUt3LWT5fx0ysnct30IcddzhizwVpbFMTSjpvbUBQKz2V/d7znQNntnrLrrFDKLSi7cvJONbuOTRg1xqQDVwDDgEFAojFmYRfL3WaMWW+MWV9dXR3sMsUlPN4AQMgchUO5FbdSdsWtlF0JJie7jQuBfdbaamutF/grMOvjC1lrF1lri6y1RdnZ2UEvUtyhzecHIC5EjgOt3IpbKbviVsquBJOTDfRBYKYxJsEYY4ALgB0O1iMu9sEItM5EKCIiIn3NsW7DWrsWeAF4D3i/s5ZFTtUj7ubxhtYItIiIiIQvR89EaK29F7jXyRokPLT5PhiBVgMtIiIifUvfd0tY+GAEWidSERERkb6mbiNI9u3bx7x580hPTycvL48//OEPvXr/dXV1LFiwgMTERIYOHcozzzxz3GXPO+884uLijp0daPTo0ad8X6HigxFoncq7f+jr7enhhx+mqKiI2NhYbr755m6XPdH24sbtSfpGX+a2ra2NW2+9laFDh5KcnMzkyZN59dVXj7u8cis90devuQsXLmTgwIGkpKRQWFjI448/ftxlQyW7aqCD5Oqrr+aiiy6ipqaGxx57jB//+Me9ev933HEHMTExVFZWsnjxYr7yla+wbdu24y7/8MMP09TURFNTE7t27Tqt+woFGoHuX/p6exo0aBD33HMPX/jCF0647Im2FzduT9I3+jK3Pp+PwYMH89Zbb1FfX8+Pf/xjPve5z7F///4ul1dupSf6+jX3rrvuYv/+/TQ0NLB06VLuueceNmzY0OWyoZJddRtBsGXLFmpra7nzzjuJjOwYIe3NQ+w0NzezZMkS7r//fpKSkpgzZw6XX345Tz31lKP3FUxtH+xEqBHosNfX2xPAlVdeyWc+8xkyMzO7Xe5E24tbtyfpfX2d28TERO677z4KCgqIiIjg0ksvZdiwYV02Icqt9EQwXnPHjx9PbGwsAMYYjDHs3bv3E8uFUnbVQAfB22+/zZw5cwgEAmzYsIE777yTr3zlK10ue+mll5KWltbl5dJLL+3yb3bv3k1UVBSFhYXHrps0aVK3n7juuususrKymD17NitWrDit+woFH07hUKTDXV9vTz1xou3FrduT9L5g57ayspLdu3czfvz4T9ym3EpPBCu7t99+OwkJCYwZM4aBAwcyf/78TywTStl19Cgc/cWmTZsoKiri/PPPZ+XKlUyZMoUFCxZ0uexLL73U4/tvamoiJSXlI9elpqbS2NjY5fI///nPGTduHDExMTz33HNcdtllbNq0iREjRvT4vkLFh1M4NAId7vp6e+qJE20vbt2epPcFM7der5frr7+em266iTFjxnziduVWeiJY2X3kkUd46KGHWL16NStWrDg2Iv2fQim7Gq4Lgk2bNnHmmWeyfPlyiouLycjI4Pvf/36v3X9SUhINDQ0fua6hoYHk5OQul58xYwbJycnExsZy0003MXv2bF555ZVTuq9Q4fEGiDAQHWmcLkX6WF9vTz1xou3FrduT9L5g5TYQCHDDDTcQExPDww8/3OUyyq30RDBfcyMjI5kzZw5lZWU8+uijn7g9lLKrBrqP+f1+duzYwZQpU4iIiGDEiBHMnj37uMvPmzfv2NExPn6ZN29el39TWFiIz+djz549x67bvHlzl1/ddcUYg7W2V+7LKW0+P3HRkXSc1FLCVTC2p5440fbi1u1Jelewcmut5dZbb6WyspIlS5YQHR3d5XLKrZwsp15zfT5fl3OgQyq71lrXXKZNm2a7sn379i6vDwXbtm2zkZGR9oUXXrA+n89u3LjR5uXl2dWrV/fq41xzzTX22muvtU1NTXbVqlU2JSXFbt269RPLHTlyxL722mu2tbXVer1e+/TTT9uEhAS7a9euHt/XB0Jh/d/zt/ft5B+9fsLlgPU2RHIbikLhuexOsLYnr9drW1tb7Q9+8AO7cOHCY9tLV060vfTW9qTsdi+Usxus3H7pS1+yM2bMsI2NjSdcNpxza5XdXhOM7FZWVtpnn33WNjY2Wp/PZ1977TWbkJBgX3zxxS6XD5XsOt4U9+Tixgb66aefthMnTrRDhw61SUlJdtKkSXbJkiW9/ji1tbX2iiuusAkJCXbw4MF28eLFx267+OKL7QMPPGCttbaqqsoWFRXZpKQkm5qaamfMmGHfeOONk76vroTC+v/uXzbZmT/55wmXUxPSvVB4LrsTrO3p3nvvtcBHLvfee6+19qPbk7Un3l56a3tSdrsXytkNRm73799vARsbG2sTExOPXZ5++mlrbf/KrVV2e00wsltVVWXPOeccm5qaapOTk+2ECRPsokWLjt0eqtk1Hcu4Q1FRkV2/fv0nrt+xYwdjx451oKIT++53v0tGRgZ33XWX06X0mVBY/19/diPvl9ez/DvndbucMWaDtbYoOFV1OF5uQ1EoPJfd6c/bk7LbvVDOrnIb3NyCsttblN3jZ1dzoPvYxo0bQ3bDCCcer18nUekHtD2JGym34lbK7vGp4+hjmzdv7vIwQtK72nwBYnUSlbCn7UncSLkVt1J2j0/Hge5j1dXVTpfQL3i8fuI0Ah32tD2JGym34lbK7vGp45Cw4NEItIiIiASJGmgJC20agRYREZEgCZuOw01HEwknobLeNQe694TKc9ofad2fHq0/Z2i9nz6tQ2ecznoPiwY6MjISr9frdBn9ktfrJSrK+an0GoHuHdHR0bS2tjpdRr/V2tp63LPHSff0PuCcUHkfcCtl1zmnk92w6DjS0tKorKwkEAg4XUq/EggEqKysJDU11elSaGzzkRirF/DTlZOTQ3l5OS0tLRoRCSJrLS0tLZSXl5OTk+N0Oa6k9wFnhNL7gFspu8443ew62nEYY9KAx4EJdJzt6wvW2tU9vZ+srCzKysrYtWtXL1coJ5KYmEhWVpajNXj9ARo9PtITYhytIxykpKQAcOjQIY2IBFl0dDS5ubnHngPpGb0POCcU3gfcTNl1zulk1+khuweB16y1VxtjYoCEU7mTiIgIhgwZ0ruViWscbelo9NIT9dV3b0hJSVETJ66j9wFxK2XXnRxroI0xqcA5wM0A1tp2oN2pesS9jrZ0xCZNI9AiIiISBE7OgR4GVAN/MMZsNMY8boxJdLAecakjH4xAJ2gEWkRERPqekw10FDAVeNRaOwVoBn7w8YWMMbcZY9YbY9brjDjSlSOdI9ChNAdauRW3UnbFrZRdCSYnG+gyoMxau7bz9xfoaKg/wlq7yFpbZK0tys7ODmqB4g4fTuEInRFo5VbcStkVt1J2JZgca6CttYeBUmPM6M6rLgC2O1WPuNcHUzgyEkNnBFpERETCl9NH4fgasLjzCBwlwC0O1yMudKS5nZioCOJ1JkIREREJAkcbaGvtJqDIyRrE/Y60tJOeEI0xxulSREREpB8IizMRSv92pMUbUjsQioiISHhTAy2ud7SlPaR2IBQREZHwpgZaXE8j0CIiIhJMaqDF9TpGoNVAi4iISHCogRZXs9Z2jkBrCoeIiIgEhxpocbUGjw9/wGoKh4iIiASNGmhxtVA8C6GIiIiENzXQ4mofnIVQI9AiIiISLGqgxdUO13sAyEmJdbgSERER6S/UQIurlR1pAWBweoLDlYiIiEh/oQZaXK38aCsJMZGaAy0iIiJBowZaXK3sSCv56fEYY5wuRURERPoJNdDiauVHWslLi3e6DBEREelH1ECLq5UdaSFf859FREQkiNRAi2s1eLw0eHzkpWsEWkRERIJHDbS4VvmRVgDy1UCLiIhIEKmBFtf6sIHWFA4REREJHjXQ4lofHANaOxGKiIhIMKmBFtcqO9JKbFQEWUk6jbeIiIgEjxpoca09VU2MyE7SMaBFREQkqBxvoI0xkcaYjcaYl5yuRdxl5+EGxgxIdroMERER6Wccb6CBbwA7nC5C3OVIczuVDW2MGagGWkRERILL0QbaGJMPXAI87mQd4j47DzcCMHpAisOViIiISH/j9Aj0r4HvAYHjLWCMuc0Ys94Ys766ujpohUlo23W4ASBkp3Aot+JWyq64lbIrweRYA22MuRSostZu6G45a+0ia22RtbYoOzs7SNVJqNtV2Uh6QjQ5ybFOl9Il5VbcStkVt1J2JZicHIGeDVxujNkPPAd8yhjztIP1iIvsqGhk9IBkHYFDREREgs6xBtpae5e1Nt9aWwBcCyyz1i50qh5xD68/wM7DDYwdqPnPIiIiEnxOz4EW6bGt5fV4vAHOLMhwuhQRERHph6KcLgDAWrsCWOFwGeIS6/bXAVBUkO5wJSIiItIfaQRaXOfdfUcoyEwgJznO6VJERESkH1IDLa4SCFg2HKjT9A0RERFxjBpocZW91U0cafFy5jA10CIiIuIMNdDiKit2dRwc/6zhmQ5XIiIiIv2VGmhxlX/trGTMgGQGZyQ4XYqIiIj0U2qgxTXqW7ys23+ET43JcboUERER6cfUQItrrNhdhT9guWBsrtOliIiISD+mBlpc47Wth8lKimHy4DSnSxHpVYGAZfHaA9z2p/VYa50uR0RETkANtLhCfYuXf+2o4tIzBhEZYZwuR6TX7Kls5JpFq/nh37bS1OajweNzuiQRETmBkDgTociJvPT+Idr9Aa6amu90KSK9wuP185vlxfz2rb0kxkbxi6vP4LPT8jFGHxBFREKdGmhxhSUbyhiVk8SEvBSnSxE5bSt3V/N/XtzKgdoWFkzJ455LxpKZFOt0WSIicpLUQEvI21pez3sHj3L3/DEanRNXq6hv5ccv7eDl9ysYnpXI4i/OYPbILKfLEhGRHlIDLSHvD2/vJyEmkmuKhjhdisgpafcF+MPb+3jwX3vwByzfvqiQ284dTmxUpNOliYjIKVADLSGturGNf2w+xLXTB5OaEO10OSI9tmpPDfcu3cre6mYuGJPDfZeP14mARERcTg20hLTfvbUXXyDALbOHOV2KSI+U1rXw45e38/q2SoZkJPDETUU6hrmISJhQAy0hq6rBw1NrDvCZKXkMy0p0uhyRk9Lc5uORFcU89u99RBrDdz89mlvnDCMuWtM1RETChRpoCVkPLSvGF7B844JRTpcickL+gGXJe2X88vVdVDW28ZnJg/j+vDEMTI13ujQREellaqAlJO2oaGDx2gMsnDmUoZkafZbQ9k5xDT9+eQfbKxqYPDiN394wjalD0p0uS0RE+ogaaAk5gYDl3qXbSI2P5s6LCp0uR+S4dlc28rNXd7JsZxV5afE8eO1kLp80SIdbFBEJc4410MaYwcCfgFzAAoustQ86VY+EjmfePci7++r42ZUTSUuIcbockU+oqG/l12/u4S8bSkmMjeIH88Zw86wCzXMWEeknnByB9gHftta+Z4xJBjYYY9601m53sCZxWGldCz99ZQdzRmZxzZmDnS5H5COOtrTz6Iq9PPnOfqyFW2YP46vnjyQ9UR/0RET6E8caaGttBVDR+e9GY8wOIA9QA91PtfsCfPWZ94iIMPzsqon6GlxCRlObj9+v2sdjK0toavexYEoe37qwUMdzFhHpp0JiDrQxpgCYAqzt4rbbgNsAhgzRmejC2U9e2cHmsnp+u3Aq+enubkyU2/DQ2u7nqTX7+e1bJdQ1t3PRuFy+M3c0owckO11an1F2xa2UXQkmxxtoY0wSsAT4prW24eO3W2sXAYsAioqKbJDLkyB59t2DPPnOfm6dM4yLJwx0upzTpty6m8frZ/Hagzy6Yi81TW2cPSqLb88dzeTBaU6X1ueUXXErZbeDtZbKhjZ2VTZSUt1EaV0rlY0e6praaWzz0uYNYIFIY4iLjiAlPpqspFhyU+IYnBHPyOwkxgxI0dl/T8DRBtoYE01H87zYWvtXJ2sR5yzbWck9f9/KuYXZ3DVvjNPlSD/W2u5n8doD/G5lCdWNbZw1PJNHrp/K9GEZTpcmYcbnD1DV2EZlg4fapnaOtnppbvPR5vPjC1gMhuhIQ0JMFKnx0WQmxTAgJY6BaXHERmlnVfmQtZbtFQ28U1zL2n11bCo9Qk1T+7Hb46MjGZAaR0ZiDFlJscRFRRIZYfAFArR6A9S3etlX00xlgwev/8PPHUMyEpg6JI0ZwzOZMzJLU9Y+xsmjcBjgCWCHtfZXTtUhzlpbUsvti99j7MBkfnP9VKIiI5wuSfqhRo+Xp9cc5PF/l1Db3M5ZwzN56LopzBye6XRp4nL+gGVvdRPbDtWzs6KR3ZWNlNQ0U36kFV+g54OkxkB+ejyjc5MZNyiVKUPSKBqaTnKcRgv7E58/wOqSWl7deph/bq+kqrENgGFZiZxbmMMZ+amMHpDMiOwkspJiTmqfIn/AUlHfSnFVEzsqGtlSdpRVxbX8fdMhAEZkJzJ3/AAumTiQ8YNS+v1+Sk6OQM8GbgDeN8Zs6rzubmvtK86VJMG0tqSWW55cR15aPE/eMp2kWMdnFEk/U9fczpNv7+PJd/bT4PFx9qgsvn7BKM4s0IiznBqP18/Gg0dZU1LL+gN1bC6tp6nNB0BMZATDsxOZkJfKpWcMJC8tgdyUWLKSYklLiCYxNoq46EiiIgzWduxY3eL1Ud/qpbapnYp6DwfrWiipbmLX4UaW7awiYCHCwBn5aZw/Ooe543MZMyC53zc34WrX4UaeX1fK0s3l1DS1kxgTybmjs/nUmFzmjMxiQGrcKd93ZIQhPz2B/PQEzhudA3SMbu+tbmLl7hqW7azisZUlPLpiL8OzE7lqaj5XT8snN+XUH9PNnDwKxypAW3g/9a8dldy++D3y0+N59r9mkpUU63RJ0o+U1rXw+L9LeH59KR5vgE+Pz+X280YyqR/McZbet7+mmWU7q1ixu5q1JbW0+QJEGBgzIIUFU/KYNDiNiXmpjMhO7NG3bPExkaQSfdzTwTe3+dhUepS1JbW8taeGX/9rN//7z92MzEliwZS8ft3chBOvP8CrWw/zp3f2s/7AEaIjDReMyeUzU/I4b3R2nx5/3hjDyJxkRuYk84U5wzjS3M5r2w7zt43l/N/Xd/GrN3dz4dgcbp41jJnDM/rVBzcN+UnQ/fGd/fzoH9uYkJfKH24+k0w1zxIkW8qOsmhlCa+8X0FkhOGKyXl8+dzhjMwJ36NqSO+z1rK1vIFXt1bwxvZKiquagI6vuK+bPoQ5I7OYPjyDlD6eVpEYG8XskVnMHpnFnXNHU9Xo4Y1tlby46cPm5uLxA7j17GE6tbwLNbX5eO7dgzyxah8V9R4KMhO455KxXDk1nwyHjj2fnhjDddOHcN30IeyvaebZdQf587pSXt9WyfhBKdx2znAumTiwX0zHVAMtQePx+rlv6TaeW1fKhWNzefDaySRq2ob0MX/A8s8dlTyxah/v7qsjOTaK/zp7ODfPLjjuyJ5IV3YdbmTp5nL+sbmCg3UtREYYZgzL4PoZQ7hwbK7jO1nlJMexcOZQFs4cyr6aZp599yDPvXuQl9+vYObwDL55YaHm9btAo8fLk2/v54m393G0xcvM4Rk8sGAC5xXmEBEROiO8BVmJ3DVvLN+6sJC/byxn0b9L+MZzm/jfN3dzx/kjWTAlL6wbaXUvEhQl1U187dmNbDvUwB3nj+DOi0YTGUIvBBJ+6lu9/GV9KX9cvZ/Sulby0uK555KxXHPmYO1wJSetqtHD0k2HWPJeOTsqGoiMMMwakckd549g7rgBIXsWymFZidw9fyzfuGAUz757kEUrS7h20RrOKczm7vljGDMgxekS5WNa2/38cfV+fvvWXo62eLlgTA5f/dRIpoT4twdx0ZFcO30InysazBvbD/PQsmK++8IWfrO8mG9dVMhlZwwKqca/t6iBlj5lreXptQf5ycs7iIuO4PEbi7hwXK7TZUkY21HRwJ9WH+DvG8tp9fo5syCdu+eN5aJxuWE9GiK9x+sPsHxnFX9eX8ryXdX4A5ZJg9O477JxXHLGILKT3TPtLDE2ii+ePZyFM4fy1OoDPLy8mPkP/puFM4fy7bmjSY3Xh0mn+QOWJRvK+NWbuznc4OHcwmy+PbeQM/LTnC6tRyIiDBdPGMinxw/gze2V/OrN3XzjuU0sWlnC3fPHMntkltMl9io10NJnSqqbuPtv77OmpI6zR2Xxi6vP0Ffm0ic8Xj+vbq3g6TUH2XDgCLFREVwxeRA3nlXAhLxUp8sTlyita+G5dQf58/oyqhvbyE6O5YtnD+Oz0/JdP08+LjqS/zpnOJ8tyud/39zNU2sO8OrWw9x/xQQunjDA6fL6rbeLa7j/pe3sPNzIlCFpPHjtZGa4fJqNMYa54wdw4dhclm4+xC/f2MX1j6/l/NHZ/PCScYzMSXK6xF6hBlp6XVObj0eWF/PYv0uIi47kp1dO5NozB/ervXMlOPZUNvLculKWvFfG0RYvw7ISueeSsVw9LZ+0hND8al1Ciz9gWbazisVrD/DW7moMcP7oHK6bPoTzRmeH3bcWaQkx/OiKCXy2aDDfe2ELX356A5+ZPIgfXTFBo9FBdLC2hftf3s6b2ysZnBHPbz4/lfkTB4TV+2REhOEzU/KYN3EAf3xnPw8tK+bTv17JjWcN5ZsXFro+b2qgpdd4/QGeX1fKr/+5h5qmNq6cmscP5o0hJ1mHUZLe0+jx8vKWCv68vpT3Dh4lOtIwd9wArps+hFkjMsNyrp30vtqmNp5bV8ozaw9SfrSV3JRYvv6pUVxz5mAGpYX/N2UT8lJ58auz+c3yYh5aVsy6/Ud46PNTdLSOPtba7ueRFcX8bmUJURGG7356NLfOGdanh6JzWmxUJLedM4Krpubzyzd28+Q7+1m66RDfv3gMV0/Ld+1rthpoOW3tvgB/31jOw8uLOVjXwpkF6Tx+UxGTdUxd6SX+gOWdvTX89b1yXt1agccbYGROEnfPH8OVU/N1HHE5aZtKj/Knd/bz0pYK2v0BZo3I5J5LxnLhuFyiw2y0+USiIyP45oWFnFuYzdee3cjnfruaey4Zy02zCsJqJDQUWGt5c3slP/rHdsqPtnLF5EHcPX9svzpOd2ZSLD+9ciLXzxjCfUu38b0lW3hu3UH+54oJrpxqpwZaTlmjx8vz60r5/ap9HKr3MDEvld/fXMT5o3P04iunzVrL9ooGXtx0iBc3lVPZ0EZKXBRXdp79asrgNOVMTkqbz88r71fw5DsH2Fx6lMSYSK6dPpgbzxrq+rnNvWHKkHRe/vrZfPvPm7jvH9vZeqiBBxZMIDYqfEdFg6m0roX7lm7jXzurGJ2bzPO3zXT9POfTMSEvlb98+Sz++l45P311B5c/vIobzyrgzrmFfX7s9N6kBlp6bE9lI4vXHuSFDWU0tfmYXpDBA1dO5LzCbDU0ctqKq5p4eUsFSzeXs7e6magIw3mjs7n3snw+NSYnrL/qlN51uN7DM2sP8My7pdQ0tTE8O5H7LhvHVdPydSjDj0mNj2bRDUU8+K89PPivPRyobWbRDUUhe5g+N2j3BXjs3yU8tGwPEcZw9/wx3DJ7WL/7pqMrxhiumpbPheNy+eXru/jj6v288n4F/33ZOC6ZONAVvYQaaDkpTW0+XtlSwV82lLJuf8epRC+ZOJBbZg/T6Y/ltFhrKa5q4tWth3nl/Qp2Hm7EGJhekMEX5gxj/oSBehOXk2atZcOBIzz5zn5e23oYv7V8anQON80qYM7ILNfOtwyGiAjDty4qZFRuEnf+eTNXPfoOf/zCdMdPEONGa0pquefvWymuauLi8QP478vG9Yu59T2VGh/N/Z+ZwNXT8vnh39/nq89s5PlRpdx/xQQKshKdLq9baqDluDxePyt3V/OPLRW8uf0wHm+A4VmJ/GBex8R/zTuVUxUIWDaVHeXN7ZW8vu0wJdXNGANFQ9O597JxzJ84sF/NDZTT19ru58VN5fxp9QG2VzSQEhfFzbMKuOGsoQzNDO034lBz6RmDyE2J49Yn13H1b9/hqVtnUJirqS4no6apjZ+8soO/vldOfno8v7+5iE+N0bkPTmTS4DRevGMOT63ez//3xm7m/nolt583gi+fOyJkv3VUAy0fUd/q5a3d1byx7TDLd1bR3O4nPSGaq6fls2BKPlOHaN6pnJqmNh+r9tSwfGcVy3ZVUd3YRlSEYcbwDG6ZVcCnxw8gR02z9FBxVROL1x5gyYYyGjw+xgxI5oEFE1gwJY+EGL3FnaozCzL4y5dnccMTa7nmd6t56tYZrtzRK1j8Acszaw/wf1/fRavXzx3nj+Cr548iPiY0m79QFBlhuHn2MOZPHMj9L+/g1//cw982lnPf5eM5f3SO0+V9gl5d+rlAoGNHrZV7qnlrVzXrDxzBH7BkJsZw+eRBXDxhILNGZGrOlvSYtZZdlY2s3F3NW7ureXdfHV6/JTk2inNGZzN3XC7nFeaQmqC5qNIzbT4/b2yr5Jm1B1ldUkt0ZMcZ0G6YOZQzC9L1Ib+XjB6QzJ+/dBbXP76W6x9fy+IvqonuyoYDddy7dBtbyxuYNSKT/7liQticLMQJOSlxPHTdFK49czD/5+9bueUP67hoXC7/fem4kJpOpAa6nwkEOpqad/fVsaakljUltRxp8QIwbmAKXzpnOBeMzWHy4HQiNVdQeqj8aCvvFNfwzt5aVhXXUN3YBkBhbhK3zB7GeaOzObMgQx/I5JTsqWzk+XWl/HVjOXXN7eSlxfPdT4/mc0WDXXV6bTcpyErkudtmcs3vVrPwibU8f9tZjB6g6RwAFfWt/PzVnfx90yEGdDZ9l57hjh3g3GD2yCxe/ebZPLFqHw8vK+aCX73Fl84ZzpfPHUFirPPtq/MVSJ860tzOlvJ6Nh08ynsHj/DewSM0enwA5KXF86kxucwemcmckVn6+lx6xFpLaV0r7+6vY21JLWv31XGwrgWAzMQYZo/MYs7ILM4uzNIp3OWU1bd4een9Q7ywoYyNB48SFWG4cGwu180YwpyRWfqgHwSDMxJ49raZfPa3HU30C18+q1/PK29u8/G7lSUsWrmXgIU7zh/B7eeNDImmLtzERkVy+3kjWTAlj5+9upOHlhXz/LpSvjN3NFdNy3d0+9ezHSastVTUe9h5uIEdFY1sO1TP++X1lNa1AmAMFOYkc+kZgygams70YRnkp8frk7KcNI/Xz9byejZ2fhhbf+DIsRHmtIRophdkcPOsAs4akcno3GQd7UBOmcfrZ/nOKv6+qZzlO6tp9wcozE3ih/PHsmBqnnZgdsDQzEQWf3EGn/vdam78/bu88OVZ/W7Uv90X4Nl3D/LQsmJqmtq4ZOJAfjBvTEhNKwhXA1PjefDaKdx4VgH3v7Sd7y3ZwuOrSvjO3NFcNC7XkV5GDbTL+AOWQ0db2VvdRHFVE3urm9hT2cTuykYaOkeWAYZkJDAxL5XPTx/KpPxUJuan6rinctI8Xj+7Djey9VA9W8vr2VJWz87DjfgDFoDBGfHMHpHJtIIMphdkMConSQ2znJaWdh8rd1fzyvuH+deOSprb/WQnx7Jw5lCunJrH+EEp+sDvsFG5yTxx85l8/rE13PrHdTx328x+saOmx+vnhQ1lPLK8mEP1HmYMy+CxG6cxRac9D7ppQ9P52+2zeHXrYX75+i5ue2oDZ+Sn8o0LRvGpMcE9iVv4J9+F6lu9lB9ppfxoK6V1LRzsvByobaa0rpV2f+DYsmkJ0RTmJHPZpEGMGZDMmIEpjBmQrGZZToo/YDlY18Keykb2VDWx83AjOyoaKKluorNXJjU+mol5qXz53OFMyk9j8pA0cpI13UdOX2ldC2/trmb5zipWFdfQ5guQnhDNZZMGcdmkQcwcnqkpGiFm6pB0Hr5uKrc9tZ6vP7uJ390wLWyfo6Mt7Tzz7kGefHs/VY1tTBmSxs+uOoOzR2Xpw5yDjDHMnziQueNy+et75Ty0fA+3/nE9YwYk88Wzh3PpGQODcug7RxtoY8zFwINAJPC4tfZnTtbT11rb/dQ0tVHd1EZ1YxtVDR6qGtuobPBQUe/hcOelsc33kb9LiIlkSEYCI3OSuHBsLsOyEhmencSI7EQyEmO0IUu3/AFLRX0rpXWtHKxrZn9tC/uqmympaWJ/TctHPpDlpcUzdmAy8yYMYNzAFCbkpWqqj/Saw/Ue1u3v2IH5nb217KtpBjq+0bhu+hDmjs9lekEGUdrJNKRdOC6Xey8bz71Lt/GL13dy17yxTpfUawIBy9p9dbywoYyXthyizRdgzsgs/veaycwakanXwhASFRnB584czIKpeSzddIhFK0v4zl8285NXdnD1tHyumprfpzu8OtZAG2Migd8AFwFlwDpjzFJr7XanajoRay1tvgBNbT6a23w0ej64eGnw+Gho9VLfeTnS0s7Rlo6ftU3t1DW30+r1f+I+IwxkJcUyIDWO4dmJzB6ZxaC0OPLSEshPjyc/PV5NsnQpELA0eLzUNrdT09hGTVM7VY0eDjd4qKz3cKjew6GjrRyu9+D7YDgZiI40DM5IYHhWIuePzmFEThIjc5IozE0mSTvBSC/wByzlR1rZU9XIzsONx6YBlR/t2CcjMSaSGcMzWThzKOcWZjEiO0mvcS5z06wC9lQ18ru3ShgzIJkFU/KdLumUNXq8rNtfx4pd1byxrZLDDR6SYqO4elo+N5w1lDEDUpwuUboRHRnBVdPyuXJqHm8X1/L0mgP8ftU+Fq0soTA3ibnjBnBOYTaTB6cRE9V7H86dfLecDhRba0sAjDHPAVcAPWqgNxw4wq7DjVgstrNHsHQ0FwFr8R/7Cf5AAK/f4uv86fUH8PoDtPs6Lm2+AB6vH483gMfX+dPrp6XdR0u7n5Z2/7E5oN1JjosiPSGG9IRo0hJiGJGdREZiDJlJMWQlxZKdFEt2ciw5ybFkJMZotKWfenFTOc1tfgL2w6z6/JZ2f4A2rx9PZx6b2/w0t/loauv4sNbo8XGkpZ36Vi9dxTEmMoKclFgGpcZTNDSdQWnx5KcnMCSj4zIoLU6Zk1NW29TG69sq8Qc6XjNb2/0dH+Sa2qlqbKP8aCvlRz461WxoZgJTh6bzhTnDKBqazvhBKcpgGLj3svEUVzXxgyXvMyonOeSPEb259Cjvl9fj8fqpaWqn9EgLuw83UlzdhLUQHx3JnFFZ3HXGGOaOG6CToLiMMYY5o7KYMyqLmqY2Xt5SwcvvV/DoW3t5eHkxMVERjB2QzIicJPLS4klPiCEuOpKzR2Wd0o6gTjbQeUDpf/xeBsz4+ELGmNuA2wCGDBnyiTt5eUsFv397X48eODLCEBVhiImMICYqgujOn7FREcRGRxAXFUliTBSZiRHEx0QRHx1BQkwUCTGRJMZGkRQbdexnSlwUyXHRJMdFkRofTUp8dNjOB5OTd6LcAvz81Z0cqvcc9z5ioyJIiIn8SPZSE2IYnJFAanx0x4e0xBgyOz+cdXwoiyMtPlo79MkpO1F2y4+2cvff3v/IdXHREWQmxpKTEsu4gSnMHZ/L8KxERmQnUTggmRTtkxGWoiMjePjzU7n8oVX8Znkxjy6c5mg9J8ru69sO88iKvUDHN3F5afGMyE7ikjMGMr0gg6lD00P2tNHSM1lJsdw0q4CbZhVQ3+Jl7b5a1h84wrZD9bxTXEtVo+fYANRjNxadUgNtrD3xiGpfMMZcDVxsrf1i5+83ADOstV893t8UFRXZ9evXf+S6Bo+X1vaOqRHm2H8g0hgijCEiwhAZYYg0HT+jI42+KuzHjDEbrLVFwXzMrnILUNXowdqOyEZ0ZjQ6KoLoyI4Pd8qp/KdQyW67L0BdczsRER3HaI2PjuzVr0XFffbVNDMwNa7L5tOJ3ELX2a1v9dLm8xMbFUlKXJReY/sxf8DS0Oql3R8gJS66y28bTpRdJ0egy4HB//F7fud1PZISF63RDXElHclC3CgmKoIBqcqufGhYljtOqpIaHw2oX5COmQjpiTGndR9ODhusA0YZY4YZY2KAa4GlDtYjIiIiInJCjo1AW2t9xpivAq/TcRi731trtzlVj4iIiIjIyXD0mFXW2leAV5ysQURERESkJ7Tnh4iIiIhIDzh2FI5TYYypBg50cVMWUBPkckKR1sOHjrcuhlprs4NZSDe5BT1nH9B66NDdelB2Q5PWQ4eQec0F9QsnQevhQ6eUXVc10MdjjFnvxGFyQo3Ww4fcsi7cUmdf03ro4Kb14KZa+5LWQwe3rAe31NnXtB4+dKrrQlM4RERERER6QA20iIiIiEgPhEsDvcjpAkKE1sOH3LIu3FJnX9N66OCm9eCmWvuS1kMHt6wHt9TZ17QePnRK6yIs5kCLiIiIiARLuIxAi4iIiIgEhRpoEREREZEecFUDbYy52BizyxhTbIz5QRe3xxpjnu+8fa0xpsCBMvvcSayHm40x1caYTZ2XLzpRZ18zxvzeGFNljNl6nNuNMeb/71xPW4wxU4Nd43/Uouyi7IJy60bKbQdl132U3Q59kl1rrSsuQCSwFxgOxACbgXEfW+Z24Led/74WeN7puh1aDzcDDztdaxDWxTnAVGDrcW6fD7wKGGAmsDaEnzNl1/aP7Cq37rootx/5/1R2XXRRdj/y/9nr2XXTCPR0oNhaW2KtbQeeA6742DJXAH/s/PcLwAXGGBPEGoPhZNZDv2CtXQnUdbPIFcCfbIc1QJoxZmBwqvsIZbeDsoty60LKbSdl13WU3U59kV03NdB5QOl//F7WeV2Xy1hrfUA9kBmU6oLnZNYDwFWdX0O8YIwZHJzSQs7JrqtQqEPZ/VB/z65yG1qU25On7IYWZffk9Ti7bmqg5eT9Ayiw1p4BvMmHn7JFQp2yK26k3IpbKbunyE0NdDnwn5+M8juv63IZY0wUkArUBqW64DnherDW1lpr2zp/fRyYFqTaQs3JZCZU6lB2UXY7KbehRbk9ecpuaFF2T16Ps+umBnodMMoYM8wYE0PHpP+lH1tmKXBT57+vBpbZztnhYeSE6+Fj83YuB3YEsb5QshS4sXPv2plAvbW2woE6lN0Oyu7JUW5Di3J78pTd0KLsnrweZzcqOHWdPmutzxjzVeB1OvYs/b21dpsx5n+A9dbapcATwFPGmGI6Jotf61zFfeMk18PXjTGXAz461sPNjhXch4wxzwLnAVnGmDLgXiAawFr7W+AVOvasLQZagFucqFPZ7aDsdlBu3UW5/ZCy6y7K7of6Irs6lbeIiIiISA+4aQqHiIiIiIjj1ECLiIiIiPSAGmgRERERkR5QAy0iIiIi0gNqoEVEREREekANtIiIiIhID6iBDhPGmK8aY4qNMa3GmNeMMdlO1yQiIiISjtRAhwFjzAPAd4DbgBnAcOAXjhYlIiIiEqZ0IhWXM8YUAe8Cs6y1azqv+zpwj7U2x9HiRERERMKQRqDd7zvAyg+a507VQJZD9YiIiIiENTXQLmaMiQYuA/76sZvigfrgVyQiIiIS/jSFw8WMMWfSMX3DA/j/46ZoYKO1dqYjhYmIiIiEsSinC5DTMhpoByZ+7PpngbeDX46IiIhI+FMD7W6pQI21tviDK4wxWcBk4OtOFSUiIiISzjQH2t1qgGRjzH8+j3cBq621qx2qSURERCSsaQTa3ZbR8Rz+0BjzFHA1cAMw29GqRERERMKYRqBdzFpbDdwIfBHYDlwEnGut3eNoYSIiIiJhTEfhEBERERHpAY1Ai4iIiIj0gBpoEREREZEeUAMtIiIiItIDaqBFRERERHpADbSIiIiISA+ogRYRERER6QE10CIiIiIiPaAGWkRERESkB/4fWIEN0qPzIAIAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 864x864 with 16 Axes>"
       ]
@@ -605,6 +679,23 @@
     "1. probably biased towards heads\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# qs 1\n",
+    "#alpha = 2, beta = 2. as the probability is maximised at 0.5, where the coin is fair\n",
+    "\n",
+    "# qs 2\n",
+    "#alpha = 1, beta = 1. as the probability is constant for all biased (non-0.5) values\n",
+    "#ideally we would expect something opposite from the answer in qs 1.\n",
+    "\n",
+    "# qs 3\n",
+    "#alpha = 1 or 2 or 3, and beta = 0.5. as all these distributions show an asymtopic relationship towards 1 (heads)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -627,7 +718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 46,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -657,7 +748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 47,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -672,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 48,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -683,13 +774,13 @@
    "outputs": [],
    "source": [
     "trials = [0, 1, 2, 3, 4, 8, 16, 32, 50, 150]\n",
-    "data = [0, 1, 1, 1, 1, 4, 6, 9, 13, 48]\n",
+    "data =   [0, 1, 1, 1, 1, 4, 6,  9,  13, 48]\n",
     "theta = np.linspace(0, 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 49,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -706,7 +797,7 @@
        "       1.00674662e-06, 2.31423249e-05])"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -719,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 50,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -731,10 +822,10 @@
     {
      "data": {
       "text/plain": [
-       "0.009392766325311568"
+       "0.009392766325310326"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -745,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 51,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -768,7 +859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 52,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -780,10 +871,10 @@
     {
      "data": {
       "text/plain": [
-       "1.5956869799149278"
+       "1.5956869799149325"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -794,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 53,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -806,16 +897,16 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fc640c74e10>]"
+       "[<matplotlib.lines.Line2D at 0x1cb2a2f7580>]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -832,21 +923,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "beta_params = ((2, 2), (1, 1), (1, 0.5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 864x864 with 10 Axes>"
       ]
@@ -879,10 +961,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 47,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "74.55494833068363"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
+    "from scipy import integrate\n",
+    "from scipy.optimize import fmin\n",
+    "\n",
     "def hpd(posterior, alpha=0.05):\n",
     "    \"\"\"\n",
     "    A (1 - α) credible interval\n",
@@ -900,7 +996,21 @@
     "        A tuple (lower, upper) giving the lower and upper bounds for\n",
     "        the (1 - α) credible interval\n",
     "    \"\"\"\n",
-    "    ..."
+    "\n",
+    "    def errfn(p, alpha, data):\n",
+    "\n",
+    "        def fn(x):\n",
+    "            pdf = posterior(x, data)\n",
+    "            return pdf if pdf > p else 0\n",
+    "\n",
+    "        prob = integrate.quad(fn, 0, 1)[0]\n",
+    "        return (prob + alpha - 1.0)**2\n",
+    "\n",
+    "    p = fmin(errfn, x0 = 0, args = (alpha, (30, 50)))[0]\n",
+    "\n",
+    "    return(p)\n",
+    "\n",
+    "# i dont understand why the posterior function produces numbers greater than 1????? skipping this part for now ..."
    ]
   },
   {
@@ -968,11 +1078,111 @@
     "1. The prior probability can simply be 50% for each theorem, as without data, we don't really have any way to change from this.\n",
     "1. The probability of the data does not need to be computed for comparing hypothesis"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(5050, 2)\n",
+      "            goldusd  gbpusd\n",
+      "Date                       \n",
+      "2019-12-23  1483.95  1.2926\n",
+      "2019-12-24  1490.85  1.2948\n",
+      "2019-12-27  1510.60  1.3094\n",
+      "2019-12-30  1511.50  1.3115\n",
+      "2019-12-31  1523.00  1.3210\n",
+      "Linear regression outputs:\n",
+      "2153.673499860183 [-767.98584762] 0.11715969520089464\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Are the two correlated according to linear regression? False\n",
+      "Our hypothesis (gold correlated with gbp) has 5.86% probability of being true\n"
+     ]
+    }
+   ],
+   "source": [
+    "# download data\n",
+    "import quandl\n",
+    "import scipy\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "\n",
+    "quandl.ApiConfig.api_key = 'hPGPuToY_GBPFpx5zsyM'\n",
+    "\n",
+    "# 1 - define if two series are correlated\n",
+    "def is_correlated(val):\n",
+    "    \n",
+    "    #cortest = scipy.stats.pearsonr(x1, x2)\n",
+    "    #return(cortest.statistic > 0.7 and cortest.pvalue < 0.05)\n",
+    "    return(val > 0.7)\n",
+    "\n",
+    "# 2 - get data, match and clean\n",
+    "# download data\n",
+    "gold = quandl.get(\"LBMA/GOLD\")[['USD (AM)']]\n",
+    "gbpusd = quandl.get(\"BUNDESBANK/BBEX3_D_GBP_USD_CM_AC_000\")[[\"Value\"]]\n",
+    "\n",
+    "# match data to have same dates\n",
+    "gold = gold.loc['2000-01-01':'2020-01-01']\n",
+    "gbpusd = gbpusd.loc['2000-01-01':'2020-01-01']\n",
+    "out = gold.merge(gbpusd, left_index=True, right_on='Date')\n",
+    "out.columns = [\"goldusd\", \"gbpusd\"]\n",
+    "print(out.shape)\n",
+    "print(out.tail())\n",
+    "\n",
+    "# add lag of 1 day\n",
+    "gold, gbp = out[\"goldusd\"].to_numpy()[:-1], out[\"gbpusd\"].to_numpy()[1:]\n",
+    "\n",
+    "# 3 - perform regression\n",
+    "model = LinearRegression()\n",
+    "model.fit(gbp[:, None], gold)\n",
+    "R_val = model.score(gbp[:, None], gold)\n",
+    "y_pred = model.predict(gbp[:, None])\n",
+    "\n",
+    "print(\"Linear regression outputs:\")\n",
+    "print(model.intercept_, model.coef_, R_val)\n",
+    "\n",
+    "plt.plot(gold)\n",
+    "plt.plot(y_pred)\n",
+    "plt.show()\n",
+    "\n",
+    "print(\"Are the two correlated according to linear regression? \" + str(is_correlated(R_val)))\n",
+    "\n",
+    "# 4 - set probabilities (not exactly sure which values to use for each???)\n",
+    "prior = 0.5\n",
+    "likelihood = R_val\n",
+    "norm_factor = 1\n",
+    "\n",
+    "# 5 - result\n",
+    "posterior = prior * likelihood / norm_factor\n",
+    "print(\"Our hypothesis (gold correlated with gbp) has \" + str(np.round(posterior*100, 2)) + \"% probability of being true\")\n",
+    "\n",
+    "# not exactly sure how to relate this to bayes theroy, skipping this for now ...."
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -986,7 +1196,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.5.3 - Representing Prior information.ipynb b/notebooks/Module 1.5.3 - Representing Prior information.ipynb
index 993e047..8397caa 100644
--- a/notebooks/Module 1.5.3 - Representing Prior information.ipynb	
+++ b/notebooks/Module 1.5.3 - Representing Prior information.ipynb	
@@ -72,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -80,7 +80,19 @@
      "n": "3"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'dict' object has no attribute 'head'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\86155\\OneDrive\\Job Stuff\\Past Jobs + Applications\\Tibra\\QuantFinance\\notebooks\\Module 1.5.3 - Representing Prior information.ipynb Cell 2\u001b[0m in \u001b[0;36m<cell line: 12>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/86155/OneDrive/Job%20Stuff/Past%20Jobs%20%2B%20Applications/Tibra/QuantFinance/notebooks/Module%201.5.3%20-%20Representing%20Prior%20information.ipynb#W1sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m postcodes_by_state \u001b[39m=\u001b[39m \u001b[39mdict\u001b[39m((\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/86155/OneDrive/Job%20Stuff/Past%20Jobs%20%2B%20Applications/Tibra/QuantFinance/notebooks/Module%201.5.3%20-%20Representing%20Prior%20information.ipynb#W1sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m     (\u001b[39m'\u001b[39m\u001b[39mAustralian Capital Territory\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mset\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m2600\u001b[39m, \u001b[39m2620\u001b[39m)) \u001b[39m|\u001b[39m \u001b[39mset\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m2900\u001b[39m, \u001b[39m2920\u001b[39m))),\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/86155/OneDrive/Job%20Stuff/Past%20Jobs%20%2B%20Applications/Tibra/QuantFinance/notebooks/Module%201.5.3%20-%20Representing%20Prior%20information.ipynb#W1sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m     (\u001b[39m'\u001b[39m\u001b[39mNew South Wales\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mset\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m2000\u001b[39m, \u001b[39m3000\u001b[39m)) \u001b[39m-\u001b[39m \u001b[39mset\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m2600\u001b[39m, \u001b[39m2620\u001b[39m)) \u001b[39m-\u001b[39m \u001b[39mset\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m2900\u001b[39m, \u001b[39m2920\u001b[39m))),\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/86155/OneDrive/Job%20Stuff/Past%20Jobs%20%2B%20Applications/Tibra/QuantFinance/notebooks/Module%201.5.3%20-%20Representing%20Prior%20information.ipynb#W1sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m     (\u001b[39m'\u001b[39m\u001b[39mNorthern Territory\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mset\u001b[39m(\u001b[39mrange\u001b[39m(\u001b[39m800\u001b[39m, \u001b[39m900\u001b[39m)))\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/86155/OneDrive/Job%20Stuff/Past%20Jobs%20%2B%20Applications/Tibra/QuantFinance/notebooks/Module%201.5.3%20-%20Representing%20Prior%20information.ipynb#W1sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m ))\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/Users/86155/OneDrive/Job%20Stuff/Past%20Jobs%20%2B%20Applications/Tibra/QuantFinance/notebooks/Module%201.5.3%20-%20Representing%20Prior%20information.ipynb#W1sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39mprint\u001b[39m(postcodes_by_state\u001b[39m.\u001b[39;49mhead())\n",
+      "\u001b[1;31mAttributeError\u001b[0m: 'dict' object has no attribute 'head'"
+     ]
+    }
+   ],
    "source": [
     "postcodes_by_state = dict((\n",
     "    ('Australian Capital Territory', set(range(2600, 2620)) | set(range(2900, 2920))),\n",
@@ -91,7 +103,9 @@
     "    ('Western Australia', set(range(6000, 6798))),\n",
     "    ('Tasmania', set(range(7000, 7800))),\n",
     "    ('Northern Territory', set(range(800, 900)))\n",
-    "))"
+    "))\n",
+    "\n",
+    "#print(postcodes_by_state)"
    ]
   },
   {
@@ -375,13 +389,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.000252\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0.001"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# prior_postcode_given_state(3122, 'Victoria')\n",
+    "def prior_postcode_given_state(postcode, state):\n",
+    "\n",
+    "    # find postcodes given state key\n",
+    "    all_postcodes = postcodes_by_state[state]\n",
+    "\n",
+    "    # if post code is valid, return probability of 1 in length(all post codes)\n",
+    "    if postcode in all_postcodes:\n",
+    "        return(1 / len(all_postcodes))\n",
+    "    # invalid postcode returns 0\n",
+    "    else:\n",
+    "        return(0)\n",
     "\n",
-    "# prior_postcode(3122)"
+    "# probability of postcode\n",
+    "print(prior_postcode(3122))\n",
+    "\n",
+    "# conditional probability of postcode given state\n",
+    "prior_postcode_given_state(3122, 'Victoria')"
    ]
   },
   {
@@ -393,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -427,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -443,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -458,7 +504,7 @@
        "array([   0,    1,    2, ..., 9997, 9998, 9999], dtype=uint16)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,7 +522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -492,24 +538,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "attributes": {
-     "classes": [],
-     "id": "",
-     "n": "19"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# def in_nsw(postcodes):\n",
-    "#     return [postcode in postcodes_by_state['New South Wales'] for postcode in postcodes]\n",
-    "# etc."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -527,7 +556,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -545,7 +574,7 @@
        "      dtype='object', name='State')"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -556,7 +585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -572,7 +601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -595,7 +624,7 @@
        " <function __main__.in_given_state.<locals>.in_state(postcodes)>]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -613,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -637,7 +666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -661,7 +690,7 @@
        "Name: %, dtype: float64"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -673,7 +702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -688,7 +717,7 @@
        "99.9"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -706,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -722,7 +751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -737,7 +766,7 @@
        "array([[1.   , 0.32 , 0.252, 0.201, 0.071, 0.108, 0.021, 0.01 , 0.016]])"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -748,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -763,7 +792,7 @@
        "True"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -781,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -789,43 +818,14 @@
      "n": "31"
     }
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/ashkfarjami/opt/anaconda3/lib/python3.8/site-packages/sklearn/base.py:209: FutureWarning: From version 0.24, get_params will raise an AttributeError if a parameter cannot be retrieved as an instance attribute. Previously it would return None.\n",
-      "  warnings.warn('From version 0.24, get_params will raise an '\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "MinDivergenceModel(features=[<function is_valid at 0x7fe5b3444e50>,\n",
-       "                             <function in_given_state.<locals>.in_state at 0x7fe5b34444c0>,\n",
-       "                             <function in_given_state.<locals>.in_state at 0x7fe5b345a040>,\n",
-       "                             <function in_given_state.<locals>.in_state at 0x7fe5b345a0d0>,\n",
-       "                             <function in_given_state.<locals>.in_state at 0x7fe5b345a160>,\n",
-       "                             <function in_given_state.<locals>.in_state at 0x7fe5b345a1f0>,\n",
-       "                             <function in_given_state.<locals>.in_state at 0x7fe5b345a280>,\n",
-       "                             <function in_given_state.<locals>.in_state at 0x7fe5b345a310>,\n",
-       "                             <function in_given_state.<locals>.in_state at 0x7fe5b345a3a0>],\n",
-       "                   samplespace=array([   0,    1,    2, ..., 9997, 9998, 9999], dtype=uint16),\n",
-       "                   vectorized=True)"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model.fit(k)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 50,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -837,12 +837,12 @@
     {
      "data": {
       "text/plain": [
-       "array([[-4.04218898e-08, -7.21599679e-08, -7.89633941e-07,\n",
-       "         7.45644801e-07, -3.70129965e-07,  1.00904637e-06,\n",
-       "         5.08948233e-08, -2.96428910e-07, -1.79097180e-07]])"
+       "array([[-1.68397139e-07,  1.76789375e-07, -3.56737769e-07,\n",
+       "         1.95550522e-08,  5.82835325e-08,  7.04978328e-08,\n",
+       "         9.42427836e-08,  7.65139526e-08,  1.74051289e-07]])"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -853,7 +853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 51,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -868,7 +868,7 @@
        "True"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -886,7 +886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 52,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -898,11 +898,11 @@
     {
      "data": {
       "text/plain": [
-       "array([2.02108897e-10, 2.02108897e-10, 2.02108897e-10, ...,\n",
-       "       2.32417815e-07, 2.32417815e-07, 2.32417815e-07])"
+       "array([8.41984986e-10, 8.41984986e-10, 8.41984986e-10, ...,\n",
+       "       2.32338077e-07, 2.32338077e-07, 2.32338077e-07])"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -913,7 +913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 53,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -937,7 +937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 54,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -949,10 +949,10 @@
     {
      "data": {
       "text/plain": [
-       "array([2915, 2910, 2917, ...,  128,  127,    0])"
+       "array([2915, 2910, 2917, ...,  128,  127,    0], dtype=int64)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -971,7 +971,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 55,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -987,7 +987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 56,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -1002,13 +1002,13 @@
        "Text(0, 0.5, 'probability')"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 864x360 with 1 Axes>"
       ]
@@ -1038,7 +1038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 26,
    "metadata": {
     "attributes": {
      "classes": [],
@@ -1051,6 +1051,66 @@
     "CBD_POSTCODES = {2000, 2001, 3000, 3001, 4000, 4001, 5000, 5001, 6000, 6001}"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n",
+      "[[1.    0.32  0.252 0.201 0.071 0.108 0.021 0.01  0.016]]\n",
+      "[[1.    0.32  0.252 0.201 0.071 0.108 0.021 0.01  0.016 0.4  ]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1af3626e740>]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def in_cbd(postcode):\n",
+    "    return [p in CBD_POSTCODES for p in postcode]\n",
+    "\n",
+    "features2 = features + [in_cbd]\n",
+    "print(len(features2))\n",
+    "\n",
+    "# define new k\n",
+    "k = np.array(k)\n",
+    "print(k)\n",
+    "\n",
+    "new_k = np.append(k, [[0.4]], axis = 1)\n",
+    "print(new_k)\n",
+    "\n",
+    "# fit model\n",
+    "model = MinDivergenceModel(features2, samplespace, vectorized=True)\n",
+    "model.fit(new_k)\n",
+    "\n",
+    "# plot distribution\n",
+    "p = model.probdist()\n",
+    "plt.semilogy(samplespace, p)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1076,7 +1136,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 864x360 with 1 Axes>"
       ]
@@ -1088,7 +1148,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 864x360 with 1 Axes>"
       ]
@@ -1351,7 +1411,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 864x360 with 1 Axes>"
       ]
@@ -1581,7 +1641,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 864x360 with 1 Axes>"
       ]
@@ -1613,7 +1673,7 @@
    "lastKernelId": null
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1627,7 +1687,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.6.1 - Time Series moving average.ipynb b/notebooks/Module 1.6.1 - Time Series moving average.ipynb
index 18aed4b..b4bf9ad 100644
--- a/notebooks/Module 1.6.1 - Time Series moving average.ipynb	
+++ b/notebooks/Module 1.6.1 - Time Series moving average.ipynb	
@@ -91,7 +91,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1fbc5e11048>]"
+       "[<matplotlib.lines.Line2D at 0x218cdb7e230>]"
       ]
      },
      "execution_count": 4,
@@ -100,7 +100,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -127,17 +127,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from statsmodels import api as sm\n",
-    "model = sm.tsa.ARMA(X_t, order=(0, 1))"
+    "# ARMA depreciated now\n",
+    "import statsmodels.api as sm\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "model = sm.tsa.ARIMA(X_t, order=(1, 0 ,0 ))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,84 +148,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<table class=\"simpletable\">\n",
-       "<caption>ARMA Model Results</caption>\n",
+       "<caption>SARIMAX Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>         <td>y</td>        <th>  No. Observations:  </th>    <td>999</td>   \n",
+       "  <th>Dep. Variable:</th>           <td>y</td>        <th>  No. Observations:  </th>    <td>999</td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>            <td>ARMA(0, 1)</td>    <th>  Log Likelihood     </th> <td>-1396.691</td>\n",
+       "  <th>Model:</th>            <td>ARIMA(1, 0, 0)</td>  <th>  Log Likelihood     </th> <td>-1426.779</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.979</td>  \n",
+       "  <th>Date:</th>            <td>Wed, 04 Jan 2023</td> <th>  AIC                </th> <td>2859.557</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>          <td>Thu, 17 Feb 2022</td> <th>  AIC                </th> <td>2799.383</td> \n",
+       "  <th>Time:</th>                <td>16:41:14</td>     <th>  BIC                </th> <td>2874.277</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>              <td>19:24:23</td>     <th>  BIC                </th> <td>2814.103</td> \n",
+       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>2865.152</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>2804.978</td> \n",
+       "  <th></th>                      <td> - 999</td>      <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
+       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "     <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>const</th>   <td>    0.0061</td> <td>    0.016</td> <td>    0.371</td> <td> 0.711</td> <td>   -0.026</td> <td>    0.038</td>\n",
+       "  <th>const</th>  <td>    0.0004</td> <td>    0.023</td> <td>    0.019</td> <td> 0.985</td> <td>   -0.045</td> <td>    0.046</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ma.L1.y</th> <td>   -0.4712</td> <td>    0.028</td> <td>  -16.906</td> <td> 0.000</td> <td>   -0.526</td> <td>   -0.417</td>\n",
+       "  <th>ar.L1</th>  <td>   -0.3901</td> <td>    0.028</td> <td>  -13.899</td> <td> 0.000</td> <td>   -0.445</td> <td>   -0.335</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sigma2</th> <td>    1.0185</td> <td>    0.047</td> <td>   21.815</td> <td> 0.000</td> <td>    0.927</td> <td>    1.110</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
-       "<caption>Roots</caption>\n",
        "<tr>\n",
-       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
+       "  <th>Ljung-Box (L1) (Q):</th>     <td>5.30</td> <th>  Jarque-Bera (JB):  </th> <td>0.69</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Q):</th>                <td>0.02</td> <th>  Prob(JB):          </th> <td>0.71</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Heteroskedasticity (H):</th> <td>1.14</td> <th>  Skew:              </th> <td>0.05</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>MA.1</th> <td>           2.1223</td> <td>          +0.0000j</td> <td>           2.1223</td> <td>           0.0000</td>\n",
+       "  <th>Prob(H) (two-sided):</th>    <td>0.22</td> <th>  Kurtosis:          </th> <td>2.92</td>\n",
        "</tr>\n",
-       "</table>"
+       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                              ARMA Model Results                              \n",
+       "                               SARIMAX Results                                \n",
        "==============================================================================\n",
        "Dep. Variable:                      y   No. Observations:                  999\n",
-       "Model:                     ARMA(0, 1)   Log Likelihood               -1396.691\n",
-       "Method:                       css-mle   S.D. of innovations              0.979\n",
-       "Date:                Thu, 17 Feb 2022   AIC                           2799.383\n",
-       "Time:                        19:24:23   BIC                           2814.103\n",
-       "Sample:                             0   HQIC                          2804.978\n",
-       "                                                                              \n",
+       "Model:                 ARIMA(1, 0, 0)   Log Likelihood               -1426.779\n",
+       "Date:                Wed, 04 Jan 2023   AIC                           2859.557\n",
+       "Time:                        16:41:14   BIC                           2874.277\n",
+       "Sample:                             0   HQIC                          2865.152\n",
+       "                                - 999                                         \n",
+       "Covariance Type:                  opg                                         \n",
        "==============================================================================\n",
        "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
        "------------------------------------------------------------------------------\n",
-       "const          0.0061      0.016      0.371      0.711      -0.026       0.038\n",
-       "ma.L1.y       -0.4712      0.028    -16.906      0.000      -0.526      -0.417\n",
-       "                                    Roots                                    \n",
-       "=============================================================================\n",
-       "                  Real          Imaginary           Modulus         Frequency\n",
-       "-----------------------------------------------------------------------------\n",
-       "MA.1            2.1223           +0.0000j            2.1223            0.0000\n",
-       "-----------------------------------------------------------------------------\n",
+       "const          0.0004      0.023      0.019      0.985      -0.045       0.046\n",
+       "ar.L1         -0.3901      0.028    -13.899      0.000      -0.445      -0.335\n",
+       "sigma2         1.0185      0.047     21.815      0.000       0.927       1.110\n",
+       "===================================================================================\n",
+       "Ljung-Box (L1) (Q):                   5.30   Jarque-Bera (JB):                 0.69\n",
+       "Prob(Q):                              0.02   Prob(JB):                         0.71\n",
+       "Heteroskedasticity (H):               1.14   Skew:                             0.05\n",
+       "Prob(H) (two-sided):                  0.22   Kurtosis:                         2.92\n",
+       "===================================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
        "\"\"\""
       ]
      },
-     "execution_count": 11,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,7 +268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -275,7 +289,7 @@
        "Name: InterestRate, Length: 378, dtype: float64"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -286,103 +300,119 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
-      "  % freq, ValueWarning)\n"
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
+      "  self._init_dates(dates, freq)\n"
      ]
     }
    ],
    "source": [
-    "model = sm.tsa.ARMA(interest_rates, order=(0, 1))\n",
+    "model = sm.tsa.ARIMA(interest_rates, order=(1, 0 ,0 ))\n",
     "interest_results = model.fit()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<table class=\"simpletable\">\n",
-       "<caption>ARMA Model Results</caption>\n",
+       "<caption>SARIMAX Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>   <td>InterestRate</td>   <th>  No. Observations:  </th>    <td>378</td>  \n",
+       "  <th>Dep. Variable:</th>     <td>InterestRate</td>   <th>  No. Observations:  </th>   <td>378</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>            <td>ARMA(0, 1)</td>    <th>  Log Likelihood     </th> <td>-709.234</td>\n",
+       "  <th>Model:</th>            <td>ARIMA(1, 0, 0)</td>  <th>  Log Likelihood     </th> <td>-12.946</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>1.576</td> \n",
+       "  <th>Date:</th>            <td>Wed, 04 Jan 2023</td> <th>  AIC                </th> <td>31.892</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>          <td>Thu, 17 Feb 2022</td> <th>  AIC                </th> <td>1424.468</td>\n",
+       "  <th>Time:</th>                <td>16:41:40</td>     <th>  BIC                </th> <td>43.697</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>              <td>19:25:18</td>     <th>  BIC                </th> <td>1436.273</td>\n",
+       "  <th>Sample:</th>             <td>01-31-1990</td>    <th>  HQIC               </th> <td>36.577</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Sample:</th>           <td>01-31-1990</td>    <th>  HQIC               </th> <td>1429.153</td>\n",
+       "  <th></th>                   <td>- 06-30-2021</td>   <th>                     </th>    <td> </td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th></th>                 <td>- 06-30-2021</td>   <th>                     </th>     <td> </td>   \n",
+       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>    <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>const</th>              <td>    4.8175</td> <td>    0.155</td> <td>   31.031</td> <td> 0.000</td> <td>    4.513</td> <td>    5.122</td>\n",
+       "  <th>const</th>  <td>    8.1883</td> <td>    8.457</td> <td>    0.968</td> <td> 0.333</td> <td>   -8.388</td> <td>   24.765</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ma.L1.InterestRate</th> <td>    0.9176</td> <td>    0.014</td> <td>   63.839</td> <td> 0.000</td> <td>    0.889</td> <td>    0.946</td>\n",
+       "  <th>ar.L1</th>  <td>    0.9995</td> <td>    0.003</td> <td>  308.052</td> <td> 0.000</td> <td>    0.993</td> <td>    1.006</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sigma2</th> <td>    0.0616</td> <td>    0.002</td> <td>   31.730</td> <td> 0.000</td> <td>    0.058</td> <td>    0.065</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
-       "<caption>Roots</caption>\n",
        "<tr>\n",
-       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
+       "  <th>Ljung-Box (L1) (Q):</th>     <td>5.18</td> <th>  Jarque-Bera (JB):  </th> <td>1900.88</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>MA.1</th> <td>          -1.0898</td> <td>          +0.0000j</td> <td>           1.0898</td> <td>           0.5000</td>\n",
+       "  <th>Prob(Q):</th>                <td>0.02</td> <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
        "</tr>\n",
-       "</table>"
+       "<tr>\n",
+       "  <th>Heteroskedasticity (H):</th> <td>0.09</td> <th>  Skew:              </th>  <td>-2.03</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>13.21</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                              ARMA Model Results                              \n",
+       "                               SARIMAX Results                                \n",
        "==============================================================================\n",
        "Dep. Variable:           InterestRate   No. Observations:                  378\n",
-       "Model:                     ARMA(0, 1)   Log Likelihood                -709.234\n",
-       "Method:                       css-mle   S.D. of innovations              1.576\n",
-       "Date:                Thu, 17 Feb 2022   AIC                           1424.468\n",
-       "Time:                        19:25:18   BIC                           1436.273\n",
-       "Sample:                    01-31-1990   HQIC                          1429.153\n",
+       "Model:                 ARIMA(1, 0, 0)   Log Likelihood                 -12.946\n",
+       "Date:                Wed, 04 Jan 2023   AIC                             31.892\n",
+       "Time:                        16:41:40   BIC                             43.697\n",
+       "Sample:                    01-31-1990   HQIC                            36.577\n",
        "                         - 06-30-2021                                         \n",
-       "======================================================================================\n",
-       "                         coef    std err          z      P>|z|      [0.025      0.975]\n",
-       "--------------------------------------------------------------------------------------\n",
-       "const                  4.8175      0.155     31.031      0.000       4.513       5.122\n",
-       "ma.L1.InterestRate     0.9176      0.014     63.839      0.000       0.889       0.946\n",
-       "                                    Roots                                    \n",
-       "=============================================================================\n",
-       "                  Real          Imaginary           Modulus         Frequency\n",
-       "-----------------------------------------------------------------------------\n",
-       "MA.1           -1.0898           +0.0000j            1.0898            0.5000\n",
-       "-----------------------------------------------------------------------------\n",
+       "Covariance Type:                  opg                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const          8.1883      8.457      0.968      0.333      -8.388      24.765\n",
+       "ar.L1          0.9995      0.003    308.052      0.000       0.993       1.006\n",
+       "sigma2         0.0616      0.002     31.730      0.000       0.058       0.065\n",
+       "===================================================================================\n",
+       "Ljung-Box (L1) (Q):                   5.18   Jarque-Bera (JB):              1900.88\n",
+       "Prob(Q):                              0.02   Prob(JB):                         0.00\n",
+       "Heteroskedasticity (H):               0.09   Skew:                            -2.03\n",
+       "Prob(H) (two-sided):                  0.00   Kurtosis:                        13.21\n",
+       "===================================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
        "\"\"\""
       ]
      },
-     "execution_count": 14,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -410,7 +440,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -425,228 +455,248 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "adj_close    154.137248\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data.mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
        "<table class=\"simpletable\">\n",
-       "<caption>ARMA Model Results</caption>\n",
+       "<caption>SARIMAX Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>     <td>adj_close</td>    <th>  No. Observations:  </th>    <td>308</td>   \n",
+       "  <th>Dep. Variable:</th>       <td>adj_close</td>    <th>  No. Observations:  </th>    <td>308</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>            <td>ARMA(0, 1)</td>    <th>  Log Likelihood     </th> <td>-1105.727</td>\n",
+       "  <th>Model:</th>            <td>ARIMA(1, 0, 0)</td>  <th>  Log Likelihood     </th> <td>-646.794</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>8.735</td>  \n",
+       "  <th>Date:</th>            <td>Wed, 04 Jan 2023</td> <th>  AIC                </th> <td>1299.588</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>          <td>Thu, 17 Feb 2022</td> <th>  AIC                </th> <td>2217.453</td> \n",
+       "  <th>Time:</th>                <td>16:45:59</td>     <th>  BIC                </th> <td>1310.779</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>              <td>19:52:56</td>     <th>  BIC                </th> <td>2228.643</td> \n",
+       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>1304.063</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>2221.928</td> \n",
+       "  <th></th>                      <td> - 308</td>      <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
+       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>const</th>           <td>  154.0698</td> <td>    0.968</td> <td>  159.159</td> <td> 0.000</td> <td>  152.173</td> <td>  155.967</td>\n",
+       "  <th>const</th>  <td>  146.0996</td> <td>   20.345</td> <td>    7.181</td> <td> 0.000</td> <td>  106.225</td> <td>  185.975</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ma.L1.adj_close</th> <td>    0.9479</td> <td>    0.012</td> <td>   76.023</td> <td> 0.000</td> <td>    0.923</td> <td>    0.972</td>\n",
+       "  <th>ar.L1</th>  <td>    0.9963</td> <td>    0.007</td> <td>  152.715</td> <td> 0.000</td> <td>    0.984</td> <td>    1.009</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sigma2</th> <td>    3.8426</td> <td>    0.203</td> <td>   18.953</td> <td> 0.000</td> <td>    3.445</td> <td>    4.240</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
-       "<caption>Roots</caption>\n",
        "<tr>\n",
-       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
+       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.51</td> <th>  Jarque-Bera (JB):  </th> <td>96.43</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>MA.1</th> <td>          -1.0549</td> <td>          +0.0000j</td> <td>           1.0549</td> <td>           0.5000</td>\n",
+       "  <th>Prob(Q):</th>                <td>0.47</td> <th>  Prob(JB):          </th> <td>0.00</td> \n",
        "</tr>\n",
-       "</table>"
+       "<tr>\n",
+       "  <th>Heteroskedasticity (H):</th> <td>0.30</td> <th>  Skew:              </th> <td>-0.21</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th> <td>5.71</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                              ARMA Model Results                              \n",
+       "                               SARIMAX Results                                \n",
        "==============================================================================\n",
        "Dep. Variable:              adj_close   No. Observations:                  308\n",
-       "Model:                     ARMA(0, 1)   Log Likelihood               -1105.727\n",
-       "Method:                       css-mle   S.D. of innovations              8.735\n",
-       "Date:                Thu, 17 Feb 2022   AIC                           2217.453\n",
-       "Time:                        19:52:56   BIC                           2228.643\n",
-       "Sample:                             0   HQIC                          2221.928\n",
-       "                                                                              \n",
+       "Model:                 ARIMA(1, 0, 0)   Log Likelihood                -646.794\n",
+       "Date:                Wed, 04 Jan 2023   AIC                           1299.588\n",
+       "Time:                        16:45:59   BIC                           1310.779\n",
+       "Sample:                             0   HQIC                          1304.063\n",
+       "                                - 308                                         \n",
+       "Covariance Type:                  opg                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const        146.0996     20.345      7.181      0.000     106.225     185.975\n",
+       "ar.L1          0.9963      0.007    152.715      0.000       0.984       1.009\n",
+       "sigma2         3.8426      0.203     18.953      0.000       3.445       4.240\n",
+       "===================================================================================\n",
+       "Ljung-Box (L1) (Q):                   0.51   Jarque-Bera (JB):                96.43\n",
+       "Prob(Q):                              0.47   Prob(JB):                         0.00\n",
+       "Heteroskedasticity (H):               0.30   Skew:                            -0.21\n",
+       "Prob(H) (two-sided):                  0.00   Kurtosis:                         5.71\n",
        "===================================================================================\n",
-       "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
-       "-----------------------------------------------------------------------------------\n",
-       "const             154.0698      0.968    159.159      0.000     152.173     155.967\n",
-       "ma.L1.adj_close     0.9479      0.012     76.023      0.000       0.923       0.972\n",
-       "                                    Roots                                    \n",
-       "=============================================================================\n",
-       "                  Real          Imaginary           Modulus         Frequency\n",
-       "-----------------------------------------------------------------------------\n",
-       "MA.1           -1.0549           +0.0000j            1.0549            0.5000\n",
-       "-----------------------------------------------------------------------------\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
        "\"\"\""
       ]
      },
-     "execution_count": 36,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# qs 1\n",
+    "# load data\n",
     "data_n2 = data[\"adj_close\"].dropna()\n",
-    "data_n2\n",
-    "model2 = sm.tsa.ARMA(data_n2, order=(0, 1))\n",
+    "\n",
+    "# fit model (previous ARMA model is depreciated)\n",
+    "model2 = sm.tsa.ARIMA(data_n2, order=(1, 0, 0))\n",
+    "\n",
+    "# summarize model\n",
     "results2 = model2.fit()\n",
-    "results2.summary()"
+    "results2.summary()\n",
+    "\n",
+    "# qs2.\n",
+    "# The const coef if 146 is the mean price, and the ma.L1.adj_close indicates the price is increasing by 0.99 multiplied by the previous days error."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:215: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
-      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: An unsupported index was provided and will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n"
      ]
     },
     {
      "data": {
       "text/html": [
        "<table class=\"simpletable\">\n",
-       "<caption>ARMA Model Results</caption>\n",
+       "<caption>SARIMAX Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>     <td>adj_close</td>    <th>  No. Observations:  </th>    <td>307</td>  \n",
+       "  <th>Dep. Variable:</th>       <td>adj_close</td>    <th>  No. Observations:  </th>    <td>307</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>            <td>ARMA(0, 1)</td>    <th>  Log Likelihood     </th> <td>-641.062</td>\n",
+       "  <th>Model:</th>            <td>ARIMA(1, 0, 0)</td>  <th>  Log Likelihood     </th> <td>-641.081</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>1.953</td> \n",
+       "  <th>Date:</th>            <td>Wed, 04 Jan 2023</td> <th>  AIC                </th> <td>1288.162</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>          <td>Thu, 17 Feb 2022</td> <th>  AIC                </th> <td>1288.124</td>\n",
+       "  <th>Time:</th>                <td>16:43:58</td>     <th>  BIC                </th> <td>1299.342</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>              <td>19:34:44</td>     <th>  BIC                </th> <td>1299.305</td>\n",
+       "  <th>Sample:</th>                  <td>0</td>        <th>  HQIC               </th> <td>1292.633</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>1292.595</td>\n",
+       "  <th></th>                      <td> - 307</td>      <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>   \n",
+       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>   \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>const</th>  <td>   -0.1743</td> <td>    0.116</td> <td>   -1.504</td> <td> 0.133</td> <td>   -0.401</td> <td>    0.053</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>const</th>           <td>   -0.1741</td> <td>    0.115</td> <td>   -1.513</td> <td> 0.131</td> <td>   -0.400</td> <td>    0.051</td>\n",
+       "  <th>ar.L1</th>  <td>    0.0283</td> <td>    0.045</td> <td>    0.621</td> <td> 0.534</td> <td>   -0.061</td> <td>    0.117</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ma.L1.adj_close</th> <td>    0.0327</td> <td>    0.062</td> <td>    0.528</td> <td> 0.598</td> <td>   -0.089</td> <td>    0.154</td>\n",
+       "  <th>sigma2</th> <td>    3.8135</td> <td>    0.201</td> <td>   18.953</td> <td> 0.000</td> <td>    3.419</td> <td>    4.208</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
-       "<caption>Roots</caption>\n",
        "<tr>\n",
-       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
+       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>101.97</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Q):</th>                <td>0.98</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>MA.1</th> <td>         -30.5971</td> <td>          +0.0000j</td> <td>          30.5971</td> <td>           0.5000</td>\n",
+       "  <th>Heteroskedasticity (H):</th> <td>0.28</td> <th>  Skew:              </th>  <td>-0.24</td>\n",
        "</tr>\n",
-       "</table>"
+       "<tr>\n",
+       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>5.78</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                              ARMA Model Results                              \n",
+       "                               SARIMAX Results                                \n",
        "==============================================================================\n",
        "Dep. Variable:              adj_close   No. Observations:                  307\n",
-       "Model:                     ARMA(0, 1)   Log Likelihood                -641.062\n",
-       "Method:                       css-mle   S.D. of innovations              1.953\n",
-       "Date:                Thu, 17 Feb 2022   AIC                           1288.124\n",
-       "Time:                        19:34:44   BIC                           1299.305\n",
-       "Sample:                             0   HQIC                          1292.595\n",
-       "                                                                              \n",
+       "Model:                 ARIMA(1, 0, 0)   Log Likelihood                -641.081\n",
+       "Date:                Wed, 04 Jan 2023   AIC                           1288.162\n",
+       "Time:                        16:43:58   BIC                           1299.342\n",
+       "Sample:                             0   HQIC                          1292.633\n",
+       "                                - 307                                         \n",
+       "Covariance Type:                  opg                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -0.1743      0.116     -1.504      0.133      -0.401       0.053\n",
+       "ar.L1          0.0283      0.045      0.621      0.534      -0.061       0.117\n",
+       "sigma2         3.8135      0.201     18.953      0.000       3.419       4.208\n",
+       "===================================================================================\n",
+       "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):               101.97\n",
+       "Prob(Q):                              0.98   Prob(JB):                         0.00\n",
+       "Heteroskedasticity (H):               0.28   Skew:                            -0.24\n",
+       "Prob(H) (two-sided):                  0.00   Kurtosis:                         5.78\n",
        "===================================================================================\n",
-       "                      coef    std err          z      P>|z|      [0.025      0.975]\n",
-       "-----------------------------------------------------------------------------------\n",
-       "const              -0.1741      0.115     -1.513      0.131      -0.400       0.051\n",
-       "ma.L1.adj_close     0.0327      0.062      0.528      0.598      -0.089       0.154\n",
-       "                                    Roots                                    \n",
-       "=============================================================================\n",
-       "                  Real          Imaginary           Modulus         Frequency\n",
-       "-----------------------------------------------------------------------------\n",
-       "MA.1          -30.5971           +0.0000j           30.5971            0.5000\n",
-       "-----------------------------------------------------------------------------\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
        "\"\"\""
       ]
      },
-     "execution_count": 32,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "# qs 3\n",
     "data_n = data[\"adj_close\"].diff().dropna()\n",
-    "data_n\n",
-    "model1 = sm.tsa.ARMA(data_n, order=(0, 1))\n",
+    "\n",
+    "# fit model\n",
+    "model1 = sm.tsa.ARIMA(data_n, order=(1, 0, 0))\n",
     "results1 = model1.fit()\n",
-    "results1.summary()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "2. The const coef explains the mean price of 154.0698, and the ma.L1.adj_close indicates the price is increasing by  0.9479 multiplied by the previous days error.\n",
-    "4. The mean change in price is negative 17 cents, and the ma.L1.adj_close indicates the price is increasing by  0.0327 multiplied by the previous days error."
+    "results1.summary()\n",
+    "\n",
+    "# qs4.\n",
+    "# The mean change in price is negative 17.4 cents, and the ma.L1.adj_close indicates the price is increasing by  0.0283 multiplied by the previous days error."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -658,7 +708,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -677,7 +727,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -696,7 +746,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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Hvdp1EUIkAhOBXZ08bRFCHBBCfCaEGNPF6xcJIbKEEFlq40XhqWTkWpmYEEGA0TmK8+1R+pmqBv6yu9Apcyi8E7sduhAiBPgb8JCUsvqyp/cBw6WU44GXgH90dg0p5RopZaqUMjUmJqaPJisU+lFZ18TRs9VOSbdczIwR0UxNiuLlzTk0NKsoXWEfdjl0IYQRzZm/J6X86PLnpZTVUsoLbb9/ChiFEM79xCsUOrAzrxwpIX2E4zdEL0YIwSMLUiipaeTdnaecOpfCe7CnykUArwPHpJQruhgzuG0cQoipbddV/UAVXkdmbhmBRh/Gx0U4fa5pySZmjozm91tyqW1scfp8Cs/Hngh9OvA9YN5FZYlfE0IsFkIsbhtzC3BYCHEAeBG4TarjZgovJDPPSmpiJH6+rjn08/CCFMprm3gro8Al8yk8mx5PRUgpdwDdNmmWUq4GVjvKKIXCHSmtaST7/AVumui6Az8TEyKZNyqWNdvy+J5lOGEBRpfNrfA8VC8XhcJOMvO0LGJv9UP7y8MLUqiqb+b17fkunVfheSiHrlDYSWaulVB/X64ZGubSea8ZFs71Ywbzxo58KmqbXDq3wrNQDl2hsJPM3DKmJUe5vGkWwPIFKVxoamHN9jyXz63wHJRDVyjsoLiyngJrHRYn1593xVWDQ/nmuKG89XkBZRcadbFB4f4oh65Q2EFmblv+3EGCFn1h2bUjaWxp5ZUtubrZoHBvlENXKOwgM9dKZJCRUYNDdbPBHBPCzZPieHfnKc5XN+hmh8J9UQ5doegBKSWZuWVYzCYMhm4reJ3OsvkjabVJXt6co6sdCvdEOXSFogdOWes4U9Wga7qlnfioIG5Njef93acpqqjT2xyFm6EcukLRA1/Wn7tHe6IfzRuBQLB6k4rSFZeiHLpC0QMZuVZiQ/0xxwTrbQoAQyMCuWNaAn/dW0RBWa3e5ijcCOXQFYpu0PLnVixmE23959yC++eYMfoIXtx4Um9TFG6EcugKRTfklFyg7EKjU/RD+0NsWAB3WRL5x/5ickpq9DZH4SYoh65QdENGW/25swUt+sJ9s5IJMPqwcoOK0hUayqErFN2QkVtGXGQg8VFBeptyBaYQf+6ZnsQnB89y7OzlImKKgYhy6ApFF9hskp155W5RrtgVP5yZTGiALyvWZ+ttisINUA5doeiCo2erqapvdrrcXH8IDzLygxnJrD96noNFlXqbo9AZ5dAVii74sn+L++XPL+aeGYlEBBlVlK5QDl2h6IqM3DKSo4MZHB6gtyndEhpg5L5ZZracKGXvqXK9zVHoiHLoCkUnNLfa2J1f7nJ1or6yMH040SF+PL9ORekDGeXQFYpOOFRcRW1Tq1uWK3ZGkJ8vS+aMICPXSkZumd7mKHRCOXSFohPa8+dpyVE6W2I/352WwKAwf1asy0ZKqbc5Ch1QDl2h6ITMXCujBodiCvHX2xS7CTD68OC8kWSdqmDbSRWlD0SUQ1coLqOxpZU9BZ6TP7+Y76TGMywikOfXnVBR+gBEOXSF4jK+OF1JY4vNrQ8UdYWfr4Fl80dysKiKDcdK9DZH4WJ6dOhCiHghxGYhxFEhxBEhxLJOxgghxItCiBwhxEEhxCTnmKtQOJ/MXCsGAdM80KED3DxpGImmIJ5fdwKbTUXpAwl7IvQW4BEp5WggDXhACDH6sjFfBUa2/SwCXnGolQqFC8nMtXLNsHDCA416m9InfH0MLLt2JMfP1fDZ4XN6m6NwIT06dCnlWSnlvrbfa4BjwLDLht0I/Elq7AQihBBDHG4tcLi4iiXv7qWhudUZl/c4Np8o4ZODZ/U2w2uoa2rhi8IKj8yfX8wN44cxIjaElRuyVZQ+gOhVDl0IkQhMBHZd9tQwoPCiv4u40ukjhFgkhMgSQmSVlpb20lSNuqZWPjt8jnd3nurT672JmoZmlq/dz//752F10zqIrIIKmlulR+bPL8bHIFg0K5mckgscVZ0YBwx2O3QhRAjwN+AhKWWfPiFSyjVSylQpZWpMTExfLsHUpChmjozm91tyqW1s6dM1vIU3dhRQWddMeW0TJ84rkQNHkJlnxdcgmJLoOfXnXTE7RbvH1EGjgYNdDl0IYURz5u9JKT/qZEgxEH/R33FtjzmFhxekUF7bxFsZBc6awu2prGvij9vzmJgQAXx5EEbRPzJyrUyIjyDY31dvU/rNoLAAkmOC1WdjAGFPlYsAXgeOSSlXdDHsY+CutmqXNKBKSum0xO7EhEjmj4plzbY8qhuanTWNW/Pa9jwuNLXwm5vHMtwU1KGso+g71Q3NHCqq9Pj8+cWkm03szi+nudWmtykKF2BPhD4d+B4wTwixv+3na0KIxUKIxW1jPgXygBzgNeB+55j7JcsXpFBV38zr2/OdPZXbYb3QyJufF/D1sUMYNTiMdLOJXflWWlUevV/syS/HJvEyhx5NbVMrB4uq9DZF4QJ6XFdKKXcA3cqdS+1I2gOOMsoerhkWzlevGcwbO/K5Oz2RyGA/V06vK69uzaWhuZWHrk0BwGKO5v3dhRw5U8W4uAh9jfNgMnKt+PkamJQQqbcpDiOtbXN3Z56VycO959+l6ByPPim6fEEKF5paWLM9T29TXEZJdQN/yjzF/0zUytKAjooMlXbpHxm5ViYnRBJg9NHbFIcRFezHqMGhamN0gODRDj1lUCg3jB/KW58XUHahUW9zXMLvt+TSYpMsmz+y47GYUH9GxoYoh94PKmqbOHa2mnQvSre0k26OJquggsYWdXbD2/Fohw6wbP5IGltaeWVLrt6mOJ3iynr+vOs0t06OY7gp+JLn0s0msgrKaWpRm199YWee9mXozvqhfcViNtHYYuOL05V6m6JwMh7v0JNjQvjWpDje3XmK89UNepvjVFZvygHgRxdF5+1YzNHUNbUqoeA+kpFrJcjPxyv3IKYmRWEQKiU3EPB4hw6wdP5IWm2Slzfn6G2K0zhtreOvWYXcNlVrj3o5aclRCHXT9pnMPCtTEqMw+njFLXEJ4YFGxg4LJ1Pl0b0er/j0xkcF8e0p8by/+zRFFXV6m+MUXth4Eh+D4IG5Izp9PiLIj9FDwtTmVx8oqW4gp+SCV+bP20kzm9hfWEld08A+Xe3teIVDB/jRvBEIITrSEt5EbukF/v5FEd9LG86gsK4V6NPNJvadrlSNy3pJZnv+3EP0Q/tCujma5lZJVkGF3qYonIjXOPQh4YHcMTWBv+4toqCsVm9zHMqqDScJMPqweI6523EWs4mmFhv7Tqmbtjdk5FgJC/Bl9NAwvU1xGlMSI/E1CJWS83K8xqED3D/XjNFH8OLGk3qb4jBOnKvh3wfPcHd6ItE96FtOSYzCR920vSYzz8q0ZBM+hm7Pz3k0QX6+TIiP6FiNKLwTr3LosaEBLLQk8vf9xeSUeEf3wZXrswnx82XRrOQex4YGGBkXF65u2l5QWF7H6fI6r86ft5NuNnGoqHLA9j8aCHiVQwe4b7aZIKMPKzd4fpR+uLiK/xw5x70zk4gIsq+1gSXZxIHCSi4M8NbC9tL+5edN/Vu6wmKOxiZhd1653qYonITXOfSoYD/umZHEJwfPcszDG/uvWJ9NeKCRe2Yk2f2adHM0LTbJngJ109rDzlwrpmA/UmJD9TbF6UxMiMDf16BScl6M1zl0gB/MSCY0wJcV67P1NqXP7D1VwabjJSyalUxYgP3alpOHR+LnY2Cnuml7REpJRq6VNLMJgxfnz9sJMPoweXikSsl5MV7p0MODjPxwZjLrj5732JOTK9dnYwr24+70xF69LtDPh4kJESoKs4P8slrOVTcMiPx5O+lmE8fOVlNe26S3KQon4JUOHeD70xOJDDLy/DrPi9J35lnZkVPGkjnmPinnWMwmDp+poqpObX51R0f+3MP1Q3uDpa3WfqeK0r0Sr3XooQFG7pttZmt2KXtPeU4+WUrJinXZxIb6c2fa8D5dI90cjZSwK1/dtN2RkWtlcFgASdHBPQ/2EsbFhRPk56Nk6bwUr3XoAHdZhhMd4u9RUfqOnDJ2F5Tz4LwRfe7LPSE+ggCj2vzqDptNsjPXSrrZhKayODAw+hiYmhSlWkR4KV7t0IP8fLl/jpmMXKtHfICllDy3Lpuh4QF8Z0p8zy/oAj9fA1MSo1QU1g3ZJTVYa5tIG0D583bSzSZyS2u9vjvpQMSrHTrAHdMSGBwWwIp12WhKee7LpuMlHCisZOn8kfj79k81x2I2ceJ8zYAR/ugt7V92A2lDtB1Lssqjeyte79ADjD48OG8EWacq2Jpdqrc5XWKzSVaszyYhKohvTY7r9/XS1eZXt2TkWkmICiIuMkhvU1zO6KFhhAX4kpGjPhvehtc7dIBvp8YTFxnIivXuG6X/98g5jpyp5qFrRzqkJ/c1Q8MI8fdVefROaLVJduZZB1R1y8X4GARpySYy8tw/DanoHQPCofv5Glg6fyQHi6rYcKxEb3OuoNUmWbkhG3NMMDdOGOaQa/r6GJiWpPLonXH0TDU1DS1eKTdnL+lmE4Xl9RSWe6d+wEBlQDh0gJsnDiMpOpjn153AZnOvKP3fB8+Qff4CD12b4tCOfxazifyyWs5W1Tvsmt5A+wb5QI3Q4ct6dHVq1LsYMA7d18fAsvkjOX6uhs8On9PbnA5aWm2s2nCSUYND+frYIQ69dnvDKRWlX0pGrpURsSHEdiMW4u2kDArBFOynPhteRo8OXQjxhhCiRAhxuIvn5wghqoQQ+9t+fuF4Mx3DN8cPZWRsCCs3ZNPqJlH6378oJr+sluULUhzeT+TqwWFEBBlVHv0imltt7CkoH9DROYAQAovZREZumdvuKyl6jz0R+lvA9T2M2S6lnND286v+m+UcfAyC5QtSyCm5wMcHivU2h6YWGy9sPMnYYeFcN3qQw69vMAgsySYyc63qpm3jYFEldU2tA7Jc8XIsZhPnqxvJ9zKFr4FMjw5dSrkN8Jyz8z1w/ZjBXD0kjBc2nKS51aarLX/dW0hRRT0PX5fitNOKFrOJ4sp6CstVHh3oKNVLG+AROnxZ2qpWcN6Do3LoFiHEASHEZ0KIMV0NEkIsEkJkCSGySkv1qQk3GASPLEihwFrHR/uKdLEBoKG5ldWbcpiUEMGclBinzdMeiXrCSVlXkJln5eohYUQG2ycY4s0kmoIYEh6g8uhehCMc+j5guJRyPPAS8I+uBkop10gpU6WUqTExznNiPTH/6ljGx0fw4sYcGltadbHh/d2nOVvVwKPXXeXUXiLmmBBiQv1VFIb2JZp1qkKlW9oQQkvJ7cyzul3ll6Jv9NuhSymrpZQX2n7/FDAKIaL7bZkTEUKL0osr6/lgT6HL569vauXlzbmkJUeRPsK5b5UQgnSzicw8lUffd7qCphabcugXYTGbsNY2ke0lGrwDnX47dCHEYNEWYgohprZd0+3DwZkjo5mSGMnqzTk0NLs2Sn9nZwFlFxp55LqrXDKfJdlEaU0juaUXXDKfu5KZa8UgYEpSlN6muA3tpa2qDYB3YE/Z4vtAJnCVEKJICHGvEGKxEGJx25BbgMNCiAPAi8Bt0gNCQSEEDy+4ivPVjby367TL5r3Q2MIrW3KZlRLDlETXOBa1+aWRmWtlbFxEryT9vJ24yCASooLUASMvoUc5HCnl7T08vxpY7TCLXIjFbGL6CBOvbMnh9qnxBPn1Xh2ot7z1eT4Vdc08vCDF6XO1Ex8VyLCIQDJzrdxlSXTZvO5EbWML+wsr+eGsZL1NcTvSzSY+OXSWVpt06EllhesZMCdFu+LhBVdRdqGJtzNOOX2uqvpm1mzL49qrBzEhPsLp87XTfogkcwBvfu0pKKfFJgf8gaLOsJhN1DS0cORMld6mKPrJgHfok4dHMveqGP6wLZeaBudqcL6+PY/qhhaXRuftpJtNVNY1c+xctcvndgcy86wYfQSpiZF6m+J2qBYR3sOAd+igRemVdc28saPAaXOU1zbxxucFfH3sEEYPDXPaPF0x0G/azFwrE+MjXZJW8zRiQwMYERsy4PdYvAHl0IGxcdrR+z9uz6Oyrskpc/xhWy61TS08dO1Ip1y/J4aEB5IUHTwgHXpVfTOHi6s6vtQUV5JuNrGnoJymFn1PTyv6h3LobTx8XQoXmlp4bXuew69dUtPA2xkF3Dh+KCMHhTr8+vZiMZvYlV9Oi84tD1zN7vxybBLl0Lsh3WyirqmVg0WVepui6AfKobcxanAYXx87hDc/L8DqYB3OV7bk0twqWXat63PnF5NuNnGhsYVDxQNr8ysjtwx/XwMTEyL0NsVtmZZkQoiBm5LzFpRDv4iHrk2hobmVV7fmOuyaZ6vqeW/Xab41SRPY0JP2hlQDreY4M9fKlMSofgtvezORwX5cPThM5dE9HOXQL2JEbAj/M3EYf8o8RUl1g0Ou+fLmHKSU/GiePrnzi4kO8eeqQaEDKgqzXmjk+LkalW6xg3Szib2nK1x+clrhOJRDv4xl80fSYpP8fkv/o/TC8jrW7inkO1PiiY9yD3V5S9vml15NyVzNzjyt87Ny6D1jMZtoarGx73SF3qYo+ohy6Jcx3BTMt1Pj+POu0xRX9q+H+EubTiKE4MG5+kfn7aSbTTQ02zhQODDy6Bm5ZYT4+zJuWLjeprg9U5Oi8DGIAbWC8zaUQ++EB9vSI6s35fT5GvlltfxtXzF3ThvO4HD30a5s3/waKP3Rtfx5JL4+6qPeE6EBRsYOC1d5dA9Gfco7YVhEILdNjeevWYWcttb16RovbMjGz8fAkjlmB1vXP8KDjFwzdGDctOeqGsgrq+1oTqbomXSziQOFldQ2tuhtiqIPKIfeBQ/MHYGPQfDCxpO9fu3J8zX888AZFqYnEhPq7wTr+ke62cT+05XUN3l3Hj0zT1uFqPy5/VjMJlpskj0FXqM6OaBQDr0LBoUF8L204fz9i6Je9xFfuSGbYD9f7nPTzn5pZhNNrTb2nvLuza+MHCvhgUZGD3F9qwVPJXV4FEYflUf3VJRD74bFc8wEGH1YtcH+KP3ImSo+PXSOe6Ynuq1u5ZTEKHwNwuvz6Jl5VtKSozColrB2E+jnw8SEyAF3VsFbUA69G6JD/Lk7PZF/HzzDcTu7FK5cf5KwAF/uneme0TlAiL8v4+MjvDqPXlheR1FFvcqf9wFLsonDxVVU1Tm3+6jC8SiH3gOLZiUT4ufLyvXZPY7dX1jJhmPnWTQrmfBA91bFsSSbOFRc5fSWwXrRvvpQ+qG9J91swiZhV773fuF7K8qh90BEkB/3zkziv0fOc7iHHigr1mcTFezH3dOTXGRd30k3m2j14s2vzFwr0SH+jIgN0dsUj2NCQgQBRoNKu3ggyqHbwT0zkggPNLKimyh9T0E527JLWTw7mRB/9++5PWl4JH6+Bq8UB5ZSkpFrxWI20aZfrugF/r4+pA6PUhujHohy6HYQFmDkvtnJbDpe0mVlyPPrThAT6s/30hJda1wfCTD6MCkhwiujsNzSWkpqGlW6pR9YzCaOn6uhzMGdRxXORTl0O1loScQU7NdpLj0jp4ydeeU8MMdMoJ/ndPRLN0dz9Gw1FbXOEfXQi8y2/LnSD+077V+GO73wC9+bUQ7dToL9fVkyx8yOnLJLPuRSSp5bd4Ih4QHcNjVBRwt7T7rZhPTCza/MPCtDwwMYbnKPhmieyNhh4YT4+6q0i4ehHHovuDNtOLGh/qxYl42UEoAt2aXsO13Jj+aNJMDoOdE5wLi4CIL8fLzqprXZJJm5VizmaJU/7we+PgamJqk8uqehHHovCDD68OC8EewuKGdHThlSSlasyyY+KpBbU+P0Nq/X+PkaSE2M8qp69OPnaqioa1b5cweQbjaRV1bLuSrHaAMonE+PDl0I8YYQokQIcbiL54UQ4kUhRI4Q4qAQYpLjzXQfvjMlnqHhATy3Lpt1R89zqLiKpfNGYvTQbn7pZhMnSy5QUuMdN237Jq/q39J/2t/D9p44CvfHHi/0FnB9N89/FRjZ9rMIeKX/Zrkv/r4+LJ0/kgOFlfzkbwdJjg7mponD9Darz7RHst6ytM7MLSPRFMTQiEC9TfF4rh4cRkSQ0StLW72VHgumpZTbhBCJ3Qy5EfiT1JLKO4UQEUKIIVLKs44y8hIKdsD6X8Kdf4PACKdM0RPfmhzH77fkcrq8jidvGKNvr+39f4a6ckh/sE8vHzM0nNAAX3bmWblxgud+MQE0t9rYlVfON8YP1deQCyXw7+XQ3LfWyw4lZBB88wXw7X3XT4NBkJZk8pqU3C//eZjQACOPfuUq/YyQEt67Fcb8D0y80+GXd4QnGgYUXvR3UdtjVyCEWCSEyBJCZJWWlvZtNv8wKM6Cnb/v2+sdgNHHwP/9zzV8OzWOb4zT0XnUlsEnj8L6/welPbcm6Awfg2Cal9y0f80qoqaxha+MGaSvIVt/B9n/gcYafX/qyuHA+7DvT33+p6SPMFFcWU9huRt8OfWDgrJa3t11mtomnfu8n/gUctZrjt0JuPRIo5RyDbAGIDU1tW//oiHjYPSNkPl7mLYYgqIcaaLdzE6JYXZKjC5zd7BjJbTUg28AbH0GbnmjT5dJN5vYcOw8xZX1DPPQVEVDcysvbTrJxIQIff+/VJ6GvW9p0dc3X9DPDtCcxptfhe3Pa/YYe///tr2WPyO3jO9EeVZZ7sW8sPGk/oIzNhts/jVEJcP4250yhSMi9GIg/qK/49oecx5zfgpNF+BznW8YPak+C3v+CONug7QlcPhvcP5Iny5l8YI8+l92n+ZsVQOPLLhK33LFbc+CEDDrMf1saEcImPdzqDkLWX37sh8RG0J0iL9Hr+BOnq/hH/uLuSt9OLGhOspBHv0HnD+s+S8f58TSjnDoHwN3tVW7pAFVTsuftxN7NYy9FXav0fKVA5EdK8DWArN/DJYHtVTU5l/36VJXDQolKtjPY/uj1ze18vKWXKYlRTF9hI7VLdZc+OI9mPx9CHeTMtbEGZA0W1vNNfZOqAVACEG62URmrrXj7IWnsWrDSYL9fFk8S8/ovBW2PAMxo+CabzltGnvKFt8HMoGrhBBFQoh7hRCLhRCL24Z8CuQBOcBrwP1Os/Zi5jwOLY2wY5VLpnMrKgu1Zf2E70JUkpZ2sjwAx/8NZ77o9eUMBoEl2XNv2nd2FlBa08gj1+kcnW/9HfgYYebD+tnQGfN+DrWlWgDUByxmEyU1jeSW1jrYMOdz9Ew1nxw6q7/gzKEPoeyEFp0bnHcAsUeHLqW8XUo5REpplFLGSSlfl1K+KqV8te15KaV8QEppllKOlVJmOc3aizGZtTzUnj9C9RmXTOk2bHtW++/Fy/q0JRAY2ecoPc1s4mxVA6f6KIqtFxcaW3h1ax4zR0YzNUmf/RRA25Q+9AFM/SGEDtbPjs6Inwojr4OMF6HBPqGWi/mytNXzVnAr1mfrLzjT2gxbfgODxsLVNzh1Ks88DdPO7B+DtGmbPgOF8jzY/x5MvhsiLtq6CAiH9KVwch0U7u71ZdtvWk/Llb71eT7ltU08cp2OpWig3bC+gTD9IX3t6Iq5T0B9Bezs/TGRhKgghkUEetxn44C7CM4ceB8q8mHez8DgXJfr2Q49cjhM+h7sfVurLhgIbP0dGHxh5iNXPjftPgiOgU1P9fqyydHBDArz96g8elV9M2u25TF/VCwT4iP0M+TcYTjykbZKCnZTybuhE2HUNyBztVbO2AuEEFjMJnbmWbHZPCcl9/z6bCKDjPoKzrQ0avfssMmQ0t35TMfg2Q4dYOajIAzam+btlGbDwbUw5QedL+v9gmHGcsjfCvnbe3VpIbQ8+s48z8mjv74jn+qGFpYvSNHXkC2/Af/wPh/uchlzn9Dq0zNX9/qllmQTFXXNHD9X4wTDHE9Wm+DMkjlmfQVn9v0Jqgph7s+0qiMn4/kOPXwYpN6jnZi05uptjXNpX9bPWN71mNR7IHQIbH6614cX0s3RlF1o4mRJ76shXE1FbRNv7Mjnq9cM5pph4foZUrxP24y2PKDtYbgzg8bAmJtg56vaobReYDF/WY/uCTy/Llt/wZnmei0dnGAB8zyXTOn5Dh00B+fjB1t/q7clzuP8kbZl/eLul/XGQC0dczoTcjf1aoqOmzbH/W/aP2zLo7bJDaLzzb/WHHnaEn3tsJc5P9UOo+1Y2auXDY0IJCk62CMELzJyysjMs3K/3oIzWW9oZwBcFJ2Dtzj00EEwbREc/ABKjuttjXPY/Gut1txix7J+0l0QHt/rKD0+Koj4KPff/CqtaeTtjAJuGD+UlEGh+hlyepd2jHv6MggI08+O3hCTAuO+o1WH1Zzr1UvTkk3syiunpdXmJOP6j5SS59dnMyQ8gNv1FJxpvKB9aSbNhqSZLpvWOxw6QPoyLYe85Td6W+J4znzRtqx/0L5WB77+WgVQ8V6tp0gvsCSb2JVfTqsbb369siWXxpZWls0fqa8hm5/SNqGnLtLXjt4y+8daKV0vq8PSzSZqGls4fKb3pY+uYkt2KXtPVfDgvBH6Cs7sXqPV/s/7uUun9R6HHmzSlr1H/wHnDultjWPpy7J+/O0QmaRF6Tb7I6p0czRV9c0cO+ueN+25qgbe3XWKb02KIzkmRD9D8rdpPzMe1gIJTyIqWevtsvct7ZCanaQlu3eLCCklK9dnExcZyK2T43t+gbNoqNZq/kcs0M4AuBDvceigRbAB4bDZi6L0wt1abXn60t4t632M2mnac4fg2Md2v8zdN79Wbz6JzSZZqmd0LiVselrbfE69Rz87+kP7obT2Q2p2EBPqT8qgELf9bKw/ep6DRVUsmz8SP18dXdvOV7Sa/3k/c/nU3uXQAyPA8iM48YmWbvAGNrUt66fd1/vXjr0VolO0NJSt1a6XDAoLIDkm2C2jsKKKOtbuKeQ7U+KJj9JRADp3IxTuhFmPglHHZk/9ISIeJi3UDqmV59v9snRzNFkFFTS1uFce3WaTrFifrb/gTF25VhY66hta7b+L8S6HDloVSGBUn4/AuxX527Wa8hnL+7asN/hoVQ2lx+HwR3a/LN1sYnd+Oc1utvn10sYchBA8OG+Efka0R+fhCTDxLv3scAQzH9EOqfXiDIfFbKK+uZUDRZXOs6sPfHr4LMfP1bDs2pH6Cs5krtZq/ec+ocv03ufQ/UNhxkOQswFO79Tbmr4jpZb/7u+yfvT/wKBrtCi91b7m/unmaGqbWjlYVNX3eR1MQVktH+4r4o6pCQwJ17Fn+4nP4Mw+bWPRV8dmT44gbIh2SO3gX+wWSElLMiEEbiVL12rTcucpg0L4pt6CMztf1Wr9B43RxQTvc+gAU34IwbF9OgLvNuRu0mrJZz7SJ2GCDgwGLVooz9VuXDto3/xyp5rjFzaexOgjuH+udwsUuJzpD2mH1bY+Y9fw8CAjY4aGuVUe/Z/7i8ktrWX5tSkYDDp222wXnJnzU91M8E6H7hekOcKC7ZC3VW9rek97dB4er9WU95ervqbl87b+FlqaehweFezHqMGhbnPTtgsULLQk6itQcOxjOH8IZj/uNIEClxPStj9z+CO7BVLSzdF8cbqShmb79mWcSXOrjVUbTjJmaBhfGaNjl8uac22CM9/Rav11wjsdOmjdCMOG9ekIvO5k/0fb1J394z6J+16BEDD351oDsy/esesl7Ztf7nDTrtpwkiCjD/fN1lmgYPOvIfoqGHuLfnY4g/QfaalKO/edLGYTTa029p6qcLJhPfO3vUWcLq/j4QU6R+fbn9dq+2f/WD8b8GaHbgzQqhAKd0HORr2tsR+bTfsSikxy7LJ+xHyInwbbnoPmhh6HW8wmGltsfHG60nE29IEOgYIZSUTpKVBw+G+aQMFc5woU6EIvBVKmJEbhYxC6r+AaW1p5aVMOE+IjmDcqVj9D2gVnJt6ppeN0xHsdOsCEOyEiQTvR5ylR+vF/abXjcx7XaskdhRBaT4maM7D3zR6HT02KwiAgU+c8+or12YQG+PKDGXoKFLRcJFBwo352OJO0JRAQYVeUHuLvy/i4cN1bRKzdU0hxZT2PXJeiv44suIWOrHc7dF8/mP0TLeo48ane1vRMx7I+RashdzTJsyFxJmxfAU3dKxOFBxoZOyxcV5WaDoGCmcmEB+ksUFCep20uO1mgQDcCwrWeNHYKpKSbozlYVMWFRvsqpxxNQ3MrqzflMDUxihkjdOxBX56v1fJPWnip4IxOeOmn8yLG3QZRZs1R9uIIvC4c/kirGXem7uC8n0NtCex5rcehFnM0+wsrqWvS56Zd0SZQ8P0ZegoUNGl12kMnwVVf1c8OVzB1EQRFaym/HrCYTbTaJHvyeyeW4Sje3XmKkppG/aPz7gRndMD7HbqPr+Ygzx/W+ry4Kx3L+mu02nFnkZAGI67VxLUbuxcrsJhNNLdKsgpcv/mVVVDO1uxSFs/WWaDgiz9B1WntGLeejsMV+Idoh9jytkDBjm6HTh4eiZ+PQZc8em1jC69syWXGiGimtZXY6kJptlYKPOUHWk2/G+D9Dh3gmpshZlSvjsC7nIN/0WrFXbGsn/sE1JdrhyC6YUpiJL4GoUuu9Pl12USH+HOXJdHlc3fQXK9tIsengXm+fna4kin3Qshg7TRsN/tOAUYfJg2P0GWP5e3MAqy1TTx8nc698Lc+43Y6sgPDoRt8NCdWlg2H/qq3NVfS0qTViA+dqNWMO5thk7V5Ml7Smgh1QZCfLxMTIlyeR3cfgYI3NYGCeT/3/ui8HWOgVh12OqNHgRRLcjRHzlRTWdfz2QZHUd3QzB+25jFvVCyTEnRUiDp/REuRTrtPq+V3EwaGQwcY9U0YPBa2PKPVi7oTX7yj1YjPdaHjmPsENFZB5svdDrMkmzhUXEV1g2ves3aBgsFhAdwxTUeBgqZa2LECkma5VKDALZh0F4TF9XiGI32ECSlhZ57r8uhv7Minqr6Zh91Bqco/VKvhdyMGjkM3GDSHWZGv6Y+6C80Nbcv6aVqtuKsYPFbL1e98BWq7XjZbzNHYJOx20U271W0ECl7TBArmulagwC24RCDlv10OGx8XQaDRx2UtIirrmnh9ez7Xj9FZR7ZDcOYB+wRnXIhdDl0Icb0Q4oQQIkcI8Xgnz98thCgVQuxv+/mB4011AClfgWGpWt1oS6Pe1mjsfVOrDddjWT/3CWiug89XdTlkYkIE/r4Gl+TRpdRaoMZFBvLtVJ0FCj5fpQkUJEzTzw49mXAHRCZqZzi6qA7z8zWQmhjpso3RNdvyuOAuOrIBEW6pI9ujQxdC+AAvA18FRgO3CyFGdzJ0rZRyQtvPHx1sp2MQQnNiVYWw7096W6PVgm9fodWGJ81y/fwxV2n17rtfg5rznQ4JMPowebhrbtp2gYKlegsU7HpV21vQqQWqW+Bj1KrDzh3SDrt1Qbo5muzzFyitcW6AVHahkbcyCvjGuKFcNVhHHdl2wZnpS7XafTfDnrtmKpAjpcyTUjYBfwE897iceR4kpLcdga/X15Y9r2k14S7WHbyE2T+B1iYtX9wF6WYTx8/VUF7rvM2vdoGCpOhgbtZToKC+AjJWw1Vfh2GT9LPDHWgXSNn86y6rw9LNrunM+eqWXBqaW3noWr11ZJ/WavWn9kFwxgXY49CHARcLDxa1PXY53xJCHBRCfCiE6HS9LIRYJITIEkJklZaW9sFcByCEVlN84RxkvaGPDaDVgO9YpdWEJ6TpZ4fJrC2vs96AquJOh1jM2kk8Z9607QIFD+ktUJCxWtssHsjReTsGH60FRTcCKWOGhhEa4OvUlNz56gbe2XmKmybGYdZTR7Zgh1ajP2O5VrPvhjjqzvkXkCilHAesB97ubJCUco2UMlVKmRoTo2OpT+IMSJ6jpTsaL+hjw85XtVpwd3Acs3+sVTNsf67Tp8fFhRPk5+O0tEu7QMHI2BC+obdAwa42gYLB1+hnhzsx+iaIHdOlQIqvj4FpSVFOLW19eXMOrTbJMnfQkQ0ZrNXquyn2OPRi4OKIO67tsQ6klFYpZXsS7Y/AZMeY50Tm/hzqymD3GtfPXV+h1YBf9XWtJlxvIhJg8kJtX6Gi4IqnjT4GpiZFOU1ntF2g4OEFKfjo2QL181XaJrGOAgVuhx0CKRZzNAXWOs5UOj6FWVxZz192F3JrajwJJj11ZDdptfmzHu2f4IyTsceh7wFGCiGShBB+wG3AJTLyQoiLz73eABxznIlOIn4KjLwOPn8BGlwstZb5svst62c+AsIHtnauAp9uNpFbWsv56p5b7/aG5lYbL2w8yeghbiBQsPuPMPbb2max4ktGfR2GTOhSIKU9j+6ML/zVm04C8CO9dWQ3P63V5jtCcMaJ9OjQpZQtwIPAf9Ec9QdSyiNCiF8JIW5oG7ZUCHFECHEAWArc7SyDHcrcJ6ChUqvFdhW1Vm2+0f/jXsv6sKHaUvLAn6Es54qn09vy6I6+aT/aV8QpqzsIFKzQNod1FihwS4TQNu67EEi5alAokUFGh+fRT1lr+SCriDumJTA0QseoOPu/jhWccSJ25dCllJ9KKVOklGYp5dNtj/1CSvlx2+8/lVKOkVKOl1LOlVIed6bRDmPoRBj1DS1irnPRabf2Zb07ReftzFgOvgGd6ktePSSMsABfhzr0xpZWXtyYw/j4COZfraNAQVWRdh5g4ne1TWLFlYy4FuKmdiqQYjAILGYTmbllSAfqDryw8SS+BsH9c/TWkX1Kq8mfcId+dtjJwDkp2hVzn9AqTjJecv5cNee1mu+xt7rnsj4kVmuheuhDKLk0a+ZjEKQlm8jIc9zm1wftAgUL9BYoeE5bVruBQIHb0h6l15zR1Hkuw2KO5kxVA6fLu++zby85JRf4xxfFLExPJDZMRx3ZDsGZnzpWcMZJKIc+aIzWjXHXH7QqB2eyY2Xbsv4nzp2nP0xfBn4hnSrXpJtNFJbXU+iAm7ahWZMPm5oYxcyROgsUfPGOpkEboWPvGE+gQyDl+SsEUixtbWwdlXZZtSGbAKMP983SUanK2YIzTkA5dNC+fVvqNYfrLKqKIet1bdnmzsv6oCiw3K8p3J89eMlTFgfm0dsFCh7WW6Bg27NuJVDg9sz9WacCKeaYYGJD/R3i0I+drebfB8/y/emJmEJ0zFl3CM487jE6ssqhA0SP1JSN9vwRqs86Z47tbct6T9h0S7tfO9Z8WZSeMigEU7Bfv3tg1za28OrWXKaPMJGmp0BBWY4mL5d6r9sIFLg9wy1ab/jLBFKEEKSbTWTmWvudR1/ZpiO7aKaOgU+74EzsGK0W30NQDr2d2T8GW0u3R+D7TEWBVuM9eaFnLOsDIyB9KWR/BkVZHQ8LoW1+ZfRz8+vtzALKLjTx8AKd9xG2/EbbBJ6xXF87PI25P+tUICXdHE3ZhUZySvp+WO9QURXrjp7nBzN01pF1peCMA/EcS51NVBJM+K624VNZ2OPwXrH1Wa3G25OW9dMWQ5DpCn1Ji9nE+epG8spq+3TZmoZm1mzLY+5VMUwerqdAwVE4/De3EyjwCOI6F0ixmPufR39+/QkigozcMyOxv1b2nXbBmSETtBp8D0I59Itpr3LY1vnhmj7Rvqyf8gOt1ttTaNeXzN0EpzI6Hu5vPfobOwqorGt2g+i8XaBgqb52eCqdCKTERwURFxnY58/G3lMVbDlRyn2zzIQG6BidtwvOeKBSlXLoFxMRr1U77H8PyvMcc82tz2iHETxxWZ96L4QMukRfMtEUxJDwgD7dtJV1Tfxxex5fGTOIsXE6th49ewCO/UvbK3AzgQKPYfBYGH3jFQIp6WYTmXlWbLbep+RWrD9BdIgfC9OHO9LS3tEuOBM3Vau99zCUQ7+cmY9oVQ9bf9f/a5Uc02q6py7yzGW9X5D2fpzaAflbgbY8enLfbtrXtruZQIHlfn3t8HTmPKFJ9WW80PGQxWyiqr6Zo2ere3WpzFwrn+dYWTJnBEF+vo621H72vqWf4IwDUA79ckIHa+mRg2uhNLt/19ryG62me/oyx9imB5Pv1npYbHqqI0q3mE2U1zaRXVLT/WsvwnqhkTc/1wQKRg0Oc5KxdlC4B7L/47YCBR5F7CitPnvXmg6BFEty71NymlLVCQaF+fNdXXVk67Qa+8SZWs29B6IcemfMWA6+gZpD7itnD8LRf2pRoCcv6339tQ5zRXvg5Hrgos2vHPtv2le3KoECr2TO420CKdoZjsHhASTHBPeqtHXbyTL2FFTw4FyddWTbBWfm/kw/G/qJcuidERwNaYvhyEdw/kjfrtGhO+gFy/qJd36pLyklcZFBJEQF2V3NUFLdwJ8y3UGg4HPI2+zWAgUeh8kME27XDs21CaRYkk3syrPS3Nq5FunFSClZse4EwyIC+fYUHXVk2wVnzPO1WnsPRTn0rrA8CP5hnR6B75GiLK2GO/1HWk23p+Nj1NoVnD2gqZ2jbX7tyrfSakce3W0ECja7v0CBRzLrUoGUdHM0tU2tHCruuS31xmMlHCiqYun8Efj76hiddwjOeG50Dsqhd01QlObUj/8bznzRu9duflqr4Z622Dm26cHYb4NpRJu+pA2L2URNQwtHznR/0xZX1vP+7kJuTY3TV6Agbwuc+lzb5HVjgQKPJHK41id83ztQUUBaspZi7CmPbrNJnl+fTaIpiJsnxbnC0s7pEJz5mlZj78Eoh94daUsgMLJ3UfqpDK1229uW9T6+Ws+bkqNw5CO7D5Gs3qT1Vn9wnt7yYU9pm7uTF+pnhzcz61EQBtj6LKYQf0YNDu3Rof/nyDmOna1m2bUjMeqpI+uOgjN9RDn07ggI0w6enFwHhbt7Ht+hOzhIq+H2NsbcDLGjYcszxAb5MiI2pFuHftpax1+zCrl9ajzD9BQoOLkOirNg9mNuL1DgsYQNhdR7tEN0ZTlYzCb2FJTT2NLa6fBWm2TF+mxGxIZww/jONOddRIfgzI1abb2Hoxx6T0y7D4JjtAivJ/K3ajXbMx/Vari9jXZ9SetJOPQB6WYTWQXlNLV0vvn1wsaT+BgED8zVWT5sU7tAwXf1s2MgMGO59oW59RnSzdE0ttjYf7qy06H/OnCGnJILLL9WZx3ZjBe0Wvo5nh+dg3LoPeMXrH1Q87dC/vauxw2UZf2ob8CQ8bD1t0xPCqOuqZWDRZVXDMspucDfvyjiLstwfQUKjv0Lzh2E2Y97hECBRxM6CKb+EA59SFpICQbReUqupdXGqg3ZjBocylev0VNH9rxWQz/2Vq2m3gtQDt0eUu+B0CHaZmdXXQZPrtdqtWc96t3LeiG0SoCKAmZcWI/o4qZ9YeNJAow+LJ6tp3xYm0CBaSSM+7Z+dgwkpj8EfiGE7nyWa4aFd5pH/2hfMQXWOh657ip9dWTbBWfmPK6fDQ5GOXR7MAZq1RGnM7UNz8uR8kvdwYl3utw8lzPyOoibQvDOFYwddGUzpuPnqvn3wTP6CxQc+TuUHoO5P/UYgQKPJyhKKyY4+k9uHGzli8IK6pu+zKM3tdh4YeNJxseFc62uOrLtgjO3u7fgTC9RDt1eJt0F4fGXHIHv4Pi/tRrt2T8ZGMv69ii9uoj7w7az93QFDc1f3rQr12cT4ufLD2fqKB/moQIFXoHlAQgI58aKt2hulWSd+lKAfW2WpiO7XG8d2XbBmVkeIDjTC5RDtxdff00E48w+rRdIOzbbl8v6sQNoWZ88B4bPYG7JOxha6tl3SuuLfbi4iv8eOc8PZiYTEeSnn32HPgBrjscJFHgFgRGQ/iOiizcxySe3IyXX0NzKy5tySB0eyewUHZvVVRRoNfOT7tJq6L0I9UnvDeNvh8gkLZdua6vsOPp3rTZ7zuNarfZAQQiY9zP8G0q5y3dDR++OFeuz9RcoaG2GLc94pECB1zBtMQRG8Yvgv3ek5P686zTnqhv015Hd+qxWMz/rUf1scBLKofcGH6PmuM8d0kSUW1tg82+02uwxN+ttnesZng7Jc3nA+C/2nSxi76kKNh0vYdGsZJ0FCt6FylNaWsgDW6B6Bf6hMOMhJjTtw794JyXVDfx+Sw6WZFOHSIoudOjI3uNZgjN2YpdDF0JcL4Q4IYTIEUJcsSUshPAXQqxte36XECLR4Za6C2NvhegULT978C9aTfZAXtbP+znhsppJZ9fym0+PER3ix93pifrZ09ygKU7FTYWRC/SzQwFTfkhTQDTLff/K0vf3UXahiUeu07kXvicLzthBj15ICOEDvAx8FRgN3C6EGH3ZsHuBCinlCGAl8FtHG+o2GHy0I/Clx+GTR7Sa7FHf0Nsq/YhLxTpsLvf6/JsTp4pZPNusr0DBvrehuhjmqehcd/yCELMeIc1wDMOp7cxOiSE1UcdW0h2CMz/Uaua9ENGTersQwgL8r5TyK21//xRASvmbi8b8t21MphDCFzgHxMhuLp6amiqzsrK6etq9sdngDzPh/GG446+Qcp3eFulKw+kvCHhjDueIJjbahEFPR1p5GoZNhoX/Ug7dHWhuwPrMGHxa6gmIHKpvv/M6q7aCe+igR2sUCCH2SilTO3vOnlBqGFB40d9FwLSuxkgpW4QQVYAJKLvMkEXAIoCEBB2VSfqLwQDffBFO/lct64GAhIlkpTzM8PqjGEJ1PlQVO7qtUZRy5m6BMYDzc54l4NB7JEcH620NXH2DRzvznnDp2lhKuQZYA1qE7sq5HU7cZI9vtelIUu/4pd4mKNyU0TNvhpkDsGhAB+zZySsGLpYSiWt7rNMxbSmXcKD3svAKhUKh6DP2OPQ9wEghRJIQwg+4Dfj4sjEfA+0dqW4BNnWXP1coFAqF4+kx5dKWE38Q+C/gA7whpTwihPgVkCWl/Bh4HXhHCJEDlKM5fYVCoVC4ELty6FLKT4FPL3vsFxf93gDc6ljTFAqFQtEbBuhpGIVCofA+lENXKBQKL0E5dIVCofASlENXKBQKL6HHo/9Om1iIUuBUH18ezWWnUAc46v24FPV+fIl6Ly7FG96P4VLKThvK6+bQ+4MQIqurXgYDEfV+XIp6P75EvReX4u3vh0q5KBQKhZegHLpCoVB4CZ7q0NfobYCbod6PS1Hvx5eo9+JSvPr98MgcukKhUCiuxFMjdIVCoVBchnLoCoVC4SV4nEPvSbB6ICGEiBdCbBZCHBVCHBFCLNPbJr0RQvgIIb4QQvxbb1v0RggRIYT4UAhxXAhxrE1OckAihFjedo8cFkK8L4QI0NsmZ+BRDt1OweqBRAvwiJRyNJAGPDDA3w+AZcAxvY1wE14A/iOlHAWMZ4C+L0KIYcBSIFVKeQ1aG3CvbPHtUQ4dmArkSCnzpJRNwF+AG3W2STeklGellPvafq9Bu2GH6WuVfggh4oCvA3/U2xa9EUKEA7PQtAqQUjZJKSt1NUpffIHANkW1IOCMzvY4BU9z6J0JVg9YB3YxQohEYCKwS2dT9GQV8GPAprMd7kASUAq82ZaC+qMQwg1Uml2PlLIYeA44DZwFqqSU6/S1yjl4mkNXdIIQIgT4G/CQlLJab3v0QAjxDaBESrlXb1vcBF9gEvCKlHIiUAsMyD0nIUQk2ko+CRgKBAsh7tTXKufgaQ7dHsHqAYUQwojmzN+TUn6ktz06Mh24QQhRgJaKmyeEeFdfk3SlCCiSUrav2D5Ec/ADkWuBfCllqZSyGfgISNfZJqfgaQ7dHsHqAYMQQqDlSI9JKVfobY+eSCl/KqWMk1Imon0uNkkpvTIKswcp5TmgUAhxVdtD84GjOpqkJ6eBNCFEUNs9Mx8v3SC2S1PUXehKsFpns/RkOvA94JAQYn/bY0+0acAqFD8C3msLfvKA7+tsjy5IKXcJIT4E9qFVhn2Bl7YAUEf/FQqFwkvwtJSLQqFQKLpAOXSFQqHwEpRDVygUCi9BOXSFQqHwEpRDVygUCi9BOXSFQqHwEpRDVygUCi/h/wP9AjQgdpb21QAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -715,7 +765,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -734,7 +784,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -753,7 +803,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -765,7 +815,7 @@
     }
    ],
    "source": [
-    "epsilons = np.array([1, 0, 0, 1, 0, 1, 1, 0, 1, 0]) # Try different combinations here.\n",
+    "epsilons = np.array([1, 0, 1, 0, 0, 1, 1, 0, 0, 1]) # Try different combinations here.\n",
     "theta_list = [0.5, -0.5, 2, -2, 100,-100]\n",
     "\n",
     "for theta in theta_list:\n",
@@ -826,27 +876,22 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Extended Exercise\n",
     "\n",
     "1. Does an MA(2) model have an expected value of 0?\n",
-    "2. Does an MA(2) model have a covariance tending to 0 with increasing time lags?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1. Yes, would still have mean 0.\n",
-    "2. Yes, because i > 1."
+    "- yes as properties dont change compared to MA(1)\n",
+    "2. Does an MA(2) model have a covariance tending to 0 with increasing time lags?\n",
+    "- yes because i > 1 as mentioned above where the covariance for lagged intervales would approximate to 0 for lages larger than the preiod of the MA of model"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -860,7 +905,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.6.2 - Stationarity.ipynb b/notebooks/Module 1.6.2 - Stationarity.ipynb
index 0f4d2bc..5be731e 100644
--- a/notebooks/Module 1.6.2 - Stationarity.ipynb	
+++ b/notebooks/Module 1.6.2 - Stationarity.ipynb	
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,22 +60,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ef1eca6708>]"
+       "[<matplotlib.lines.Line2D at 0x2244b3caa40>]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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7Rxe8VKACYpQxO9aKVTuVS1eKxG1qrON8TgHkv4ZxDP/aF2xUj9MQX+hVmkd6u01516vuGS189bbJq1EhKR7VyyXi6xHt1T+I4Hdwy7mM86BGaLN6ntqWkfOhhzE8OWMLVuw/h8ZVgnpZJtPR3AXynf/evD0AwGTkyk1ZzRK042JeIVq/uTB4QcXnun3yGjzap6FpNPgWDglpUqs+m/e3cFHytaWtPs4zUw8t1kC7YUAkPDtZi4oWOs5mF+BsdgF26UgEwUnkbhM7zOrcCbZj5JuOZiErt1CwWsoNTva9XL8Xm5IeLGjIH/5ZKOx0MyTZLccvYvXBc3i4ZwNNz42bvwc1yiWKFradp0InCkWoNySn1zxzKT/wPhmXC/DKnzu0Ey+BEIlcYqaoVYlYGXsnGvi4cDxrSc6hV21GWVsn0X2BGpHRXrjmYNABSaVErm0rrZkeNbAdI7/1S59O7o3BzQLX1GlWBOf0DPM9VseFUwetqM/n/c23/S0s9uLSlSKkJiubO97szwSvlZF/ufQgAKB59RRf+/7vKfxiXip+jz83n7TEzpaK/hCDoy8rrwhFHi9i3exjoSKPF2/N3oXMy6GmZGIduR5K/bRYLJKnH8tC21rlVNLi+5f/PloY9ZK96s4chG0w7zOfoSLPWiOfL7/IgwMZOWhevYxiWS8nkdvoBNFGpIaCL5GrGX4bj14IcWQJxMbmlZFTrUTDtjhk0eG9waM/p6PDO4uYz/Dx5+aTimX+2nISe86EStlfLz8k/YBo10BFDGHDkQuK7RqBGin5762n8NQvbJt8wHcY+r81R0Vb8WgJgdD+7YV4fPpm2TLL9ipb5si9jhb/p3u+3aBcSNg2KAqK2WarLFrEErl+jP1tG274fCUyLstHeAQAj0YduRpYPYpKBiNX8ZWOns/DmJ/EE4Flmx16Xxd5uqCXaSzYeVaxzJlL+SFS8dztp5HBCFv6+PQtgch9HN6Zu5tHo+9fteaH4YCcVMmXgmdtlfZIlZqywi7535ojOHY+TyuJsm2owbmcQsWMRTEaGE9ADOB/O5POGaRQ5KFIe3m+6Dpz2Bs0PxRisz8fbl6B8kJipdWKVQKhfRk574vIBs2SuB50qAm5CsDnEPDH5hMhdWv+/iZwNGGbTIcgle0IJaGHf0zHnV+vNUAdG14qZgdLVUiKxto0vuBKSV9CKfXduXswbMoaXW1YnSEoRocuwMoE1mrrnbNdvEBRiFUrZtCghpFqtVqJBtiWkbs0qlaE4FQTrEVg1Lcb8OSMrbrqFkZsMxMsNdDpS8pbRYA9mU5dVPesXH2iAyi/zp4DIcDJi+oSIxg+TIO2/rpS6MHY37bhYl6hpPTFGh/Z+fqSTRR7KN6ctQtZvJjnWtHy9QWBZM5CqJHIharCUAcopWf1QUn1xc21kGdMVq1oCbzFl8i9Kiay1LidseFYYBfoeHZKICZEtaLjK3EMl2e0oqRaWbo3Q3V2c13ebEr3GQWWqfRYNGrOJaJFgloKoNlrC3TVOWHRfvk2KcX4BXuw89SlYGP+fzhq/t2doRhXm8PMTccxfcNxfPTPvhDBgI+X/tiuqi41mL/jDKauOozeHy3Fd6uP6KojO78Yr/+9k3lP6h34EFuthNwF4GNk53OCh75GrUXUTM86Y+cEwlsAwJUiD96eszu0kJHxqkEiD5gfugiKvGKrNrX85vnftuPRnzl1rs6dvUrYlpGrZkIS35z1uIThWuCvUd9uCMluPpUR5VAY/+V8TgFzVZ++/pjompgg6ZdkLzraVExqPmF+EVunKBUZUBRrRcPI/X0zW9LkUOShmLTkIG4ReBPym5yx8Tie/mWLqvb5lErxQM572Axw3yYrrwivSTBjNZBi2Fp05BxCg4T5/n1nzm60e3uRyCFKv5ezOpzi7dx+WHsUfwvONMKh6CjyeAMLiNtFUOwRU2/Mckn/s3KwLSPnMxItH3bl/nMA1H9QOZvSN2fvknyOAjibnY92by/CZ/+KJc2xv4slPS02zHLBqJjPMu4pHR6tO3Qeny0OpZ2rRyrYv7AdLdKc2n7kFsbg9wi1lDnKO4xU27oW5w+9c9Es80MpNVCMW41qRfo3t9As2HkGgC9kMYCwGcDzxwrLv0P4/dTuvELrkL/PX0wIAZORC4WVaLBrKhmMXMOnfPRnX2IGlo7cDKuVgI7cSwPJbBft9lmVrDl4XvZZxfcwNGK0q1bumLIWX/jtx0W1SRwCC1sxUwLhvo+IGZlxsBwB+9LCYi8+mL8HK/ZrOwyWolSTRM7pyHn80rrDTvMrnrX1FJrzVHi5Ckw9mOlL/hvxSXUTtmpFuBlV83qOjlwCaiVyIXMsFgSp59fDlnLleyD9WJYkgw7oIf1VKFmJiKVZwX25m8L7ArA0IUbMq7zUF3BJFH5U0NBlDQeDSoN91tbT/nI0pLwZjF2NNBuASTx/xoZj+GLpQdz9zXomM5eyuZbqNrcKsxjhtwm1WqEh//IKGYKex1l9eCAjJ3B4KDwbem/ebvEDjPq0dJ3bRZgqxHM5BboXJ6u8U+3LyBm6PTXgtkp8d3EOcomCC4rZrvxDvljNZNCUajcR1ILnZm5jtCmjI2eqVrS3y1Wz6sA5NHhpHjYdzZItf8GAhYYQz/zKtiSi1BivoaCaFjW9U1HYP/wxdTJLbNkzyR+VUNQ+j1Z+nZqsVgLP8+mTbsf3rD6oHf9quiB4eBiKXAX7cLWOfaHnJmxG3vX9f/H92qPyFYUZtnPR5+ANkcjVDzG59F1Mz05/cbUeaVwdXkp5pojq6FMqZWRBYKlt+Mzr1b+0xT7hJMg1h0J3I4Zo1BgPRdX3Zczc/CIP5u84o5U80xHCkBn3z0ksgvw34o9nLXbPgRAVvGuXC4oxeZlYlWY8JLO65/nUKzFc4W2lN5c0l5WB20UkKV++7xxGdKnjqzsKtOSmSOSEkKmEkAxCiHmRkBQQqiOXhnCOF3q8aPbqfNUdyjGXQgmJXPI5GN9GiQ4SFQaMrGqFQT6/+v+tOYr/rVGWMsTRAEN/G0kXpuXJB7/fiA1HsgLPqfFQ5PDe3N14YsYWrD7AX4TUt26FPp312aT4Mr95/s40VkI9JHUoWFjsxcW80MWCi87JplHdNyrWGVueL2NpP5tS0H2rqEP4fm4XkXxnrQZCXC3RbrUyDcAAk+pSBf5h8pVCD9q9tRBjfxOrG4RBkAAgt9DD/KDMoFn+f6VUK1LlKaUBTz49vO18ToHmQxX5OBraDztl25JoM1yyCT8sgRyDYb0i50R1uaDIX4ZoUs/p/W6yAgfrMFpCEHCFqFbY1zmsPXQezV9bgKVckCt/+Yt5hWj08jz0/miZ6BkRbRq+zY6Tl9DgpXlYwkvkofZ5tcmuWfFSFFUmgfMUefUjfyy5CMHUlUeYZbWGCNFix64HpjBySulyANZGRhKAr1p57e+dOJ9biOkbjovKfbRwH/t5jdt4raAITizVUqq/2LmcAp/Z4mJ5Bxnj0D6qxHpTeQldC4ypjoyVtdqqQAlaJHI+w+YzPxYj3+DPkMQFLuNKi5xtTAKX+o2fkUntp1U7Tzq+s1h0TbvhpQ8HMoKZlYTt5xd5MHXVYeZzWg0Ftp64qKm8Vtj2sLNYiwjFep5hH8qCVv1X8BCJhujL1bj6cjifw9aNKurQQZFTUMzMo6iFUci2IVKtCGnQD726RiEN+87maPregDaVkP7DTpl7jGvfqVB1eSQqPZiZg/u/24h8/9mOFsbDOkw2ArMlckAs2aqVyH9cewwPfb8pcJ2f3Fm0A9bQPh+Fxd5AHlTAl2lqilwEURMQNkZOCBlNCNlICNmYmWk8iJLcxPtk4T7UGTtH9nnWQsCqU+964bNaCZofqlHNcIxMisEqJmemwP/9sAlDvlgddOYQ1M2HGds8kXNEBERb1rtxOUCF75hf5GGYdVJNzCorrwheL0V+kSfgPKOOTnPAfyevxFnRy3/swKLdZ7HhcJb/GR27L70ECqB2geYvSkpPrDoQesiutFBx9U1ccgDzJfpMSKdcjS6Zg+qJ/+7H49O3BH4XSHhHm4mwMXJK6RRKaXtKafvU1FTD9cmt3p+qUEmwGDlrMusO5ITQCItqkgsr6dHUULLj5CUAPnd2Psy2Iw/QpFGPr6UuLc+pTcnW+b3gtpyvg9baz5OWHMBrf+3Eg99vwjYzts0a2pdSrRQWezHq2/XYfTo7wJQCkfwCQoW291x/+ALu/99GTc/ohZZdlDAQm7JErlw3pcB+XiwluTrldrNS1kaAdXbktjU/VLMNk+s8D9NjS8D8GFnUpbDnTHZIHG8vpYhBcPKoGkiBv8xgsMqSspFWpJxxjGi8+I+evnQFvT9chpn/1wXNqpVRRUtoXWxCLuaJEyoL21aDf/dmICHGDQDI0RkNUW/7UlYr209ewtK9mbiYV4SEWFfIfb0RWd+eEwxDYWShVQMtqhUhzLBa8VKKB3lqFzm6pQ6cASAuAqmFzDI//BnAGgBphJAThJD7zKhXDmp0mnLjghkMR/D71KUryGZkUmdh+vrQg1Y+eV6qjcFJTjqFOkLUOSoeNcOMTrhAGJmM/Krm7ziDK0UezGAcYIueY1zj6GB66zLK6zGb1KrTlwvLqqV5PhPhyyPcuOG/C9cdaiIj6sWBjMvMJCUc9DByrf2h9HZSCzgfmuaoTM7g+BgZtmpRN5gikVNK7zSjHi1Qc9hZzDKelnleONGuGbdEO2GBungmeqAaD3LYva2GcbA89li/lSDlMCOsR/haRhg5f0JwUm7peOUhypr0l/OL8eeWU7LBsIJejkSXtBl4RuXktERHLjCXA3x9ELgckMjZCzwLnHkmReirrTxwjlmeOzA88v4g5n1dOnKNH0tOTciK4Dlt1WGM6lY35JqaXWywPWlahIycX4tVy6ltrVbUMAy5Mqx7pmYk4R2gsXS4zGcUtsGKduQyE4bF7ORCczz0wybpmzL1yi2eWsA5sZROUCFrMF779b934pU/d2DVQTHzYXrwaqQvtL7QGqVC/8pBi+5ayrNz4pIDgWvcVe62W4dPg1Z9+kZeblY9mYeMqVak7+UVivvj9VniyKXC5uXIkVs44gSM3IiTnFrYlpGr8bSUk9pZYTLNtLjwVUUDf1uRMUiIfWdzcF7ioIV1kKTn4EUpbK3QZV8L+HUFGLkKiZy1gHExXpSi4gHA8v2ZGDl1vUoqfdh5KhvrDrNdJ76UiBgpB206coJtJy7ibHY+k0mEqvVCJXJNNGkcs0O/Ck2Bt+PkJU2hH7whErm2xl2E4Kd17Bj/6kM/CNSEchK5jEge7z87CdQThslv28NONZKfR8ZW3HKJnPIOBClVtSoLdsPMOuVw86RVvLpCC7OtVhRJkqQhqDYKxQfz92qvVKIuILgAycVGYX0XVnRLURn/QnZURzJlYTo7PoQJGQKQ6T9NnqUABk9cBRcBFj3VQ3Tfw9PrBS2hCPKLPKJkDVZi+NdrkZ1fjP7Nqqgqz5et9EzFFyWyOam14RaOFbk5Gzp3QgdAfGyofCy0ILMCtpXIf1jLXn0Pn8sNuM/KSeRsRm6iRM5jpedyCjUFwZeiQhN1Ih05QyI34bBTq+ONWnASJGe2efhcrmRZCm2Ln9kU87/igYwcbPebgCqBT4cWCTToMcxeAHzWVtRfxvevmwTj4quFYUcgjfXoTRYDyO84Jqtk5O3eXhTyW25sy6pWBFYrIaGyLfLRt61ELoW520/DRQAP5KUxJpM3cYbzM7sXerwYMGG5/AMIlpdaULRMdmFJ1jZRXxhbwfbTREbOfz9uMS4ShB1mPyd9T1YiN3FOffzPXnwmEXaWg1w4Ay2MK5RueYGE+9NDgbKJceobgXarHCmw1JgshNCt1aNaR18qOQ2q15GHFhTqyMOhWrGtRC6FYk8wtrSc+oU1uEyVyCkNGYxa+tIMMpSsSwB9J+hiRyMzdzFicB6xcrRSUNHE59QmcnpOs0gnhCgycVZ7fNNWTSECZGyYfXWJhYK3Zu/CtpMXVbcBAD3GL8XWE+p2F3JQ4wwHGJPIrZBz1QoBQlqPXQhV1ZllACCHksfIvd4QMyzJcirsyI1Abruv+KzEg7O3nVZdh8i5iVFnkYeqlpY4CA+ZzZXIxfUWSbjaSz0XAGdTzTzk9T9nUo/rlez5zF8LJXz9LOs5vtUK/76R8wtd4HakKiOHhkrk2mCFxkI+qibPll9QbsKiUM9yPq+ZtfUUjl/QfiajhBLHyIs8NDDQ5XTkLCnBXIncACOXuK7loMor8CZlDcpjF/LQ44MlmmgTMn45iVcr+DRyfaeGCfg8Z9n3wmH6NW+7ungrcqToV62IEeoQZP37K0GtsBAqkWuj24xwE0LIjW05iVwIIR86JHPeoxclTkde7AlK5HKHFdZbrYi3+2phxuQTLiRSc+nUJWmPPBaEC6BVfIILoRCQyGU2zxTAakHeVK50fpH4xf/ZdVaxTi2QCnUqhGwsbA1jJTRGjPj+6Uv5AeYRKT7Op1FtpNFiA6oVK6CWHyiVE85nOSc1vSiBErk3sFpqDnVrJiM38Ozv6SeVCykg/VgW6r04N/DbLMmsyErVCu9vru+4rEVaVStc+V2ns02izlrolcilFgAuoUokolECvv7jFv0CtYedBlz0rYBaiyylRVi4kFkRLcHWEjkry3WxlwYsHrQyGaE6wgiMqFbMiF0srMOs9xJK5KaeyPurOp9TgDP+nYKaQa/33aKBWXDQYsapxYQtHI5oLPy8PmgeLFz8pcCnVasQZkUsGbnxEeLFqkCqcI5YQau9GTkh8AhWw1CrFe2M3CzGFGkmIXZuMKdezmqFYzxWWK3w7XnVJBTWS0G4+0jWTFIDLRdyg+kLlR6L9DgENOjIebRqnYcxGpljnNulaE0jR8LWE5cwZflB9G5cWVGQEFqtaEmSrRa2Vq2wPkiRxxtY8VihauVAYd7hnV6JfI4GyxQ5aPFS0wNukbTaRrbI41tc5aRQpmpFhf47DFZhIZD7Ulq+4/ELwVjcIxRCC0QBH1fNyKkBRq51fMvFGQrUqUDDu3P34LavVisKSSKJ3NGRh4K1Chd5eVYrMocsrG9JqXmTO6+wGEU6KluyN0O5kAoIB4/ZDNcSiVyirm7v/6tgRy6GmrkSfolcuj29qQtZycX50GpeagWmrT6qqhx/vmrtG62fT015NTTkFQp1AmII+9YKidzWqhXWZC32eAOnwnLMS6qPzJLI7/tuIyokafOkA8yToITvYTbP4uq3QEUuwhmZWNeAfh15uIVVuW9l1aIiGfcljDiXI7/YcHhzdjAioVpLFw6aGb+a6Kkqq1Qaf2KJXF29WmBrRh7DyMThs1oJ6sgJ9YKCqPYYKDZRgpGKRCgHsw4lhRPBbEbBTQTzI0ayIdd9rHeLRolcj2BhFGpyxYYLzc4exN3pc+AlLmSULoftVRpgcYNOzLIeL0Xly+dwNrmiqrq1fj91iWlUWq0oFBNK5I5qRYAYF8HQdjVCri3anRHI5+elFPOnjsFP019SXafeLa5ZMIu5iD07Tak2gGKv+RK5HOSGPiu6nBqt1uZjF3XTowdyfWtV8LFowiuLv8awbf9g+Nb5qHnxDL757S1Uy/apElPyc/DNzDcwYtMsNMw8ijoHtmHdF6Nw/Z6VqurW+v1UqVZMMj8U1uOoVgQgBLi9fU3M3HSCXSAvF2nnjiErIQv/t/ZXeIgLUzrdKltnOALcyMGs1kWHnSa/l8cKiVzm7eUOO1l9VirOzSgZWch6doaPjIghPyYeADCp822Yl9YNt+5cgtVf3osX+o/Bvoq10OfgBvQ5uCHkmfJ5l9Dm5B4UumOws0oDybqtmLbzZEIncyBEWWgIh47c1hK5i5AQfVO17AxM+vM9tDm5B5Uun0fPtvUAAGMHPornl32HF5d+q7gPivThkFkD0mqrFa4+M6vVW9fHC/eJrhndWRFq/jiQlcijwbzEBMixqFG3vY4mT87E+B4jsaNyfXzlF6reWzARFfIuYWq7wSHlf2/WCzNb9MEfPzyDT2Z/LNtuJL+fkkQutJ5zVCs8pOZcwAu/fwTC+4ivLv4ag/auwpd/vou7tswDAPzQeiD+adg5UKbjiZ1ILMxHfFEBUvJzRPXqlcjNcyQypx7hYafcaymF82TWz0nkmp+UhrAuvlWS1rFvZGd1465l2PvhEHQ5uk13HSzIMRvqpeh2ZAuSCowHVKpZPtFwHWbC7fVgxKZZqHHpLK7EJfguEoL3e4zCXXe8jWeufwJra7XAm31Ho8Ezf2JyxyE4VLMRnrrhaeTHJmBa2xvQ6PwxdDm6VbKNSDJypaEm1pGbT4PtGHmNi2eQlnkEaZlHccOm+Ug44vNgLFV4BdftW4vNVdNQJecCBu1Zia3vT8LL/R8BJS7cdcfbKHDH4pefxmL3J0MxYN9qTPxrnIiZ683mYZYkbZrVisWHnZ6Ajty6CcSP66x17OsNHVr//HF8Pms84rzFaHdSnNfRCGQX061r8eOMl7Fzwu0oc+WyoXasiOVhBCu+ug9vLpqMlZPvD+jEAQCEYFWd1pjZoi+yE0oDAIrdMXiv1714buzUQLH3et6DvNh4DNi3WrINK8eh0u5Mq2eno1qhFCsn348FU8cgNy4RLkpRavdOAAChFK/1exAv938YQ/4zHjeP+Bgnr78l8OiqOq1x9x1vBX4P3r0c3Y9sxtil34Y0wR2UaoXegRTrKcLjK39CszO+kKZp29egYm4W7ti6ALdt+0dXnQBLIjd3oDc+thtTf30dZXOy0H/faiQUaQu+xYSAxJAA/YTg9q3/4Mi4GzB411IAQNcjW1Aujx0vW61EnlSQhydX/IC++9eh7cndmDrzDXzV6VZkJpVF7Sx1UQ35uKFlVcl7crutvHkLAn+PSJ+tuV0+rHAB14r4ooLA7uLGkRMC16/49eRKKOZ9qoLYeKyp1RI9DqX7mCrjO5qhEW15eh9WfnkvamUFnfLqnT+BPR8NQe8DbMcrn+OZ/FgTx1q52g87LwTjG1y/13eanXDiKFJz3Jg77TG8dN0j2Fm5fqCMkHltrpaGcT1G4vll3wUOVYZvXYAFjbpiWb12AKApWSwfSmzD5fWgw4ld2FK1EeK8xShdkIfTKam4b8NfeHLVT6iccx4Tug3Hg/99BlUbXYNqlzNQ9koOfm15nS56hNs5sw87X/v7EzTKOIIHV07HyA1/Y3rL6zB24GOG6qSguMLLeM5PmVUpfS0+mP8ZAGDo9sVIKCrEB/M/w8zmffDMoCdFdanRkScW5mPF5PtR/kpoYK1aWaexsXpT1Mk6hfjiQhTEqPcHiGWYxHLgFheX1wMXpWiceQSzv3sCg0Z9inoXTuJA+RpIKC5EvQv6gqaVy7uEzZ/fhUJ3LBo/NRNeV+QOfD+bNR79968FANR97m98eO1/UD4vG1mlyqh6Xjh3l9Rrjw7Hd+LwBz49+qTOt+GXlv2wbMpovNn7AeS0f0QXnW6vB15CQIkL7y6YhBrZGRi4bxW6HdmKyjnnMbnTrYj3FGPAvtX4t0FHCVrl23AkciGO+jzEnh34GN7pfT/OJVdApU8/RPOzB5GaexFXYkNXe+EHLnLH4svOt2HU0NdQ6IrBz34m2STjcKCM1pgNHJR024P2rMSMn1/A9J9fxIJvHsGaL+/B0skP4Nnl//PTcATrvxiJ+MJ8fN3xZqyp1RLNMg4hLfOILnqEjNvsU/2yeT7mN3LD3wCAYdv+QdkrxiINFnkomrw6HwAQV1yERBIUs/o/MgwA8G7PezDy9jcCTL3H4U3MuoSmhdWyMxBX7HOO6XlwA6pfysDwLfMCTHzwiI+RmVQWALCpehN82u1O1Lp0Bgu+eQT9965Gi9P7MXbpt6iQG1qvEHLjx0sBUIppv76OP75/Gvds8n27OdMeR/MzB3CoQg0cLVcFN+xZgdScLNl2WLhl51IAQJynCF///jYeWPe7JYe2SkguyA0wcQCof/4EJnYdhjf7jlZdh1CK/aHN9ejw6A+B34+s/RU3+9/3pSXfYNP8Nfj6t7dEY1D4/l2ObsUTK39EqcIrqHvhJA6OvwmfzP4IjTMOo/nZg8iNTUDZKznofmQz0s4dg9vrwcbqTVD3gnQuAOVYKzZh5ISQAYSQvYSQA4SQsWbUyUTp0jiZnIr0ak0AAEtb9QCNi0O7k7sBACfKVA4pLvWBl9bvgBZP/oIXBj6Gsf3H4N/67QP3ypbS7o0JyJggUYpmZw6ghV910ub0XlS7fA4AUOfiabipF3cOewelinwqndykFOyrWBvftxkEAFgwdQweWjsTd2vcbpuqWvE/WyH3IkZt/BupOVn4sPc9yPdLqkfKVsXu1DqodVG7KoKPUoVX8OqiKShz5TJqZ53CF1OeRKynCN0PbcL8//6B8dfejSmdbgUlLrzex8cUjpepjHfnT0RNmbaTCvKw+st78d6Cz9HtyBZMm/kGFv/3IeyqXA8ra7dC3/u+wLaqjfDwTWPxbs978EPbQdhTqS46PzwNpQvzMPnPd/HWwi/w0Lrf8PSKHyTbAdhOagAASnHTop8w67sn0P3IZrQ4exC9D/h2hWdLl8eZ5IpYXqcNFtfvhFivB+8umKjuo1GKO7YuQFJBHppmHMKl+CRsq98Kp5Mr4qWlU3H4g8F4feFX6uoyCQ3OHceFxBTcced7qPfsXzhQsZbmOkSqMUJQEBOHwSM+xomUVLx83cP4qvNQzE67Bm7qxZuLvkS/A+uw5bPhqJB7ES6vB/9Jn4Mv/3zP9zylaHl6H36e/hKeWPUzdn1yG+ZPHQMAuHnXMvz5/dMAgNuHv49xPUbCQ3z9uLtSPeyoXB8dTu7CyE2zRHReKfJgyd5MhXcJZQ5WHGEYVq0QQtwAJgHoB+AEgA2EkL8ppeaeFAFAo0bo88CXyI/1nXx/cdOjaDrjW9To59tuZSSVCykux7y47fL01gNCrtermIRNR7VLQ1JtNck8jDnfPYHPu9yBB295Ef9Jn4sWZw+gLO+Q9XC56rh9+DiMWTMDe4c/AJyhuJSYjMkdh+DB9b9j7LJpAIAFDbsgI7mCKnqE5Ojl45/M+hD1L5zA4zc+i0J3LF5fPAWvL56Cax79Do2f/h0xLoJiL0VqThY6nNiJjKTyOJOizhtPiH771+LeTX+j9sXT+G+HW9Ds6C7s/9B3zrGu3juY1PWOQNlp7QdjWvvBuG7fGkz54x10OLET/e7/kllvxxO+c5Tuh9Jx/Z5VAICE4kJsqt4U/xn2TqDchprNsaFm88BvSlwodMcCAGpe9CWjaHtyt89bmLAZdqw7dJY2O3MA3Y9sxn0b/kRFnj4/LzYexW43htw1Huk1moQ8UzEvC7GeYiQX5GLwrmX4uVV/STVJ25N7MG7+5+h+OB2PDX4OX3S+DQnNmuDsweP4j99ya1T6bHzdcQhGr/8NnY9tx213fRA4XDQb7twcbK6Whg5jvvcxQ51cSypUxraqjXDN/wXPtT7rNgw37F2Jbn4Lo8txifjmtzdRLTsTy+q2w3X71iKxMB8vLp2KuzfPRXZ8ElIKfBl6eoz+Gp/OGo+UglzMb9QVVS+fC9iq3zZ8HDof344dVRqg3JVsjEqfjYq5F7Hz46F4YcCj+LtpD9XvIpLIo1RH3hHAAUrpIQAghEwHcBMA0xk5pTTAxAEABEhauxI371qGw+WqIje+VEh5NQcg8UUFWPXVvXBRiraP/qj7IIKluqiYm4V53/r0xnMaX4M9lepiQaOuAIBrD6djZ+X6yIuNR0FMHChx4Z3e96NP2VTgjO9k/71e9+K9nvfgmRXfY8yaX1Av6ySaZhzGrkp1Ee8pQk5comqdo9YYMhVyL2LqzDfwe/PeGLx7OZZ8/SDW12gauN/54GbMbNE3sIB1O7oFE2Z/BAAYNfQ1LK3fAYDPlO/zWeNxx53vYV2tFrJtlvEvbolFBUiv3hj7ajVBo2O7kR1XCocH3QbMEduL76jiOxNpeP442p7cjfTqTURlrjmyBQDw1ctfYezLd2F6y+swvvsIFMbEKn6HD3qMxITZH2FeWjfsqFwf7y+YiLvT5yClIBfftbsRl+OTAACDdq/AoQrV4XbVCTzb49AmfPfra9hYvUmAife570tUz87AyjqtJZnzBz1GocyVy9g+wbdwnShTOXCGA/jN+dJnY3mdtqh/wecMN2jvKjzp9eJQhRpo4XbhfFJZ9L7/KxS5Y/D80mlod3I3RqbPwepaLRXfWS/GLv0Ww8f9hhvjEtFr9BScEwhWWqD2sHp/xVr4stNQDN2xCP807Iydlevj3QWTAAC37fCFQ979ydBA+b0Va+PJG55CqaJ8nEmpiDvuGsesN71Gk8ACu6JuW/S5/0v839qZSCrKx+OrftbEyIW+KdGqWqkO4Djv9wn/tRAQQkYTQjYSQjZmZspvRaQg5EUuQlDQ7Vr0fGAy7rjzfVF5L6Uokyg/WT0uNyrmXUL5K9lomnFYt0MQS41z7eHNAIACdwz2CbaXK+q2xYVSZZAfmxAi3YlqIQTft7keANDz4Eb8Z/McrP9iJFZMvh+vLv4aozb+rUQYBuxdhezMC3h94VeY9Od7eHjNL7K608G7lmHGT2PR/OxBbKzeBFmJyQCABY26YsRtb+BiQmkcLO8LjcDNt3U8SXbazDcw0O9afeOeFQCAiX+NY7dJKYZuX4RaWafR5+AG5MQl4vEbn0VBTBw+vf8NjBn8HFo//jO8ccHzj8ZVkgN/n0qphGZP/AIA+P2HZ0PN2/yonHMB62s0xbmqtfBh97vxbq97cd6vD1fCn816of6zf+HVfg9heqv+WFa3Ld5cNBnPrPgB2yfcgbjiIqRlHsGkv8dh3rePoVSuz3Sw5el9mDrzDQDArCbdAQD7azTCwYo1sbxeO8WDyEuJyfiriY9ZdPDvKDh0O7IFry3+Gp/NGo8j5YJWMr38B/gcnzhUoQaOl62CMTePxWezxgMAxncfEZDGW5zejwmzxqNqtr75yEeV7HN4aN1vAIDkwisYuHeVofrUmo9S4sK4nqPQYcwPeKn/GKyp1RIF7lgsaNgZkzsOCZR78OYXAQBzG3fDibJVsC+1jiZ6DlaoiXE9RmJil9sx7M73ND07acnBkN+2TixBKZ0CYAoAtG/f3pSjNxfx2RcfKS9aN7g2lQ8i3DF44oanMWH2R0jLPIJCTzddtHCS6WuLJuN8qTKY2HUYUgpysb5GU9w17B3VFgQsFc3Z5IrYW7EW2pzeh/RqjdHXP2Fv2bUUXY5tw7T2g0XPNMk4hI7Hd6LNqT24edcynFz8X1S/7Juwg/auQvXsDLzUfwzeXjAJuyvVxY9trkfpgjwkFBcEJv1XHYdgZ5UGeKfXfbhx93LMaHkdcuJLofXj00XtnU5JRZ3nZ6PTse1ofuYAFvqdsEYPeRkjNs3Cm4smo9Xp/dhSLS3wTFJBHjqc2IkP504AAMxufC3Gdx+BzNI+SS6rSk2sbuLbZfE3SjEC9UVufCncP+QVdD62Ddce3owVddvgVEolgFKULryC1bVb4kCFmqgSn4DJnYZCKzy8vht5+5tofWpvQKd6/4Y/8Jz/wPrF/o8gOaUMgPNofvYg3NSLX1r0xXftbsSGGs0Q27gRkFWsut3HBz+L2hdPo+PxoCUVod7Aotks4xAOl6+OOs/NQqvT+7Crss+TWcgoYj3BCIjHy1bGjbuWITuhNL779TUAvsPIM8kV8Ubf0aJzJiESYl3iPKiUIjshCQ/fNBYDK1JMz0nGlqqNVL8nC3rDSR8uXx2Nn/4tIBxN6DY84ITUfsz3OFeqrG6aMkuXx4fdR+h+nkO0qlZOAqjJ+13Df810CFkcAZGNweGl6jwPZze+FuPmfYq0c0fxq84cjxQ+Kewe/4HI9Fb98V27G/Fduxu11SNB8PBh7yIrMRn1z59Am1N7MC+tG5ILcvHMih/Q5uQeHKhYEwXuOBTGxCK+uBB/fP8MEoqD0RerX87EmMHPoVRhPj6Y/xlW1GmDWdMeR4uzB5GVkIyuR7diEE+K2lG5Pr72SzR/NO+NP5r3VkX/ulotsK5WC4zYNAuVcrPwYfcRWFK/A7BoMhplHg0yckqxc8LtIXbF37S/CZurNw78DrEj58HNyAqwqGEnHC9bGQumjsHFhNIYPOIT9Dm4HtfvWYXb73oflLhwi0mS0JZqaXjm+idwKaE0zvtVW9nxSfip1QB88+d3uHDiCtKrNcaRslUxL80nGOyqXA8NYxMAiL2J5TAn7RrUunQGby+YhFt3/IvE4gIMGvUpht35Lqb//CLmffsoOoz5AVt5C6SQURS5Y9HiiRnIiUtE52M78Pms8Sh0Baf+5E63YtjWBXh01XQ8P/AxWb22UOPRMPMoFk59BM8PeBQzWvXHXADQd0QSAiOeufwdbsCTFDCk6jET0WpHvgFAQ0JIXfgY+DAAw02oVwShdE2IvLurRyUnL3bH4NHbX8OuMtVwIjPXx001fGxCvRjx6Vh8Va0jvm13I+7ZNAvfz3gFN4z6NESaUwOpQ1NOFbA/tTaGDfepkTof8x3wfPPbmyh/JRu5sQl4s88DuFCqDBKKC7Gofgd80eV2TPvlNZxKScX8Rl1R7I7BzBZ9UOviGbQ469vyfXztXXiLZ9lQ53ljDikAUP/CCYxMn4Mxa35B2tO/4+/ut+Ivnl7xJr9TT2JxARY26Ih+B9YHJEoO8TxG/sLv2wN/uyW6ptfBjQCAsvk5WD7lgcB16vcLNVM3ObNFXwBA6YI8DBo5Absq10Ojc0fRZ+o49AHwat8H0fPBr0Oe0WM59HUn32L60ZyPkVjsi+v94Lrf8NiNz+LrDjdjQaMuomdYjILT5R8vUwkAEOctxojb3sCa2i1R5I7FDXtW4I7tC5F27ghuHvGJJD0xxYXwenxzJtZThIVTffbbby78EjNa9df8flLIL/YoF7Ip1GQn0lyn0QoopcUAxgBYAGA3gF8opTvln9LZFuOa3OpGqVI4myBe/+JpnChTGbduX4x1X4xEw0x1WU0AYPIf76Ld2n9QPu8SPu86DLmxCUguyEWMR/02Wg/W1mqJqe0GB2yhk4ryMW7+57g7fQ4yS5XFQ7e8hPTqTdDyyV8w4L5JKHb71m2vy40j5avj42vuAgD80qIf+t07Cd+2uxGjb1Ef8lcOn3YLruUFMXGYMvRxFLlj0PSsL6RC52M+xry8Ths8Ovg5tHx8usjxJi6GvQjGSMyErMRkUeCl+4e8EliUrdjS5sSXws4qDUCJCxcTgrr7TIb0Z8QC9LW+D2FCtzuxtUpD/NGsF0AI3ul9PzbWaCYqK8cozvqtns4npmB5vXYo8lvlbKvSEADQ+vR+xHqK4PJ6AEpRNTsT786fiEG7VyCuuAibProDBz68GUfG3YDnln0XqLfzw9+JGzOAi3nmJsS4tW0N5UJhQrSqVkApnQv4dlXhhIsQhYQD6oNQVbt8Di/++w1Gb/gDALBw6iNIe/p3Ra++R1bPwHV+54dlddvhQqkyuOahbwD4XIu1Qii1Na2agl0y6p6JXe/A4gYd8fK//4XH5cbOSvXQ5+B6TG/VP8C4pfBZ12GY2OV2eF1u7E+tjTf6PqiZXilcKFUGB8tXR6x/MXMRgqHbF+OdfyZhQ41mmJvWDbf850MUumOQH5uAfMaZtNCUj4OUZD2jVX+AUvzasi+u37MK9c8fx+IGHQL3rXZdz0iugE//SsdPC7YxEyLozT71dL9G8FLgk/hSmOBffOUgJ9wUuWPxfzeNxXZBSNhvOtyMzKRymNXkWty1eR5eXzwFOyvVw5t9HsDwrfMxfOt8PDL4ecR4g8LJ2lot8HOrATharqrmnWe40axaCn5LjzQVPlz18chFVisueV2aV4NEDpcLt+xaghMpqZibdg2OlK+Ggpg4VMzNktStpeTnwOsXf4bcNT5gP63WJJAF4Ts2rSbPyB8a2gnvzi2DgfcGHUgI9SK+WEV2IkLgJdZNwAH3TgyoNQghyCqVglivB12PbUPXY9tQ79m/ZA+B4yV05F3qV8CaQ+fZDxGC3ZXqYXeleqJbMt7zpsGTmCSZ1UavU9ajfRri93SJmPsMKDGKeY2vEV0riInDry374e702Xh98RQAvsPUGK8HN9/9Ef78/mlcv3cl2j72E4bs+BdL67XD0XLVtL1IBBEF4WcCkDvX0wtbuegL2TIBkWXkHi9VP3mqV0fX//sWfe//Eu/2vg8/tR6IP//3JDZOvBuf/zUO8UUFqHPhZAinfXb5/zBy0yz0ve8LkVOHXvDpnfvYtZJSKYca5UqJrlHiCrW3jxCK3LGBXQEBsLBBJ7zW90FkJSTjwVtehFfCqYZDvIRqpUa5RF1SjZRKRg7XNtR2cidH1fEL+gKyAdr8aowwitW1W+Gfhp3R4okZ+L1ZL9TNOoUt1dKwtUpDDNq7Ck3PHsJ37W60lIk3q5Yie79iae3e19EQSMxK2FsiJ/LbVUq16SWL3LEBnSEATG1/Ez6b9SFu3LMC5a5k45qjW7G9cn38W78j9qbWxt2b52JOWjddLsiSNPBiTPgWLvkBaMUJuBVwEQCEaLLkkbJaAfRJt1q/VYWkOLw8qCn6T1iu+hmrukOLe4PC2h9A90apWL4v1Ib8YIWaGD3kZQDAUzc8Hbj+8M0v4MO5n+BQ+aCuuX+zyliw86x6wlRCbpEeO7Axlu/LxLkciR2ZBOwxS/TDVoxcBCIvkWtSrTDwd9OeWFOrFTZMuhtX/BJui7MHA9YeHuLC+z3vMdCCGIWiZLnyb2AXQUPPgiO3G9GjpdCqWtn0Sj/tbVjEybVEr1T7rVMS1E//k2Uq4U6BI4wVul5AmX5dGiqbCDx6YSvVihAEQIUk6QNFX7Q5Y21kli6HUUNfx0O3vIhP/JYYuyrVBeCLmHe8bBVjDQhQwDO7olTZMYI/6M2aWHe0r6lcSCP0zKM4t7n6+3Bsr63iF1pS16l9T+F4UVLjidqx6GWVIpDqEc/sIvDoha0YOUu1UqWMtC544a4zKDQh4vzS+u3hcbnxabc70fOBybj57o/RY/QU2WSwelEgksjlwVf7Xt9COqmBFjSvLq+jrFcxSXOdRMPmtks9n4mcnGpFiAVPdFcsozdEsRZYcZAFiCPoyUHtrkDIyD8Yqi0Oi1XvqrQQ6ZHItYw/O8JejFywEitJBOmCmNSGQQiOlK+OwphYyw578otCHSGUpA9LTsBdRDZGja4m9UjkGhh5+STlA7BwpECzTkeunnuppUG4sGmVeaz6mkr9pGeTzX/VkqhlsRcjF4coLnHgZ8hRc1hrxfaWgMja3+tR4egRhrUwcjX1h0W1YhF706JakRsSfIsQYT9qPUC2av4pji8BmRVULOJ8WuPCYYcaZtj6jey2XapeVjm7eXZ+0OGCqtAGhkgaOukSQm6Cuoi+xUPPM/EaJpya+sNh4WPVWqGFyaotKtzNaU0HaNXXVFpwherS5tWV/Tb47yrln2Bn2OqNTgkSI2udl7MfFTtChBOtampzFAq3RF4qTvlwcdmzvXSpc7TZQfv+lZLI+zQRR+hTx8jV06AXVi0WWiRytRBSqrUJq95VyRtbuKjlFSqHwuBTKhX6gcPE4W0k7/VMS8WQNuxoq5GErRj5F0tD4/pqHUdqVm4roYcBKurI9RLDQKm4oPOOVKs1y5fSxRD1THopRs7S3yv4FvnKmMx4xjMOB60S+rVIy2ppEJbTGkJALtG0lfhsWCijzS1QDrDl0iCRD5IxGph2T0d0rFtesb1ww1aMXAi7OMNw0EovDfxPGmYyJ26AS1X53xG+3Kbh+u5aGIUaisymu3EVeeseI3h/SGg2JU06cpXLu7Cc2rhEHOqmJuG1G5sqF9QIJTLqCKymrhSpYOS8oaTEyJXmlJ69ETd3rILDyMMIPdQqDRozPwGXsEGKEbj9941K5FPubidTkkePyboQs4cLy+PfrDE5rGOot/A9Xeuqf1ivRK5Rt0IpcE+3umG10WY1pUYlyB/T93SrY4gGPeaPfZvKJ+wwClsxcmEnapkzvRtXUiwz7lb5nJLhhpoMR2YuZgEJWEK3QoTlNIA/2ZVo5m4bjS8i1HVyJW5spc90dECzUOcvlnWFVbJFmVLK+UW1QkiqVkbO6arN1t63rKFNBfrR7a0Uy/D75WaDOm45dWejytYktVaCrRi5Gvz7tDgp6pN9G2HqqA6M0qEwEi9aDeQmOUv6bFwlRZNEbpSJKDFojgEnqpCAhOAzWiU6uX5gMUop70P+1X+e7I5Ph7XGDS1DGTZHv1YVAoeveDuJ+U9cy9y52GmXSAhBr7TUwG+949/sedO+TjnVZQkBKiUrB4gLHX/G+shqPqEHtmbkrElTp0IS9r09MOSa2n6zun/kyNjy2nVIjA1lkIlxbqY3q9xvI4gNqFbY4L5jQqx2Rq7FIYN7ZxcheLxPw5B78x6/VrGtRpWTcVNrsdSld/6ueK4XVj7fK+Ra4yopzG8f7a7gwoX/23s6Bn5rtSPXcgArHNtq0LRqimJ/t6lZVp0PgYlmunJvHSkmb2tGXi9V7CpOiNjaIVyTa3R3cQxstXATEhJnhYMoT6mIG5n3ctyuQEpiCUjkOiYln04liYiznnARgkopobF0ypViO3+oYdJ6JbGa5UsxwwUzq4tyiZzPaIQ7Cq1WK1o0MXqcyCqnxKNJ1RRsebUf7uzIjjA67d6OqvqVL/S5CMH7Q1ogNVl74hdAfkcXKWHdXoxc0F8vDBTHAGd1qtoJbGQ1rZKSgMZVkmXLyNFBCHtiCAeN0H3ZTEmDU61ImR9y9eth5EQDnTTAyMW7LiPbYvMXdMZYM7sJHVBLg9D+WqtDkJSuuAXDzJfrtjoVSuGzO0PPLoQCUE3/otmlvi/mTtlScUj2R2oUtpiSEKuqX/lFCPEdJj+oUfBqUMmn/5bjE3rVdkZhL0YugBYXbjUwEvSWEGWpQ66ThQyrqj8YmPAJoaWEmeaHit/T3xRLRz60nXxORKnDTlaSAI6fuF1ENEkl1T4q2JfZ+mtWdWa0YbZJ3wsDGwf+5pN3LqcgpJzWWCtSfJ8V94bb7bkIwWCFw+aGlZOxamxvPHCtOkar5pub0fUd/Lp7RyKPEJQ6Uc6TS3UbMDaJ+YvAnR1rYub/dfX9EOnIpSVyIV69gc0QqqQkBCQcPrhFQeo1uLZZOnIl6YaEqFaC1xtUKh2Ii/1k30Z4ql+jgPUEIUS0UBmZkGZrPazSrEipEdRC+M1G8cztPrwtaOEhZMSak3VIlGcdmnPZntTaw1cvm6haSFHHyENVK4C8ZC2XrNnRkUcIStJay+plAYR2wqfDWmtrgxBD8cD5jw5uVT0Ql0Up4iOR0T1XSolnBgha+2If1CrP0PkG6mFLHXKqFaVJx99J8Gnmz+t2tcvhsT4NAwzF7RL3nFRfWqkj11KfGeobo2TKfbNGlYLqPyHj1qoWkOLJLMsibrfHMnE0+slU9T2jvNwOvEY56bhI0ahasVWGIL1BspQ6mnW/f7MqaFwlGXvOXFbdhhGJnM8U+AuCktWKXJNuQjTNEq5uqe/MBTOKYUxUJQYWKhEFr/MHPlckwMgJEX9TIxK5/kdV12fGYmF2MLhYN8GornVwU+tqIeNFyFO1HnZKMcIYhvDAMfciE/IDCKE1YJqarysrdeu8ZyVsxcj1Qu204HeCVumaEPM8EfnzQMTIBW3IjWGtYVuVmJDcXaXJFHJIG8LIxe1yORQIES8aRvhkOHTkZrRg9qEsIQSvD24mus4toutf6oNiD8X/1hzVVK82idy3i9PqdKQGqg47GWc0ejVJQqn7hpZVUSk5AVNXHbanaoUQchshZCchxEsIsTaYgAGolsh5vSAV3F6KWRMEVStSuRDV9jGf4QilngRB5LYQ1YqgHq2JFEJUK6z7JHhgJYTSwsf/bqHvJ27fG7BaISJ9q1Qr6lQrymWU8PWI9gGrC5bkbIpEHiYTRu47V0pOQLWyiRjSVpvHo5ROPZYRu4CLb2JUImd9Ga0LdFC1Yg4mDm+LkV1r++u0p9XKDgBDAKhPMx4BKAkBTF2nizDTxEnFdXCRoATMsjnWAjnVSu0KpfDBrcGoe3IDh6VjloPaBY8piSo8y98d8IuO7FpH9Oyku9rirk61kFYlWczITTA/NDLV+jWtHLC6YFutGKjcj/CwcbF03KhyMt69RTlMReAdJT4kS/UmpyM3Cq2LuNGFkrWA6ZXyzYIhRk4p3U0p3WsWMVahWEEKkOrW7CtFomtJ8Wxp20VIQAKW6ku1w0dKYuXu3d4hmBxZbuC4XERXvBIlhyC2R6N61QqfqYeYovkv108tjXduaQG3i4hVK1K0q/i64ZB0zWiCX0c/HcGW1NKgl6dygoaURM4yY+UO3bk2vxnJ28Ab/Gbh2sFwkD/sDB8dfIRNR04IGQ1gNADUqqXPvEpvfymZPHH1Viwd6ul1yc/IUxJiApl7mlVLwelL+YxKgpYZRk+u5SRy4TeQa0qraoUrL80suX+1q1akJHKleoXbdEPmhxLt666PqSM3T7Vy6N3rLXUUZY3TQoZ3sRA++qjk2GPFi+dUK9yOlpUcRA53dqyF39NP6A54xuoX3bFlWPWHdy0RQVEiJ4QsIoTsYPx3k5aGKKVTKKXtKaXtU1NTlR8wEcUedT02oHlodLsi/3NleW7hb93cnJkhhACIYcU11QG+dPvGTaGHVMIBI2f763JpYyxyCwifLj2HfHwducgQRWYWiCVyiTMKFa/pUtgxaYXVkqBvR6W9DbVPsOSbK0XKOmyuK6UPOxkSOcfI4/WEdwDqVkzCxpf7oZqKdIlMaPyMens2aj07KaV9KaXNGf/9FQ4CzUCRV0m1wlYpNKnqSxzA3yomxLjRixES12dH7vtbqi/VdjF/HgjzfApVGCF1CkafnETOolHpECioI1c+5BvVtU7I76evayRZln/IKoRYRy5BG/tyCExaZzW1aSX+07kW07FLLVhCQL6aJA2BBZE9UlhqNu6QMykusoZyrWuWDfyt92CSNXdK+1Wu7etEJnvQVeEQxM9Mz4IUc/j5gU5Y8ER3pq2zqA7ID/D+zaS3kh/xvO0A8USYMbqzZDB8OYnc7SKqBustbarj36d7BNqVirnBkcXSohACTLijdeA339wtJSEmZFcjUq3IcESpsLV6wGIwajKwSyHS22kAhrYXrG7OV6FaGdi8KhJj3ZIeqKzxwQWoGsY733luQJo6Qk0Ev8+0Cs9y/V22VBwWPNEdHzDS/4UDRs0PbyGEnADQBcAcQsgCc8iSaE/nczn58slZpeotWyoOaYxAWGxrhaD5IWuAyKUFG9QyNEegUN/cqV4FdK1fMdAOH0qHnWoGa2pyPOqllg606/FS5gIQ2Lkw6iCQDtgvdmqSUI8wrgnVVZILqUau+suDXfDTA500O8GEtMmguFYFYxZLWiBFutpvwVqw8xWEHgCoXjYBu98agEaVxXNj/NCWgXHEXyRTS8djz1sDMFKwUwPMOVd4RSIcRbAN8zCiS23m9bQqybIhnr+9pwP+fKSbiZQEYdRq5Q9KaQ1KaTyltDKltL9ZhJmJywUKWba1WHbI6GjldLBadGcsRidUP/xwXyc82KNe6G5BQJubEFUCGzehAxK5BK0BiVyjjZ2wPvHrsVVbgHoduRrw6+9Ytzy61q+INryttvb6xNfa1iqnuz59ROh/lNXPcvkvq6QoJ3AAguOob5PKeKKvP548IUiIdVt2rnDfNXVRs7xO/TkPZVVkYkqKjxFli1KDXmmVQlQ7ZuKqUK3wJfJ9bw/E9NGdQ+5rYg5E2hGEYzospk2hfs7JWYBwk+SahhXxwsAmsoza7SKqtt6cYBaws5aU9Pz/+n/35GWX4STb9S/2wcaX+4Y8J6xO+P1kvVOF+nQTrFb4GH9bK0wdpc+XLdKalQql42VT8imBqVqROew8k+2z1tp+8pJkGUKCEStDdnVRklZH7tskxrrx79M9Je+HxHJX8ZHfurm5arqMokQwcu6j8hkIf8Xkb5/jYlyyB2jNqslnRidE2mpDzo5cyzhmMXLuceEducMulo6cpXPme1Jyv5mHoQLJOSUhFpX9iR84R49KKQkiM07hdlNktSJx3UcTuyyrXMc65fHVf6QTO3N9O5BnnVSxdDzSZNResoggJx93awuM6dXAUB0sgUNNRnpWLBUOBHw7c92kmQ7WTkD4/vUrJTFD8OrFrRo9ZY3AVrFWFD0PeX9/dXc7eL0UHy3cKzqUkdMM/PZ/XWUPRwnY89fH4KXNPryUfezYt0nlgI1tkD7pQScckI2rpGBg8yqYt+OM6Pu4BTry3W8OYFDAY+R+MqT0xizPTk6HLeexJ3w/sfmh/1/Gs2oTSxBC8MtDXSRpAIB6qaVx4J2BIkaklx+bHdxKC+7oUIsjQgQl9cWCJ7qj/4TlTNXKM9elISM7H1tPSEvdSuDaD6meOaZ1N6EZanpKKtQys6yKCo1EQ9WKEiGRcxAOYJeL4Nn+jUUu8+JQsEEkxLpRTmZV9sXIZl2XN9+jYDsm/XdkexHdrAHAPcpqm2UOCfjek99iYpw7kBSCf10kkSuIUnx78kl3tcWAZlVQtYy0flJpoZJjiHJ9pQcsaVKvukbtc0/2bcS8/suDXfBgD/3pAaWgxD/k7MDTqiTjrzHXMJ97268qkFu0CQmOX7XmfWapzdMqS++stLahtMioWcRZMWesgq0YudLHU7sAGkkfJr21D6VOmKzCSyk8Kh2T2PbfHLOVvAUA2MRTL/kkcuU2+Rl5+L+FIIJ/KfXZ5X51dztZ6UNRtcKQ9DlUTI5jljUTusMjqygT53bh8b4Nmfc61i3PTFdoFErhEojKBVsILl+qkqd0QMXILxYG8XvCsNai8y8h+HNdyWtaviLlIlqNAozAVoxcCWonpJSOVv2zjMNO3t+UUtzQshrSX+mH2pw5GlWfHYUweiUgkStQW4Gnn1ZrtUJZOnIWXf6mtUabE8beEB12yjxbKi4mZHJaYfVgpT14GIUy1W0qxUqRAneY75FxsAvZmWqo3wzddOn4GHSuVwHLnu2pyjpEfAhvX9iKkStOON0SuXz52hWSlCslhMcIfZfKJ8VhuF8/76VU9cRhSeRCPXZo2+x6XC51ghA//jcgt3UmIeXUQmjiJy2oSOi/tTWnGXrrV2ODbnYMdDVQG1de62Ekd1CuFPKC+ywhaiwFmv54uKsmWta80BuzJFRAtSskIUUQ78Xs84xoY/q2YuR8SOmp1UCUwFihWybw0r4RsHXk1zWtzNORBwc6n7lzbspf/aetbHvyOnJG4xLzSu1hCydpuXkSuRy0SsUvC5w1xKoV+QXC8uh2OqvnFrwqKQkokxiLh3vWF5XReuA17Z4O+ojhQRjWQQglfwEpuFUcbBMQFPi9Q4VnI3JQJSzxULVMIlrUKKO6vNwQuqZBReVCovqii5XbymqFD77USqDNTltr+rCUhODqTojYhG/zK/1QtlQsjl+4AoBtb0ppcAIoBdeStVqReU54T030w2sbVsRzA3xZ1kN05HIeoxrHsNGY4larGo1KzeWT4jD38WuZ97Qy8p5p7INrLRjdvR7qVkzCwz+mM+8Ho3Rqq5cLfKa0Eyko9gks8THqAmRZ0b1Sap1QFah+OqKLjdtYImcdJOjNuq11HvdoVAlNqwZPyMslxYEQgjJ+r7DrWwRd7gOmWKBBRq4QP0SNQ5AauFwEX94lL/0/P6BxIOwoUZDU5A4ltUD4uJz5oRntKYFVvZo2q5ZJxJs3NcPUUdJSdCRUKzEuEjIGheDUDlpjnXOMXE61QgiPkceqYy/hsEJk2whwJr2M8qyNL4/QqmXUebmGC7Zl5CxpU71Err9dzryKH82PQ5nEWGx+pR+e90u4fJq0SeTiaxxzZWtW2FPBTQgGykxoYX0hnp2swR341xhzklpIpRdi/e2pcZphRnNUWf+ILnVQRWZSW8HIld5JSaBJSYjFppf7KsYnEYITQJQO7QuKtKtWIoFqfpNZLvSA3GdrV7tcSJCvpxjzXwrlVLj9G0V0f2kB+B+aJbWq1pEbsE0Oejey75dLiguhjX+Cz00Ape022wvN968cYxDe0mr+xA+aFWtgx6MEsWZL6XuIr31yRyvc0FJ+kQKAZ/orR9hj9YdZDJjTKj3Vr5EotK9eqHknJVQoHa9Z7cPpyJXMFvmqlR6NfGEcuH9ZCMuehdHIbe1r4L8j2uMOXkRGKYy7tWWIRZhatdGK53phyTM91VKpG7Zi5HyEMks/c1U5JAzZkQfUANraoghK5HEx7GebV5d2aAiaH6qH1onK0erxUvz2cFeMlIjyZhRSsVakqGVpem5pUwMTh8urjdSCJTmaYQNco1wi3rzJ50TzWJ+GzEz2dkJAtSJrfkh4jNyFNrXK4cj7g9CudjlRWSuTMCjFC/L9TdCXp16S73F9tNYsXyokhLNVsO9hpwGJ3BRhS2NbXp5ELqVa+emBzjiZdYV5T8pF33eP3bbWVG/BBLIUjauk4Im+jfDdmqOB+2ZJTlrt+K3OusLKaGPkXcf0aoBm1VKYaq1HetXHpCUHDdSuHute7KP7PaqVScApQUrDoB25fH/c2bEWftl4XJRxSwpWHCMI1Y1Gc7pGScwvSdiWkRvZ+hqRtojgX7XlfTpyn6TCLULCV0hJiEVKVbY+jRtIcq8tHKz89UJN2E2h67aUB6ZerHiuF9wuoniYKoTVc8hs1Yqc6uOJvo3Cxsgrqww7y8Kfj3TD/owc3PXfdYFrQYk8tEeGtK2O39NPAvCN97QqydglEdeHhUgzyRY1yqB7o1S8MLCxZJko5+N2Y+TBycUKwKZeR26AgoDNs7yuXFjeyz/s9Es2WpgFlXPRlwAnkR95f5Cq8i6BeZnYA9MYJ69Z3ufleupi6K5DSTXGTfQOdcTbc6tglcmjVfrgeY9fi4GfrjCtvkopCagkWAik7Mg/vr018os8mLtdHLgt2sAaY/Exbvzv3o7M8i1rlMGJrCuoWS58CUP0wGaMPIj2tYO58bRaU4iz7Khfb7knuYmu1GKwqaD5YZzbhT6NK+FuDTpoORd9Ker16sjVxuDQO2m1qla8MmolqyCV7cgorHqHRJnMNGZBSiLnQ8ti37JGWQBAGwuScRiKo8JDz0apeOq68Kek0wpbMvK7OtVimk6FQ0cuPOxUmphBvXNQ0o1xufCNjO0xCwGrFZnjaSmPSXZ94snYr2kljJu/J5h6zmTVSqAeUcXy9QcYuc72KiTF4XxuoaZnuENKs2E2Gw+nAFyxdDxub18D/+ksFkD0qEe6N0rF+hf7hEj+FUubczBoxNnHKGJcRHVcJdPaDGtrJqFJ1ZSQiHouFwG87GQILBizWglVqShK5P5/vZQGHCncOhIKB/XK1g3JBpWSQ9QwVgnAapNFBKDC9FIOi57qgUtXijQ9Y1UsabO/aWBUWNRXZRJjA9+OEOCDoa1ky2ulg8/EVzzXSzZRihHoZqs6Puzqsb1xIU+b4GAUtmLkUt/0j4e7Yv6OM6Ioe1IQMoQ4mYwnHNwuEqIbDKhzFHXkvn8pBd4Y3AxvzNqFVEEGHTWwesKqAT9UgSFo2DkA8rHY1aBcUpxsjPlwwirVilVJLta+0AfHLuRhy/Es2cTCZhxYcmcoliIM84d1vmA1bMXIpdCsWhk0q6Y+gA4nbMW4CPa+PVCV9DVrzDVYsjcjeEGgYpFCQ3+m8Xa1y+G6ZlVwnY6krQACM0WNoNi4SjL2nLmsrx0e+E2tGts7EILAKKQcsqStVqS9Wv2bMVujS70KWHPovK5nreZLiXFupFVJRlqV5KigRy3UJrUwgpFdauNCnradnlUoEYxcK/hSkdotdNNqKWjKy+ep5OHJoW2tcljxXC/UKGcsw7faeOQAMOPBLsi8nK9YTgn878SKqKdXChO+gdI3lPNq3fmGejO3aMXPozujztg5iuUmDm+DQ5m5zHuRthYJB+PUAuHY5Hbrpu0qAbxh0TmKHtiSkRsdMsEs3/qhJYCUGVtGLnFw53oVRPeEg7ZMYmwgEJYS5BYGq3iDZN5NiRblrFa41HVXA25oWS3SJEhCjZ9DJNGmZlm8ckNTDLHIGinSsBUjN2uM6M2QwqojXM4M7euUx6aX+4bEexBCyySaOLwtpiw/qHrLbCbEh53yu5sAk7CQpkVPdYeXAtd9stzCVhxECoQQ3HdNXe0PRtpbSSUMxVohhIwnhOwhhGwjhPxBCClrEl2WwoyASDEmLAZaIcfEtaJBpdL4YGgrWdWSVdKVVKwVqU9Z1q+br1tRW/IBLWhQKdnS+q1GpCVhLvBUC79teKRhdFZadXhsFYwGzVoIoDmltCWAfQBeME6S9VBiHGrAxeeIhoM2q/STVg1mVk5SQPo92tQqh2/v6YAXrpd2oTYD9pq60YU+TSrjyPuDFLMThQ1RMC/DCUOMnFL6D6W02P9zLYAaxkmyHqZI5O7wS+TKsAcrEh92KqupeqVVUh06VC+iLX2XFtiZdgfGYWYY23sBzJO6SQgZTQjZSAjZmJmZaawlg8zTTNVKVPFxk6HWRl4rjMSDtxLRQoce2Jl2B8aheNhJCFkEgGX8/BKl9C9/mZcAFAP4UaoeSukUAFMAoH379rrYn1lChxlOe0pZfsIJuy0mZkdVNAvRQocWOJI4G9FmDmk1FBk5pbSv3H1CyCgANwDoQ60OHG0SuMH/VD/16ZqEUMq7GQnYZU5rPewMFyLJFBc91UOU1FsNgnHqzabI3oj0WAo3DJkfEkIGAHgOQA9KaZ45JIUHakO7SoGVjKCkwTKrFQUPzpKO0d3roWdaaOqzBpVKG6qTgOC5AWnILShWLuxANewyIo3akU8EEA9goV+aWUspfcgwVQqIho8bY1XAah2w6ntYZbUijG3DtXO1SFEvXt/EtLr4u4iHeyonmnZQMmGIkVNKwzpyosm2MyYKJfLo+TryEGZoCqhWIkCL3eGoVtgwayzZ5bNGHzeyCaJJIrcK4WIOXDM2OWKJSpT80agNV9tYspWLPodo6KNoOuy8pU11LN+Xicf7NDS13rC9IWdHHq72ShAcqxUHgM0YedC6IfJTPjaKzA9Lx8fg6xHtTa83XEzCYUUmwPmIVzWihxupQDSNVaGe14F+RIv5oZ0RTedH0YBo8vMIB2wlkTsIL8LFGoLtOJzcgTn4ZFhrTFt1GA/1qI+z2QW667HLiHQYuQNJqE34YBYciVw/HFV5KKqXTcRLg3wJ2vVEDbXb97QlI4+W+V62VCxGdKkTaTLCjkrJvolRL1V/2NdVY3vD4wlNGBEt/WpH2IzvODAZtmLk0XZCv+XV6yJNgqWQ+t5dG1TEj/d3Qqe65XXXzQ93GjQ/1F2dAwdXNWzFyB1ED7o1qKhYZs0LveFREbA9mqyR7IpoE3IchBcOI3dgGaqWUZdkIOCibyUxGnB7e1uE1Q+Bw8avbjiM3EHkEUVcyGgwNQclA3ExPvNFuwTHsyUjd3bgJQuOjtw4HM2KuRjVtQ4uXSnCA9fWizQpqmCP5cZBiUa/ppUBADXKRUm+RxvCWQTNRUKsG88PaIzEOGvTC5oFW0rkDkoW7rumLm5rXxNlEmMjTYoDB7aEI5E7iDgIIQ4TNwhHtXJ1w5aM3NlFOnAQCke1cnXDVozckTocOAiFMyccADZj5A4cOAiFI4k7ABxG7sCBAwe2hy0ZuePK7cCBD45qxQFgM0buBM+PDByLkuiFI9M4AGzGyLs38gVqale7XIQpuXowdVR7zH382kiT4cCBAxnYyiGoZ1ol7H17AOJj7OFtVRLQu3HlSJPgQAaOasUBYFAiJ4S8RQjZRgjZQgj5hxBSzSzCpOAwcQcOgigV68wHB8ZVK+MppS0ppa0BzAbwqnGSHDhwoBY/PtAZzw1IQ7mkuEiT4iCCMMTIKaXZvJ9JcJwuHTgIK+pWTMLDPRtEmgwHEYZhHTkh5B0AIwBcAtBLptxoAKMBoFatWkabdeDAgQMHfhAlm2xCyCIAVRi3XqKU/sUr9wKABErpa0qNtm/fnm7cuFErrQ4cOHBwVYMQsolS2l54XVEip5T2VdnGjwDmAlBk5A4cOHDgwDwYtVppyPt5E4A9xshx4MCBAwdaYVRH/j4hJA2AF8BRAA8ZJ8mBAwcOHGiBIUZOKb3VLEIcOHDgwIE+2MpF34EDBw4ciOEwcgcOHDiwORTNDy1plJBM+HTqelARwDkTybEDnHe+OuC889UBI+9cm1KaKrwYEUZuBISQjSw7ypIM552vDjjvfHXAind2VCsOHDhwYHM4jNyBAwcObA47MvIpkSYgAnDe+eqA885XB0x/Z9vpyB04cODAQSjsKJE7cODAgQMeHEbuwIEDBzaHrRg5IWQAIWQvIeQAIWRspOkxA4SQmoSQJYSQXYSQnYSQx/3XyxNCFhJC9vv/Lee/Tgghn/m/wTZCSNvIvoF+EELchJDNhJDZ/t91CSHr/O82gxAS578e7/99wH+/TkQJ1wlCSFlCyExCyB5CyG5CSJeS3s+EkCf943oHIeRnQkhCSetnQshUQkgGIWQH75rmfiWEjPSX308IGamFBtswckKIG8AkAAMBNAVwJyGkaWSpMgXFAJ6mlDYF0BnAI/73GgtgMaW0IYDF/t+A7/0b+v8bDeDL8JNsGh4HsJv3exyATyilDQBkAbjPf/0+AFn+65/4y9kRnwKYTyltDKAVfO9eYvuZEFIdwGMA2lNKmwNwAxiGktfP0wAMEFzT1K+EkPLwhQDvBKAjgNc45q8KlFJb/AegC4AFvN8vAHgh0nRZ8J5/AegHYC+Aqv5rVQHs9f89GcCdvPKBcnb6D0AN/wDvDV++VwKft1uMsL8BLADQxf93jL8cifQ7aHzfMgAOC+kuyf0MoDqA4wDK+/ttNoD+JbGfAdQBsENvvwK4E8Bk3vWQckr/2UYiR3BQcDjhv1Zi4N9KtgGwDkBlSulp/60zACr7/y4p32ECgOfgC4EMABUAXKSUFvt/898r8M7++5f85e2EugAyAXzrVyf9lxCShBLcz5TSkwA+BHAMwGn4+m0TSnY/c9Dar4b6206MvESDEFIawG8AnqChSa1BfUt0ibETJYTcACCDUrop0rSEETEA2gL4klLaBkAugtttACWyn8vBl3CmLoBq8CVoF6ogSjzC0a92YuQnAdTk/a7hv2Z7EEJi4WPiP1JKf/dfPksIqeq/XxVAhv96SfgO3QAMJoQcATAdPvXKpwDKEkK4GPn89wq8s/9+GQDnw0mwCTgB4ASldJ3/90z4GHtJ7ue+AA5TSjMppUUAfoev70tyP3PQ2q+G+ttOjHwDgIb+E+84+A5N/o4wTYZBCCEAvgGwm1L6Me/W3wC4k+uR8OnOuesj/KffnQFc4m3hbAFK6QuU0hqU0jrw9eO/lNK7ACwBMNRfTPjO3LcY6i9vK8mVUnoGwHHiy6gFAH0A7EIJ7mf4VCqdCSGl/OOce+cS2888aO3XBQCuI4SU8+9krvNfU4dIHxJoPFC4HsA+AAcBvBRpekx6p2vg23ZtA7DF/9/18OkGFwPYD2ARgPL+8gQ+652DALbDZxEQ8fcw8P49Acz2/10PwHoABwD8CiDefz3B//uA/369SNOt811bA9jo7+s/AZQr6f0M4A34cvnuAPA9gPiS1s8AfobvDKAIvp3XfXr6FcC9/nc/AOAeLTQ4LvoOHDhwYHPYSbXiwIEDBw4YcBi5AwcOHNgcDiN34MCBA5vDYeQOHDhwYHM4jNyBAwcObA6HkTtw4MCBzeEwcgcOHDiwOf4fVMnCvg+oQx0AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -96,22 +96,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ef1ed17388>]"
+       "[<matplotlib.lines.Line2D at 0x2244d4c33d0>]"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -133,22 +133,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\86155\\AppData\\Local\\Temp\\ipykernel_9380\\3441026984.py:12: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  X_AR1 = np.array(X_AR1)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ef21691a48>]"
+       "[<matplotlib.lines.Line2D at 0x2244e523e20>]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -192,22 +200,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\86155\\AppData\\Local\\Temp\\ipykernel_9380\\220313886.py:11: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  X_AR1 = np.array(X_AR1)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ef217166c8>]"
+       "[<matplotlib.lines.Line2D at 0x2244e9ad030>]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -221,7 +237,7 @@
    "source": [
     "# X generated as a relationship to the lagged value, therefore having an inconsistent mean\n",
     "c = 0.5\n",
-    "beta = 0.9\n",
+    "beta = 0.8\n",
     "X_AR1 = [1, ]\n",
     "\n",
     "for i in range(1, 1000):\n",
@@ -233,34 +249,39 @@
     "SMA_X_AR1 = simple_moving_average(X_AR1, n=50)\n",
     "\n",
     "plt.plot(X_AR1)\n",
-    "plt.plot(SMA_X_AR1, 'r--')  # Note how the moving average clearly changes over time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The difference is that this model doesn't continously grow, unlike a model with a beta > 1."
+    "plt.plot(SMA_X_AR1, 'r--')\n",
+    "\n",
+    "# qs 1\n",
+    "# we can see that a smaller beta results in more \"noisy\" data with less obvious trends\n",
+    "# the model doesn't continously grow, unlike a model with a beta > 1."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\86155\\AppData\\Local\\Temp\\ipykernel_9380\\3397989508.py:11: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.\n",
+      "  X_AR1 = np.array(X_AR1)\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1ef227e5548>]"
+       "[<matplotlib.lines.Line2D at 0x2244fc02e60>]"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -274,7 +295,7 @@
    "source": [
     "# X generated as a relationship to the lagged value, therefore having an inconsistent mean\n",
     "c = 0.5\n",
-    "beta = -1.1\n",
+    "beta = -1.01\n",
     "X_AR1 = [1, ]\n",
     "\n",
     "for i in range(1, 1000):\n",
@@ -286,14 +307,11 @@
     "SMA_X_AR1 = simple_moving_average(X_AR1, n=50)\n",
     "\n",
     "plt.plot(X_AR1)\n",
-    "plt.plot(SMA_X_AR1, 'r--')  # Note how the moving average clearly changes over time"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When < -1, it is mean reverting and continiously growing, whereas between 0 and -1 it is mean reverting however not continously growing"
+    "plt.plot(SMA_X_AR1, 'r--')  # Note how the moving average clearly changes over time\n",
+    "\n",
+    "# qs 2\n",
+    "# when greater than -1 but below 0, the data is still looking very random. The price always reverts back to 0, meaning convergence\n",
+    "# but when less than -1, even by a fraction, the dataseems to diverge as absolute value of beta is now greater than 1"
    ]
   },
   {
@@ -319,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -336,6 +354,68 @@
     "2. Perform the same tests for the Augmented Dickey-Fuller (ADF) test, also in statsmodels"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "kpss tests\n",
+      "0.1\n",
+      "0.01\n",
+      "0.1\n",
+      "adf tests\n",
+      "2.2414923995431472e-13\n",
+      "0.9103948611654578\n",
+      "0.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:2022: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
+      "look-up table. The actual p-value is greater than the p-value returned.\n",
+      "\n",
+      "  warnings.warn(\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:2018: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
+      "look-up table. The actual p-value is smaller than the p-value returned.\n",
+      "\n",
+      "  warnings.warn(\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:2022: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
+      "look-up table. The actual p-value is greater than the p-value returned.\n",
+      "\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "# import kpss\n",
+    "from statsmodels.tsa.stattools import kpss\n",
+    "\n",
+    "print(\"kpss tests\")\n",
+    "_, p_val, _, _ = kpss(X_stationary)\n",
+    "print(p_val)\n",
+    "_, p_val, _, _ = kpss(X_non_stationary)\n",
+    "print(p_val)\n",
+    "_, p_val, _, _ = kpss(X_AR1)\n",
+    "print(p_val)\n",
+    "\n",
+    "# import adf\n",
+    "print(\"adf tests\")\n",
+    "from statsmodels.tsa.stattools import adfuller\n",
+    "\n",
+    "_, p_val, _, _, _, _  = adfuller(X_stationary)\n",
+    "print(p_val)\n",
+    "_, p_val, _, _, _, _  = adfuller(X_non_stationary)\n",
+    "print(p_val)\n",
+    "_, p_val, _, _, _, _  = adfuller(X_AR1)\n",
+    "print(p_val)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -408,6 +488,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -415,32 +496,41 @@
     "\n",
     "1. Review the documentation for `statsmodels.tsa.tsatools.detrend`.\n",
     "2. Remove the linear trends from the three datasets from above (`X_stationary`, `X_non_stationary` and `X_AR1`) and visualise the new data compared to the old data.\n",
-    "3. Perform the ADF test on the new resulting data and check for stationarity.\n",
-    "\n",
-    "\n",
-    "#### Extended exercise\n",
-    "\n",
-    "Detrending is an area with lots of different methods and techniques used.  Often the context matters. Review the documentation for `scipy.signal.detrend` and review its terminology compared to the statsmodels version. Does a linear detrend in scipy correlate to a statsmodels detrending? Can you find another Python package that includes detrending? What terminology does it use?"
+    "3. Perform the ADF test on the new resulting data and check for stationarity."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<function matplotlib.pyplot.show(*args, **kw)>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -449,42 +539,64 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.5350261217451813e-13\n",
+      "0.0\n",
+      "0.973105436396821\n"
+     ]
     }
    ],
    "source": [
-    "#From Solutions\n",
+    "# qs 1\n",
     "from statsmodels.tsa.tsatools import detrend\n",
     "\n",
-    "Xst_detrended = detrend(X_stationary)\n",
-    "Xnonst_detrended = detrend(X_non_stationary)\n",
-    "Xar1_detrended = detrend(X_AR1)\n",
-    "\n",
-    "\n",
-    "from statsmodels.tsa.stattools import adfuller\n",
-    "\n",
-    "# X_stationary\n",
-    "plt.plot(X_stationary, alpha=0.5)\n",
-    "plt.plot(Xst_detrended, 'r--', alpha=0.5)  # No difference\n",
+    "# qs 2\n",
+    "# detrend \n",
+    "X_stationary_2 = detrend(X_stationary)\n",
+    "X_non_stationary_2 = detrend(X_non_stationary)\n",
+    "X_AR1_2 = detrend(X_AR1)\n",
     "\n",
-    "adf, p_value, *_ = adfuller(Xst_detrended)\n",
-    "\n",
-    "p_value < 0.05, p_value  # Reject hypothesis of a unit root, which indicates stationary\n",
-    "\n",
-    "plt.plot(X_non_stationary, alpha=0.5)\n",
-    "plt.plot(Xnonst_detrended)  # Removed linear increase\n",
-    "\n",
-    "adf, p_value, *_ = adfuller(Xnonst_detrended)\n",
-    "\n",
-    "p_value < 0.05, p_value  # Reject hypothesis of a unit root, which indicates stationary\n",
+    "plt.plot(X_stationary)\n",
+    "plt.plot(X_stationary_2)\n",
+    "plt.show()\n",
     "\n",
-    "plt.plot(X_AR1, alpha=0.5)\n",
-    "plt.plot(Xar1_detrended)  # linear detrends aren't perfect\n",
+    "plt.plot(X_non_stationary)\n",
+    "plt.plot(X_non_stationary_2)\n",
+    "plt.show()\n",
     "\n",
-    "adf, p_value, *_ = adfuller(Xar1_detrended)\n",
+    "plt.plot(X_AR1)\n",
+    "plt.plot(X_AR1_2)\n",
+    "plt.show()\n",
+    "\n",
+    "# qs 3\n",
+    "_, p_val, _, _, _, _  = adfuller(X_stationary_2)\n",
+    "print(p_val)\n",
+    "_, p_val, _, _, _, _  = adfuller(X_non_stationary_2)\n",
+    "print(p_val)\n",
+    "_, p_val, _, _, _, _  = adfuller(X_AR1_2)\n",
+    "print(p_val)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Extended exercise\n",
     "\n",
-    "p_value < 0.05, p_value  # Do NOT hypothesis of a unit root, which indicates stationary\n",
+    "Detrending is an area with lots of different methods and techniques used.  Often the context matters. Review the documentation for `scipy.signal.detrend` and review its terminology compared to the statsmodels version. Does a linear detrend in scipy correlate to a statsmodels detrending? Can you find another Python package that includes detrending? What terminology does it use?\n",
     "\n",
-    "# In this case, linear detrend is not sufficient to make stationary"
+    "- The scipy version seems to do the same thing as the statsmodels version\n",
+    "- However, scipy offers linear and constant fitting while statsmodel offers polynomial fitting\n",
+    "- Depending on what type of data you have, one library might be preferably over another\n",
+    "- Another library is meegkit.detrend\n",
+    "    - this library offers weighting for regerssion, polynomial, or sin/cosine (wavelets)\n",
+    "    - it also offers slightly more control over the optimization method such as number of iterations or threshold for outlier removal\n",
+    "- All these libraries would work on simple data, but depending on the specific complex data, one might choose different libraries over others"
    ]
   },
   {
@@ -497,7 +609,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -511,7 +623,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.6.3 - White Noise.ipynb b/notebooks/Module 1.6.3 - White Noise.ipynb
index 238efee..1eb1fae 100644
--- a/notebooks/Module 1.6.3 - White Noise.ipynb	
+++ b/notebooks/Module 1.6.3 - White Noise.ipynb	
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -75,22 +75,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1781559c6c8>]"
+       "[<matplotlib.lines.Line2D at 0x1b8a68de590>]"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -107,7 +107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -116,7 +116,7 @@
        "0.021943786501463503"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -136,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,16 +145,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(-0.014453270331154722, 0.05834084333408174)"
+       "(-0.014453270331154726, 0.05834084333408173)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,7 +181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -219,7 +219,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,7 +229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -249,7 +249,7 @@
        "(-0.01434853490028654, 0.058486871505393195)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -266,6 +266,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -273,7 +274,44 @@
     "\n",
     "The constant variance tests in `statsmodels` are not well documented and poorly implemented for directly calling them functions. Instead, use the `scipy` methods for computing constant variance, such as using Bartlett’s and Levene's test. That said, in both cases, these functions don't actually give us the value we are looking for. Instead, they tell whether two datasets have the same variance. A way to use this is to test the first half of the data against the second half.\n",
     "\n",
-    "Check the documentation for these two methods, and use the more robust of the two to perform the above test. \n",
+    "Check the documentation for these two methods, and use the more robust of the two to perform the above test. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LeveneResult(statistic=0.3806018631741954, pvalue=0.5373281521503752)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import levene\n",
+    "from statsmodels.stats.diagnostic import het_white\n",
+    "\n",
+    "# documentation for statsmodels is very lacking so using scipy for testing\n",
+    "\n",
+    "# perform tests between 1st half and 2nd half of data\n",
+    "i = int(np.floor(len(X)/2))\n",
+    "levene(X[:i], X[i:])\n",
+    "#het_white(X[:i], X[i:])\n",
+    "\n",
+    "# results show that the data does not have equal variance"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "\n",
     "#### Extended Exercise\n",
     "\n",
@@ -285,22 +323,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x17825571588>]"
+       "[<matplotlib.lines.Line2D at 0x1b8abb93d00>]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -312,27 +350,19 @@
     }
    ],
    "source": [
-    "X = distribution.rvs(1000)  # Random data, between 0 and 1, then with 0.5 subtracted\n",
-    "# Note we are considering this as a \"time series\" data, so X[1] happens after X[0]\n",
-    "\n",
-    "#From solutions\n",
-    "from scipy import stats\n",
-    "\n",
-    "n = int(len(X) / 2)\n",
-    "\n",
-    "stats.levene(X[:n], X[n:])\n",
-    "\n",
     "# Extended exercise solution\n",
     "import statsmodels.api as sm\n",
     "\n",
-    "# Create some random \n",
-    "y = np.random.random(len(X))\n",
-    "est = sm.OLS(y, X).fit()\n",
+    "# generate randomy y predictor variable\n",
+    "y_true = np.random.random(len(X))\n",
     "\n",
-    "y_pred = est.fittedvalues\n",
+    "# fit X values with the labels above\n",
+    "y_pred = sm.OLS(y_true, X).fit().fittedvalues\n",
     "\n",
-    "residuals = y_pred - y\n",
+    "# compute error\n",
+    "residuals = y_pred - y_true\n",
     "\n",
+    "# plot\n",
     "plt.plot(y_pred, residuals, 'ro')  # No clear pattern == white noise"
    ]
   },
@@ -354,22 +384,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x178255c7e88>"
+       "<AxesSubplot:xlabel='Lag', ylabel='Autocorrelation'>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -402,32 +432,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\misc.py:409: UserWarning: Requested projection is different from current axis projection, creating new axis with requested projection.\n",
-      "  ax = plt.gca(xlim=(1, n), ylim=(-1.0, 1.0))\n"
+      "1.5014538130881663\n"
      ]
     },
     {
      "data": {
+      "image/png": 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",
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x1782564f148>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
+       "<Figure size 432x288 with 1 Axes>"
       ]
      },
      "metadata": {
@@ -437,33 +480,35 @@
     }
    ],
    "source": [
-    "#From solutions\n",
+    "# requirements for white noise:\n",
+    "#   1 mean not 0\n",
+    "#   2 constant variance as random values\n",
+    "#   3 no auto correlation (no correlation with lagged versions of itself)\n",
     "\n",
-    "# Mean not 0\n",
-    "\n",
-    "X = np.random.random(100) + 10  # Mean is about 10.5\n",
+    "# crit 1\n",
+    "X = 1 + np.random.random(1000)  # Mean is about 10.5\n",
+    "print(np.mean(X))\n",
     "\n",
     "plt.plot(X)\n",
-    "\n",
     "pd.plotting.autocorrelation_plot(X)\n",
+    "plt.show()\n",
     "\n",
-    "# Variance not constant\n",
-    "\n",
-    "i = np.arange(100)\n",
-    "X = np.random.random(len(i)) * i\n",
+    "# crit 2\n",
+    "i = np.array([i for i in range(1000)])\n",
+    "X = np.random.random(1000) * i\n",
     "\n",
     "plt.plot(X)\n",
-    "\n",
     "pd.plotting.autocorrelation_plot(X)\n",
+    "plt.show()\n",
     "\n",
-    "# Lagged correlation\n",
+    "# crit 3\n",
     "X = [1] * 100\n",
     "for i in range(1, 100):\n",
     "    X[i] = X[i-1] * np.random.random() * 2\n",
-    "    \n",
-    "plt.plot(X)\n",
     "\n",
-    "pd.plotting.autocorrelation_plot(X)\n"
+    "plt.plot(X)\n",
+    "pd.plotting.autocorrelation_plot(X)\n",
+    "plt.show()"
    ]
   },
   {
@@ -476,7 +521,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -490,7 +535,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.6.4 - ARMA.ipynb b/notebooks/Module 1.6.4 - ARMA.ipynb
index 4a0d801..4e8eee9 100644
--- a/notebooks/Module 1.6.4 - ARMA.ipynb	
+++ b/notebooks/Module 1.6.4 - ARMA.ipynb	
@@ -104,7 +104,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2a173df4808>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
      "execution_count": 5,
@@ -113,7 +113,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -130,25 +130,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1358: RuntimeWarning: invalid value encountered in sqrt\n",
-      "  return rho, np.sqrt(sigmasq)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "pac = sms.tsa.pacf(employment)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -157,12 +148,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -193,22 +192,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x2a175746308>"
+       "<AxesSubplot:xlabel='Lag', ylabel='Autocorrelation'>"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -225,40 +224,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:541: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.\n",
-      "  warnings.warn(msg, FutureWarning)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "ac = sms.tsa.acf(employment,nlags=24)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2a175858188>]"
+       "[<matplotlib.lines.Line2D at 0x180f9bda290>]"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -290,10 +280,14 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "The autocorrelation is the correlation of time t with time with lag n i.e. how correlated is the current day with each of previous days individually. This highlights to me how important each of these lags are individually. The partial autocorrelation is a regression of all the lags up to n, where the value is the beta of the final lag n. It controls for the intermediate lags, ie how much extra does the final lag yield above the intermediate lags. "
+    "# autocorrelation simply tests the correlation of a series with itself\n",
+    "# partial autocorrelation requires an additional input specifying the amount of lag. If not provided it uses 10*log10(N) samples.\n",
+    "# the difference is that the partial version tests how much the highest lag (specified as input) differs from all the values before it."
    ]
   },
   {
@@ -307,7 +301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -316,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -325,19 +319,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/rob/anaconda3/envs/quant/lib/python3.6/site-packages/scipy/signal/signaltools.py:1341: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
-      "  out_full[ind] += zi\n",
-      "/home/rob/anaconda3/envs/quant/lib/python3.6/site-packages/scipy/signal/signaltools.py:1344: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
-      "  out = out_full[ind]\n",
-      "/home/rob/anaconda3/envs/quant/lib/python3.6/site-packages/scipy/signal/signaltools.py:1350: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
-      "  zf = out_full[ind]\n"
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
      ]
     }
    ],
@@ -347,89 +347,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [
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-      "text/plain": [
-       "             0            1            2\n",
-       "0 -1425.572126 -1671.849901 -1805.260817\n",
-       "1 -1960.391602 -1961.924479 -1965.757912\n",
-       "2 -1964.580178 -1960.742190 -2003.768340\n",
-       "3 -1963.836622 -1983.750752 -2024.629486\n",
-       "4 -1967.600391 -1983.436176 -1988.273046"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             0            1            2\n",
+      "0 -1473.728271 -1739.463070 -1876.189397\n",
+      "1 -2041.330410 -2041.665546 -2043.957741\n",
+      "2 -2043.945388 -2024.094170 -2055.411497\n",
+      "3 -2042.386581 -2034.084434 -2056.249903\n",
+      "4 -2045.349240 -2035.966562 -2026.214095\n",
+      "(3, 2)\n"
+     ]
     }
    ],
    "source": [
-    "statistics['bic']"
+    "print(statistics['bic'])\n",
+    "print(statistics['bic_min_order'])"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -438,11 +379,18 @@
     "Review the documentation for this function and identify:\n",
     "\n",
     "1. What does 'bic' stand for?\n",
+    "    - Bayesian Information Criterion\n",
     "2. What other options can be selected?\n",
+    "    - example usage: res = sm.tsa.arma_order_select_ic(y, ic=[\"aic\", \"bic\"], trend=\"n\")\n",
+    "    - shows AIC and BIC can be selected\n",
     "3. What the columns specify (i.e. $p$ or $q$)?\n",
+    "    - q-values (columns)\n",
     "4. What does the index represent?\n",
+    "    - p-values (rows)\n",
     "5. Which value should we select? Are higher or lower values better?\n",
-    "6. Find a shortcut parameter that gives us this value without the need to compute it?"
+    "    - from the output above, we can see that it is (3, 2)\n",
+    "6. Find a shortcut parameter that gives us this value without the need to compute it?\n",
+    "    - the key 'bic_min_order' in the example above tells us this"
    ]
   },
   {
@@ -467,140 +415,155 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from my_secrets import p, q  # No cheating! Overwrite these values yourself: p, q = ?, ?"
+    "#from my_secrets import p, q  # No cheating! Overwrite these values yourself: p, q = ?, ?\n",
+    "p = 3\n",
+    "q = 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
-    "from statsmodels import api as sms\n",
-    "model = sms.tsa.ARMA(employment, order=(p, q))"
+    "# this code is depreciated\n",
+    "#from statsmodels import api as sms\n",
+    "#model = sms.tsa.ARMA(employment, order=(p, q))\n",
+    "\n",
+    "from statsmodels.tsa.arima.model import ARIMA\n",
+    "model = sms.tsa.ARIMA(employment, order=(p, 0, q))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    }
+   ],
    "source": [
     "results = model.fit()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<table class=\"simpletable\">\n",
-       "<caption>ARMA Model Results</caption>\n",
+       "<caption>SARIMAX Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>       <td>Value</td>      <th>  No. Observations:  </th>    <td>323</td>   \n",
+       "  <th>Dep. Variable:</th>         <td>Value</td>      <th>  No. Observations:  </th>    <td>331</td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>            <td>ARMA(3, 2)</td>    <th>  Log Likelihood     </th> <td>1032.537</td> \n",
+       "  <th>Model:</th>            <td>ARIMA(3, 0, 2)</td>  <th>  Log Likelihood     </th> <td>1048.432</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.010</td>  \n",
+       "  <th>Date:</th>            <td>Wed, 04 Jan 2023</td> <th>  AIC                </th> <td>-2082.865</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>          <td>Mon, 29 Apr 2019</td> <th>  AIC                </th> <td>-2051.073</td>\n",
+       "  <th>Time:</th>                <td>20:10:03</td>     <th>  BIC                </th> <td>-2056.250</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>              <td>16:59:59</td>     <th>  BIC                </th> <td>-2024.629</td>\n",
+       "  <th>Sample:</th>             <td>04-01-1949</td>    <th>  HQIC               </th> <td>-2072.250</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Sample:</th>           <td>04-01-1949</td>    <th>  HQIC               </th> <td>-2040.517</td>\n",
+       "  <th></th>                   <td>- 10-01-2031</td>   <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th></th>                 <td>- 10-01-2029</td>   <th>                     </th>     <td> </td>    \n",
+       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>const</th>       <td>   -0.0024</td> <td>    0.005</td> <td>   -0.455</td> <td> 0.649</td> <td>   -0.013</td> <td>    0.008</td>\n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ar.L1.Value</th> <td>    1.8295</td> <td>    0.066</td> <td>   27.897</td> <td> 0.000</td> <td>    1.701</td> <td>    1.958</td>\n",
+       "  <th>const</th>  <td>   -0.0359</td> <td>    0.046</td> <td>   -0.775</td> <td> 0.438</td> <td>   -0.127</td> <td>    0.055</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ar.L2.Value</th> <td>   -1.2166</td> <td>    0.109</td> <td>  -11.201</td> <td> 0.000</td> <td>   -1.429</td> <td>   -1.004</td>\n",
+       "  <th>ar.L1</th>  <td>   -0.7646</td> <td>    0.044</td> <td>  -17.343</td> <td> 0.000</td> <td>   -0.851</td> <td>   -0.678</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ar.L3.Value</th> <td>    0.3207</td> <td>    0.061</td> <td>    5.297</td> <td> 0.000</td> <td>    0.202</td> <td>    0.439</td>\n",
+       "  <th>ar.L2</th>  <td>    0.8919</td> <td>    0.018</td> <td>   48.914</td> <td> 0.000</td> <td>    0.856</td> <td>    0.928</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ma.L1.Value</th> <td>   -1.2591</td> <td>    0.032</td> <td>  -39.824</td> <td> 0.000</td> <td>   -1.321</td> <td>   -1.197</td>\n",
+       "  <th>ar.L3</th>  <td>    0.8181</td> <td>    0.037</td> <td>   22.066</td> <td> 0.000</td> <td>    0.745</td> <td>    0.891</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ma.L2.Value</th> <td>    0.9061</td> <td>    0.025</td> <td>   36.253</td> <td> 0.000</td> <td>    0.857</td> <td>    0.955</td>\n",
+       "  <th>ma.L1</th>  <td>    1.6659</td> <td>    0.059</td> <td>   28.352</td> <td> 0.000</td> <td>    1.551</td> <td>    1.781</td>\n",
        "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<caption>Roots</caption>\n",
        "<tr>\n",
-       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
+       "  <th>ma.L2</th>  <td>    0.6990</td> <td>    0.058</td> <td>   12.134</td> <td> 0.000</td> <td>    0.586</td> <td>    0.812</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>AR.1</th> <td>           1.2078</td> <td>          -0.0000j</td> <td>           1.2078</td> <td>          -0.0000</td>\n",
+       "  <th>sigma2</th> <td> 9.725e-05</td> <td> 2.27e-06</td> <td>   42.809</td> <td> 0.000</td> <td> 9.28e-05</td> <td>    0.000</td>\n",
        "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>AR.2</th> <td>           1.2929</td> <td>          -0.9540j</td> <td>           1.6068</td> <td>          -0.1012</td>\n",
+       "  <th>Ljung-Box (L1) (Q):</th>      <td>0.06</td>  <th>  Jarque-Bera (JB):  </th> <td>21301.10</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>AR.3</th> <td>           1.2929</td> <td>          +0.9540j</td> <td>           1.6068</td> <td>           0.1012</td>\n",
+       "  <th>Prob(Q):</th>                 <td>0.80</td>  <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>MA.1</th> <td>           0.6948</td> <td>          -0.7880j</td> <td>           1.0505</td> <td>          -0.1350</td>\n",
+       "  <th>Heteroskedasticity (H):</th> <td>254.08</td> <th>  Skew:              </th>   <td>-0.98</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>MA.2</th> <td>           0.6948</td> <td>          +0.7880j</td> <td>           1.0505</td> <td>           0.1350</td>\n",
+       "  <th>Prob(H) (two-sided):</th>     <td>0.00</td>  <th>  Kurtosis:          </th>   <td>42.25</td> \n",
        "</tr>\n",
-       "</table>"
+       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                              ARMA Model Results                              \n",
+       "                               SARIMAX Results                                \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  Value   No. Observations:                  331\n",
+       "Model:                 ARIMA(3, 0, 2)   Log Likelihood                1048.432\n",
+       "Date:                Wed, 04 Jan 2023   AIC                          -2082.865\n",
+       "Time:                        20:10:03   BIC                          -2056.250\n",
+       "Sample:                    04-01-1949   HQIC                         -2072.250\n",
+       "                         - 10-01-2031                                         \n",
+       "Covariance Type:                  opg                                         \n",
        "==============================================================================\n",
-       "Dep. Variable:                  Value   No. Observations:                  323\n",
-       "Model:                     ARMA(3, 2)   Log Likelihood                1032.537\n",
-       "Method:                       css-mle   S.D. of innovations              0.010\n",
-       "Date:                Mon, 29 Apr 2019   AIC                          -2051.073\n",
-       "Time:                        16:59:59   BIC                          -2024.629\n",
-       "Sample:                    04-01-1949   HQIC                         -2040.517\n",
-       "                         - 10-01-2029                                         \n",
-       "===============================================================================\n",
-       "                  coef    std err          z      P>|z|      [0.025      0.975]\n",
-       "-------------------------------------------------------------------------------\n",
-       "const          -0.0024      0.005     -0.455      0.649      -0.013       0.008\n",
-       "ar.L1.Value     1.8295      0.066     27.897      0.000       1.701       1.958\n",
-       "ar.L2.Value    -1.2166      0.109    -11.201      0.000      -1.429      -1.004\n",
-       "ar.L3.Value     0.3207      0.061      5.297      0.000       0.202       0.439\n",
-       "ma.L1.Value    -1.2591      0.032    -39.824      0.000      -1.321      -1.197\n",
-       "ma.L2.Value     0.9061      0.025     36.253      0.000       0.857       0.955\n",
-       "                                    Roots                                    \n",
-       "=============================================================================\n",
-       "                  Real          Imaginary           Modulus         Frequency\n",
-       "-----------------------------------------------------------------------------\n",
-       "AR.1            1.2078           -0.0000j            1.2078           -0.0000\n",
-       "AR.2            1.2929           -0.9540j            1.6068           -0.1012\n",
-       "AR.3            1.2929           +0.9540j            1.6068            0.1012\n",
-       "MA.1            0.6948           -0.7880j            1.0505           -0.1350\n",
-       "MA.2            0.6948           +0.7880j            1.0505            0.1350\n",
-       "-----------------------------------------------------------------------------\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "const         -0.0359      0.046     -0.775      0.438      -0.127       0.055\n",
+       "ar.L1         -0.7646      0.044    -17.343      0.000      -0.851      -0.678\n",
+       "ar.L2          0.8919      0.018     48.914      0.000       0.856       0.928\n",
+       "ar.L3          0.8181      0.037     22.066      0.000       0.745       0.891\n",
+       "ma.L1          1.6659      0.059     28.352      0.000       1.551       1.781\n",
+       "ma.L2          0.6990      0.058     12.134      0.000       0.586       0.812\n",
+       "sigma2      9.725e-05   2.27e-06     42.809      0.000    9.28e-05       0.000\n",
+       "===================================================================================\n",
+       "Ljung-Box (L1) (Q):                   0.06   Jarque-Bera (JB):             21301.10\n",
+       "Prob(Q):                              0.80   Prob(JB):                         0.00\n",
+       "Heteroskedasticity (H):             254.08   Skew:                            -0.98\n",
+       "Prob(H) (two-sided):                  0.00   Kurtosis:                        42.25\n",
+       "===================================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
        "\"\"\""
       ]
      },
-     "execution_count": 28,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -611,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -620,22 +583,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fe3788d2b00>]"
+       "[<matplotlib.lines.Line2D at 0x180fcc64400>]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -668,39 +631,153 @@
     "Investigate the results further. If we *ignore* the impact of the global financial crisis (as a one-off event that can't easily be modelled with the data on hand), does it do well on the years 2000-2005, and 2012-2019?"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*For solutions, see `solutions/overfitting_arma.py`*"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 2\n",
+      "                               SARIMAX Results                                \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  Value   No. Observations:                  331\n",
+      "Model:                 ARIMA(4, 0, 2)   Log Likelihood                1036.316\n",
+      "Date:                Wed, 04 Jan 2023   AIC                          -2056.631\n",
+      "Time:                        20:20:30   BIC                          -2026.214\n",
+      "Sample:                    04-01-1949   HQIC                         -2044.499\n",
+      "                         - 10-01-2031                                         \n",
+      "Covariance Type:                  opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const         -0.0012      0.010     -0.112      0.911      -0.021       0.019\n",
+      "ar.L1          0.0813      0.340      0.239      0.811      -0.586       0.749\n",
+      "ar.L2          0.2299      0.170      1.354      0.176      -0.103       0.563\n",
+      "ar.L3          0.4198      0.237      1.769      0.077      -0.045       0.885\n",
+      "ar.L4          0.1036      0.056      1.846      0.065      -0.006       0.214\n",
+      "ma.L1          0.7396      0.356      2.080      0.037       0.043       1.436\n",
+      "ma.L2          0.5565      0.209      2.667      0.008       0.147       0.966\n",
+      "sigma2         0.0001   2.96e-06     37.282      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.27   Jarque-Bera (JB):             16248.13\n",
+      "Prob(Q):                              0.60   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):             414.20   Skew:                            -0.91\n",
+      "Prob(H) (two-sided):                  0.00   Kurtosis:                        37.28\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x180fee1c8b0>]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "# This next cell runs the solution file, in place, as if I copied the contents into this cell.\n",
-    "# Useful for removing large bits of code from your nice looking notebooks"
+    "train, y_true = employment.loc[:\"1999-12-31\"], employment.loc[\"2000-01-01\":]\n",
+    "\n",
+    "# calculate ic's with above code\n",
+    "statistics = stattools.arma_order_select_ic(train)  # May take a few minutes\n",
+    "p, q = statistics.bic_min_order\n",
+    "print(p, q)\n",
+    "\n",
+    "# train model with above code\n",
+    "model = sms.tsa.ARIMA(employment, order=(p, 0, q))\n",
+    "model = model.fit()\n",
+    "print(model.summary())\n",
+    "\n",
+    "# predict using trained model\n",
+    "y_pred = model.predict(start = y_true.index.min(), end = y_true.index.max())\n",
+    "\n",
+    "# plot tru vs pred\n",
+    "plt.plot(y_pred)\n",
+    "plt.plot(y_true)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/rob/anaconda3/envs/quant/lib/python3.6/site-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
-      "  \"Check mle_retvals\", ConvergenceWarning)\n"
-     ]
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -712,6 +789,47 @@
     }
    ],
    "source": [
+    "# redo by ignoring 2008\n",
+    "y_true1, y_true2 = employment.loc[\"2000-01-01\":\"2005-12-31\"], employment.loc[\"2012-01-01\":\"2019-12-31\"]\n",
+    "\n",
+    "# predict using trained model\n",
+    "y_pred1 = model.predict(start = y_true1.index.min(), end = y_true1.index.max())\n",
+    "y_pred2 = model.predict(start = y_true2.index.min(), end = y_true2.index.max())\n",
+    "\n",
+    "# plot tru vs pred\n",
+    "plt.plot(y_pred1)\n",
+    "plt.plot(y_true1)\n",
+    "plt.show()\n",
+    "\n",
+    "plt.plot(y_pred2)\n",
+    "plt.plot(y_true2)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*For solutions, see `solutions/overfitting_arma.py`*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This next cell runs the solution file, in place, as if I copied the contents into this cell.\n",
+    "# Useful for removing large bits of code from your nice looking notebooks"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# code is depreciated as ARMA function doesnt exist\n",
     "%run -i solutions/overfitting_arma.py"
    ]
   },
@@ -728,12 +846,42 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -745,7 +893,17 @@
     }
    ],
    "source": [
-    "train_residuals = training['Value'].values - results.predict().values\n",
+    "# have to re-run the code with updated function for it to work\n",
+    "training = employment.loc[:\"2000-01-01\"]\n",
+    "testing = employment.loc[\"2000-01-02\":]\n",
+    "\n",
+    "statistics = stattools.arma_order_select_ic(training)  # May take a few minutes\n",
+    "model = sms.tsa.ARIMA(employment, order=(p, 0, q))\n",
+    "results = model.fit()\n",
+    "results.summary()\n",
+    "y_pred = results.predict(start=testing.index.min(), end=testing.index.max())\n",
+    "\n",
+    "train_residuals = training['Value'].values - results.predict(start=training.index.min(), end=training.index.max())\n",
     "plt.plot(train_residuals);"
    ]
   },
@@ -762,7 +920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -776,7 +934,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -799,11 +957,49 @@
     "\n",
     "Perform a formal analysis to identity if the residuals are white noise in both the training and testing case."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "pd.plotting.autocorrelation_plot(train_residuals)\n",
+    "plt.show()\n",
+    "pd.plotting.autocorrelation_plot(test_residuals)\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -817,7 +1013,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 1.7.1 - Kalman Filter Introduction.ipynb b/notebooks/Module 1.7.1 - Kalman Filter Introduction.ipynb
index ed93640..61c71db 100644
--- a/notebooks/Module 1.7.1 - Kalman Filter Introduction.ipynb	
+++ b/notebooks/Module 1.7.1 - Kalman Filter Introduction.ipynb	
@@ -38,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,7 +78,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -97,7 +97,7 @@
        "27.999993"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -108,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -117,7 +117,7 @@
        "27.297585340624874"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -128,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -137,7 +137,7 @@
        "27.23728301758805"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -148,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -157,7 +157,7 @@
        "26.283589051582236"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -168,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -180,16 +180,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "25.371744120457674"
+       "26.364029449567244"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -199,6 +199,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -208,11 +209,13 @@
     "2. Plot the estimated value over time\n",
     "3. Modify the code to perform estimates for two values - say temperature and humidity, as they are provided at the same time do the update function like this:\n",
     "\n",
-    "    ```measurements = np.array([temperature, humidity])\n",
-    "    filter.update(measurements)```\n",
     "\n",
-    "Note how the variation in the error becomes small\n",
-    "\n"
+    "measurements = np.array([temperature, humidity])\n",
+    "\n",
+    "filter.update(measurements)\n",
+    "\n",
+    "\n",
+    "Note how the variation in the error becomes small"
    ]
   },
   {
@@ -224,22 +227,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a43cd60cc8>]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -258,7 +251,9 @@
     "        self.current_estimate = original_estimate\n",
     "        self.estimated_measurement_error = estimated_measurement_error\n",
     "        self.current_error = original_estimated_error\n",
-    "        self.estimates = []\n",
+    "\n",
+    "        # this records all estimates given over time (same code as above)\n",
+    "        self.estimates = [] \n",
     "        \n",
     "    def update(self, new_data):\n",
     "        kalman_gain = 1e-6 + self.current_error / (1e-6 + self.current_error + self.estimated_measurement_error)\n",
@@ -268,34 +263,40 @@
     "        self.current_error = (1 - kalman_gain) * self.current_error  # Update error\n",
     "        self.estimates.append(self.current_estimate)\n",
     "        return self.current_estimate\n",
-    "    \n",
-    "data = np.random.randn(1000) * 5 + 25\n",
+    "\n",
+    "# generate some random data\n",
+    "data = np.random.randn(1000) * 10 + 10\n",
+    "\n",
     "temperature_filter = KalmanFilter(30, 5, 2)\n",
+    "[temperature_filter.update(d) for d in data]\n",
     "\n",
-    "for measurement in data:\n",
-    "    estimated_temperature = temperature_filter.update(measurement)\n",
     "# Question 2\n",
-    "plt.plot(temperature_filter.estimates)"
+    "plt.plot(temperature_filter.estimates)\n",
+    "plt.plot(data, alpha=0.5)\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1a43cec8048>]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[14.76181852 50.93005174]\n",
+      " [23.15683548 93.56694406]\n",
+      " [21.15785243 59.52995992]\n",
+      " ...\n",
+      " [25.0889557  63.15523565]\n",
+      " [25.918384   69.08183641]\n",
+      " [18.82947022 67.2329374 ]]\n"
+     ]
     },
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\n",
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84x+PdjEO6Qc/+AFtbW2jXYwuR9KFcglwb/D8XuDSIy6NiByXjpUAd86Ry+UO+PrhBHgmkznSYh3QYAPcAU+Y2Vozuy4YN8k5tzN4/iYwaaA3mtl1ZrbGzNY0NDQcYXFFZCy68cYb2bJlCwsWLODLX/4yt956K2eeeSZnnHEG3/jGNwCora1lzpw5rFy5kpNPPpmrrrqKJ598krPPPpvZs2ezevVqAG666SY+8pGPsGTJEmbPns1Pf/rTruUcaL6nnHIKH/3oR5k3bx7btm3j+uuvp7q6mrlz53ZNd/vtt7Njxw6WLl3K0qVLASguLu6a90MPPcTKlSsBWLlyJZ/85Cd529vexg033MCWLVu46KKLWLx4Me985zvZtGnTsGy3wX4K5R3Oue1mNhH4k5n1WrpzzpmZG+iNzrk7gTsBqqurB5xGRI4dN//uVTbs2Des8zxtSinfeO/cA75+yy23sH79etatW8cTTzzBQw89xOrVq3HOsWLFCv785z8zffp0Nm/ezIMPPshdd93FmWeeya9+9Suee+45Vq1axT/90z/xyCOPAPDKK6/w17/+ldbWVhYuXMh73vMe1q9fT01NzYDzramp4d577+Wss84C4Dvf+Q7l5eVks1mWLVvGK6+8wmc/+1m+973v8cwzzzBhwoRDrnNdXR3PP/880WiUZcuW8ZOf/ITZs2fzwgsv8KlPfYqnn376iLfroALcObc9eKw3s4eBtwK7zGyyc26nmU0G6o+4NCJy3HviiSd44oknWLhwIQAtLS3U1NQwffp0Zs6cyemnnw7A3LlzWbZsGWbG6aefTm1tbdc8LrnkEgoKCigoKGDp0qWsXr2a55577oDznTFjRld4AzzwwAPceeedZDIZdu7cyYYNGzjjjDOGtB6XX3450WiUlpYWnn/+eS6//PKu11Kp1OFunl4OGeBmVgREnHP7g+cXAN8EVgFXA7cEj48OS4lEZFQdrKV8NDjn+OpXv8onPvGJXuNra2vJz8/vGo5EIl3DkUikV19z309zmNlB51tUVNQ1/Prrr3Pbbbfx4osvMn78eFauXHnAj/b1XE7faTrnmcvlGDduHOvWrTvUqg/ZYPrAJwHPmdnLwGrg9865P+CD+3wzqwHOC4ZFRIaspKSE/fv3A3DhhRdy11130dLifwd3+/bt1NcP7QL/0UcfJZlM0tjYyLPPPsuZZ5456Pnu27ePoqIiysrK2LVrF48//viA5QSYNGkSGzduJJfL8fDDDw9YltLSUmbOnMmDDz4I+BPUyy+/PKT1OZBDtsCdc38H5g8wvhFYNiylEJHjWkVFBWeffTbz5s3j4osv5sorr2TJkiWAv1H4b//2b0Sj0UHP74wzzmDp0qXs3r2br33ta0yZMoUpU6awcePGQ853/vz5LFy4kDlz5nDiiSdy9tlnd7123XXXcdFFFzFlyhSeeeYZbrnlFpYvX05lZSXV1dVdJ4e+fvnLX3L99dfz7W9/m3Q6zRVXXMH8+f1idcjMuaN3X7G6utqtWbPmqC1PRAZn48aNnHrqqaNdjGFx0003UVxczJe+9KXRLsphGaguzGxtj//B6aJ/pRcRCSl9mZWIjCk33XTTaBfhqFELXEQkpBTgIiIhpQAXEQkpBbiISEgpwEXkmFBbW8u8efOGfb5f//rXefLJJ/uNf/bZZ1m+fDkAq1at4pZb/P8iPvLII2zYsGHYyzES9CkUERnTvvnNbx5ymhUrVrBixQrAB/jy5cs57bTTRrpoR0wtcBE5ZmSzWT7+8Y8zd+5cLrjgAtrb2znnnHPo/AfA3bt3U1VVBcA999zDpZdeyvnnn09VVRU/+tGP+N73vsfChQs566yz2LNnD+C/2vWhhx4C4A9/+ANz5sxh0aJF/Pa3v+1a7j333MOnP/1pnn/+eVatWsWXv/xlFixYwJYtW1i0aFHXdDU1Nb2GR5ta4CLS2+M3wpt/G955nnA6XHzor0uqqanhvvvu46c//Skf+MAH+M1vfnPQ6devX89LL71EMplk1qxZfPe73+Wll17iC1/4Ar/4xS/4/Oc/3zVtMpnk4x//OE8//TSzZs3igx/8YL/5vf3tb2fFihUsX76cyy67DICysjLWrVvHggULuPvuu7nmmmuGtu4jSC1wETlmzJw5kwULFgCwePHiXl8RO5ClS5dSUlJCZWUlZWVlvPe97wXo9/WyAJs2bWLmzJnMnj0bM+PDH/7woMr0sY99jLvvvptsNsv999/PlVdeOdTVGjFqgYtIb4NoKY+Unl8XG41GaW9vJxaLdf3MWd+vbB3s18seife///3cfPPNnHvuuSxevJiKiophme9wUAtcRI5pVVVVrF27FqCrL/twzJkzh9raWrZs2QLAfffdN+B0fb8yNpFIcOGFF3L99dcfU90noAAXkWPcl770Je644w4WLlzI7t27D3s+iUSCO++8k/e85z0sWrSIiRMnDjjdFVdcwa233srChQu7wv6qq64iEolwwQUXHPbyR4K+TlZExtTXyY6E2267jebmZr71rW+N+LKG8nWy6gMXETmI973vfWzZsmVYfoR4uCnARUQO4kA/lXYsUB+4iEhIKcBFBPA/tiuja6h1oAAXERKJBI2NjQrxUeSco7GxkUQiMej3qA9cRJg2bRp1dXU0NDSMdlGOa4lEgmnTpg16egW4iBCPx5k5c+ZoF0OGSF0oIiIhpQAXEQkpBbiISEgpwEVEQkoBLiISUgpwEZGQUoCLiISUAlxEJKQU4CIiIaUAFxEJqUEHuJlFzewlM3ssGJ5pZi+Y2WYzu9/M8kaumCIi0tdQWuCfAzb2GP4u8H3n3CxgL3DtcBZMREQOblABbmbTgPcAPwuGDTgX6PyJ6HuBS0egfCIicgCDbYH/ALgByAXDFUCTcy4TDNcBUwd6o5ldZ2ZrzGyNvqpSRGT4HDLAzWw5UO+cW3s4C3DO3emcq3bOVVdWVh7OLEREZACD+T7ws4EVZvZuIAGUAj8ExplZLGiFTwO2j1wxRUSkr0O2wJ1zX3XOTXPOVQFXAE87564CngEuCya7Gnh0xEopIiL9HMnnwL8C/E8z24zvE//58BRJREQGY0g/qeacexZ4Nnj+d+Ctw18kEREZDP0npohISCnARURCSgEuIhJSCnARkZBSgIuIhJQCXEQkpBTgIiIhpQAXEQkpBbiISEgpwEVEQkoBLiISUgpwEZGQUoCLiISUAlxEJKQU4CIiIaUAFxEJKQW4iEhIKcBFREJKAS4iElIKcBGRkFKAi4iElAJcRCSkFOAiIiGlABcRCSkFuIhISCnARURCSgEuIhJSCnARkZBSgIuIhJQCXEQkpBTgIiIhpQAXEQmpQwa4mSXMbLWZvWxmr5rZzcH4mWb2gpltNrP7zSxv5IsrIiKdBtMCTwHnOufmAwuAi8zsLOC7wPedc7OAvcC1I1ZKERHp55AB7ryWYDAe/DngXOChYPy9wKUjUUARERnYoPrAzSxqZuuAeuBPwBagyTmXCSapA6Ye4L3XmdkaM1vT0NAwDEUWEREYZIA757LOuQXANOCtwJzBLsA5d6dzrto5V11ZWXl4pRQRkX6G9CkU51wT8AywBBhnZrHgpWnA9uEtmoiIHMxgPoVSaWbjgucFwPnARnyQXxZMdjXw6AiVUUREBhA79CRMBu41syg+8B9wzj1mZhuAX5vZt4GXgJ+PYDlFRKSPQwa4c+4VYOEA4/+O7w8XEZFRoP/EFBEJKQW4iEhIKcBFREJKAS4iElIKcBGRkFKAi4iElAJcRCSkFOAiIiGlABcRCSkFuIhISI2JAK/d3cov/lI72sUQETmqBvNlVqPunv98nZZUhk+fO3vA16/86V/Z0Zxk+9525p84jpxzLD9jylEupYjI0RWKAF9du4dNO/cPGOAvvbGXHc1JAP7lz3/vGn/Tqg08++VzKM4f/Com01liEcMBzkFebExcoIjIGBWKAD9xfCFPbqgnl3NEItY1/p+f2cytf3yNiSX53PHhxbz/jucByItG2N2S4rY/vsZNK+YedN65nOOJDbt4cuMufvfyDjqyOZyDkvwY15xdxcIZ40l2ZFnylgryYhFueXwTr+9upaqiiM+cO4ui/BiZrKO2sZXSgjgzJxSN6LYYio5Mjv3JNK2pLC2pDC2pDEX5UcYV5lGUFyXnuqd1rnsgGjGS6RwliRhtHVnq9ycpiEeJmJHJ5dja2IYZmBmvN7SSF4tQvz9FLGKkMllyDiYU51OaiFGSiFOaiNGRzXVtq7xYhL2tHSTiURpbU5gZ2VyObXva2b63nWjUSKVztKczXeWYP20ck0oTtHVkaG5P09SWZkJxHuXF+V1lT6Vz7EumaUn5X/pLxKMkYhEyOce+ZIZsLgeAYViwGxnQOdC5Z5n5abqf9xhv3ftf9zyMkkSMaMRo68gSMSjKjxExo70jgwPaO7Jsb2qnJZUhFjFaO7IkYlHKCuJ0ZLOk0jnKi/KIRS2oD6jfn6KpLU1xfpSCvCiliTjji/Iozo+RF4tQEI9SEI8SixrprKMk4ZeZc450Nkc257d1XjRCLBohakZ+PIJzkM05cs7hHOScIxoxsjlH1jnyopGu+exLpsnl/DRdf72GIZNzRMwfd8VBGaIRY3xhHOf8D+jGoxFiESMejRCPGrHOx0iEbM7RkcmRCrbDvmSalmSGVCZH1jmcc2RzvsxmEDGjI5Pr2m4OiAaVkcrmSKWzZHLOr0/PP9f9PJXJ0t6RI5nO0pHt3C+6697XdbAfmD+WmtvSXdsqYkYk4l9PprMAZJ3ryqhYxCjM8/tEPGok4lFuvHgOk8sKDvt4Hkg4Ary8kI5sjvr9KU4oS3SNv/WPrwHwjlkTWDxjPJ85dxZbG9u4/UML+fgv1vCLv9Ty6xffIJnOMf/EcUwsyWf5GZN5eVszu1tSADy3eTd7WjtIxCNcNO8EkuksFcX5rN/ezO1Pb+5aVucODjCjopDnNu/mX/+6tV9Z55xQQmVJPgunj+ddJ0/gxPGFmBkFedFDXg2kMlma29I0tnawp7WDml37qalv6eoOakllmDWxmHjUyGQd25va2d2S4t9fa6C1I0Nbh9+R0tkcbzYn2dGcpCOTO4ItP3TRiI++TM+zwxBUluSTyzkScR9a8WiE9dtTPLpuxxGXrfvqypet80rraCkriFOcHyOTy1GUF6M9naW5PU00YhTmRdnblibXY7uVF+VRXpRHa4c/kTW3p496fY4VEfP7ZjRiRM0Hauc+FuvRKPQnHNd14nHO4fAnp3GFcWIW6To5pLP+JJYIGjedJ5fOE2hTWwc554/H9nSWTHb4d7bQBDjAG3vayORyTBtfyN7Wjq7XP7PMd6188YJTusZ959J5JNNZ/qNmNwAvb2sC4E8bdvWadzRifOuSuXzwzOn9uky27Wmjpn4/uRz8+381EI0YF887gbedVMGGHft4fP1Otje1UxCPcurkUprb0/z174007E9x+1M13P5UTde88mIR5k0ppak9TdSM8qI8CvOizKgoIptz1O9P8vyWRvYnM/T1yxfeOOj2KcqLMmVcARa0/GIRo2pCEeecMpGTKosozItRlBelOBGjNZWhqS1NW0eWaI8dF7pblMl0lng0QltHlpJEjNJEHIdveZnBjIoiMtkckYgxo7yQnINxhXGiQcvDOdifyrCvPU1ze5p9yTSZrCOZzlKYFyOZzlJenEc641udqUyOgrwoU8cVkIhHB1zHHU3t7E9mKIj7VmthfpQ9rR3sbkkFy42QH4tQGoQk+BNiMp0jFjWK82K9rt4OxAWtUuCAYd95gHe/B/Yl0zgHiaCF25LKkHOOwrwYZr7+SxPxQy7/UGVr7cjSlvKt02Q6S3s6SzqbIx6NsD+ZwbnusIpFjY6MI5PLBS1yv00iZkSCsOlsSaazjljEiESMdCZHzjnMjNJEPAg+f/URMR+AnWEVidBV56mMbz0TtMqb2tNdVy++DD7YMp2POUcmmyMaifgrhVhQh4kYxflxEvEIkUj3MiORIGCD7s384D2dV4YA+bEoeTHfuu8M62jEel05jSXmjmITpLq62q1Zs2bI73t9dytLb3uWK848kV+/uI1vXTLXh+TTm3n8c+/k1MmlB33/5voWwPGrF7axuraRT50zi/KiPE4oTVBW4C9Lh9vGnftYs3Uve1o62JdM05rKsHbrXgrzY6TSWfa0dtCRzZHNOvanMkwpS/C2kyo4dXIJhXkxKorymF5RyMSSBC9vayI/HiERj/JGYxtt6Sz50QjTyguoKMpnenkhBXkDB5+IhJ+ZrXXOVfcbH4YAT2WyzPnaH/pd7k4vL+TPNywdptKNnmQ6e8CWp4jIgQI8FB+zyI9FB+yrvOGiU/qPDCGFt4gcjlAEOPh+vZ6WnlKpz3qLyHEtFDcxAR77zDv5/d928LF3nMSLtXs4s6p8tIskIjKqQhPgp00p5bQp/mblBXNPGOXSiIiMvtB0oYiISG8KcBGRkFKAi4iElAJcRCSkFOAiIiGlABcRCSkFuIhISCnARURC6qh+mZWZNQD9v0R7cCYAu4exOGGgdT4+aJ2PD0eyzjOcc5V9Rx7VAD8SZrZmoG/jGsu0zscHrfPxYSTWWV0oIiIhpQAXEQmpMAX4naNdgFGgdT4+aJ2PD8O+zqHpAxcRkd7C1AIXEZEeFOAiIiEVigA3s4vM7DUz22xmN452eYaDmZ1oZs+Y2QYze9XMPheMLzezP5lZTfA4PhhvZnZ7sA1eMbNFo7sGh8/Momb2kpk9FgzPNLMXgnW738zygvH5wfDm4PWqUS34YTKzcWb2kJltMrONZrZkrNezmX0h2K/Xm9l9ZpYYa/VsZneZWb2Zre8xbsj1amZXB9PXmNnVQynDMR/gZhYF/hm4GDgN+JCZnTa6pRoWGeCLzrnTgLOA/xGs143AU8652cBTwTD49Z8d/F0H3HH0izxsPgds7DH8XeD7zrlZwF7g2mD8tcDeYPz3g+nC6IfAH5xzc4D5+HUfs/VsZlOBzwLVzrl5QBS4grFXz/cAF/UZN6R6NbNy4BvA24C3At/oDP1Bcc4d03/AEuCPPYa/Cnx1tMs1Auv5KHA+8BowORg3GXgteP4vwId6TN81XZj+gGnBjn0u8Bhg+P9Oi/Wtb+CPwJLgeSyYzkZ7HYa4vmXA633LPZbrGZgKbAPKg3p7DLhwLNYzUAWsP9x6BT4E/EuP8b2mO9TfMd8Cp3tn6FQXjBszgkvGhcALwCTn3M7gpTeBScHzsbIdfgDcAOSC4QqgyTmXCYZ7rlfXOgevNwfTh8lMoAG4O+g2+pmZFTGG69k5tx24DXgD2Imvt7WM7XruNNR6PaL6DkOAj2lmVgz8Bvi8c25fz9ecPyWPmc95mtlyoN45t3a0y3IUxYBFwB3OuYVAK92X1cCYrOfxwCX4k9cUoIj+XQ1j3tGo1zAE+HbgxB7D04JxoWdmcXx4/9I599tg9C4zmxy8PhmoD8aPhe1wNrDCzGqBX+O7UX4IjDOzWDBNz/XqWufg9TKg8WgWeBjUAXXOuReC4YfwgT6W6/k84HXnXINzLg38Fl/3Y7meOw21Xo+ovsMQ4C8Cs4M72Hn4myGrRrlMR8zMDPg5sNE5970eL60COu9EX43vG+8c/9HgbvZZQHOPS7VQcM591Tk3zTlXha/Hp51zVwHPAJcFk/Vd585tcVkwfahaqs65N4FtZnZKMGoZsIExXM/4rpOzzKww2M8713nM1nMPQ63XPwIXmNn44MrlgmDc4Iz2TYBB3ih4N/BfwBbgH0e7PMO0Tu/AX169AqwL/t6N7/t7CqgBngTKg+kN/2mcLcDf8Hf4R309jmD9zwEeC56fBKwGNgMPAvnB+EQwvDl4/aTRLvdhrusCYE1Q148A48d6PQM3A5uA9cC/AvljrZ6B+/B9/Gn8lda1h1OvwH8P1n0zcM1QyqB/pRcRCakwdKGIiMgAFOAiIiGlABcRCSkFuIhISCnARURCSgEuIhJSCnARkZD6/yrrTxhnAGLNAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -304,129 +305,46 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[25.17385185 69.66055355]\n",
+      "[0.00498503 0.004995  ]\n"
+     ]
     }
    ],
-   "source": [
-    "plt.plot(data, 'o', alpha=0.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [],
    "source": [
     "# Question 3\n",
     "# Variance of 5 degrees in temperature, 10 in humidity\n",
     "# Mean of 25 for temperature, 70 for humidity\n",
-    "data = np.random.randn(1000, 2) * [5, 10] + [25, 70]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "original_estimate = np.array([30, 60])\n",
-    "estimated_measurement_error = np.array([5, 5])\n",
-    "original_estimated_error = np.array([2, 10])\n",
-    "weather_filter = KalmanFilter(original_estimate=original_estimate,\n",
-    "                              estimated_measurement_error=estimated_measurement_error,\n",
-    "                              original_estimated_error=original_estimated_error)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "data = np.random.randn(1000, 2) * [5, 10] + [25, 70]\n",
+    "print(data)\n",
+    "\n",
+    "# pass variables through original function\n",
+    "# note that as long as the shape of the inputs are the same, we can re-use the function\n",
+    "# in the same way as all operations are vectorized\n",
+    "weather_filter = KalmanFilter(original_estimate = np.array([30, 60]),\n",
+    "                              estimated_measurement_error = np.array([5, 5]),\n",
+    "                              original_estimated_error = np.array([2, 10]))\n",
+    "\n",
+    "# fit data\n",
     "for row in data:\n",
-    "    weather_filter.update(row)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([25.13008319, 70.22646745])"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weather_filter.current_estimate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "estimates = np.array(weather_filter.estimates)  # Convert to array so dimensional indexing works"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1a43cf68348>"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(estimates[:,0], 'r-', label=\"temperature\")\n",
-    "plt.plot(estimates[:,1], 'g-', label=\"humidity\")\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([0.00498503, 0.004995  ])"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weather_filter.current_error"
+    "    weather_filter.update(row)\n",
+    "\n",
+    "# store\n",
+    "overall_estimates = np.array(weather_filter.estimates)\n",
+    "\n",
+    "# plot\n",
+    "plt.plot(overall_estimates[:,0], label = \"temperature\")\n",
+    "plt.plot(overall_estimates[:,1], label = \"humidity\")\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# print outputs\n",
+    "print(weather_filter.current_estimate)\n",
+    "print(weather_filter.current_error)"
    ]
   },
   {
@@ -505,6 +423,57 @@
     "Compute this both by hand (the number of computations is reasonably small but non-trivial) and also using NumPy, and compare the results."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[100   0]\n",
+      "[-122.5  -49. ]\n",
+      "[-22.5 -49. ]\n",
+      "predicted prediction is -22.500000000000014 metres\n"
+     ]
+    }
+   ],
+   "source": [
+    "# assume 1 dimensional problem, only 1 x and 1 v is needed\n",
+    "# assume ground is 0\n",
+    "# X(t) = A X(t-1) + B u(t) (ignore w(t))\n",
+    "# where\n",
+    "#   x(t) = [x, v]\n",
+    "#   A is 2 x 2\n",
+    "#   u(t) = [-9.8]\n",
+    "#   B is 2 x 1\n",
+    "\n",
+    "# qs 1\n",
+    "# x(t - 1) = [100, 0] # starting from 100, with speed of 0\n",
+    "# A = [1       delta_t\n",
+    "#      0            1]\n",
+    "# B = [0.5 delta_t^2\n",
+    "#            delta_t]\n",
+    "# u(t) = [-9.8]\n",
+    "\n",
+    "# qs 2\n",
+    "t = 5\n",
+    "x0 = np.array([100, 0])\n",
+    "A = np.array([[1, t],\n",
+    "              [0, 1]])\n",
+    "B = np.array([[0.5 * t**2],\n",
+    "              [t]])\n",
+    "u = np.array([-9.8])\n",
+    "\n",
+    "x = np.matmul(A, x0) + np.matmul(B, u)\n",
+    "print(np.matmul(A, x0))\n",
+    "print(np.matmul(B, u))\n",
+    "print(x)\n",
+    "\n",
+    "print(\"predicted prediction is \" + str(x[0]) + \" metres\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -546,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -555,13 +524,13 @@
        "(-0.1, 0.1)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -591,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,22 +571,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7feec28d8898>"
+       "<AxesSubplot:xlabel='Date'>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -641,7 +610,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -655,16 +624,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(6.968591685738987e-06, 0.11821204178195427)"
+       "(6.968591685742221e-06, 0.11821204178195706)"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -675,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -684,22 +653,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7feec2053be0>]"
+       "[<matplotlib.lines.Line2D at 0x1ea58c95990>]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1152x288 with 1 Axes>"
       ]
@@ -725,22 +694,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7feec1fddda0>]"
+       "[<matplotlib.lines.Line2D at 0x1ea58c11ff0>]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 1152x288 with 1 Axes>"
       ]
@@ -767,11 +736,82 @@
     "\n",
     "For an added challenge, ensure the function does not alter the internal state of the Kalman Filter instance. That is, you should be able to run your `predict` function twice and get the same result."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Date\n",
+      "2018-03-18       NaN\n",
+      "2018-03-19    157.35\n",
+      "2018-03-20    156.20\n",
+      "2018-03-21    156.69\n",
+      "2018-03-22    152.09\n",
+      "2018-03-23    148.89\n",
+      "2018-03-24       NaN\n",
+      "2018-03-25       NaN\n",
+      "2018-03-26    153.37\n",
+      "Freq: D, Name: Adj. Close, dtype: float64\n",
+      "[151.91, 151.91000000123967, 151.91000000247934, 151.910000003719, 151.91000000495868, 151.91000000619835, 151.91000000743801, 151.91000000867768, 151.91000000991735, 151.91000001115702, 151.9100000123967]\n"
+     ]
+    }
+   ],
+   "source": [
+    "class KalmanFilter:\n",
+    "    \n",
+    "    def __init__(self, original_estimate, estimated_measurement_error, original_estimated_error=0):\n",
+    "        self.current_estimate = original_estimate\n",
+    "        self.estimated_measurement_error = estimated_measurement_error\n",
+    "        self.current_error = original_estimated_error\n",
+    "        self.estimates = []\n",
+    "        \n",
+    "    def update(self, new_data):\n",
+    "        kalman_gain = 1e-6 + self.current_error / (1e-6 + self.current_error + self.estimated_measurement_error)\n",
+    "        self.current_estimate = self.current_estimate + kalman_gain * (new_data - self.current_estimate)\n",
+    "        self.current_error = (1 - kalman_gain) * self.current_error \n",
+    "        self.estimates.append(self.current_estimate)\n",
+    "        return self.current_estimate\n",
+    "\n",
+    "    # not exactly sure how to predict with KF\n",
+    "    def predict(self, n, x_0):\n",
+    "        \n",
+    "        pred = [x_0]\n",
+    "        x_t = x_0 # starting is current estimate\n",
+    "        A = 1\n",
+    "        B = self.current_estimate\n",
+    "        u = self.current_error # add error \n",
+    "        w = 0\n",
+    "\n",
+    "        for i in range(n):\n",
+    "            \n",
+    "            # compute transition steps\n",
+    "            x_t = A * x_t + B * u + w\n",
+    "\n",
+    "            pred.append(x_t)\n",
+    "            \n",
+    "        return pred\n",
+    "\n",
+    "# use kf filter\n",
+    "kf = KalmanFilter(original_estimate=1,\n",
+    "                  original_estimated_error=1,\n",
+    "                  estimated_measurement_error=std)\n",
+    "smoothed = pd.Series([kf.update(row) for row in movement], index = movement.index)\n",
+    "\n",
+    "print(close.iloc[-10:-1])\n",
+    "print(kf.predict(10, close.iloc[-1]))\n",
+    "\n",
+    "# not exactly sure how to use kalman filter as predict, what is the transition matrix here? skipping for now ..."
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -785,7 +825,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3 (tags/v3.10.3:a342a49, Mar 16 2022, 13:07:40) [MSC v.1929 64 bit (AMD64)]"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 2.1.1 - Hypothesis Testing.ipynb b/notebooks/Module 2.1.1 - Hypothesis Testing.ipynb
index b892d08..87e2bb1 100644
--- a/notebooks/Module 2.1.1 - Hypothesis Testing.ipynb	
+++ b/notebooks/Module 2.1.1 - Hypothesis Testing.ipynb	
@@ -82,6 +82,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -93,8 +94,14 @@
     "- Ten experiments\n",
     "- Twenty experiments\n",
     "\n",
-    "The above results can be computed with a calculator (or by hand if you are fine with leaving a fraction).\n",
-    "\n",
+    "The above results can be computed with a calculator (or by hand if you are fine with leaving a fraction)."
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "#### Extended exercise\n",
     "\n",
     "Write a program with a function that does the following:\n",
@@ -136,22 +143,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -160,6 +157,16 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of tests with mean > 0.166 for n = 1000:\n",
+      "0\n",
+      "Number of tests with mean > 0.166 for n = 100:\n",
+      "500\n"
+     ]
     }
    ],
    "source": [
@@ -167,20 +174,26 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "def rand_sample_mean(n):\n",
-    "    samp = np.random.randn(n)\n",
-    "    return np.mean(samp)\n",
-    "\n",
     "mean_list = []\n",
-    "for i in range(0,10000):\n",
-    "    mean_list.append(rand_sample_mean(1000))\n",
+    "for i in range(10000):\n",
+    "    mean_list.append(np.mean(np.random.randn(1000)))\n",
     "    \n",
-    "\n",
     "plt.hist(mean_list)\n",
+    "plt.show()\n",
     "\n",
-    "(np.array(mean_list) > 0.166).sum()\n",
+    "print(\"Number of tests with mean > 0.166 for n = 1000:\")\n",
+    "print(np.sum(np.array(mean_list) > 0.166))\n",
     "\n",
-    "#None, but if we reduce the sample size we get more"
+    "#None, but if we reduce the sample size we get more\n",
+    "# if we reduce the number of samples each test to 100, we get more\n",
+    "# the more values we generate, the closer to the mean = 0 we get\n",
+    "\n",
+    "mean_list = []\n",
+    "for i in range(10000):\n",
+    "    mean_list.append(np.mean(np.random.randn(100)))\n",
+    "\n",
+    "print(\"Number of tests with mean > 0.166 for n = 100:\")\n",
+    "print(np.sum(np.array(mean_list) > 0.166))\n"
    ]
   },
   {
@@ -209,23 +222,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 16,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function RandomState.randint>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "np.random.randint?"
+    "np.random.randint"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "359\n"
+      "347\n"
      ]
     }
    ],
@@ -236,7 +260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -245,12 +269,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -281,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -290,7 +314,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -307,6 +331,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -320,24 +345,68 @@
     "4. Review the documentation for all of the `scipy.stats.ttest_???` functions and identify when each would be needed.\n",
     "\n",
     "\n",
-    "Note there are also T tests in the `statsmodels` package, which can be called in a similar way.\n",
+    "Note there are also T tests in the `statsmodels` package, which can be called in a similar way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# qs 1\n",
+    "# - if we reject h0 when our p-value is significant, then it means that there is a 5% chance that that this conclusion was reached randomly\n",
     "\n",
+    "# qs 2\n",
+    "# - from the output above, we see that p = 0.0008 (0.08%) meaning that is the likelihood this was obtained by chance, so we are very confident in our conclusion\n",
     "\n",
-    "#### Extended Exercise\n",
+    "# qs 3\n",
+    "# - from the results above, we cannot draw that conclusion as we are not testing if 6 occurs more, we are testing if the mean of the dice rolls are 3.5 or greater. \n",
+    "# - our results show that we can be confident that the expected value is more than 3.5, but it does not tell us exactly why that is occuring\n",
     "\n",
-    "Create an alternative hypothesis and experiment to address question 3 above. How can we test if a dice rolls 6s more frequently?"
+    "# qs 4\n",
+    "# - ttest_1samp: for means of 1 group of values\n",
+    "# - ttest_ind:   for means of 2 independent sets of values\n",
+    "# - ttest_ind_from_stats: for means of 2 independent sets of values from descriptive statistics\n",
+    "# - ttest_rel: t-test for 2 non-independent sets of values (rel = related)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "#### Extended Exercise\n",
+    "\n",
+    "Create an alternative hypothesis and experiment to address question 3 above. How can we test if a dice rolls 6s more frequently?\n",
+    "\n",
     "1. Reject the null hypothesis, the dice is biased or more specifically the mean is significantly different to 350.\n",
     "2. 0.08%\n",
     "3. Not necessarily rolls more 6's\n",
     "4. One sample t test used above"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# qs 1\n",
+    "# H0 is p(dice = 6) = 1/6\n",
+    "# H1 is p(dice = 6) > 1/6\n",
+    "\n",
+    "# qs 2\n",
+    "# simulate 1000 rolls, and record number of 6s\n",
+    "# repeat this experiment 10000 times as above\n",
+    "\n",
+    "# qs 3\n",
+    "# repeat experimetn with biased dice \n",
+    "\n",
+    "# qs 4\n",
+    "# perform t-test with ttest_1samp as answered above"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -415,6 +484,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -445,8 +515,80 @@
     "#### Exercises\n",
     "\n",
     "1. Download the USD/AUD prices from Quandl\n",
-    "2. Identify whether each change is \"up\" or \"down\" and compute the sample proportion value (it was 0.6 in the artificial data above)\n",
-    "\n",
+    "2. Identify whether each change is \"up\" or \"down\" and compute the sample proportion value (it was 0.6 in the artificial data above)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "             Value\n",
+      "Date              \n",
+      "1983-12-12  0.9175\n",
+      "1983-12-13  0.9010\n",
+      "1983-12-14  0.9000\n",
+      "1983-12-15  0.8978\n",
+      "1983-12-16  0.8928\n",
+      "Proportion of down days\n",
+      "48.101666315123396\n"
+     ]
+    }
+   ],
+   "source": [
+    "import quandl\n",
+    "import my_secrets\n",
+    "quandl.ApiConfig.api_key = my_secrets.QUANDL_API_KEY\n",
+    "\n",
+    "# qs1 \n",
+    "usdaud = quandl.get(\"RBA/FXRUSD-AUD-USD-Exchange-Rate\")\n",
+    "usdaud = usdaud.dropna()\n",
+    "print(usdaud.head())\n",
+    "\n",
+    "# qs2\n",
+    "# negative means down, positive means up\n",
+    "movement = usdaud.diff()\n",
+    "\n",
+    "prob = np.sum(movement[\"Value\"] < 0)/movement.shape[0]*100\n",
+    "print(\"Proportion of down days\")\n",
+    "print(prob)\n",
+    "# shows ~50%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.48459915611814347"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "xdelta = usdaud.diff().dropna()\n",
+    "xdelta['lag'] = xdelta['Value'].shift(1)\n",
+    "xdelta = xdelta.dropna()\n",
+    "xdelta['matched_movement'] = xdelta['Value']*xdelta['lag'] > 0 # compare values to lagged values\n",
+    "samp_prop = np.sum(xdelta['matched_movement'])/xdelta.shape[0] # get proportion of movements that match the previous movement\n",
+    "samp_prop"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "#### Extended Exercise\n",
     "\n",
     "1. Create and run the simulation mentioned above, where we simulate a random walk scenario and compute the proportion of times a change corresponds with the previous change.\n",
@@ -456,19 +598,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "t stat: 13.7602, p value: 0.000831\n"
+      "Ttest_1sampResult(statistic=0.8307602931056941, pvalue=0.4081081202748419)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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UF90Xcxfk7mLuguMq4CvAJZPuaUS9vQX4WLf8VOBe4HmT7mnQvoB1PcuXAXd0y2czd+1ndfd6ADh70j2NoK/TmTupeNOk+xhlXydscxWNXVBdFmfudZLHFSR5BzBTVSvyuTTD9FVVP05yA3PP6Snm/vm4bP7s1JC/sw8BH0uyHwhzQb9v/FX3N2Bfb+j+BfIY8AhzwUBVPZLkncz9zgDeUcvkWsIwfQGXA38MPD3J8bGrquruJWxhXkP21TQfPyBJDVoRF1QlSQtjuEtSgwx3SWqQ4S5JDTLcJalBhrskNchwl6QG/Q+vplequbdx6QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ttest_1sampResult(statistic=0.09210885896408241, pvalue=0.9266300165861547)\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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deVj9eOADwN8MuslxzLhventwleSuJPcn+avBtvoCs+n7K8B/09vj/A/gI1V1cIC99puy7847u0MhX0ly6MuE0112EGbT928kWUtvz/XHg2nzRWbcd5KXAB8Fhnq4tTObn/frgf9K8rUk/5rk79K70eRR41gJ/Ul1v0C3ANeOM/t64GNV9cxQm5qGKfpeDLwN+LPu+eIkZw+xvQlN0fda4Hl6hxpOA65N8tohtjeVf6J3uOmN9Pbmt8xzP9M1ad9JTgG+AFxZVf83D/1NZKK+/wLYVlV7562zyU3U92LgD+l9WP0+vUNEV8xHgxM5VkJ/qts7nAD8HvC9JHuAs4Ct3cnFM4G/7erXAB/qvkA2DLPpey9wd1X9tPvv4zbgjKF0Pbu+3wV8q6r+t6oOAP8MDOv+JVPeBqQ7TPar7uWngbdMd9kBmk3fJHklcCfw11V1z4B77Tebvv8AuLr7/fkIcHmSgf3tjcPMpu+9wM7u0NBzwD8yvH+X0zPfJxXm4kHv0/UxenuOh068vGGS8d/jsBO5Xf16hnsid8Z90zuZeD+9k6GLge8AFyyAvj8AfLabfgW922q/8WjpGzilb/pi4J5u+iR6532WdI/HgZMWQN/HATuAa4bR61z1fdiYKxjuidzZ/LwXdeOXdq8/C1w17J/9ZI+j7i6bM1ET3N4hyQ3ASFUdlff3mU3fVfV0klvo3c+o6P1XeCg3/p7lz/tW4LNJdgOh9wGwa/BdT7vvv0zyJ8BzwEG6/5pX1cEkN9L7eQPcUEM6FzGbvoFLgD8CXpXkUO2Kqtp5lPc9b2b5e/J8epd970gS4D7gU/PxPibibRgkqSHHyjF9SdI0GPqS1BBDX5IaYuhLUkMMfUlqiKEvSQ0x9CWpIf8PB4MVcIAUn88AAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -477,43 +650,65 @@
       "needs_background": "light"
      },
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ttest_1sampResult(statistic=0.07442680822316887, pvalue=0.9406722669022444)\n"
+     ]
     }
    ],
    "source": [
-    "#from solutions\n",
-    "#1 \n",
-    "import quandl\n",
-    "xrate = quandl.get(\"RBA/FXRUSD-AUD-USD-Exchange-Rate\")\n",
+    "probs = []\n",
     "\n",
-    "#2\n",
-    "xdelta = xrate.diff().dropna()\n",
-    "xdelta['lag'] = xdelta['Value'].shift(1)\n",
-    "xdelta = xdelta.dropna()\n",
-    "xdelta['matched_movement'] = xdelta['Value']*xdelta['lag'] > 0 # compare values to lagged values\n",
-    "samp_prop = np.sum(xdelta['matched_movement'])/xdelta.shape[0] # get proportion of movements that match the previous movement\n",
-    "samp_prop\n",
+    "for i in range(100):\n",
+    "    \n",
+    "    fx = stats.norm(0, 1).rvs(250) # 250 trading days a year\n",
+    "    movement = fx[1:] - fx[0:-1]\n",
+    "    prob = np.sum(movement < 0)/movement.shape[0]\n",
+    "    probs.append(prob)\n",
     "\n",
+    "plt.hist(probs)\n",
+    "plt.show()\n",
     "\n",
-    "#Extended Exercise\n",
+    "print(stats.ttest_1samp(probs, 0.5))\n",
     "\n",
-    "iters = range(1000) #Repeat our experiment 1000 times.\n",
-    "results = []\n",
-    "distribution = stats.norm(0, 1)\n",
+    "probs = []\n",
     "\n",
-    "for i in iters:\n",
-    "    X = distribution.rvs(365) # create randomly distributed data for a year\n",
-    "    data = pd.DataFrame([X[i] for i in range(len(X))])\n",
-    "    data.columns = ['Value']\n",
-    "    data['lag'] = data['Value'].shift(1)\n",
-    "    data = data.dropna()\n",
-    "    data['matched_movement'] = data['Value']*data['lag'] > 0 # compare values to lagged values\n",
-    "    data_prop = np.sum(data['matched_movement'])/data.shape[0]\n",
-    "    results.append(data_prop)\n",
+    "for i in range(1000):\n",
+    "    \n",
+    "    fx = stats.norm(0, 1).rvs(250) # 250 trading days a year\n",
+    "    movement = fx[1:] - fx[0:-1]\n",
+    "    prob = np.sum(movement < 0)/movement.shape[0]\n",
+    "    probs.append(prob)\n",
     "\n",
-    "plt.hist(results)\n",
+    "plt.hist(probs)\n",
+    "plt.show()\n",
     "\n",
-    "stats.ttest_1samp(results, 0.5)\n",
-    "print('t stat: {:.4f}, p value: {:4f}'.format(t_stat, p_val))"
+    "print(stats.ttest_1samp(probs, 0.5))\n",
+    "\n",
+    "probs = []\n",
+    "\n",
+    "for i in range(10000):\n",
+    "    \n",
+    "    fx = stats.norm(0, 1).rvs(250) # 250 trading days a year\n",
+    "    movement = fx[1:] - fx[0:-1]\n",
+    "    prob = np.sum(movement < 0)/movement.shape[0]\n",
+    "    probs.append(prob)\n",
+    "\n",
+    "plt.hist(probs)\n",
+    "plt.show()\n",
+    "\n",
+    "print(stats.ttest_1samp(probs, 0.5))"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Conclusion: we want to accept the null hypothesis"
    ]
   },
   {
@@ -613,11 +808,83 @@
    "source": [
     "*For solutions, see `solutions/simulation_ttest.py`*"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           5         10        20        50        100\n",
+      "0.01 -4.409879 -3.735847 -3.239313 -3.103881 -3.070684\n",
+      "0.02 -3.830221 -3.025188 -2.897161 -2.822613 -2.475009\n",
+      "0.05 -2.479881 -2.329461 -2.081622 -2.019847 -1.815448\n",
+      "0.10 -1.779322 -1.715669 -1.647659 -1.471962 -1.454650\n",
+      "0.20 -1.206808 -1.145209 -1.048965 -0.940665 -0.943134\n",
+      "0.50 -0.049559 -0.028120 -0.007540  0.050559 -0.013117\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "def run_experiment(degrees):\n",
+    "    \"\"\"Runs a single t-value experiment\"\"\"\n",
+    "    \n",
+    "    # Create random sizes for each dataset\n",
+    "    n1 = np.random.randint(3, degrees + 2 - 1)\n",
+    "    n2 = (degrees + 2) - n1\n",
+    "\n",
+    "    # generate random datasets\n",
+    "    X1 = np.random.randn(n1)\n",
+    "    X2 = np.random.randn(n2)\n",
+    "    \n",
+    "    # compute standard error\n",
+    "    e1 = np.std(X1) / np.sqrt(n1)\n",
+    "    e2 = np.std(X2) / np.sqrt(n2)\n",
+    "    \n",
+    "    # compute standard error of test\n",
+    "    sed = np.sqrt(e1**2 + e2**2)\n",
+    "    \n",
+    "    # compute t-statistic\n",
+    "    t_stat = (np.mean(X1) - np.mean(X2)) / sed\n",
+    "\n",
+    "    return t_stat\n",
+    "\n",
+    "N_EXPERIMENTS = 1000\n",
+    "\n",
+    "results = {}\n",
+    "\n",
+    "for DOF in [5, 10, 20, 50, 100]:\n",
+    "\n",
+    "    # run n experimetns and sort so we can find confidence levels\n",
+    "    values = [run_experiment(DOF) for _ in range(N_EXPERIMENTS)]\n",
+    "    values.sort()  # In place operation\n",
+    "\n",
+    "    # crreate dict to\n",
+    "    results[DOF] = {}\n",
+    "\n",
+    "    # test against different levels of confidence\n",
+    "    for p_value in [0.01, 0.02, 0.05, 0.1, 0.2, 0.5]:\n",
+    "\n",
+    "        # convert p value to number of samples\n",
+    "        n = int(N_EXPERIMENTS * p_value)\n",
+    "\n",
+    "        # store sorted value at the p-value\n",
+    "        results[DOF][p_value] = values[n]\n",
+    "    \n",
+    "tstats = pd.DataFrame(results)\n",
+    "print(tstats)\n"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -631,7 +898,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 2.2.1 - Linear regression models.ipynb b/notebooks/Module 2.2.1 - Linear regression models.ipynb
index 4fcb6f9..7d3a42b 100644
--- a/notebooks/Module 2.2.1 - Linear regression models.ipynb	
+++ b/notebooks/Module 2.2.1 - Linear regression models.ipynb	
@@ -86,11 +86,17 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "1. The ratio should not change because they are both being multiple by a constant ratio\n",
-    "2. Kelvin would be interval data. Nevermind, I was wrong, Kelvin has a clear 0."
+    "# qs 1\n",
+    "# - as long as u are using the same currency for both stocks, the ratio does not change\n",
+    "# - the absolute values though would change\n",
+    "\n",
+    "# qs 2\n",
+    "# - kelvin is a ratio, as u cannot have negative kelvin, hence all reference to it is to 0 K"
    ]
   },
   {
@@ -150,13 +156,22 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {},
+   "outputs": [],
    "source": [
-    "1. Ratio\n",
-    "2. Ratio\n",
-    "3. Ordinal\n",
-    "4. Nominal"
+    "# qs 1\n",
+    "# ratio as u cannot have negative age, so all reference is to 0\n",
+    "\n",
+    "# qs 2\n",
+    "# ratio as u cannot have negative time, so all reference is to 0\n",
+    "\n",
+    "# qs 3\n",
+    "# ordinal, as it can be ordered from worse to best, kind of like a ordered factor in R\n",
+    "\n",
+    "# qs 4\n",
+    "# nominal, categorical data which is exactly like a factor in R"
    ]
   },
   {
@@ -206,6 +221,88 @@
     "1. Load some data that contains ordinal and nominal variables (e.g. Boston house prices). Convert the ordinal and nominal variables to encoded forms using the above scikit-learn classes."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            Under $100_000  $100_000 - $199_999  $200_000 - $299_999  \\\n",
+      "Date                                                                   \n",
+      "1990-08-31            60.0                 23.0                 10.0   \n",
+      "1993-11-30            54.0                 27.0                  7.0   \n",
+      "1993-12-31            53.0                 28.0                  9.0   \n",
+      "1994-01-31            57.0                 21.0                  9.0   \n",
+      "1994-02-28            55.0                 29.0                  7.0   \n",
+      "\n",
+      "            $300_000 - $399_999  $400_000 - $499_999  $500_000 +  \n",
+      "Date                                                              \n",
+      "1990-08-31                  1.0                  0.0         1.0  \n",
+      "1993-11-30                  3.0                  1.0         3.0  \n",
+      "1993-12-31                  2.0                  1.0         1.0  \n",
+      "1994-01-31                  4.0                  0.0         1.0  \n",
+      "1994-02-28                  2.0                  2.0         2.0  \n",
+      "[[40. 12.  4.  0.  0.  0.]\n",
+      " [37. 16.  1.  2.  1.  2.]\n",
+      " [36. 17.  3.  1.  1.  0.]\n",
+      " ...\n",
+      " [ 4.  2. 10. 12. 14. 28.]\n",
+      " [ 2.  3. 11. 12. 14. 28.]\n",
+      " [ 0.  0. 13. 16. 13. 30.]]\n",
+      "[[0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 0. 0. ... 0. 0. 0.]\n",
+      " [0. 0. 0. ... 0. 0. 0.]\n",
+      " ...\n",
+      " [0. 0. 0. ... 1. 0. 0.]\n",
+      " [0. 0. 1. ... 1. 0. 0.]\n",
+      " [1. 0. 0. ... 0. 0. 1.]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# qs 1\n",
+    "# sklearn.preprocessing.OrdinalEncoder\n",
+    "# - The input to this transformer should be an array-like of integers or strings, denoting the values taken on by \n",
+    "#       categorical (discrete) features. The features are converted to ordinal integers. \n",
+    "\n",
+    "# sklearn.preprocessing.OneHotEncoder\n",
+    "# - Encode categorical features as a one-hot numeric array.\n",
+    "# - this is used in deep learning\n",
+    "\n",
+    "# extended exercise\n",
+    "# qs 1\n",
+    "# it convertes it into a ordered categorical variable, but the order may not be in the sequence u want\n",
+    "\n",
+    "# qs 2\n",
+    "# copied from solutions\n",
+    "import quandl\n",
+    "import my_secrets\n",
+    "quandl.ApiConfig.api_key = my_secrets.QUANDL_API_KEY\n",
+    "\n",
+    "# \n",
+    "housing_prices = quandl.get(\"UMICH/SOC22-University-of-Michigan-Consumer-Survey-Current-Market-Value-of-Primary-Residence\")\n",
+    "housing_prices = housing_prices.iloc[:,0:6] #Trim data down to price categories only\n",
+    "print(housing_prices.head())\n",
+    "\n",
+    "from sklearn.preprocessing import OrdinalEncoder, OneHotEncoder\n",
+    "\n",
+    "ord_enc = OrdinalEncoder()\n",
+    "ord_enc.fit(housing_prices)\n",
+    "ord_prices = ord_enc.transform(housing_prices)\n",
+    "print(ord_prices)\n",
+    "\n",
+    "hot_enc = OneHotEncoder(categories='auto')\n",
+    "hot_enc.fit(housing_prices)\n",
+    "hot_enc = hot_enc.transform(housing_prices).toarray()\n",
+    "print(hot_enc)\n",
+    "\n",
+    "# ordinal encoder tells u which category each data point belongs to\n",
+    "# 1-hot encoder arranges it in a sparse matrix such that the value is 1 at the index of the category (where the categories are arranged by index)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -242,6 +339,18 @@
     "nx1"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# qs\n",
+    "# - for each sample there is an error term \n",
+    "# - so if Y is n x 1, then y is also n x 1\n",
+    "# - or it could just be n if 1 column. but if there is more than 1 column, we need to use n x n_cols"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -291,6 +400,19 @@
     "X dimensions is n x k, B dimensions is k x 1, hence needs to be that way."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# qs\n",
+    "# - order is important in matrix operations\n",
+    "# - X - n x k\n",
+    "# - B is k x 1\n",
+    "# - so the cols of x need to match the rows of B for matrix multiplication"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -343,6 +465,26 @@
     "Multiple train test splits, can use smaller test sets depending on sample size, and average the results."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# qs 1\n",
+    "# - cross validation trains and tests your model on all combinations of train-test splits\n",
+    "# - using 1 split would cause some potential biase as you might have picked good or bad subsets\n",
+    "# - running X-validation would ensure your model is trained and tested on the entire dataset\n",
+    "# - for 10 fold X-validation you would test ur model on all combinations of 9:1 splits (train-test)\n",
+    "# - this way you iterate ur model through all data\n",
+    "\n",
+    "# qs 2\n",
+    "# - we want to develop a robust model first using cross-validation for training-validation\n",
+    "# - testing should be done on an independent dataset to further evaluate our model\n",
+    "# - this is why data is usually split into train-val-test sets, where validation set is used for model tuning\n",
+    "# - you never tunr ur model on the test set directly"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -353,7 +495,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -367,7 +509,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 2.2.2 - ARIMA.ipynb b/notebooks/Module 2.2.2 - ARIMA.ipynb
index 800bd0a..609e769 100644
--- a/notebooks/Module 2.2.2 - ARIMA.ipynb	
+++ b/notebooks/Module 2.2.2 - ARIMA.ipynb	
@@ -75,314 +75,173 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "XRP:  {'bic':              0            1            2\n",
-      "0  1413.433184  -682.270955 -2080.468355\n",
-      "1 -5126.140852 -5164.924819 -5164.346025\n",
-      "2 -5159.404378 -5170.639887 -5163.217534\n",
-      "3 -5155.440608 -5163.228253 -5167.248393\n",
-      "4 -5196.124260 -5189.197749 -5188.844919, 'bic_min_order': (4, 0)}\n",
-      "ETH: {'bic':               0             1             2\n",
-      "0  35897.027360  33168.546528  30890.205840\n",
-      "1  23835.428192  23841.651231  23848.916081\n",
-      "2  23841.720708  23833.604176  23839.040963\n",
-      "3  23848.822203  23839.334945  23797.097663\n",
-      "4  23856.031229  23841.249012  23802.560324, 'bic_min_order': (3, 2)}\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
-      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
-      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
+      "(2021,)\n",
+      "(2021,)\n",
+      "Date\n",
+      "2023-01-02    0.34891\n",
+      "2023-01-03    0.34410\n",
+      "2023-01-04    0.34802\n",
+      "2023-01-05    0.33845\n",
+      "2023-01-06    0.34501\n",
+      "Name: Last, dtype: float64\n",
+      "Date\n",
+      "2023-01-02    1215.5\n",
+      "2023-01-03    1215.7\n",
+      "2023-01-04    1256.3\n",
+      "2023-01-05    1251.8\n",
+      "2023-01-06    1271.2\n",
+      "Name: Last, dtype: float64\n"
      ]
     }
    ],
    "source": [
-    "#From solutions\n",
     "%run setup.ipy\n",
-    "\n",
     "import quandl\n",
     "import my_secrets\n",
-    "quandl.ApiConfig.api_key = my_secrets.QUANDL_API_KEY\n",
     "\n",
-    "xrp = quandl.get(\"BITFINEX/XRPUSD\")[\"Last\"]\n",
-    "eth = quandl.get(\"BITFINEX/ETHUSD\")[\"Last\"]\n",
+    "# configure api\n",
+    "quandl.ApiConfig.api_key = my_secrets.QUANDL_API_KEY\n",
     "\n",
-    "# Should we replace the index? Some dates are missing so we can't set a frquency to 'D' without actually\n",
-    "# replacing the entire index? This generates a lot of warnings...\n",
+    "# download data according to instructed exchange\n",
+    "xrp = quandl.get(\"BITFINEX/XRPUSD\")\n",
+    "eth = quandl.get(\"BITFINEX/ETHUSD\")\n",
     "\n",
+    "# get only closing price\n",
+    "xrp = xrp[\"Last\"]\n",
+    "eth = eth[\"Last\"]\n",
     "\n",
-    "# When we've played with values a bit, we can then use the below.\n",
-    "from statsmodels.tsa import stattools\n",
-    "xrp_stats = stattools.arma_order_select_ic(xrp) \n",
-    "eth_stats = stattools.arma_order_select_ic(eth) \n",
-    "print(\"XRP: \", xrp_stats)\n",
-    "print(\"ETH:\", eth_stats)\n",
+    "# match the lengths of data\n",
+    "eth = eth.iloc[-xrp.shape[0]:]\n",
     "\n",
-    "from statsmodels import api as sms\n",
-    "xrp_model = sms.tsa.ARMA(xrp, order=(4, 2))\n",
-    "eth_model = sms.tsa.ARMA(eth, order=(3, 2))\n",
+    "# view\n",
+    "print(xrp.shape)\n",
+    "print(eth.shape)\n",
     "\n",
-    "xrp_results = xrp_model.fit()\n",
-    "eth_results = eth_model.fit()"
+    "print(xrp.tail())\n",
+    "print(eth.tail())"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<table class=\"simpletable\">\n",
-       "<caption>ARMA Model Results</caption>\n",
-       "<tr>\n",
-       "  <th>Dep. Variable:</th>       <td>Last</td>       <th>  No. Observations:  </th>   <td>1699</td>   \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Model:</th>            <td>ARMA(4, 2)</td>    <th>  Log Likelihood     </th> <td>2624.174</td> \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.052</td>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Date:</th>          <td>Sat, 19 Feb 2022</td> <th>  AIC                </th> <td>-5232.347</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Time:</th>              <td>14:34:33</td>     <th>  BIC                </th> <td>-5188.845</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th> <td>-5216.242</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th></th>                       <td> </td>        <th>                     </th>     <td> </td>    \n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<tr>\n",
-       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>const</th>      <td>    0.4990</td> <td>    0.091</td> <td>    5.505</td> <td> 0.000</td> <td>    0.321</td> <td>    0.677</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L1.Last</th> <td>    1.2388</td> <td>    0.091</td> <td>   13.678</td> <td> 0.000</td> <td>    1.061</td> <td>    1.416</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L2.Last</th> <td>   -0.8262</td> <td>    0.193</td> <td>   -4.279</td> <td> 0.000</td> <td>   -1.205</td> <td>   -0.448</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L3.Last</th> <td>    0.8020</td> <td>    0.149</td> <td>    5.383</td> <td> 0.000</td> <td>    0.510</td> <td>    1.094</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L4.Last</th> <td>   -0.2337</td> <td>    0.025</td> <td>   -9.392</td> <td> 0.000</td> <td>   -0.282</td> <td>   -0.185</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ma.L1.Last</th> <td>   -0.0898</td> <td>    0.092</td> <td>   -0.971</td> <td> 0.332</td> <td>   -0.271</td> <td>    0.091</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ma.L2.Last</th> <td>    0.5181</td> <td>    0.148</td> <td>    3.494</td> <td> 0.000</td> <td>    0.228</td> <td>    0.809</td>\n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<caption>Roots</caption>\n",
-       "<tr>\n",
-       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.1</th> <td>          -0.1011</td> <td>          -1.2636j</td> <td>           1.2676</td> <td>          -0.2627</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.2</th> <td>          -0.1011</td> <td>          +1.2636j</td> <td>           1.2676</td> <td>           0.2627</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.3</th> <td>           1.0180</td> <td>          -0.0000j</td> <td>           1.0180</td> <td>          -0.0000</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.4</th> <td>           2.6159</td> <td>          -0.0000j</td> <td>           2.6159</td> <td>          -0.0000</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>MA.1</th> <td>           0.0866</td> <td>          -1.3866j</td> <td>           1.3893</td> <td>          -0.2401</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>MA.2</th> <td>           0.0866</td> <td>          +1.3866j</td> <td>           1.3893</td> <td>           0.2401</td>\n",
-       "</tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<class 'statsmodels.iolib.summary.Summary'>\n",
-       "\"\"\"\n",
-       "                              ARMA Model Results                              \n",
-       "==============================================================================\n",
-       "Dep. Variable:                   Last   No. Observations:                 1699\n",
-       "Model:                     ARMA(4, 2)   Log Likelihood                2624.174\n",
-       "Method:                       css-mle   S.D. of innovations              0.052\n",
-       "Date:                Sat, 19 Feb 2022   AIC                          -5232.347\n",
-       "Time:                        14:34:33   BIC                          -5188.845\n",
-       "Sample:                             0   HQIC                         -5216.242\n",
-       "                                                                              \n",
-       "==============================================================================\n",
-       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
-       "------------------------------------------------------------------------------\n",
-       "const          0.4990      0.091      5.505      0.000       0.321       0.677\n",
-       "ar.L1.Last     1.2388      0.091     13.678      0.000       1.061       1.416\n",
-       "ar.L2.Last    -0.8262      0.193     -4.279      0.000      -1.205      -0.448\n",
-       "ar.L3.Last     0.8020      0.149      5.383      0.000       0.510       1.094\n",
-       "ar.L4.Last    -0.2337      0.025     -9.392      0.000      -0.282      -0.185\n",
-       "ma.L1.Last    -0.0898      0.092     -0.971      0.332      -0.271       0.091\n",
-       "ma.L2.Last     0.5181      0.148      3.494      0.000       0.228       0.809\n",
-       "                                    Roots                                    \n",
-       "=============================================================================\n",
-       "                  Real          Imaginary           Modulus         Frequency\n",
-       "-----------------------------------------------------------------------------\n",
-       "AR.1           -0.1011           -1.2636j            1.2676           -0.2627\n",
-       "AR.2           -0.1011           +1.2636j            1.2676            0.2627\n",
-       "AR.3            1.0180           -0.0000j            1.0180           -0.0000\n",
-       "AR.4            2.6159           -0.0000j            2.6159           -0.0000\n",
-       "MA.1            0.0866           -1.3866j            1.3893           -0.2401\n",
-       "MA.2            0.0866           +1.3866j            1.3893            0.2401\n",
-       "-----------------------------------------------------------------------------\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "xrp_results.summary()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n"
+     ]
+    },
     {
-     "data": {
-      "text/html": [
-       "<table class=\"simpletable\">\n",
-       "<caption>ARMA Model Results</caption>\n",
-       "<tr>\n",
-       "  <th>Dep. Variable:</th>       <td>Last</td>       <th>  No. Observations:  </th>    <td>2126</td>   \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Model:</th>            <td>ARMA(3, 2)</td>    <th>  Log Likelihood     </th> <td>-11871.732</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>64.302</td>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Date:</th>          <td>Sat, 19 Feb 2022</td> <th>  AIC                </th>  <td>23757.464</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Time:</th>              <td>14:34:37</td>     <th>  BIC                </th>  <td>23797.098</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th>  <td>23771.971</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th></th>                       <td> </td>        <th>                     </th>      <td> </td>    \n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<tr>\n",
-       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>const</th>      <td>  748.1353</td> <td>  812.828</td> <td>    0.920</td> <td> 0.357</td> <td> -844.978</td> <td> 2341.248</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L1.Last</th> <td>   -0.8121</td> <td>    0.020</td> <td>  -40.397</td> <td> 0.000</td> <td>   -0.851</td> <td>   -0.773</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L2.Last</th> <td>    0.9087</td> <td>    0.006</td> <td>  160.375</td> <td> 0.000</td> <td>    0.898</td> <td>    0.920</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L3.Last</th> <td>    0.8981</td> <td>    0.020</td> <td>   44.303</td> <td> 0.000</td> <td>    0.858</td> <td>    0.938</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ma.L1.Last</th> <td>    1.8433</td> <td>    0.014</td> <td>  128.403</td> <td> 0.000</td> <td>    1.815</td> <td>    1.871</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ma.L2.Last</th> <td>    0.9524</td> <td>    0.014</td> <td>   67.444</td> <td> 0.000</td> <td>    0.925</td> <td>    0.980</td>\n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<caption>Roots</caption>\n",
-       "<tr>\n",
-       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.1</th> <td>           1.0014</td> <td>          -0.0000j</td> <td>           1.0014</td> <td>          -0.0000</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.2</th> <td>          -1.0066</td> <td>          -0.3141j</td> <td>           1.0544</td> <td>          -0.4519</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.3</th> <td>          -1.0066</td> <td>          +0.3141j</td> <td>           1.0544</td> <td>           0.4519</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>MA.1</th> <td>          -0.9677</td> <td>          -0.3370j</td> <td>           1.0247</td> <td>          -0.4467</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>MA.2</th> <td>          -0.9677</td> <td>          +0.3370j</td> <td>           1.0247</td> <td>           0.4467</td>\n",
-       "</tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<class 'statsmodels.iolib.summary.Summary'>\n",
-       "\"\"\"\n",
-       "                              ARMA Model Results                              \n",
-       "==============================================================================\n",
-       "Dep. Variable:                   Last   No. Observations:                 2126\n",
-       "Model:                     ARMA(3, 2)   Log Likelihood              -11871.732\n",
-       "Method:                       css-mle   S.D. of innovations             64.302\n",
-       "Date:                Sat, 19 Feb 2022   AIC                          23757.464\n",
-       "Time:                        14:34:37   BIC                          23797.098\n",
-       "Sample:                             0   HQIC                         23771.971\n",
-       "                                                                              \n",
-       "==============================================================================\n",
-       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
-       "------------------------------------------------------------------------------\n",
-       "const        748.1353    812.828      0.920      0.357    -844.978    2341.248\n",
-       "ar.L1.Last    -0.8121      0.020    -40.397      0.000      -0.851      -0.773\n",
-       "ar.L2.Last     0.9087      0.006    160.375      0.000       0.898       0.920\n",
-       "ar.L3.Last     0.8981      0.020     44.303      0.000       0.858       0.938\n",
-       "ma.L1.Last     1.8433      0.014    128.403      0.000       1.815       1.871\n",
-       "ma.L2.Last     0.9524      0.014     67.444      0.000       0.925       0.980\n",
-       "                                    Roots                                    \n",
-       "=============================================================================\n",
-       "                  Real          Imaginary           Modulus         Frequency\n",
-       "-----------------------------------------------------------------------------\n",
-       "AR.1            1.0014           -0.0000j            1.0014           -0.0000\n",
-       "AR.2           -1.0066           -0.3141j            1.0544           -0.4519\n",
-       "AR.3           -1.0066           +0.3141j            1.0544            0.4519\n",
-       "MA.1           -0.9677           -0.3370j            1.0247           -0.4467\n",
-       "MA.2           -0.9677           +0.3370j            1.0247            0.4467\n",
-       "-----------------------------------------------------------------------------\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               SARIMAX Results                                \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   Last   No. Observations:                 2021\n",
+      "Model:                 ARIMA(4, 0, 0)   Log Likelihood                3260.221\n",
+      "Date:                Sat, 07 Jan 2023   AIC                          -6508.441\n",
+      "Time:                        20:36:21   BIC                          -6474.773\n",
+      "Sample:                             0   HQIC                         -6496.086\n",
+      "                               - 2021                                         \n",
+      "Covariance Type:                  opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const          0.4924      0.131      3.750      0.000       0.235       0.750\n",
+      "ar.L1          1.1480      0.007    155.199      0.000       1.133       1.162\n",
+      "ar.L2         -0.2269      0.012    -18.610      0.000      -0.251      -0.203\n",
+      "ar.L3          0.2287      0.011     20.101      0.000       0.206       0.251\n",
+      "ar.L4         -0.1634      0.006    -27.140      0.000      -0.175      -0.152\n",
+      "sigma2         0.0023   1.87e-05    123.836      0.000       0.002       0.002\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.03   Jarque-Bera (JB):            282795.90\n",
+      "Prob(Q):                              0.87   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.70   Skew:                             2.61\n",
+      "Prob(H) (two-sided):                  0.00   Kurtosis:                        60.72\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
+      "                               SARIMAX Results                                \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   Last   No. Observations:                 2021\n",
+      "Model:                 ARIMA(1, 0, 0)   Log Likelihood              -11563.533\n",
+      "Date:                Sat, 07 Jan 2023   AIC                          23133.066\n",
+      "Time:                        20:36:21   BIC                          23149.900\n",
+      "Sample:                             0   HQIC                         23139.244\n",
+      "                               - 2021                                         \n",
+      "Covariance Type:                  opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "const       1071.8715    980.388      1.093      0.274    -849.653    2993.396\n",
+      "ar.L1          0.9977      0.001    783.544      0.000       0.995       1.000\n",
+      "sigma2      5450.3274     64.117     85.006      0.000    5324.661    5575.994\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.62   Jarque-Bera (JB):             14791.12\n",
+      "Prob(Q):                              0.43   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):              14.96   Skew:                            -0.61\n",
+      "Prob(H) (two-sided):                  0.00   Kurtosis:                        16.20\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n"
+     ]
     }
    ],
    "source": [
-    "eth_results.summary()"
+    "# using code from 1.6.4\n",
+    "from statsmodels.tsa import stattools\n",
+    "from statsmodels import api as sms\n",
+    "\n",
+    "xrp_pq = stattools.arma_order_select_ic(xrp) \n",
+    "p, q = xrp_pq.bic_min_order\n",
+    "xrp_model = sms.tsa.ARIMA(xrp, order=(p, 0, q))\n",
+    "xrp_out = xrp_model.fit()\n",
+    "\n",
+    "eth_pq = stattools.arma_order_select_ic(eth) \n",
+    "p, q = eth_pq.bic_min_order\n",
+    "eth_model = sms.tsa.ARIMA(eth, order=(p, 0, q))\n",
+    "eth_out = eth_model.fit()\n",
+    "\n",
+    "print(xrp_out.summary())\n",
+    "print(eth_out.summary())"
    ]
   },
   {
@@ -412,23 +271,20 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "#### Exercise\n",
     "\n",
     "1. What can the ARIMA(1, 0, 0) model also be known as?\n",
+    "    - Autoregressive(1) model with p = 1, d = 0, q = 0\n",
     "2. What does an ARIMA(0, 0, 0) model actually model? Hint: use the ARMA equation, and set p and q to zero.\n",
-    "3. What does an ARIMA(0, 1, 0) model represent - i.e. what other term have we seen that refers to the same idea?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "1. An AR(1) model\n",
-    "2. Constant mean plus white noise\n",
-    "3. Difference model"
+    "    - Constant (c) plus error (epsilon) with all the other terms being 0\n",
+    "    - This basically means a constant mean plus random noise\n",
+    "3. What does an ARIMA(0, 1, 0) model represent - i.e. what other term have we seen that refers to the same idea?\n",
+    "    - d is the number of times the raw observations are differenced; also known as the degree of differencing\n",
+    "    - d = 1 gives us a differened model (by 1)"
    ]
   },
   {
@@ -451,25 +307,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1685: FutureWarning: The behavior of using lags=None will change in the next release. Currently lags=None is the same as lags='legacy', and so a sample-size lag length is used. After the next release, the default will change to be the same as lags='auto' which uses an automatic lag length selection method. To silence this warning, either use 'auto' or 'legacy'\n",
-      "  warn(msg, FutureWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1708: InterpolationWarning: p-value is smaller than the indicated p-value\n",
-      "  warn(\"p-value is smaller than the indicated p-value\", InterpolationWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:1708: InterpolationWarning: p-value is smaller than the indicated p-value\n",
-      "  warn(\"p-value is smaller than the indicated p-value\", InterpolationWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
-      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
-      "  ' ignored when e.g. forecasting.', ValueWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:219: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
-      "  ' ignored when e.g. forecasting.', ValueWarning)\n"
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:2018: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
+      "look-up table. The actual p-value is smaller than the p-value returned.\n",
+      "\n",
+      "  warnings.warn(\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\stattools.py:2018: InterpolationWarning: The test statistic is outside of the range of p-values available in the\n",
+      "look-up table. The actual p-value is smaller than the p-value returned.\n",
+      "\n",
+      "  warnings.warn(\n"
      ]
     },
     {
@@ -481,130 +333,116 @@
      ]
     },
     {
-     "data": {
-      "text/html": [
-       "<table class=\"simpletable\">\n",
-       "<caption>ARMA Model Results</caption>\n",
-       "<tr>\n",
-       "  <th>Dep. Variable:</th>       <td>Last</td>       <th>  No. Observations:  </th>    <td>2126</td>   \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Model:</th>            <td>ARMA(3, 1)</td>    <th>  Log Likelihood     </th> <td>-11896.681</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>65.071</td>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Date:</th>          <td>Sat, 19 Feb 2022</td> <th>  AIC                </th>  <td>23805.363</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Time:</th>              <td>14:48:00</td>     <th>  BIC                </th>  <td>23839.335</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>Sample:</th>                <td>0</td>        <th>  HQIC               </th>  <td>23817.798</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th></th>                       <td> </td>        <th>                     </th>      <td> </td>    \n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<tr>\n",
-       "       <td></td>         <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>const</th>      <td>  980.3539</td> <td>  770.587</td> <td>    1.272</td> <td> 0.203</td> <td> -529.970</td> <td> 2490.678</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L1.Last</th> <td>    0.1663</td> <td>    0.050</td> <td>    3.296</td> <td> 0.001</td> <td>    0.067</td> <td>    0.265</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L2.Last</th> <td>    0.8629</td> <td>    0.040</td> <td>   21.468</td> <td> 0.000</td> <td>    0.784</td> <td>    0.942</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ar.L3.Last</th> <td>   -0.0318</td> <td>    0.023</td> <td>   -1.395</td> <td> 0.163</td> <td>   -0.076</td> <td>    0.013</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>ma.L1.Last</th> <td>    0.8140</td> <td>    0.046</td> <td>   17.794</td> <td> 0.000</td> <td>    0.724</td> <td>    0.904</td>\n",
-       "</tr>\n",
-       "</table>\n",
-       "<table class=\"simpletable\">\n",
-       "<caption>Roots</caption>\n",
-       "<tr>\n",
-       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.1</th> <td>           1.0014</td> <td>          +0.0000j</td> <td>           1.0014</td> <td>           0.0000</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.2</th> <td>          -1.1509</td> <td>          +0.0000j</td> <td>           1.1509</td> <td>           0.5000</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>AR.3</th> <td>          27.2954</td> <td>          +0.0000j</td> <td>          27.2954</td> <td>           0.0000</td>\n",
-       "</tr>\n",
-       "<tr>\n",
-       "  <th>MA.1</th> <td>          -1.2285</td> <td>          +0.0000j</td> <td>           1.2285</td> <td>           0.5000</td>\n",
-       "</tr>\n",
-       "</table>"
-      ],
-      "text/plain": [
-       "<class 'statsmodels.iolib.summary.Summary'>\n",
-       "\"\"\"\n",
-       "                              ARMA Model Results                              \n",
-       "==============================================================================\n",
-       "Dep. Variable:                   Last   No. Observations:                 2126\n",
-       "Model:                     ARMA(3, 1)   Log Likelihood              -11896.681\n",
-       "Method:                       css-mle   S.D. of innovations             65.071\n",
-       "Date:                Sat, 19 Feb 2022   AIC                          23805.363\n",
-       "Time:                        14:48:00   BIC                          23839.335\n",
-       "Sample:                             0   HQIC                         23817.798\n",
-       "                                                                              \n",
-       "==============================================================================\n",
-       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
-       "------------------------------------------------------------------------------\n",
-       "const        980.3539    770.587      1.272      0.203    -529.970    2490.678\n",
-       "ar.L1.Last     0.1663      0.050      3.296      0.001       0.067       0.265\n",
-       "ar.L2.Last     0.8629      0.040     21.468      0.000       0.784       0.942\n",
-       "ar.L3.Last    -0.0318      0.023     -1.395      0.163      -0.076       0.013\n",
-       "ma.L1.Last     0.8140      0.046     17.794      0.000       0.724       0.904\n",
-       "                                    Roots                                    \n",
-       "=============================================================================\n",
-       "                  Real          Imaginary           Modulus         Frequency\n",
-       "-----------------------------------------------------------------------------\n",
-       "AR.1            1.0014           +0.0000j            1.0014            0.0000\n",
-       "AR.2           -1.1509           +0.0000j            1.1509            0.5000\n",
-       "AR.3           27.2954           +0.0000j           27.2954            0.0000\n",
-       "MA.1           -1.2285           +0.0000j            1.2285            0.5000\n",
-       "-----------------------------------------------------------------------------\n",
-       "\"\"\""
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               SARIMAX Results                                \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   Last   No. Observations:                 2021\n",
+      "Model:                 ARIMA(4, 1, 0)   Log Likelihood                3251.387\n",
+      "Date:                Sat, 07 Jan 2023   AIC                          -6492.774\n",
+      "Time:                        21:52:40   BIC                          -6464.719\n",
+      "Sample:                             0   HQIC                         -6482.478\n",
+      "                               - 2021                                         \n",
+      "Covariance Type:                  opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1          0.1539      0.008     19.280      0.000       0.138       0.170\n",
+      "ar.L2         -0.0711      0.009     -8.293      0.000      -0.088      -0.054\n",
+      "ar.L3          0.1536      0.007     21.755      0.000       0.140       0.167\n",
+      "ar.L4          0.0132      0.011      1.164      0.245      -0.009       0.036\n",
+      "sigma2         0.0023   1.83e-05    127.889      0.000       0.002       0.002\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):            273295.78\n",
+      "Prob(Q):                              0.99   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):               0.69   Skew:                             2.05\n",
+      "Prob(H) (two-sided):                  0.00   Kurtosis:                        59.84\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
+      "                               SARIMAX Results                                \n",
+      "==============================================================================\n",
+      "Dep. Variable:                   Last   No. Observations:                 2021\n",
+      "Model:                 ARIMA(1, 1, 0)   Log Likelihood              -11556.082\n",
+      "Date:                Sat, 07 Jan 2023   AIC                          23116.165\n",
+      "Time:                        21:52:40   BIC                          23127.386\n",
+      "Sample:                             0   HQIC                         23120.283\n",
+      "                               - 2021                                         \n",
+      "Covariance Type:                  opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1         -0.0185      0.012     -1.584      0.113      -0.041       0.004\n",
+      "sigma2      5454.5219     64.633     84.392      0.000    5327.843    5581.201\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):             15032.59\n",
+      "Prob(Q):                              0.99   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):              15.02   Skew:                            -0.78\n",
+      "Prob(H) (two-sided):                  0.00   Kurtosis:                        16.27\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
+      "  self._init_dates(dates, freq)\n"
+     ]
     }
    ],
    "source": [
-    "#From solutions\n",
-    "# Use KPSS to test for stationarity.\n",
+    "# copy tests from previous module for testing if series are stationary\n",
     "from statsmodels.tsa.stattools import kpss\n",
-    "\n",
-    "kpss_stat, p_value, lags, crit = kpss(xrp)\n",
+    "_, p_value, _, _ = kpss(xrp)\n",
     "print(\"XRP p_value:\", p_value)\n",
-    "\n",
-    "kpss_stat, p_value, lags, crit = kpss(eth)\n",
+    "_, p_value, _, _ = kpss(eth)\n",
     "print(\"ETH p_value:\", p_value)\n",
     "\n",
+    "# copy code from above, except change to d = 1\n",
+    "xrp_pq = stattools.arma_order_select_ic(xrp) \n",
+    "p, q = xrp_pq.bic_min_order\n",
+    "xrp_model2 = sms.tsa.ARIMA(xrp, order=(p, 1, q))\n",
+    "xrp_out2 = xrp_model2.fit()\n",
     "\n",
-    "from statsmodels.tsa.arima_model import ARIMA as arima\n",
-    "\n",
-    "xrp_model_arima = arima(xrp, order=(4,1, 2))  #Remember if we use d=0 we're back to using the ARMA model!\n",
-    "eth_model_arima = sms.tsa.ARMA(eth, order=(3, 1, 2))\n",
+    "eth_pq = stattools.arma_order_select_ic(eth) \n",
+    "p, q = eth_pq.bic_min_order\n",
+    "eth_model2 = sms.tsa.ARIMA(eth, order=(p, 1, q))\n",
+    "eth_out2 = eth_model2.fit()\n",
     "\n",
-    "xrp_results_arima = xrp_model_arima.fit()\n",
-    "eth_results_arima = eth_model_arima.fit()\n",
-    "\n",
-    "xrp_results_arima.summary()\n",
-    "eth_results_arima.summary()"
+    "print(xrp_out2.summary())\n",
+    "print(eth_out2.summary())"
    ]
   },
   {
@@ -633,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -642,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -651,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -660,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -669,7 +507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -678,27 +516,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
-      "  % freq, ValueWarning)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
-      "  % freq, ValueWarning)\n"
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n"
      ]
     }
    ],
    "source": [
-    "model = ARIMA(changes, order=(1, 1, 1))"
+    "# previous code depreciated so updated package\n",
+    "model = sms.tsa.ARIMA(changes, order=(1, 1, 1))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -707,92 +548,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<table class=\"simpletable\">\n",
-       "<caption>ARIMA Model Results</caption>\n",
+       "<caption>SARIMAX Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>      <td>D.Value</td>     <th>  No. Observations:  </th>    <td>329</td>   \n",
+       "  <th>Dep. Variable:</th>         <td>Value</td>      <th>  No. Observations:  </th>    <td>330</td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>          <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th> <td>1018.750</td> \n",
+       "  <th>Model:</th>            <td>ARIMA(1, 1, 1)</td>  <th>  Log Likelihood     </th> <td>1018.749</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Method:</th>             <td>css-mle</td>     <th>  S.D. of innovations</th>   <td>0.011</td>  \n",
+       "  <th>Date:</th>            <td>Sat, 07 Jan 2023</td> <th>  AIC                </th> <td>-2031.498</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>          <td>Sat, 19 Feb 2022</td> <th>  AIC                </th> <td>-2029.500</td>\n",
+       "  <th>Time:</th>                <td>21:58:36</td>     <th>  BIC                </th> <td>-2020.110</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>              <td>14:49:49</td>     <th>  BIC                </th> <td>-2014.316</td>\n",
+       "  <th>Sample:</th>             <td>07-01-1949</td>    <th>  HQIC               </th> <td>-2026.955</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Sample:</th>           <td>10-01-1949</td>    <th>  HQIC               </th> <td>-2023.443</td>\n",
+       "  <th></th>                   <td>- 10-01-2031</td>   <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th></th>                 <td>- 10-01-2031</td>   <th>                     </th>     <td> </td>    \n",
+       "  <th>Covariance Type:</th>        <td>opg</td>       <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "        <td></td>           <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>const</th>         <td> 3.741e-07</td> <td> 5.24e-06</td> <td>    0.071</td> <td> 0.943</td> <td>-9.89e-06</td> <td> 1.06e-05</td>\n",
+       "  <th>ar.L1</th>  <td>   -0.1947</td> <td>    0.028</td> <td>   -6.970</td> <td> 0.000</td> <td>   -0.249</td> <td>   -0.140</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ar.L1.D.Value</th> <td>   -0.1977</td> <td>    0.054</td> <td>   -3.661</td> <td> 0.000</td> <td>   -0.304</td> <td>   -0.092</td>\n",
+       "  <th>ma.L1</th>  <td>   -1.0000</td> <td>    1.051</td> <td>   -0.951</td> <td> 0.342</td> <td>   -3.060</td> <td>    1.061</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ma.L1.D.Value</th> <td>   -1.0000</td> <td>    0.008</td> <td> -125.172</td> <td> 0.000</td> <td>   -1.016</td> <td>   -0.984</td>\n",
+       "  <th>sigma2</th> <td>    0.0001</td> <td>    0.000</td> <td>    0.953</td> <td> 0.341</td> <td>   -0.000</td> <td>    0.000</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
-       "<caption>Roots</caption>\n",
        "<tr>\n",
-       "    <td></td>   <th>            Real</th>  <th>         Imaginary</th> <th>         Modulus</th>  <th>        Frequency</th>\n",
+       "  <th>Ljung-Box (L1) (Q):</th>      <td>0.05</td>   <th>  Jarque-Bera (JB):  </th> <td>20420.85</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Q):</th>                 <td>0.83</td>   <th>  Prob(JB):          </th>   <td>0.00</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>AR.1</th> <td>          -5.0574</td> <td>          +0.0000j</td> <td>           5.0574</td> <td>           0.5000</td>\n",
+       "  <th>Heteroskedasticity (H):</th> <td>2019.58</td> <th>  Skew:              </th>   <td>-1.89</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>MA.1</th> <td>           1.0000</td> <td>          +0.0000j</td> <td>           1.0000</td> <td>           0.0000</td>\n",
+       "  <th>Prob(H) (two-sided):</th>     <td>0.00</td>   <th>  Kurtosis:          </th>   <td>41.41</td> \n",
        "</tr>\n",
-       "</table>"
+       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                             ARIMA Model Results                              \n",
+       "                               SARIMAX Results                                \n",
        "==============================================================================\n",
-       "Dep. Variable:                D.Value   No. Observations:                  329\n",
-       "Model:                 ARIMA(1, 1, 1)   Log Likelihood                1018.750\n",
-       "Method:                       css-mle   S.D. of innovations              0.011\n",
-       "Date:                Sat, 19 Feb 2022   AIC                          -2029.500\n",
-       "Time:                        14:49:49   BIC                          -2014.316\n",
-       "Sample:                    10-01-1949   HQIC                         -2023.443\n",
+       "Dep. Variable:                  Value   No. Observations:                  330\n",
+       "Model:                 ARIMA(1, 1, 1)   Log Likelihood                1018.749\n",
+       "Date:                Sat, 07 Jan 2023   AIC                          -2031.498\n",
+       "Time:                        21:58:36   BIC                          -2020.110\n",
+       "Sample:                    07-01-1949   HQIC                         -2026.955\n",
        "                         - 10-01-2031                                         \n",
-       "=================================================================================\n",
-       "                    coef    std err          z      P>|z|      [0.025      0.975]\n",
-       "---------------------------------------------------------------------------------\n",
-       "const          3.741e-07   5.24e-06      0.071      0.943   -9.89e-06    1.06e-05\n",
-       "ar.L1.D.Value    -0.1977      0.054     -3.661      0.000      -0.304      -0.092\n",
-       "ma.L1.D.Value    -1.0000      0.008   -125.172      0.000      -1.016      -0.984\n",
-       "                                    Roots                                    \n",
-       "=============================================================================\n",
-       "                  Real          Imaginary           Modulus         Frequency\n",
-       "-----------------------------------------------------------------------------\n",
-       "AR.1           -5.0574           +0.0000j            5.0574            0.5000\n",
-       "MA.1            1.0000           +0.0000j            1.0000            0.0000\n",
-       "-----------------------------------------------------------------------------\n",
+       "Covariance Type:                  opg                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "ar.L1         -0.1947      0.028     -6.970      0.000      -0.249      -0.140\n",
+       "ma.L1         -1.0000      1.051     -0.951      0.342      -3.060       1.061\n",
+       "sigma2         0.0001      0.000      0.953      0.341      -0.000       0.000\n",
+       "===================================================================================\n",
+       "Ljung-Box (L1) (Q):                   0.05   Jarque-Bera (JB):             20420.85\n",
+       "Prob(Q):                              0.83   Prob(JB):                         0.00\n",
+       "Heteroskedasticity (H):            2019.58   Skew:                            -1.89\n",
+       "Prob(H) (two-sided):                  0.00   Kurtosis:                        41.41\n",
+       "===================================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
        "\"\"\""
       ]
      },
-     "execution_count": 16,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -817,6 +662,65 @@
     "Note: There are no solutions for this exercise - just try different values and examine the result."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               SARIMAX Results                                \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  Value   No. Observations:                  330\n",
+      "Model:                 ARIMA(2, 1, 1)   Log Likelihood                1012.164\n",
+      "Date:                Sat, 07 Jan 2023   AIC                          -2016.328\n",
+      "Time:                        21:58:28   BIC                          -2001.144\n",
+      "Sample:                    07-01-1949   HQIC                         -2010.270\n",
+      "                         - 10-01-2031                                         \n",
+      "Covariance Type:                  opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1         -0.2934      0.033     -8.767      0.000      -0.359      -0.228\n",
+      "ar.L2         -0.0244      0.034     -0.707      0.480      -0.092       0.043\n",
+      "ma.L1         -0.8283      0.024    -35.025      0.000      -0.875      -0.782\n",
+      "sigma2         0.0001    2.5e-06     49.462      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):             23334.67\n",
+      "Prob(Q):                              0.97   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):            5642.63   Skew:                            -2.12\n",
+      "Prob(H) (two-sided):                  0.00   Kurtosis:                        44.04\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = sms.tsa.ARIMA(changes, order = (2, 1, 1))\n",
+    "results2 = model.fit()\n",
+    "print(results2.summary())\n",
+    "\n",
+    "# when p = 2, ar.L2 is added, an extra coefficient\n",
+    "# increasing d increseas heteroskedasticity\n",
+    "# increasing p/q increases number of coefficients"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -840,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -849,7 +753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -858,26 +762,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([ True,  True, False, False, False, False, False, False, False,\n",
-       "       False, False, False, False, False, False, False, False, False,\n",
-       "       False, False, False, False, False, False, False, False, False,\n",
-       "       False, False, False, False, False, False, False, False, False,\n",
-       "       False, False, False, False])"
+       "1      True\n",
+       "2      True\n",
+       "3     False\n",
+       "4     False\n",
+       "5     False\n",
+       "6     False\n",
+       "7     False\n",
+       "8     False\n",
+       "9     False\n",
+       "10    False\n",
+       "Name: lb_pvalue, dtype: bool"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "p_values > 0.05"
+    "out = acorr_ljungbox(results.resid)\n",
+    "out[\"lb_pvalue\"] > 0.05"
    ]
   },
   {
@@ -891,6 +802,16 @@
     "Hint: for documentation on the function itself, see https://www.statsmodels.org/stable/generated/statsmodels.stats.diagnostic.acorr_ljungbox.html"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# for lags of 1 and 2 (> 0.05), we can reject that the data is independently distributed\n",
+    "# we can say that there is indeed a correlation for the data in these lag periods"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -912,7 +833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -945,23 +866,23 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>2030-10-01</td>\n",
+       "      <th>2030-10-01</th>\n",
        "      <td>0.000026</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2031-01-01</td>\n",
+       "      <th>2031-01-01</th>\n",
        "      <td>0.000039</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2031-04-01</td>\n",
+       "      <th>2031-04-01</th>\n",
        "      <td>0.000048</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2031-07-01</td>\n",
+       "      <th>2031-07-01</th>\n",
        "      <td>0.000051</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2031-10-01</td>\n",
+       "      <th>2031-10-01</th>\n",
        "      <td>0.000042</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -978,7 +899,7 @@
        "2031-10-01  0.000042"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -989,9 +910,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\86155\\AppData\\Local\\Temp\\ipykernel_12972\\2320578269.py:1: FutureWarning: Indexing a DataFrame with a datetimelike index using a single string to slice the rows, like `frame[string]`, is deprecated and will be removed in a future version. Use `frame.loc[string]` instead.\n",
+      "  changes[\"2000\"]\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
@@ -1022,19 +951,19 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>2000-01-01</td>\n",
+       "      <th>2000-01-01</th>\n",
        "      <td>-0.000088</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2000-04-01</td>\n",
+       "      <th>2000-04-01</th>\n",
        "      <td>-0.000089</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2000-07-01</td>\n",
+       "      <th>2000-07-01</th>\n",
        "      <td>-0.000085</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2000-10-01</td>\n",
+       "      <th>2000-10-01</th>\n",
        "      <td>-0.000069</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1050,7 +979,7 @@
        "2000-10-01 -0.000069"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1061,7 +990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1077,7 +1006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1086,12 +1015,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1103,7 +1032,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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KXfZOJS8S5gp37ObRj5cz7ev1+Jzj3Nyu/Pq43nTLbOl1NIkAKnmRMLVuWzmPfLyCl2avA+Dc3G78+rhedG2jcpf9p5IXCTNri8t55OPlvDR7PXFm/HxkNlce14usjNi6NqmEhkpeJEysKS5j4ofLeSW/kPg44xeH+ss91i48LaGlkhfx2Kqt/nKfXlBIQpxx4WHdueq4XnRMT/Y6mkQBlbyIR1YU7WLih8uZUVBIi4Q4xh2RwxXH9KSDyl1CSCUv0syWbynloQ+X8/rcDbRIiOPSo3pw+TE96ZCmcpfQU8mLNJONO3dz51tLeGPeBpIT4rn86J5cfkxP2rVK8jqaRDGVvEgzKK2o5uKnvmLdtt1ceWwvLjuqB21V7tIMVPIiTazW57h+agErisp45leHcFSfdl5HkhgS53UAkWh39ztL+GDJFu44/SAVvDQ7lbxIE3oxbx2Pf7qSCw/rzoWH53gdR2KQSl6kieSt3satry7gyN5t+ePpB3kdR2KUSl6kCazfXs4Vz82mS0YyD18wnMR4/aqJN7TmiYRYWWUNlz2TR1WtjycuHklGyxZeR5IYpqNrRELI53Nc/0IBy7bs4t/jRtK7g67SJN7SlrxICN3736W8t2gzt/1sAMf0be91HBGVvEiovJq/nkc+XsHYQ7K5+Igcr+OIACp5kZCYs3Y7N708n8N6ZvLn0QMxM68jiQAqeZGgFe7YzfhnZ9MpPZlHfzFCR9JIWNGOV5EglFfVcPkzeVRW1zLl8kNpk6ojaSS8qORFDpDP5/jtC3NZsqmEJ8eNpE/HNK8jifyI3leKHKD73v+GdxZu4pafDuD4fh28jiNSL5W8yAGYUVDIQx8u5/zcblx6VA+v44jslUpepJEK1u3gxpfmcUhOJn85c5COpJGwppIXaYRNOysY/2weHdKSePSXw2mRoF8hCW9aQ0X20+6qWi5/No+yyhqevHikruwkESHokjeza81siZktNLO7QxFKJNz4fI7fvTiXBRt28uDYYfTrpCNpJDIEdQilmR0PjAaGOOcqzUyHGEhUevDDZbw5fyM3n9qfUQM6eh1HZL8FuyV/FXCXc64SwDm3JfhIIuHlzXkbuf/9ZZw9vCvjj+npdRyRRgm25PsCR5vZl2b2iZmN3NsDzWy8meWZWV5RUVGQTyvSPOav38kNLxYwonsb7jxLR9JI5GlwuMbM3gc61XPXrYH5M4HDgJHANDPr6ZxzP3ywc24SMAkgNzf3R/eLhJvNJRVc9uzXtE1N4vELR5CUEO91JJFGa7DknXMn7u0+M7sKeCVQ6l+ZmQ9oB2hTXSJaRXUt45/No7SihpevOoJ2OpJGIlSwwzXTgeMBzKwv0ALYGuQyRTzlnOPGl+Yxr3An958/lAGd072OJHLAgj1B2VPAU2a2AKgCLq5vqEYkkkz8cDmvz93A70/px0kD6xupFIkcQZW8c64K+GWIsoh47p0FG/nHe98wZlgWVx3by+s4IkHTJ15FAhYU7mTCC3MZlp3B3886WEfSSFRQyYsAW0oruPzZPDJaJvL4hSNITtSRNBIddNEQiXn+I2lms6O8mhevPJwOacleRxIJGZW8xDTnHDe/Mp+CdTt47JfDGZTV2utIIiGl4RqJaY9+soJX8wu54Sd9OWVQZ6/jiIScSl5i1n8XbuKed5dy+pAuXHNCb6/jiDQJlbzEpMUbS7j+hQIGZ7XmnnMG60gaiVoqeYk5W3dVctkzeaQnJzLpolwdSSNRTTteJaZU1tRy5XOzKS6r5MUrjqBjuo6kkeimkpeY4ZzjllcWkLdmOxMvGMbBXXUkjUQ/DddIzPjXZyt5ec56rhvVh9MGd/E6jkizUMlLTPhg8Wb+/vYSfnZwZ64b1cfrOCLNRiUvUW/pplJ+MyWfgV3SuffcIcTF6UgaiR0qeYlqxbsqufSZr0lNSuBfF+WS0kJH0khs0Y5XiVpVNT6u+s8cikoreeGKw+ncOsXrSCLNTiUvUck5x23TF/DV6m088POhDO2W4XUkEU9ouEai0pOfr+KFvHVce0JvRg/N8jqOiGdU8hJ1Plq6hTvfWszJAzsy4cS+XscR8ZRKXqLKss2l/GZyPv07pXPf+UN1JI3EPJW8RI3tZVVc9mweSYnx/OviXFq20C4nEZW8RIWqGh9XPT+bjTsqePzCEWRl6EgaEdDRNRIFnHPc/tpCZq3cxn3nD2FE9zZeRxIJG9qSl4j3zP9WM+WrtVx1XC/GDOvqdRyRsKKSl4j26TdF/PmNRfzkoI7ceFI/r+OIhB2VvESs5Vt2cfXkOfTtmKYjaUT2QiUvEWlHeRWXPfM1LeLjeOLiXFolafeSSH30myERp7rWx9WT57BhRwWTLz+Urm1aeh1JJGyp5CXi/Pn1RcxcXsw95wwmNyfT6zgiYU3DNRJRnvtiNc/NWsP4Y3pybm43r+OIhD2VvESMWSuLueP1RYzq34GbTunvdRyRiBDUcI2ZvQDsOW4tA9jhnBsaZCaRHykqreTaKfl0z2zJ/T8fSryOpBHZL0GVvHPu/D23zewfwM6gE4n8QK3Pcd3UfEp2V/PcpYeQlpzodSSRiBGSHa9mZsB5wAmhWJ5IXQ9+sIz/rSjm7rMH079TutdxRCJKqMbkjwY2O+eWhWh5IgB8tqyIBz9cxtnDu3Jurk5ZINJYDW7Jm9n7QKd67rrVOTcjcHssMKWB5YwHxgNkZ2c3MqbEos0lFVw/tYDe7VvxlzMH4n/DKCKN0WDJO+dO3Nf9ZpYAnAWMaGA5k4BJALm5ua4RGSUG1dT6uHZKPuVVtUwdP1znhhc5QKH4zTkRWOKcWx+CZYkA8M/3vuGrVf5TB/fpmOZ1HJGIFYox+Z/TwFCNSGN8tHQLj3y8gp+P7KZTB4sEKegteefcuBDkEAFgw47dTHihgP6d0rjjjIFexxGJePrEq4SN6lof10yeQ3WNj0d+MZzkxHivI4lEPO3NkrBxz7tLmbN2Bw+NHUbP9q28jiMSFbQlL2HhvUWbmfTpSi48rDunD+nidRyRqKGSF8+t21bODdMKGJSVzh9OG+B1HJGoopIXT1XV+MfhnYOHLxhOUoLG4UVCSWPy4qk731rM3PU7eeyXw+neNtXrOCJRR1vy4pm352/k6f+t5ldH5nDKoM5exxGJSip58cSa4jJ+/9I8hnTL4OZTNQ4v0lRU8tLsKqpr+fXzc4iLMyaOHUaLBK2GIk1FY/LS7P765iIWbijhiYty6ZbZ0us4IlFNm1DSrGYUFPKfWWu54pienHhQR6/jiEQ9lbw0mxVFu7jllfmM6N6G353cr+EZRCRoKnlpFhXVtVz9/BxaJMQx8YJhJMZr1RNpDhqTl2Zx+4yFLNlUytO/Gknn1ilexxGJGdqckib38uz1vJC3jquP78Vx/Tp4HUckpqjkpUkt21zKH6Yv4NAemUw4sa/XcURijkpemkx5VQ2/fn4OqUnxPDR2GAkahxdpdhqTlybhnOMPry5gedEu/nPpoXRIT/Y6kkhM0qaVNIlpeet4Jb+Q60b14cje7byOIxKzVPIScos3lvDHGQs5qnc7rj2hj9dxRGKaSl5CaldlDVc/P4f0lETuO38o8XHmdSSRmKaSl5BxznHzK/NZXVzGQ2OH0T4tyetIIjFPJS8h8/yXa3l97gZuOKkfh/Vs63UcEUElLyGyoHAnf359Ecf2bc9Vx/byOo6IBKjkJWglFdX8+vk5tG3VgvvOH0qcxuFFwoaOk5egOOe46aV5FO7YzQvjDyMztYXXkUSkDm3JS1Ce/t9q3l6wiZtO6UduTqbXcUTkB1TycsAK1u3gzrcWc+KADlx+dE+v44hIPVTyckB2lldz9fNz6JCWzL3nDsFM4/Ai4Uhj8tJozjlueHEuW0ormHbF4WS01Di8SLjSlrw02hOfreL9xZu5+dQBDMtu43UcEdmHoErezIaa2SwzKzCzPDM7JFTBJDzNXrONu95ZwikDO/GrI3O8jiMiDQh2S/5u4E/OuaHAHwM/S5TaVlbFNZPzycpI4e5zB2scXiQCBDsm74D0wO3WwIYglydhyudz/HZaAcW7qnjl10eQnpzodSQR2Q/Blvz1wLtmdi/+dwVH7O2BZjYeGA+QnZ0d5NNKc3v0kxV8vLSIv5w5iEFZrb2OIyL7qcGSN7P3gU713HUrMAqY4Jx72czOA54ETqxvOc65ScAkgNzcXHfAiaXZfbmymH/8dymnDe7MLw/VH2iRSNJgyTvn6i1tADN7Frgu8OOLwBMhyiVhYuuuSq6dkk/3tqn8/ayDNQ4vEmGC3fG6ATg2cPsEYFmQy5MwUutzXD+1gJ27q3n4guGkaRxeJOIEOyZ/OfCAmSUAFQTG3CU6TPxwOZ8v38pdZx3MQV3SG55BRMJOUCXvnPscGBGiLBJGZi7fyv0ffMNZw7I4f2Q3r+OIyAHSJ17lR7aUVHDd1Hx6tW/FX8cM0ji8SATTuWvke2pqfVw7JZ+yylomXz6cli20iohEMv0Gy/fc//4yvly1jXvPHULfjmlexxGRIGm4Rr71yTdFPPzxcs7L7co5I7p6HUdEQkAlLwBs3LmbCS8U0LdDGn86Y5DXcUQkRFTyQnWtj2sn51NZXcsjvxxOSot4ryOJSIhoTF64979LyVuznQd+PpRe7Vt5HUdEQkhb8jHug8WbefyTlfzi0GxGD83yOo6IhJhKPoat317Ob6fNZWCXdG477SCv44hIE1DJx6iqGh/XTM6n1ud4+ILhJCdqHF4kGmlMPkbd9fYSCtbt4JFfDCenXarXcUSkiWhLPga9s2ATT81cxbgjcvjpwZ29jiMiTUglH2PWFpdz40tzGdK1NTf/tL/XcUSkiWm4JkY455izdju3TV+IARMvGE5SgsbhRaKdSj7KrSjaxYz8QqYXbGDttnJSEuOZeMEwumW29DqaiDQDlXwUKiqt5PW5G5heUMi89TuJMziydzuuG9WHkwd1olWS/ttFYoV+26NEWWUN/120iVfzNzBz+VZqfY5BWen84WcDOGNIFzqkJ3sdUUQ8oJKPYDW1Pj5bvpUZ+YW8u3Azu6trycpI4cpje3Lm0Cz66FTBIjFPJR9hnHPMXb+T6fmFvDFvA1t3VdE6JZExw7MYMyyLEdltiIvTlZxExE8lHyHWFJcxPd8/zr5qaxktEuI4cUAHzhyaxbH92utIGRGpl0o+jBXvquTN+Rt5Nb+Q/LU7MIPDerTlymN7csqgzrROSfQ6ooiEOZV8mNldVct7izczPb+QT78posbn6N8pjf87tT9nDOlCl4wUryOKSARRyYeBWp/jixXFvJpfyDsLNlJWVUun9GQuPboHZw7NYkDndK8jikiEUsl7xDnHwg0lTM8v5LW5G9hSWklaUgKnDe7C6GFdOKxHW+1AFZGgeVLyCzeUcMr9n5Kd2ZKcdqlkZ7ake9uWdM9MpUtGMgnx0XtKnXXbynlt7gZezS9k+ZZdJMYbx/XrwJhhWZzQv4NO+SsiIeVJybdJTSQrI4WVW8v4+Jsiqmp83wWKM7q2SSG7bSrd95R/21S6t21JdmbLiCzBHeVVvDl/IzPyN/DV6m0AjMxpw9/GDOJnB3cmo2ULjxOKSLTypOS7tE7hyXEjAfD5HJtKKlhTXM7abWWsKS73f20rI3/tdkorar43b6f0ZLLbtqR7Pe8CWrcMn6NNKqpr+WjJFl7NL+SjpVuornX0ap/KjSf344whXXTuGBFpFp6PycfFGV0yUuiSkcLhvdp+7z7nHDvKq1ldXMbabeXf/QEo9r8DKJq9/nuPz2iZSPfMlvW+C+iQloRZ045x+3yOL1dtY3p+IW8t2EhpRQ3t05K46PAcxgzLYmCX9CbPICJSl+clvy9mRpvUFrRJbcGw7DY/ur+8qoa128pZvfX77wIK1m3nzXkb8LnvHpucGEf3zNRv3wXU/QOQlZES1H6AJZtKeDW/kNcKNrBxZwWpLeI5eVAnxgzL4ohe7YjXDlQR8UhYl3xDWrZIoH+ndPp3+vEhhtW1Pgq37/7Ru4DVW8v49JsiKuvsB4jfsx+gztDPnj8C2ZktSWnx4/0AG3fuZkbBBqbnF7JkUynxccaxfdtz808H8JMBHeudR0SkuQVV8mY2BHgMaAWsBn7hnCsJQa6gJcbHkdMutd7rl/p8ji2llawpLvt2/N+/T6Cc1wo2UPKD/QAd0pLIaet/F9AlI4WvV21j1qpinINh2Rn86YyBnDa4M21bJTXXyxMR2S/mnGv4UXub2exr4HfOuU/M7BKgh3Putobmy83NdXl5eQf8vE1tR3lVoPzLWbO1jDXbylkb+GOwuaSSHu1SGT20C2cOzdJFsEWk2ZjZbOdcbmPmCXa4pi/waeD2e8C7QIMlH+4yWrYgo2ULhnTL+NF9lTW1tIiP0w5UEYkIwX7qaCEwOnD7XKBbkMsLe0kJ8Sp4EYkYDW7Jm9n7QKd67roVuAR40MxuA14DqvaxnPHAeIDs7OwDCisiIo0T1Jj89xZk1hf4j3PukIYeG+5j8iIi4ehAxuSDGq4xsw6B73HAH/AfaSMiImEi2DH5sWb2DbAE2AD8O/hIIiISKkEdXeOcewB4IERZREQkxKL3nL4iIqKSFxGJZiE7uqZRT2pWCixt9icOndbATq9DBCGS80dydoB2wFavQwQh0v/9Iz1/H+dc68bM4NUJypY29jCgcGJmk5xz473OcaAiOX8kZwcwszyt+96JhvyNnUfDNQfmda8DBCmS80dy9mgQ6f/+MZffq+GaiN6aETlQWveluXm1Jd/otxwiUULrvjQrT7bkRUSkeWhMfh/MrKuZzTCzZWa20swmmlmSmf3EzGab2fzA9xO8zlqffeQ/xMwKAl9zzWyM11nrs7f8de7PNrNdZvY7L3NGq0he/7Xuf0clvxfmP5/wK8B051wfoA+QAtyN/xC4051zBwMXA895FnQvGsi/AMh1zg0FTgEeN7OwuhRkA/n3+Cfwtgfxol4kr/9a93/AOddkX0BXYAawDFgJTASSgLbAR8AuYGJTZggi+yjg0x9MSwe2A63qTDNgG5DkdeYDzN8D2AwkeJ25MfmBM4F7gDvwX53M88z1vAat/+GdPSbW/Sbbkm/gr1EF/itIhfPb7IHA7LoTnP/6tauB3nUmnw3Mcc5VNl+0/bLP/GZ2qJktBOYDVzrnan68CE/tK/9Q4CbgT82eaj9p/feU1v06mnK45gSgwjn3bwDnXC0wAbgI/w7fz/Gv7BHLzAYC/w+4wussjeWc+9I5NxAYCdxsZsleZ2qEO4D7nHO7vA6yD1r/w1SsrftNWfL7uyUQrhYBI+pOMLN0/FfJWmpmXYFXgYuccys8yNeQfebfM805txj/sMGgZk3XsH3lbw3cbWargeuBW8zsmuYO2ACt/97Rul+Hdrzu3QdASzO7CMDM4oF/8N246pvA/znnZnoXcZ/2lb/Tnp1NZtYd6I+/fMLJXvM750Y653KccznA/cCdzrmJniWNTpG8/mvdr6MpS36//pqGK+ff2zEGOMfMlgHFgM859zfgGvxbY3+sczhWBw/j/kgD+Y8C5ppZAf6tsV8758LqpFkN5I8EWv89onX/xwtsqj3EBuThfzsHEA/8C7i1zmPGEaZHF9Tzeo4A1gDDvc6i/N7n2Y+8Wv/D5CuSs4cif5N+4tXMugEPAwOA9sALzrkrAvetxn9YUAtgB3CSc25Rk4URaWZa/yUcNNtpDczsCGAKMMY5N6dZnlQkTGj9F6/o3DUiIlFMR9eIiESxkJS8mXUzs4/MbJGZLTSz6wLTM83svcBJdt4zszaB6WZmD5rZcjObZ2bD6yzr4sDjl5nZxaHIJ9JUQrzuv2NmO8zsDa9ej0SfUG3J1wA3OOcOAg4Drjazg4D/Az5w/o91fxD4GeBU/B/z7gOMBx4F/y8GcDtwKHAIcPueXw6RMBWSdT/gHuDC5gousSEkJe+c27hnZ5JzrhRYDGQBo4FnAg97Bv+JdQhMf9b5zQIyzKwzcDLwnnNum3NuO/Ae/jPFiYSlEK77OOc+AEqbMb7EgJCPyZtZDjAM+BLo6JzbGLhrE9AxcDsLWFdntvWBaXubLhL2glz3RZpESEvezFoBLwPXO/95Or7l/Ifx6FAeiUpa9yVchazkzSwR/0r+vHPulcDkzXveiga+bwlMLwS61Zm9a2Da3qaLhK0QrfsiTSJUR9cY8CSw2Dn3zzp3vYb/yjEEvs+oM/2iwJEGhwE7A29t3wVOMrM2gR2uJwWmiYSlEK77Ik0iJB+GMrOjgM/wn4TfF5h8C/6xyWlANv5zL5znnNsW+MWYiH+najnwK+dcXmBZlwTmBfibC5yPWyQchXjd/wz/WRFb4T8p1aXOOW3kSFD0iVcRkSimT7yKiEQxlbyISBRTyYuIRDGVvIhIFFPJi4hEMZW8RD0zqw1ch3Shmc01sxvMbJ/rvpnlmNkFzZVRpKmo5CUW7HbODXXODQR+gv9MkLc3ME8OoJKXiKfj5CXqmdku51yrOj/3BL4G2gHdgeeA1MDd1zjn/mdms/Bfm3UV/rNIPgjcBRwHJAEPO+ceb7YXIXKAVPIS9X5Y8oFpO4B++E/t63POVZhZH2CKcy7XzI4DfuecOy3w+PFAB+fcX80sCZgJnOucW9WML0Wk0RK8DiDisURgopkNBWqBvnt53EnAYDM7J/Bza/wX/lDJS1hTyUvMCQzX1OI/M+TtwGZgCP59VBV7mw24VueSkUijHa8SU8ysPfAYMDFwnvfWwEbnnA//pffiAw8tBdLqzPoucFXgtMKYWV8zS0UkzGlLXmJBipkV4B+aqcG/o3XPaYEfAV42s4uAd4CywPR5QK2ZzQWeBh7Af8TNnMCZJIv47pJ+ImFLO15FRKKYhmtERKKYSl5EJIqp5EVEophKXkQkiqnkRUSimEpeRCSKqeRFRKKYSl5EJIr9f8rh140Iv+FNAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1121,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1141,22 +1070,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x2153f372f88>]"
+       "[<matplotlib.lines.Line2D at 0x2362f8a11b0>]"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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ytaOPSLQU6HLdDAyFeONQFy80t/P8/nYOBsMLohYW57LpjgrWLprB0lnT1DNF5Cop0GVcnerr5xfvtvNcczsvv9NBz4VBstLTWFk5nQdXlXPXghnMyp8U6zJFkoICXcaUu/Neey/PNZ/kheZ2dh85RcihMHcCG6pLWLNoBrfPLdC+miLjQP+q5Jq5OzsOdlHXGOD5/e0cO3UOgKrSKXxlzTzWLppB1Q15anolMs4U6HLVBodC1DW18fcvt9J0/AzZmWncPreAL981l7sWzKA4LzvWJYqkFAW6jNq5/iF+vOso/+eVgxzpOktFwWT+cmM1n1paysQs7W4vEisK9Ks0FHJ6LwySNzF1ltWd6uvnie2H+afth+jq62fprKk8smER9ywu0soUkTigQL9Kj73Uyl898w4rK6azcflM1lUVk5OkF/qOdp3l8VcP8n/fOMq5gSHWLpzB766u5JbyaeqZIhJHkjOBroPnmk9SPCWb46fP8bUfvcX/+GkT66qK2bi8lNrKgqQ4Y917opvNLx/g528HMOD+paVsuqOCBcW5sS5NRIahQL8KPecHePtYN19aXcnD985n95FTPLX7OD9/6wQ/efM4RVMm8KllpWxcNjPhws/d2d7ayd+91Mor7wWZnJXO76wq58FVc7hh6sRYlyciV6BAvwo7Ipsn1M6djplx8+x8bp6dz599YjEv7G9ny+5jPP7KQf7+pQNUlU5h47KZfHLpDRTkTIh16Zc1OBRi2942/v6lAzQe76YgZwJfv28Bn7ttdkpdJxBJZAr0q1DfGmRCRhrLy6Z95PnszHQ2VJewobqEYO8F/j1yxv7nP9/HX2xtZvX8QjYuL+XuRUVkZ8bHapDzA0P8aOeHK1bmRFasfHpZadzUKCLRUaBfhYaWTm4pz79i4BXkTODBVXN4cNUc3jvZw5Y3j/OT3cd5YX87udkZfOLGEjYun0nN7NhcWDzV188/v3aYf2o4RKdWrIgkBQX6KHX0XOCdkz3cv+yGqN8zryiXb6xbyNfuXcBrBzp5avcx/m3PCX74+lHK8ifx6WWlbFxeyuzp498e9tip8IqVf309vGJlzcIZ/O4dFayYk68VKyIJToE+Sg2tQQBWVRaM+r3pacaquQWsmlvAN+8f5Om9bWzZfZz//cJ7fPf596iZPY2Ny2fy8RtLxnzeet+JM2x+uZV/14oVkaSlQB+lhpZOpmRnUFWad03HmTwhg43LZ7Jx+UwC3ef46ZsneGr3MR75SSP/89/3cs+iIjYuL+WO+YVXvTPP+ytWHnv5AC+/28HkrHQerC3nd27XihWRZDRioJvZLOAJoBgIAZvd/bvDjLsT+GsgEwi6++qxLTU+1LcGua1i+pjOM5fkTeRLd1bye6sraDp+hqd2H+Nnb53gPxoDTJ+cxSeX3sCvLp/Jkhui28F+KORsi/RYefvYRStWbp1N3iStWBFJVtGcoQ8CD7v7bjPLBXaZ2bPuvu/9AWY2FfgesM7dj5jZjHGqN6aOdJ7l2Klz/JePVYzL8c2M6pl5VM/M4799fBEvvdPBljeP8eRrR/jH+kPML8ph4/KZfGpp6bCNr84PDPGjXcf4/isHONypFSsiqWbEQHf3ABCIPO4xs2agFNh30bDfAra4+5HIuPZxqDXm6t+fP587fdw/KzM9jbsXF3H34iK6zw7w88YTbNl9nG/V7efb2/azqrKAjctLuW9JMQNDIf55+2F+EFmxctOsqfzp+oXcs7hYK1ZEUsio5tDNrBxYBuy45KX5QKaZ/QLIBb7r7k8M8/5NwCaAsrKy0VcbY/UtQWbkTqCyMOe6fm7epEw+e+tsPnvrbA4F+9jy5nG27D7GH/2/t5iU1QTA2X6tWBFJdVEHupnlAE8BD7n7mWGOczOwFpgIbDez19z93YsHuftmYDNATU2NX0vh11soFL7AeMf8wpiGZXnBZP7onvk8tHYeOw+f4idvHgec364tZ2HxlJjVJSKxF1Wgm1km4TB/0t23DDPkGOELoX1An5m9DNwEvDvM2IT0bnsPnX391FaO/3RLNNLSjBVz8lkxJz/WpYhInBhxPZyFT0cfB5rd/dHLDPs34GNmlmFmk4BbgeaxKzP26ls6AVg1d/Trz0VErodoztBXAQ8AjWa2J/LcI0AZgLs/5u7NZrYNeJvw0sbvu3vTeBQcKw0tQeYUTNb6bRGJW9GscnkVGHHS2N2/A3xnLIqKN4NDIXYc7OKTS6O/3V9E5Hq7ulsQU8xbx7rpvTB4Vbf7i4hcLwr0KDS0hNefr4yTC6IiIsNRoEehvjXI4pIp5E/OinUpIiKXpUAfwbn+IXYfPn1d7g4VEbkWCvQR7DzcRf9QiFotVxSROKdAH0F9SycZacaKct3AIyLxTYE+gobWIMvKpjJ5glrHi0h8U6BfQffZARqPd1Or5YoikgAU6Few/UAn7rrdX0QSgwL9Chpag0zMTGfprKmxLkVEZEQK9CuobwmyYk4+WRn6mkQk/impLqOt+zytHX1afy4iCUOBfhkNke3mdEFURBKFAv0y6ls6mTYpk8Ul2gVIRBKDAn0Y7s721iArK6eTpk2WRSRBKNCHcajzLCe6z2u6RUQSigJ9GPWRdrlafy4iiUSBPoyG1iA35GVTPn1SrEsREYmaAv0SoZCzvbWT2rkFhPfHFhFJDAr0S+wLnOHU2QFqtTuRiCSYEQPdzGaZ2Ytm1mxme83sq8OMudPMus1sT+TXn41PuePv/fXnmj8XkUQTTU/YQeBhd99tZrnALjN71t33XTLuFXf/xNiXeH3Vt3RSWTiZoinZsS5FRGRURjxDd/eAu++OPO4BmoHS8S4sFvoHQ7x+sEtn5yKSkEY1h25m5cAyYMcwL680s7fMrM7MloxBbdfdnqOnOTcwpPXnIpKQot6Gx8xygKeAh9z9zCUv7wZmu3uvmW0AfgrMG+YYm4BNAGVlZVdd9HipbwmSZrCyQhdERSTxRHWGbmaZhMP8SXffcunr7n7G3Xsjj7cCmWb2S6e57r7Z3WvcvaawsPAaSx97Da1BqkrzyJuUGetSRERGLZpVLgY8DjS7+6OXGVMcGYeZrYgct3MsCx1vfRcGefPIaU23iEjCimbKZRXwANBoZnsizz0ClAG4+2PArwFfMrNB4BzwGXf3cah33Lx+qIvBkKv/uYgkrBED3d1fBa54y6S7/w3wN2NVVCw0tATJSk+jZnZ+rEsREbkqulM0or6lk+WzpzIxKz3WpYiIXBUFOtDV18++wBlWaf5cRBKYAh3Y3hq+flurG4pEJIEp0IH61iA5EzK4aWZerEsREblqCnTCZ+i3zsknI11fh4gkrpRPsBOnz3Ew2KfpFhFJeCkf6B9uN6f15yKS2FI+0BtaOynIyWJBUW6sSxERuSYpHejuTn1LkJWV2m5ORBJfSgd6a0cv7T0XWKXt5kQkCaR0oNe3RNaf64YiEUkCKR7oQWZOm0jZ9EmxLkVE5JqlbKAPhZzXDnTqdn8RSRopG+hNx7s5c36QWi1XFJEkkbKBXt8aXn+u+XMRSRYpG+gNLZ0sKMqlMHdCrEsRERkTKRno5weGeONQl6ZbRCSppGSg7z5yiguDIV0QFZGkkpKB3tDSSXqacWuFtpsTkeSRkoFe3xrkxpl55GZnxroUEZExk3KB3nN+gLePdWu6RUSSzoiBbmazzOxFM2s2s71m9tUrjL3FzIbM7NfGtsyxs+NAF0Mh1wVREUk6GVGMGQQedvfdZpYL7DKzZ91938WDzCwd+Dbw9DjUOWYaWjuZkJHG8rJpsS5FRGRMjXiG7u4Bd98dedwDNAOlwwz9CvAU0D6mFY6xhtYgt5Tnk52ZHutSRETG1Kjm0M2sHFgG7Ljk+VLg08BjI7x/k5ntNLOdHR0do6t0DAR7L7C/rUfTLSKSlKIOdDPLIXwG/pC7n7nk5b8GvuHuQ1c6hrtvdvcad68pLCwcfbXXqKE13C5XF0RFJBlFM4eOmWUSDvMn3X3LMENqgH+N7PpTAGwws0F3/+mYVToGGlqCTMnOoKo0L9aliIiMuRED3cIp/TjQ7O6PDjfG3edcNP4HwM/jLcwhvP78torppKdpuzkRST7RnKGvAh4AGs1sT+S5R4AyAHe/4rx5vDjadZajXef44u0VsS5FRGRcjBjo7v4qEPUprbt//loKGi/1LeF2uat0QVREklTK3Cla39rJjNwJVBbmxLoUEZFxkRKB7u5sbw1SWzmdyIVbEZGkkxKB/s7JHoK9/dTO1XJFEUleKRHo9S2R9ecKdBFJYikR6A0tQcqnT6J06sRYlyIiMm6SPtAHh0LsONil6RYRSXpJH+hvHeum98KgbvcXkaSX9IHeEFl/vrJS689FJLklfaDXtwZZXDKF/MlZsS5FRGRcJXWgn+sfYvfh07o7VERSQlIH+q7Dp+gfCumCqIikhKQO9PrWIBlpxory/FiXIiIy7pI60Btagiwrm8rkCVG1fRcRSWhJG+jd5wZoPN5NrZYrikiKSNpAf+1AJyHX7f4ikjqSNtAbWoJMzExn6aypsS5FROS6SNpAr2/tZMWcfLIykvaPKCLyEUmZdifPnKelvVfrz0UkpSRloDe0hm/31wVREUklSRno9S2dTJ2UyeKSKbEuRUTkuhkx0M1slpm9aGbNZrbXzL46zJj7zextM9tjZjvN7PbxKXdk7k5DS5CVFdNJS9N2cyKSOqI5Qx8EHnb3RcBtwJfNbPElY54HbnL3pcDvAN8f2zKjd6jzLCe6z+t2fxFJOSMGursH3H135HEP0AyUXjKm19098tvJgBMj9ZF2uavULldEUsyo5tDNrBxYBuwY5rVPm9l+4D8In6UP9/5NkSmZnR0dHaOvNgoNrUFK8rKZUzB5XI4vIhKvog50M8sBngIecvczl77u7j9x94XAp4BvDncMd9/s7jXuXlNYWHi1NV9WKORsb+2ktrIAM82fi0hqiSrQzSyTcJg/6e5brjTW3V8GKs3suk9i7wuc4dTZAa0/F5GUFM0qFwMeB5rd/dHLjJkbGYeZLQeygM6xLDQa768/V/8WEUlF0fSVXQU8ADSa2Z7Ic48AZQDu/hjwq8B/NrMB4BzwGxddJL1u6ls6qSycTNGU7Ov90SIiMTdioLv7q8AVJ6Td/dvAt8eqqKvRPxjijUNd/NrNM2NZhohIzCTNnaJvHTvN2f4h3e4vIikraQK9viVImsHKCl0QFZHUlDSB3tDSSVVpHnmTMmNdiohITCRFoJ/tH+TNo6c03SIiKS0pAv31g10MDLnWn4tISkuKQG9o7SQrPY2a2fmxLkVEJGaSItDrW4Isnz2ViVnpsS5FRCRmEj7QT/X1sy9whlWaPxeRFJfwgb79QCfuqP+5iKS8hA/0+pYgORMyuGlmXqxLERGJqYQP9IbWTlbMyScjPeH/KCIi1yShU/DE6XMcDPZRq92JREQSO9A/2G5O8+ciIokd6A2tnUyfnMWCotxYlyIiEnMJG+juTn1LkJWV00lL03ZzIiIJG+itHb2091zQdIuISETCBnp9S3iHO91QJCISlrCB3tAaZOa0iZRNnxTrUkRE4kJCBvpQyNne2qmzcxGRiyRkoO890c2Z84PUql2uiMgHEjLQ358/14YWIiIfGjHQzWyWmb1oZs1mttfMvjrMmM+a2duRXw1mdtP4lBvW0BpkQVEuhbkTxvNjREQSSjRn6IPAw+6+CLgN+LKZLb5kzEFgtbvfCHwT2Dy2ZX7owuAQbxzq0nSLiMglMkYa4O4BIBB53GNmzUApsO+iMQ0XveU1YOYY1/mB3YdPc34gpAuiIiKXGNUcupmVA8uAHVcY9gWg7jLv32RmO81sZ0dHx2g++gOZ6cadCwq5tULbzYmIXMzcPbqBZjnAS8BfuPuWy4y5C/gecLu7d17peDU1Nb5z585RlisiktrMbJe71wz32ohTLpEDZAJPAU9eIcxvBL4PrB8pzEVEZOxFs8rFgMeBZnd/9DJjyoAtwAPu/u7YligiItGI5gx9FfAA0GhmeyLPPQKUAbj7Y8CfAdOB74Xzn8HL/UggIiLjI5pVLq8CV+xP6+5fBL44VkWJiMjoJeSdoiIi8ssU6CIiSUKBLiKSJBToIiJJIuobi8b8g806gKTV0IQAAALASURBVMNX+fYCIDiG5SQ6fR8fpe/jQ/ouPioZvo/Z7l443AsxC/RrYWY7tSzyQ/o+Pkrfx4f0XXxUsn8fmnIREUkSCnQRkSSRqIE+bv3WE5S+j4/S9/EhfRcfldTfR0LOoYuIyC9L1DN0ERG5hAJdRCRJJFygm9k6M3vHzFrM7E9iXU8sRbOBd6oxs3Qze9PMfh7rWmLNzKaa2Y/NbH/k78jKWNcUK2b2h5F/I01m9kMzy451TeMhoQLdzNKBvwXWA4uB3xxmw+pUEs0G3qnmq0BzrIuIE98Ftrn7QuAmUvR7MbNS4L8CNe5eBaQDn4ltVeMjoQIdWAG0uPsBd+8H/hW4P8Y1xYy7B9x9d+RxD+F/sKWxrSp2zGwm8HHCO2elNDObAtxBeHMa3L3f3U/HtqqYygAmmlkGMAk4EeN6xkWiBXopcPSi3x8jhQPsYlFu4J3s/hr4YyAU60LiQAXQAfxjZArq+2Y2OdZFxYK7Hwf+CjgCBIBud38mtlWNj0QL9OE22kj5dZeRDbyfAh5y9zOxricWzOwTQLu774p1LXEiA1gO/J27LwP6gJS85mRm0wj/JD8HuAGYbGafi21V4yPRAv0YMOui388kSX90ilY0G3iniFXAJ83sEOGpuDVm9i+xLSmmjgHH3P39n9h+TDjgU9HdwEF373D3AcL7H9fGuKZxkWiB/gYwz8zmmFkW4QsbP4txTTETzQbeqcLd/9TdZ7p7OeG/Fy+4e1KehUXD3duAo2a2IPLUWmBfDEuKpSPAbWY2KfJvZi1JeoE4mk2i44a7D5rZHwBPE75S/Q/uvjfGZcXSsBt4u/vWGNYk8eMrwJORk58DwIMxricm3H2Hmf0Y2E14ZdibJGkLAN36LyKSJBJtykVERC5DgS4ikiQU6CIiSUKBLiKSJBToIiJJQoEuIpIkFOgiIkni/wOVmvhpzDzQiQAAAABJRU5ErkJggg==\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1187,6 +1116,133 @@
     "*For solutions, see `solutions/arima_sse.py`*"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Value\n",
+      "Date                \n",
+      "2030-10-01  0.000026\n",
+      "2031-01-01  0.000039\n",
+      "2031-04-01  0.000048\n",
+      "2031-07-01  0.000051\n",
+      "2031-10-01  0.000042\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                               SARIMAX Results                                \n",
+      "==============================================================================\n",
+      "Dep. Variable:                  Value   No. Observations:                  330\n",
+      "Model:                 ARIMA(1, 1, 2)   Log Likelihood                1010.595\n",
+      "Date:                Sat, 07 Jan 2023   AIC                          -2013.190\n",
+      "Time:                        22:14:57   BIC                          -1998.006\n",
+      "Sample:                    07-01-1949   HQIC                         -2007.133\n",
+      "                         - 10-01-2031                                         \n",
+      "Covariance Type:                  opg                                         \n",
+      "==============================================================================\n",
+      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+      "------------------------------------------------------------------------------\n",
+      "ar.L1         -0.3324      0.097     -3.442      0.001      -0.522      -0.143\n",
+      "ma.L1         -0.7440      0.100     -7.425      0.000      -0.940      -0.548\n",
+      "ma.L2          0.0009      0.101      0.009      0.993      -0.197       0.199\n",
+      "sigma2         0.0001   2.38e-06     52.584      0.000       0.000       0.000\n",
+      "===================================================================================\n",
+      "Ljung-Box (L1) (Q):                   0.61   Jarque-Bera (JB):             24678.09\n",
+      "Prob(Q):                              0.44   Prob(JB):                         0.00\n",
+      "Heteroskedasticity (H):            8865.43   Skew:                            -2.20\n",
+      "Prob(H) (two-sided):                  0.00   Kurtosis:                        45.20\n",
+      "===================================================================================\n",
+      "\n",
+      "Warnings:\n",
+      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
+      "standard error 0.0014250462944652698\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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2GmrinD6nmdNPaOadp6Q4obmWlsYU+7t6ea29m+3t3Ty74wAPPfc6OYdUIsZfnNzCO0+ZyTtOmcmbZjb0L/9YqdLZwQvX55mZpqIWiZiqTwT5NQm60pn+oY9HqzeT5cFNr/MvT7zCtj2dnNBcy99edRrXL1/AlLrRX+twOsvTr7bzq21t/GpbG196aAsAp8xu5Npz5nHNOScyZ0rd2D/UcZDOhIkgfmTroDrgRaKl6hNB4ZoER5sI2jt7+fentvP9p/7Mvs5eTjuhiX94/1lcfdaJR/xCHkldTZyLT53FxafOAmDH/m5+/uJeHti0kzseeZGvrXuRt53cwrXnzOUv3zKHhlTlfN39iaDI523ScpUikVI5JdM4DVqTYMrI+27bc4i7fvMqP352J+lMjotPncmNF57EhYtnlKUpZ/70ela/fRGr376IP7d3cf+zO7n/2Z189r7n+OKDm7n6rBP54HnzOXv+1AlvOuodIRE0aypqkUip/kQwypoEmWyOx7bu5d+efI3f/amdVCLGdefO46MXLGLxrKZjFtfClgY+dekp3LxiCRu3H+BH63fwwKbXWbt+B6fObuID583nvefMZVpDzTGLYST5PoKhw0chqBHs7ug53iGJyASp+kTQNMwqZfu70qxdv50fPLWdnW8c5sQptXz2ilNZtXwB049j4WtmnLtwOucunM4X3n0GDz23i7Xrd/Dlh7Zwx8MvcvnS2Vx/3gLe/qaWcXd2j0dfmAiGjhqCILke2qumIZGoqPpEUNhH4O78/tX9/PD323n4+d2kszne/qYWvvDuM7j09FkkihR6x1NTbZJVyxewavkCtu7q4Efrd3D/szt56LldzJtWxweXzee9585j7tRj38E8UmexTtITiZbqTwRhjeDHG3fyjUe38fLeTppqE6xaPp//8hcLWTL72DX/lOL0Oc188T1LueWq01i3eTf3btjBPzy6jW88tq2/g/mqt8zp/3zlNnJncdBH4O7HpC/jje40L+zsYOuuDl4/eJi9h3pp6+hl76Ee2jvT9OWC2NwhvzxOYyrBlLokzbUJmuuSNNclmdFQw8ymFDObUsxqqg2um1PMaEgd19qVSLWr+kTQXJfEDB7buoez50/la9edydVnnkhdTXWc4FWbjLPy7LmsPHsu29u7ww7mVj5733N84YEXuHLpCVx91olcuGQGqUT5PtNoo4b6sk5vJlfyiXK5nLP59Q6eeLmN51sP8vzOg7QeONz/eENNnFnNQSG+dO4UZjamqEnE6C/GDQgXyjl4OEPH4T46evrYeeAw+zp7i06Ol4gZs5pSzJ5SywnNtcxurmVWc2rgdpg8ptQlJ7zTXqQSlCURmNmVwD8BceB77n77kMdTwL8B5wLtwAfd/bXwsb8FbgSywF+7+7qxvHdzbZI7Vy/jhOY6zjixueTPMpEWtNRz86VL+OsVi9m4/Q1+vLGVh57bxf/b9DqNqQQrTp/FVW+ewztPmVlyousd5oQyGOh36ejpG1ciaDvUyxMvtfHrbW088dI+2rvSACxsqees+VP58PkLecvcKSw9sbnkzvKeviz7OntpO9TL3kO97OnoYffBHnZ39LCno4dtew7xm5f3FT0voiYeo6UxqFXMaEwxvaGGloYapoeXlsYaptbXMLUuybT6GprrksRV05BJqOREYGZx4DvAZUArsN7MHnT3LQW73QgccPfFZnY9cAfwQTM7A7geWAqcCDxmZqe4e3YsMbzrtNmlfoyKEnQwT+PchdO47eqlPPlKOw8/v4t1m3fzwKbXqUvGefubWrhoyQwuXDK+M5lH7iMI1yToyXC0A6te3dfFzzbv5mdb9rBx+wHcoaWhhouWzOCdp87kwsUzmdmUGlOMR6M2GWfetHrmTasfcb/udIa9HWGi6OhhX2eatkO9/Ulk98Eetrzewf6udP+IqqHMgh8eU+qSNNclaK5NBpe6BI2pJI21CZpSCRprEzSkEjSm4tTXJGioSdCQitOQSlCbjFOXjJOMm2ojUjHKUSNYDrzs7q8AmNlaYCVQmAhWAl8Mb98HfNuCo2AlsNbde4FXzezl8PWeLENck0JNIsY7T5nJO0+ZyVeueTNPv7qfR17YzRMvtfH4i3sBOHFKLRcumcG5C6dx5rypLJnVOGrH+GhNQzDyVNTZnLNpxwEe37qXR7fs4aW9nQC8eW4zn1pxCitOn8UZc5orpq2+vibBohkJFs1oGHE/d6crnWV/Z5r2rl7eONzHG91pDnT19d8OmqeCZqpX9nXScThDZ29mTB3s8ZhRm4hRVxOnNhnvTxB1yTipZIzaZJxUIhY+FiOVOPJ60OMF+6cSA4+nCvZNJWJKPmOQyzl9uRzZnJPJOdlseJ1zMuH2vmxwO5P1gf0KHs/knFzBddbDxwftnyt4XuHj4XYPn5uDnHvBJfh/zeXA8UF9ahC0qsbMiMWCH5cjHYrlSARzgR0F91uB84fbx90zZnYQaAm3PzXkuXOLvYmZ3QTcBLBgwYIyhF19EvEYFyyewQWLZwCwvb2bJ15u4zcv7eORF3Zz74ZWAGqTMZaeOIWz5k3lv13yJmY0HvlLPD98dKQawdBEcKArza+2tfGLP+7lV9vaeKO7j3jMOP+k6Xz4/AVctvSE4zLi6VjKz7XUmEqwoGXkWsZQuZzTlQ6TQk+GrnSW7t7guqs3Q1c6w+F0lp6+LD19OQ73ZelOZ+nty9KTCbels3T2ZtjXmQ6292XpyeTo6cuSzgQFQynyCSGVjFMTD24n4zFqEsElGTdqEnFq4hZsi8dIxIN9knEjEYuRTBiJmBGPxcJr678edDEjFl7HYwO3C3NR/mb+UwWFWVCo5Qu8bI7+QjSTDb6DTDYopDPZYFtfLrzOOn3ZHOlMLrgOb6ezTl+4rS83cDuT8/59+8Lnp7M5MtkcJX7VJTPjyO/QCK+tv3A3Cwp8CL5PM8PdcRicMEb4PFXTWezua4A1AMuWLZvgP1FlWNBSz4dbFvLh8xeSyzmvtXfx/M6D/GHHQTbtOMBdv32Vk2bU81dvW3TEc0eqEeRHKr3w+kF2HOhm458PsHH7Af7U1gUETT7vOm0W7zptFhctmXlU8zJFQSxmNNUmg0Q6ylnu45XJ5ujNBJeevmz/dT655Lf1ZoLrdKZwW7g93C9dWGBmgkKzL+McPNxHX2agEM0XtH3ZoOBNZ3P9v3IrRX+Silt/YutPcPEYyUSMVHi/oSCpDewXPC+f6JKxGInwtfKJLhEz4vEYcQvvx4PCedA+8digpBizwUkyESbEZCxGPF7wukOfGyaAcrNbi28vRyLYCcwvuD8v3FZsn1YzSxAcJu1H+Vw5CrGYcfLMRk6e2cjKs+dyOJ3l9FsfGfaM6+FmHwWYUh8U7Lc//CIA0+qTnLNgGteeM5cLFs/grHlTK6bJJ2oS4S/0hvJ3t4xZ/ldmf9OID24GyTdz5LzwuuD5YT3APfhVa2H9ILhN/y/hfPNGUJDG+gvhZEHBqSav0pQjEawHlpjZSQSF+PXAh4bs8yCwmqDt/zrg5+7uZvYg8EMz+wZBZ/ES4PdliCnyapMxYgZdwyWCEWoEJ06p5csrl1Jfk+CtC6exqKVeB5ocwcyIG8RjcSpoPkUZh5L/fGGb/yeBdQTDR+9y981m9iVgg7s/CNwJfD/sDN5PkCwI97uXoGM5A3xirCOGpDgzoyGVoKu3+NfZO8KoITMr2pwkIpNTWfK4u/8U+OmQbbcW3O4B3j/Mc78KfLUccchgDTWJ0WsEEzzthohMPJUCk1hDKk53uniNoC+b6++4EpFoUyKYxBpSw08el84cuXC9iESTSoJJbMSmoawSgYgEVBJMYg2pOF3DNA2lMzn1D4gIoEQwqQWjhtQ0JCIjU0kwiTWkEnSniyeCXjUNiUhIJcEk1lATH7azuE9NQyISUkkwiTWkEvT0BbMgDqXOYhHJU0kwiTXUBOcLdhVpHlJnsYjkqSSYxBrCCWCKdRirs1hE8lQSTGINqWCZyWLzDalpSETyVBJMYv1NQ8PVCNQ0JCIoEUxq/U1Dw/QRJFUjEBGUCCa10ZqGUqoRiAhKBJNavkZQ7KQydRaLSJ5Kgkks30dQ7KQydRaLSJ5KgklsoGlIncUiMjyVBJNYff+ooSJ9BGoaEpGQSoJJLB4z6pLxI2oEuZyTyTlJ1QhEBCWCSa8hlThiTYJ0NlyvWDUCEUGJYNJrSB1ZI8gngpQSgYigRDDpNdQcuSZBOqMagYgMUEkwyTWkjlyToD8RqI9ARFAimPSC5SqH9BGoRiAiBVQSTHJBZ3HxPgIlAhEBJYJJr6GmSGdxWCPQ8FERASWCSa8hlaB7aNOQagQiUkAlwSTXUBM0DbkPrFucrxFo9lERASWCSa8hlSDn0NOX69+mzmIRKaSSYJJrDCeeKxxCqkQgIoVUEkxy9UWWq1QfgYgUUkkwyRVbrlKjhkSkkEqCSa7YcpX9NQIlAhFBiWDSG6lGoEnnRASUCCa9hmJ9BOosFpECKgkmuXzTUHexpiElAhFBiWDSa0wduYC9Zh8VkUIqCSa5osNHMznMgqUsRURKSgRmNt3MHjWzl8LracPstzrc5yUzW12w/atmtsPMOkuJQ4ZXk4hRE48NWq4ync1RE49hpkQgIqXXCG4BHnf3JcDj4f1BzGw6cBtwPrAcuK0gYfxnuE2Oofohy1WmMzn1D4hIv1JLg5XA3eHtu4FriuxzBfCou+939wPAo8CVAO7+lLvvKjEGGUV+4rm8dDanoaMi0q/U0mB2QUG+G5hdZJ+5wI6C+63htjExs5vMbIOZbWhraxt7pBHWmEocWSNQR7GIhBKj7WBmjwEnFHno84V33N3NzIvsVxbuvgZYA7Bs2bJj9j6TUX0qTndhH4GahkSkwKiJwN0vHe4xM9tjZnPcfZeZzQH2FtltJ3Bxwf15wC/HGKeUoDGVOGL4qBKBiOSVWho8CORHAa0GHiiyzzrgcjObFnYSXx5uk+OkfshylelsThPOiUi/UkuD24HLzOwl4NLwPma2zMy+B+Du+4EvA+vDy5fCbZjZ18ysFag3s1Yz+2KJ8UgRDanEoEnn+rKqEYjIgFGbhkbi7u3AiiLbNwAfK7h/F3BXkf0+B3yulBhkdENHDfWqs1hECqg0iIChC9irj0BECqk0iIDGVJx0Ntc/x1A6o/MIRGSASoMIyM831B02D6XVRyAiBVQaRMDQGUjTGY0aEpEBKg0ioH7IcpV9WXUWi8gAlQYRMHS5SnUWi0ghlQYRkG8a6upVIhCRI6k0iID6msFNQ73qLBaRAioNIqCwRuDuwfBR9RGISEilQQQUDh/tywYTt2rUkIjkqTSIgIHho1nS2XDhejUNiUhIpUEE1CZjxCxoGurLKBGIyGAqDSLAzPonnlONQESGUmkQEQ3hcpX5+YZ0QpmI5Kk0iIj6VJyudJZeNQ2JyBAqDSKiUTUCERmGSoOIqK+J061RQyJShEqDiMgvYN+nRCAiQ6g0iIj6/KghNQ2JyBAqDSIiv4B9Wp3FIjKESoOIaEzF6erNaNSQiBxBpUFE1NckONyXpTcTzECqNYtFJE+lQUTk5xt6o7sP0KRzIjJApUFE5Jer3N+VBtQ0JCIDVBpExECNIEwEqhGISEilQUQ0hGsSHAibhlQjEJE8lQYRkW8aOtCtpiERGUylQUTkm4YOqGlIRIZQaRAR+eUqD3T1kYwbZjbBEYlIpVAiiIjCGoFqAyJSSCVCROT7CLrTWfUPiMggKhEiIj9qCNRRLCKDqUSIiHjMqEsGtQIlAhEppBIhQhrC5iH1EYhIIZUIEdIQdhhrniERKaQSIULyQ0g186iIFFKJECGNKfURiMiRVCJESL5pSIlARAqVVCKY2XQze9TMXgqvpw2z3+pwn5fMbHW4rd7MfmJmL5rZZjO7vZRYZHT5IaTqLBaRQqWWCLcAj7v7EuDx8P4gZjYduA04H1gO3FaQML7u7qcB5wAXmNlVJcYjI2hQ05CIFFFqibASuDu8fTdwTZF9rgAedff97n4AeBS40t273f0XAO6eBjYC80qMR0aQ7yyuScQnOBIRqSSlJoLZ7r4rvL0bmF1kn7nAjoL7reG2fmY2FbiaoFZRlJndZGYbzGxDW1tbSUFHVWP/8FFNOCciAxKj7WBmjwEnFHno84V33N3NzMcagJklgHuAb7n7K8Pt5+5rgDUAy5YtG/P7yMB8Qxo+KiKFRk0E7n7pcI+Z2R4zm+Puu8xsDrC3yG47gYsL7s8Dfllwfw3wkrt/82gClvHL1wjUWSwihUotER4EVoe3VwMPFNlnHXC5mU0LO4kvD7dhZl8BpgCfKjEOOQr9o4ZUIxCRAqWWCLcDl5nZS8Cl4X3MbJmZfQ/A3fcDXwbWh5cvuft+M5tH0Lx0BrDRzDaZ2cdKjEdGoFFDIlLMqE1DI3H3dmBFke0bgI8V3L8LuGvIPq2Aei2Po/4TyuIaNSQiA/TTMELyw0eTCeVfERmgRBAh6iwWkWJUIkRIg4aPikgRKhEiZO7UOv56xRJWnF7svD8RiaqSOoulupgZf3PZKRMdhohUGNUIREQiTolARCTilAhERCJOiUBEJOKUCEREIk6JQEQk4pQIREQiTolARCTizL36Fvsys0PAH8v4klOAg2V8vRnAvjK+nuIrjeIrjeIrTSXFt9DdZx6x1d2r7gJsKPPrrVF8ik/xKb4oxufuahoK/edEBzAKxVcaxVcaxVeaSo9PiQDA3Sv6D6X4SqP4SqP4SlPp8UH1JoI1Ex3AKBRfaRRfaRRfaSIXX1V2FouISPlUa41ARETKRIlARCTiKiIRmNl8M/uFmW0xs81mdnO4fbqZPWpmL4XX08Ltp5nZk2bWa2afGfJar5nZ82a2ycw2VFJ8ZnZqGFf+0mFmn6qU+MLHbjazF8LXKTm2ccb3YTN7Lvw7/s7Mzip4rbvMbK+ZvVCO2MoZn5nVmtnvzewP4ev8fSXFFz5WCcfHcN9fpRwfI31/lXB8rAzj22RmG8zswoLXesTM3jCzh8YURLnHo45zXOwc4K3h7SZgG3AG8DXglnD7LcAd4e1ZwHnAV4HPDHmt14AZlRpfwWvGgd0EJ3hURHzAm4EXgHqC1eseAxZPQHxvB6aFt68Cni54rXcAbwVemMC/b9H4AAMaw9tJ4GngLyolvgo6PoaNr0KOj+H+vpVyfDQy0L97JvBiwWutAK4GHhpTDOX8hyjjP9YDwGUEZw/PKfiy/jhkvy9yHBJBOeMreOxy4LeVFB/wfuDOgvtfAD43UfGF26cBO4dsW0QZE0G54wu31wMbgfMrKb5KOj5G+f4m/PgYGl+FHh9vA7YO2XYxY0wEFdE0VMjMFgHnEPyamu3uu8KHdgNHs+q6Az8zs2fM7KYKjC/veuCe8kZXcnwvABeZWYuZ1QN/Ccyf4PhuBB4uZwwjKTU+M4ub2SZgL/Couz9dSfFRecfHcH/fSjk+CuOrmOPDzK41sxeBnwAfLfV9K2rxejNrBP4D+JS7d5hZ/2Pu7mZ2NGNdL3T3nWY2C3jUzF50919XUHyYWQ3wHuBvyxFXueJz961mdgfwM6AL2ARkJyo+M7uE4EC8kOOgHPG5exY428ymAveb2ZvdvSz9GWX6/irm+Bju71spx8fQ+Crp+HD3+wn+v94BfBm4tJT3rpgagZklCb6EH7j7j8PNe8xsTvj4HIJfWSNy953h9V7gfmB5JcUXugrY6O57yhFbOeNz9zvd/Vx3fwdwgKC98rjHZ2ZnAt8DVrp7ezliOJ7xufsbwC+AKyspvko5Pkb5/ib8+Bjh+6uI46Mgnl8DJ5vZjFLevyISgQWp706Ctq5vFDz0ILA6vL2aoO1spNdpMLOm/G2CdsaSf42VK74Cqyhjtbec8YW/FDGzBcB7gR8e7/jC9/4x8FfuXpYD7XjEZ2Yzw5oAZlZH0M77YgXFVxHHx1H8fSf0+Bgpvgo5PhaHz8HM3gqkgNJ+LJXasVGOC0HVy4HnCKpbmwja31qAx4GXCHrop4f7nwC0Ah3AG+HtZuBk4A/hZTPw+UqKL3ysIfyjTam07y987AlgS/gdrpig+L5H8Gsrv++Ggte6B9gF9IVx31gp8RGM4Hg2fJ0XgFsr6fujco6Pkf6+lXB8jBRfJRwf/zP8+20CniRo7iuMrw04THB8XHE0MWiKCRGRiKuIpiEREZk4SgQiIhGnRCAiEnFKBCIiEadEICIScUoEIqMws6wFMz1utmBm0f9hZiMeO2a2yMw+dLxiFCmFEoHI6A67+9nuvpTgJLGrgNtGec4iQIlAqoLOIxAZhZl1untjwf2TgfXADGAh8H2CE6EAPunuvzOzp4DTgVeBu4FvAbcTzAyZAr7j7v/7uH0IkREoEYiMYmgiCLe9AZwKHAJy7t5jZkuAe9x9mZldTDDF97vD/W8CZrn7V8wsBfwWeL+7v3ocP4pIURU1+6hIFUoC3zazswlmojxlmP0uB840s+vC+1OAJQQ1BpEJpUQgMkZh01CWYDbI24A9wFkEfW49wz0N+O/uvu64BCkyBuosFhkDM5sJfBf4tgftqlOAXe6eA/6KYIlFCJqMmgqeug74eDjdMGZ2SjgDqMiEU41AZHR1Fqw6lgQyBJ3D+emC/xfwH2b2EeARggVLIJhJMmtmfwD+FfgngpFEG8MphNuAa45P+CIjU2exiEjEqWlIRCTilAhERCJOiUBEJOKUCEREIk6JQEQk4pQIREQiTolARCTi/j8O2POy2A+x/AAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "print(changes.tail())\n",
+    "\n",
+    "# add cut off point for modelling\n",
+    "marker  = datetime(2015, 1, 1)\n",
+    "train, test = changes.loc[:marker], changes.loc[marker:]\n",
+    "\n",
+    "# copy code from above for arima model\n",
+    "changes_pq = stattools.arma_order_select_ic(changes) \n",
+    "p, q = changes_pq.bic_min_order\n",
+    "changes_model = sms.tsa.ARIMA(changes, order=(p, 1, q))\n",
+    "changes_out = changes_model.fit()\n",
+    "\n",
+    "print(changes_out.summary())\n",
+    "\n",
+    "# produce prediction\n",
+    "y_pred = changes_out.predict(marker, changes.index[-1])\n",
+    "y_pred.plot(title = \"Predictions\")\n",
+    "test.plot(title = \"Actual\")\n",
+    "\n",
+    "# compute error\n",
+    "y_true = test[\"Value\"] # Convert to a series to match our predicted data\n",
+    "sse = np.sum((y_true - y_pred)**2)\n",
+    "print(\"standard error \" + str(sse))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1208,7 +1264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1217,15 +1273,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:165: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
-      "  % freq, ValueWarning)\n"
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency QS-OCT will be used.\n",
+      "  self._init_dates(dates, freq)\n"
      ]
     }
    ],
@@ -1235,43 +1293,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 65,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    }
+   ],
    "source": [
     "results = seasonal_model.fit()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/html": [
        "<table class=\"simpletable\">\n",
-       "<caption>Statespace Model Results</caption>\n",
+       "<caption>SARIMAX Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>               <td>Value</td>             <th>  No. Observations:  </th>    <td>322</td>   \n",
+       "  <th>Dep. Variable:</th>                <td>Value</td>             <th>  No. Observations:  </th>    <td>330</td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)x(0, 1, 0, 4)</td> <th>  Log Likelihood     </th>  <td>891.773</td> \n",
+       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)x(0, 1, [], 4)</td> <th>  Log Likelihood     </th>  <td>929.738</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>                  <td>Mon, 13 May 2019</td>        <th>  AIC                </th> <td>-1777.546</td>\n",
+       "  <th>Date:</th>                   <td>Sat, 07 Jan 2023</td>        <th>  AIC                </th> <td>-1853.477</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                      <td>11:14:20</td>            <th>  BIC                </th> <td>-1766.269</td>\n",
+       "  <th>Time:</th>                       <td>22:15:29</td>            <th>  BIC                </th> <td>-1842.125</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Sample:</th>                   <td>07-01-1949</td>           <th>  HQIC               </th> <td>-1773.041</td>\n",
+       "  <th>Sample:</th>                    <td>07-01-1949</td>           <th>  HQIC               </th> <td>-1848.947</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th></th>                         <td>- 10-01-2029</td>          <th>                     </th>     <td> </td>    \n",
+       "  <th></th>                          <td>- 10-01-2031</td>          <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Covariance Type:</th>              <td>opg</td>              <th>                     </th>     <td> </td>    \n",
+       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>    \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -1279,53 +1346,53 @@
        "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ar.L1</th>  <td>   -0.3695</td> <td>    0.020</td> <td>  -18.912</td> <td> 0.000</td> <td>   -0.408</td> <td>   -0.331</td>\n",
+       "  <th>ar.L1</th>  <td>   -0.3510</td> <td>    0.019</td> <td>  -18.353</td> <td> 0.000</td> <td>   -0.389</td> <td>   -0.314</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>ma.L1</th>  <td>   -0.9999</td> <td>    1.169</td> <td>   -0.856</td> <td> 0.392</td> <td>   -3.290</td> <td>    1.291</td>\n",
+       "  <th>ma.L1</th>  <td>   -0.9992</td> <td>    0.085</td> <td>  -11.792</td> <td> 0.000</td> <td>   -1.165</td> <td>   -0.833</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>sigma2</th> <td>    0.0002</td> <td>    0.000</td> <td>    0.850</td> <td> 0.395</td> <td>   -0.000</td> <td>    0.001</td>\n",
+       "  <th>sigma2</th> <td>    0.0002</td> <td> 1.77e-05</td> <td>   10.583</td> <td> 0.000</td> <td>    0.000</td> <td>    0.000</td>\n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
        "<tr>\n",
-       "  <th>Ljung-Box (Q):</th>          <td>196.94</td> <th>  Jarque-Bera (JB):  </th> <td>7969.37</td>\n",
+       "  <th>Ljung-Box (L1) (Q):</th>      <td>0.64</td>   <th>  Jarque-Bera (JB):  </th> <td>6677.17</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(Q):</th>                 <td>0.00</td>  <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
+       "  <th>Prob(Q):</th>                 <td>0.42</td>   <th>  Prob(JB):          </th>  <td>0.00</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Heteroskedasticity (H):</th> <td>713.28</td> <th>  Skew:              </th>  <td>0.66</td>  \n",
+       "  <th>Heteroskedasticity (H):</th> <td>8732.68</td> <th>  Skew:              </th>  <td>0.54</td>  \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Prob(H) (two-sided):</th>     <td>0.00</td>  <th>  Kurtosis:          </th>  <td>27.53</td> \n",
+       "  <th>Prob(H) (two-sided):</th>     <td>0.00</td>   <th>  Kurtosis:          </th>  <td>25.18</td> \n",
        "</tr>\n",
        "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
        "\"\"\"\n",
-       "                                 Statespace Model Results                                \n",
-       "=========================================================================================\n",
-       "Dep. Variable:                             Value   No. Observations:                  322\n",
-       "Model:             SARIMAX(1, 1, 1)x(0, 1, 0, 4)   Log Likelihood                 891.773\n",
-       "Date:                           Mon, 13 May 2019   AIC                          -1777.546\n",
-       "Time:                                   11:14:20   BIC                          -1766.269\n",
-       "Sample:                               07-01-1949   HQIC                         -1773.041\n",
-       "                                    - 10-01-2029                                         \n",
-       "Covariance Type:                             opg                                         \n",
+       "                                     SARIMAX Results                                      \n",
+       "==========================================================================================\n",
+       "Dep. Variable:                              Value   No. Observations:                  330\n",
+       "Model:             SARIMAX(1, 1, 1)x(0, 1, [], 4)   Log Likelihood                 929.738\n",
+       "Date:                            Sat, 07 Jan 2023   AIC                          -1853.477\n",
+       "Time:                                    22:15:29   BIC                          -1842.125\n",
+       "Sample:                                07-01-1949   HQIC                         -1848.947\n",
+       "                                     - 10-01-2031                                         \n",
+       "Covariance Type:                              opg                                         \n",
        "==============================================================================\n",
        "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
        "------------------------------------------------------------------------------\n",
-       "ar.L1         -0.3695      0.020    -18.912      0.000      -0.408      -0.331\n",
-       "ma.L1         -0.9999      1.169     -0.856      0.392      -3.290       1.291\n",
-       "sigma2         0.0002      0.000      0.850      0.395      -0.000       0.001\n",
+       "ar.L1         -0.3510      0.019    -18.353      0.000      -0.389      -0.314\n",
+       "ma.L1         -0.9992      0.085    -11.792      0.000      -1.165      -0.833\n",
+       "sigma2         0.0002   1.77e-05     10.583      0.000       0.000       0.000\n",
        "===================================================================================\n",
-       "Ljung-Box (Q):                      196.94   Jarque-Bera (JB):              7969.37\n",
-       "Prob(Q):                              0.00   Prob(JB):                         0.00\n",
-       "Heteroskedasticity (H):             713.28   Skew:                             0.66\n",
-       "Prob(H) (two-sided):                  0.00   Kurtosis:                        27.53\n",
+       "Ljung-Box (L1) (Q):                   0.64   Jarque-Bera (JB):              6677.17\n",
+       "Prob(Q):                              0.42   Prob(JB):                         0.00\n",
+       "Heteroskedasticity (H):            8732.68   Skew:                             0.54\n",
+       "Prob(H) (two-sided):                  0.00   Kurtosis:                        25.18\n",
        "===================================================================================\n",
        "\n",
        "Warnings:\n",
@@ -1333,7 +1400,7 @@
        "\"\"\""
       ]
      },
-     "execution_count": 37,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1352,6 +1419,124 @@
     "2. Choose a seasonal commodity from Quandl, such as Wheat, and apply a Seasonal ARIMA to the data."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
+      "  self._init_dates(dates, freq)\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:471: ValueWarning: No frequency information was provided, so inferred frequency M will be used.\n",
+      "  self._init_dates(dates, freq)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>SARIMAX Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>                <td>Value</td>             <th>  No. Observations:  </th>    <td>514</td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>           <td>SARIMAX(1, 1, 1)x(0, 1, [], 4)</td> <th>  Log Likelihood     </th> <td>-2170.178</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>                   <td>Sat, 07 Jan 2023</td>        <th>  AIC                </th> <td>4346.356</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                       <td>22:17:49</td>            <th>  BIC                </th> <td>4359.053</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Sample:</th>                    <td>01-31-1980</td>           <th>  HQIC               </th> <td>4351.335</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th></th>                          <td>- 10-31-2022</td>          <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>ar.L1</th>  <td>   -0.6143</td> <td>    0.022</td> <td>  -27.314</td> <td> 0.000</td> <td>   -0.658</td> <td>   -0.570</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>ma.L1</th>  <td>    0.9995</td> <td>    0.077</td> <td>   13.014</td> <td> 0.000</td> <td>    0.849</td> <td>    1.150</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>sigma2</th> <td>  293.0192</td> <td>   23.414</td> <td>   12.515</td> <td> 0.000</td> <td>  247.129</td> <td>  338.909</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Ljung-Box (L1) (Q):</th>     <td>0.72</td> <th>  Jarque-Bera (JB):  </th> <td>923.41</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Q):</th>                <td>0.40</td> <th>  Prob(JB):          </th>  <td>0.00</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Heteroskedasticity (H):</th> <td>9.01</td> <th>  Skew:              </th>  <td>-0.13</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(H) (two-sided):</th>    <td>0.00</td> <th>  Kurtosis:          </th>  <td>9.59</td> \n",
+       "</tr>\n",
+       "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                                     SARIMAX Results                                      \n",
+       "==========================================================================================\n",
+       "Dep. Variable:                              Value   No. Observations:                  514\n",
+       "Model:             SARIMAX(1, 1, 1)x(0, 1, [], 4)   Log Likelihood               -2170.178\n",
+       "Date:                            Sat, 07 Jan 2023   AIC                           4346.356\n",
+       "Time:                                    22:17:49   BIC                           4359.053\n",
+       "Sample:                                01-31-1980   HQIC                          4351.335\n",
+       "                                     - 10-31-2022                                         \n",
+       "Covariance Type:                              opg                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "ar.L1         -0.6143      0.022    -27.314      0.000      -0.658      -0.570\n",
+       "ma.L1          0.9995      0.077     13.014      0.000       0.849       1.150\n",
+       "sigma2       293.0192     23.414     12.515      0.000     247.129     338.909\n",
+       "===================================================================================\n",
+       "Ljung-Box (L1) (Q):                   0.72   Jarque-Bera (JB):               923.41\n",
+       "Prob(Q):                              0.40   Prob(JB):                         0.00\n",
+       "Heteroskedasticity (H):               9.01   Skew:                            -0.13\n",
+       "Prob(H) (two-sided):                  0.00   Kurtosis:                         9.59\n",
+       "===================================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# qs 1\n",
+    "# above, we can see that the model is set with (0, 1, 0, 4), for (p, d, q, period)\n",
+    "# period = 4 as we have 4 seasons in a year\n",
+    "\n",
+    "# qs 2\n",
+    "price = quandl.get(\"ODA/PWHEAMT_USD\", collapse=\"monthly\")\n",
+    "seasonal_price = SARIMAX(price, order = (1, 1, 1), seasonal_order = (0, 1, 0, 4))\n",
+    "result3 = seasonal_price.fit()\n",
+    "result3.summary()\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1379,11 +1564,131 @@
     "\n",
     "Further, the documenation for this library also contains additional information on choosing parameter bounds that is worth reading for a more complete picture of ARIMA."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(4, 0)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 0)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-invertible starting MA parameters found.'\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 2)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
+      "  warn('Non-stationary starting autoregressive parameters'\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 1)\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\base\\model.py:604: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
+      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
+     ]
+    }
+   ],
+   "source": [
+    "# xrp data\n",
+    "result = stattools.arma_order_select_ic(xrp) \n",
+    "print(result.bic_min_order)\n",
+    "\n",
+    "# eth data\n",
+    "result = stattools.arma_order_select_ic(eth) \n",
+    "print(result.bic_min_order)\n",
+    "\n",
+    "# unemployment data\n",
+    "result = stattools.arma_order_select_ic(changes) \n",
+    "print(result.bic_min_order)\n",
+    "\n",
+    "# wheat data\n",
+    "result = stattools.arma_order_select_ic(price) \n",
+    "print(result.bic_min_order)"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1397,7 +1702,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 2.2.3 - GARCH.ipynb b/notebooks/Module 2.2.3 - GARCH.ipynb
index 044f042..57cc3d7 100644
--- a/notebooks/Module 2.2.3 - GARCH.ipynb	
+++ b/notebooks/Module 2.2.3 - GARCH.ipynb	
@@ -61,7 +61,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,7 +79,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -88,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,22 +99,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x20a173d7b08>]"
+       "[<matplotlib.lines.Line2D at 0x212c6e564d0>]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -131,7 +131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -140,12 +140,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -162,12 +162,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -193,12 +201,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -215,12 +223,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\statsmodels\\graphics\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\n",
+      "  warnings.warn(\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -246,70 +262,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'arch'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-15-f7e7e7ccf968>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0march\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0march_model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'arch'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from arch import arch_model"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'arch_model' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-12-ddbedf841504>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0march_model\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mNameError\u001b[0m: name 'arch_model' is not defined"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "model = arch_model(y)"
+    "model = arch_model(y, vol = 'GARCH')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Iteration:      1,   Func. Count:      6,   Neg. LLF: 2616.973909624015\n",
-      "Iteration:      2,   Func. Count:     13,   Neg. LLF: 2600.845474003604\n",
-      "Iteration:      3,   Func. Count:     21,   Neg. LLF: 2598.5124870490017\n",
-      "Iteration:      4,   Func. Count:     28,   Neg. LLF: 2596.0157763641864\n",
-      "Iteration:      5,   Func. Count:     35,   Neg. LLF: 2588.3319294869007\n",
-      "Iteration:      6,   Func. Count:     41,   Neg. LLF: 2563.815454647498\n",
-      "Iteration:      7,   Func. Count:     47,   Neg. LLF: 2560.08970419472\n",
-      "Iteration:      8,   Func. Count:     53,   Neg. LLF: 2558.689579315256\n",
-      "Iteration:      9,   Func. Count:     59,   Neg. LLF: 2558.685773298228\n",
-      "Iteration:     10,   Func. Count:     65,   Neg. LLF: 2558.6852226001606\n",
-      "Iteration:     11,   Func. Count:     71,   Neg. LLF: 2558.685119386276\n",
-      "Optimization terminated successfully.    (Exit mode 0)\n",
-      "            Current function value: 2558.6851182754017\n",
+      "Iteration:      1,   Func. Count:      6,   Neg. LLF: 324290.29566441604\n",
+      "Iteration:      2,   Func. Count:     12,   Neg. LLF: 226075.52410248906\n",
+      "Iteration:      3,   Func. Count:     19,   Neg. LLF: 6830.113526740106\n",
+      "Iteration:      4,   Func. Count:     25,   Neg. LLF: 3030.8231653584726\n",
+      "Iteration:      5,   Func. Count:     31,   Neg. LLF: 2563.815598056422\n",
+      "Iteration:      6,   Func. Count:     36,   Neg. LLF: 2560.0897150451437\n",
+      "Iteration:      7,   Func. Count:     41,   Neg. LLF: 2558.689580480894\n",
+      "Iteration:      8,   Func. Count:     46,   Neg. LLF: 2558.685772194439\n",
+      "Iteration:      9,   Func. Count:     51,   Neg. LLF: 2558.6852215224003\n",
+      "Iteration:     10,   Func. Count:     56,   Neg. LLF: 2558.685118276649\n",
+      "Iteration:     11,   Func. Count:     60,   Neg. LLF: 2558.6851193875236\n",
+      "Optimization terminated successfully    (Exit mode 0)\n",
+      "            Current function value: 2558.685118276649\n",
       "            Iterations: 11\n",
-      "            Function evaluations: 71\n",
+      "            Function evaluations: 60\n",
       "            Gradient evaluations: 11\n"
      ]
     }
@@ -320,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -329,10 +321,10 @@
        "<table class=\"simpletable\">\n",
        "<caption>Constant Mean - GARCH Model Results</caption>\n",
        "<tr>\n",
-       "  <th>Dep. Variable:</th>          <td>y</td>         <th>  R-squared:         </th>  <td>  -0.000</td> \n",
+       "  <th>Dep. Variable:</th>          <td>y</td>         <th>  R-squared:         </th>  <td>   0.000</td> \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Mean Model:</th>       <td>Constant Mean</td>   <th>  Adj. R-squared:    </th>  <td>  -0.000</td> \n",
+       "  <th>Mean Model:</th>       <td>Constant Mean</td>   <th>  Adj. R-squared:    </th>  <td>   0.000</td> \n",
        "</tr>\n",
        "<tr>\n",
        "  <th>Vol Model:</th>            <td>GARCH</td>       <th>  Log-Likelihood:    </th> <td>  -2558.69</td>\n",
@@ -347,10 +339,10 @@
        "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>1000</td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>           <td>Mon, May 13 2019</td>  <th>  Df Residuals:      </th>     <td>996</td>   \n",
+       "  <th>Date:</th>           <td>Sat, Jan 07 2023</td>  <th>  Df Residuals:      </th>     <td>999</td>   \n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>               <td>12:18:53</td>      <th>  Df Model:          </th>      <td>4</td>    \n",
+       "  <th>Time:</th>               <td>23:25:41</td>      <th>  Df Model:          </th>      <td>1</td>    \n",
        "</tr>\n",
        "</table>\n",
        "<table class=\"simpletable\">\n",
@@ -371,10 +363,10 @@
        "  <th>omega</th>    <td>    5.0192</td> <td>    1.077</td> <td>    4.662</td> <td>3.138e-06</td> <td>[  2.909,  7.129]</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>alpha[1]</th> <td>    0.6676</td> <td>    0.111</td> <td>    6.041</td> <td>1.536e-09</td> <td>[  0.451,  0.884]</td>\n",
+       "  <th>alpha[1]</th> <td>    0.6676</td> <td>    0.111</td> <td>    6.040</td> <td>1.536e-09</td> <td>[  0.451,  0.884]</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>beta[1]</th>    <td>0.0000</td>   <td>    0.145</td>   <td>0.000</td>   <td>    1.000</td> <td>[ -0.284,  0.284]</td>\n",
+       "  <th>beta[1]</th>  <td>2.6513e-13</td> <td>    0.145</td> <td>1.829e-12</td> <td>    1.000</td> <td>[ -0.284,  0.284]</td>\n",
        "</tr>\n",
        "</table><br/><br/>Covariance estimator: robust"
       ],
@@ -383,14 +375,14 @@
        "\"\"\"\n",
        "                     Constant Mean - GARCH Model Results                      \n",
        "==============================================================================\n",
-       "Dep. Variable:                      y   R-squared:                      -0.000\n",
-       "Mean Model:             Constant Mean   Adj. R-squared:                 -0.000\n",
+       "Dep. Variable:                      y   R-squared:                       0.000\n",
+       "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
        "Vol Model:                      GARCH   Log-Likelihood:               -2558.69\n",
        "Distribution:                  Normal   AIC:                           5125.37\n",
        "Method:            Maximum Likelihood   BIC:                           5145.00\n",
        "                                        No. Observations:                 1000\n",
-       "Date:                Mon, May 13 2019   Df Residuals:                      996\n",
-       "Time:                        12:18:53   Df Model:                            4\n",
+       "Date:                Sat, Jan 07 2023   Df Residuals:                      999\n",
+       "Time:                        23:25:41   Df Model:                            1\n",
        "                               Mean Model                               \n",
        "========================================================================\n",
        "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
@@ -401,15 +393,15 @@
        "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
        "------------------------------------------------------------------------\n",
        "omega          5.0192      1.077      4.662  3.138e-06 [  2.909,  7.129]\n",
-       "alpha[1]       0.6676      0.111      6.041  1.536e-09 [  0.451,  0.884]\n",
-       "beta[1]        0.0000      0.145      0.000      1.000 [ -0.284,  0.284]\n",
+       "alpha[1]       0.6676      0.111      6.040  1.536e-09 [  0.451,  0.884]\n",
+       "beta[1]    2.6513e-13      0.145  1.829e-12      1.000 [ -0.284,  0.284]\n",
        "========================================================================\n",
        "\n",
        "Covariance estimator: robust\n",
        "\"\"\""
       ]
      },
-     "execution_count": 15,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -427,18 +419,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 80,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "0.016239425305830522"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "                     Constant Mean - GARCH Model Results                      \n",
+      "==============================================================================\n",
+      "Dep. Variable:                      y   R-squared:                       0.000\n",
+      "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
+      "Vol Model:                      GARCH   Log-Likelihood:               -2915.84\n",
+      "Distribution:                  Normal   AIC:                           5843.67\n",
+      "Method:            Maximum Likelihood   BIC:                           5873.12\n",
+      "                                        No. Observations:                 1000\n",
+      "Date:                Sat, Jan 07 2023   Df Residuals:                      999\n",
+      "Time:                        23:33:58   Df Model:                            1\n",
+      "                               Mean Model                               \n",
+      "========================================================================\n",
+      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
+      "------------------------------------------------------------------------\n",
+      "mu             1.8666      0.123     15.134  9.642e-52 [  1.625,  2.108]\n",
+      "                            Volatility Model                            \n",
+      "========================================================================\n",
+      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
+      "------------------------------------------------------------------------\n",
+      "omega          9.2230      3.434      2.686  7.241e-03 [  2.492, 15.954]\n",
+      "alpha[1]       0.8143  5.184e-02     15.707  1.347e-55 [  0.713,  0.916]\n",
+      "alpha[2]       0.0000      0.397      0.000      1.000 [ -0.778,  0.778]\n",
+      "beta[1]    4.1327e-15      0.545  7.588e-15      1.000 [ -1.067,  1.067]\n",
+      "beta[2]    1.1124e-13      0.145  7.665e-13      1.000 [ -0.284,  0.284]\n",
+      "========================================================================\n",
+      "\n",
+      "Covariance estimator: robust\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\env\\lib\\site-packages\\arch\\__future__\\_utility.py:11: FutureWarning: \n",
+      "The default for reindex is True. After September 2021 this will change to\n",
+      "False. Set reindex to True or False to silence this message. Alternatively,\n",
+      "you can use the import comment\n",
+      "\n",
+      "from arch.__future__ import reindexing\n",
+      "\n",
+      "to globally set reindex to True and silence this warning.\n",
+      "\n",
+      "  warnings.warn(\n"
+     ]
     }
    ],
    "source": [
@@ -461,6 +492,275 @@
     "2. Modify the code we used to *create* the data, adding a second lag term. Fit the GARCH(1,1) model again, and see what the results look like when the model does not have enough specificity. Then fit a GARCH(2,2) model and observe the improvement."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration:      1,   Func. Count:      6,   Neg. LLF: 324290.29566441604\n",
+      "Iteration:      2,   Func. Count:     12,   Neg. LLF: 226075.52410248906\n",
+      "Iteration:      3,   Func. Count:     19,   Neg. LLF: 6830.113526740106\n",
+      "Iteration:      4,   Func. Count:     25,   Neg. LLF: 3030.8231653584726\n",
+      "Iteration:      5,   Func. Count:     31,   Neg. LLF: 2563.815598056422\n",
+      "Iteration:      6,   Func. Count:     36,   Neg. LLF: 2560.0897150451437\n",
+      "Iteration:      7,   Func. Count:     41,   Neg. LLF: 2558.689580480894\n",
+      "Iteration:      8,   Func. Count:     46,   Neg. LLF: 2558.685772194439\n",
+      "Iteration:      9,   Func. Count:     51,   Neg. LLF: 2558.6852215224003\n",
+      "Iteration:     10,   Func. Count:     56,   Neg. LLF: 2558.685118276649\n",
+      "Iteration:     11,   Func. Count:     60,   Neg. LLF: 2558.6851193875236\n",
+      "Optimization terminated successfully    (Exit mode 0)\n",
+      "            Current function value: 2558.685118276649\n",
+      "            Iterations: 11\n",
+      "            Function evaluations: 60\n",
+      "            Gradient evaluations: 11\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<table class=\"simpletable\">\n",
+       "<caption>Constant Mean - GARCH Model Results</caption>\n",
+       "<tr>\n",
+       "  <th>Dep. Variable:</th>          <td>y</td>         <th>  R-squared:         </th>  <td>   0.000</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Mean Model:</th>       <td>Constant Mean</td>   <th>  Adj. R-squared:    </th>  <td>   0.000</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Vol Model:</th>            <td>GARCH</td>       <th>  Log-Likelihood:    </th> <td>  -2558.69</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Distribution:</th>        <td>Normal</td>       <th>  AIC:               </th> <td>   5125.37</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>        <td>Maximum Likelihood</td> <th>  BIC:               </th> <td>   5145.00</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th></th>                        <td></td>          <th>  No. Observations:  </th>    <td>1000</td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>           <td>Sat, Jan 07 2023</td>  <th>  Df Residuals:      </th>     <td>999</td>   \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>               <td>23:27:53</td>      <th>  Df Model:          </th>      <td>1</td>    \n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<caption>Mean Model</caption>\n",
+       "<tr>\n",
+       "   <td></td>     <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>   <th>95.0% Conf. Int.</th> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>mu</th> <td>    0.0162</td> <td>7.743e-02</td> <td>    0.210</td> <td>    0.834</td> <td>[ -0.136,  0.168]</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<caption>Volatility Model</caption>\n",
+       "<tr>\n",
+       "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>       <th>P>|t|</th>   <th>95.0% Conf. Int.</th> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>omega</th>    <td>    5.0192</td> <td>    1.077</td> <td>    4.662</td> <td>3.138e-06</td> <td>[  2.909,  7.129]</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>alpha[1]</th> <td>    0.6676</td> <td>    0.111</td> <td>    6.040</td> <td>1.536e-09</td> <td>[  0.451,  0.884]</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>beta[1]</th>  <td>2.6513e-13</td> <td>    0.145</td> <td>1.829e-12</td> <td>    1.000</td> <td>[ -0.284,  0.284]</td>\n",
+       "</tr>\n",
+       "</table><br/><br/>Covariance estimator: robust"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                     Constant Mean - GARCH Model Results                      \n",
+       "==============================================================================\n",
+       "Dep. Variable:                      y   R-squared:                       0.000\n",
+       "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
+       "Vol Model:                      GARCH   Log-Likelihood:               -2558.69\n",
+       "Distribution:                  Normal   AIC:                           5125.37\n",
+       "Method:            Maximum Likelihood   BIC:                           5145.00\n",
+       "                                        No. Observations:                 1000\n",
+       "Date:                Sat, Jan 07 2023   Df Residuals:                      999\n",
+       "Time:                        23:27:53   Df Model:                            1\n",
+       "                               Mean Model                               \n",
+       "========================================================================\n",
+       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
+       "------------------------------------------------------------------------\n",
+       "mu             0.0162  7.743e-02      0.210      0.834 [ -0.136,  0.168]\n",
+       "                            Volatility Model                            \n",
+       "========================================================================\n",
+       "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
+       "------------------------------------------------------------------------\n",
+       "omega          5.0192      1.077      4.662  3.138e-06 [  2.909,  7.129]\n",
+       "alpha[1]       0.6676      0.111      6.040  1.536e-09 [  0.451,  0.884]\n",
+       "beta[1]    2.6513e-13      0.145  1.829e-12      1.000 [ -0.284,  0.284]\n",
+       "========================================================================\n",
+       "\n",
+       "Covariance estimator: robust\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# qs 1\n",
+    "model = arch_model(y, vol = 'GARCH', p = 1, q = 1)\n",
+    "results = model.fit()\n",
+    "results.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration:      1,   Func. Count:      6,   Neg. LLF: 5079.709058046277\n",
+      "Iteration:      2,   Func. Count:     13,   Neg. LLF: 116099.01889884044\n",
+      "Iteration:      3,   Func. Count:     19,   Neg. LLF: 7571.802017908774\n",
+      "Iteration:      4,   Func. Count:     25,   Neg. LLF: 3760.409285924256\n",
+      "Iteration:      5,   Func. Count:     31,   Neg. LLF: 2921.9540752817165\n",
+      "Iteration:      6,   Func. Count:     36,   Neg. LLF: 2932.426695223647\n",
+      "Iteration:      7,   Func. Count:     42,   Neg. LLF: 2916.410310152743\n",
+      "Iteration:      8,   Func. Count:     47,   Neg. LLF: 2915.9282362587237\n",
+      "Iteration:      9,   Func. Count:     52,   Neg. LLF: 2915.8466267019794\n",
+      "Iteration:     10,   Func. Count:     57,   Neg. LLF: 2915.836179581095\n",
+      "Iteration:     11,   Func. Count:     62,   Neg. LLF: 2915.8360085129193\n",
+      "Iteration:     12,   Func. Count:     66,   Neg. LLF: 2915.836009006801\n",
+      "Optimization terminated successfully    (Exit mode 0)\n",
+      "            Current function value: 2915.8360085129193\n",
+      "            Iterations: 12\n",
+      "            Function evaluations: 66\n",
+      "            Gradient evaluations: 12\n",
+      "                     Constant Mean - GARCH Model Results                      \n",
+      "==============================================================================\n",
+      "Dep. Variable:                      y   R-squared:                       0.000\n",
+      "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
+      "Vol Model:                      GARCH   Log-Likelihood:               -2915.84\n",
+      "Distribution:                  Normal   AIC:                           5839.67\n",
+      "Method:            Maximum Likelihood   BIC:                           5859.30\n",
+      "                                        No. Observations:                 1000\n",
+      "Date:                Sat, Jan 07 2023   Df Residuals:                      999\n",
+      "Time:                        23:33:52   Df Model:                            1\n",
+      "                               Mean Model                               \n",
+      "========================================================================\n",
+      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
+      "------------------------------------------------------------------------\n",
+      "mu             1.8666      0.116     16.036  7.200e-58 [  1.638,  2.095]\n",
+      "                               Volatility Model                              \n",
+      "=============================================================================\n",
+      "                 coef    std err          t      P>|t|       95.0% Conf. Int.\n",
+      "-----------------------------------------------------------------------------\n",
+      "omega          9.2233      1.065      8.661  4.673e-18      [  7.136, 11.310]\n",
+      "alpha[1]       0.8143      0.104      7.837  4.598e-15      [  0.611,  1.018]\n",
+      "beta[1]    3.6417e-15  3.556e-02  1.024e-13      1.000 [-6.969e-02,6.969e-02]\n",
+      "=============================================================================\n",
+      "\n",
+      "Covariance estimator: robust\n"
+     ]
+    }
+   ],
+   "source": [
+    "# qs 2\n",
+    "y2 = np.zeros(1000)\n",
+    "y2[0] = np.random.normal()\n",
+    "for i in range(1, len(y2)):\n",
+    "    y2[i] = np.random.normal() * np.sqrt(5 + 0.75 * y2[i-1]**2) + np.sqrt(5 + 0.2 * y[i-3]**2)\n",
+    "\n",
+    "plt.plot(y2)\n",
+    "plt.show()\n",
+    "\n",
+    "model = arch_model(y2, vol = 'GARCH', p = 1, q = 1)\n",
+    "results = model.fit()\n",
+    "print(results.summary())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration:      1,   Func. Count:      8,   Neg. LLF: 4588.774438635322\n",
+      "Iteration:      2,   Func. Count:     16,   Neg. LLF: 3747.6136754934696\n",
+      "Iteration:      3,   Func. Count:     24,   Neg. LLF: 2207587.7008148897\n",
+      "Iteration:      4,   Func. Count:     32,   Neg. LLF: 3697.889067901465\n",
+      "Iteration:      5,   Func. Count:     40,   Neg. LLF: 2920.3197874921975\n",
+      "Iteration:      6,   Func. Count:     47,   Neg. LLF: 2918.547607607379\n",
+      "Iteration:      7,   Func. Count:     54,   Neg. LLF: 2916.7401806468115\n",
+      "Iteration:      8,   Func. Count:     61,   Neg. LLF: 2915.9700992094427\n",
+      "Iteration:      9,   Func. Count:     68,   Neg. LLF: 2915.8447952457645\n",
+      "Iteration:     10,   Func. Count:     75,   Neg. LLF: 2915.836051079413\n",
+      "Iteration:     11,   Func. Count:     82,   Neg. LLF: 2915.8360105038623\n",
+      "Iteration:     12,   Func. Count:     89,   Neg. LLF: 2915.8360083917287\n",
+      "Iteration:     13,   Func. Count:     95,   Neg. LLF: 2915.8360089040316\n",
+      "Optimization terminated successfully    (Exit mode 0)\n",
+      "            Current function value: 2915.8360083917287\n",
+      "            Iterations: 13\n",
+      "            Function evaluations: 95\n",
+      "            Gradient evaluations: 13\n",
+      "                     Constant Mean - GARCH Model Results                      \n",
+      "==============================================================================\n",
+      "Dep. Variable:                      y   R-squared:                       0.000\n",
+      "Mean Model:             Constant Mean   Adj. R-squared:                  0.000\n",
+      "Vol Model:                      GARCH   Log-Likelihood:               -2915.84\n",
+      "Distribution:                  Normal   AIC:                           5843.67\n",
+      "Method:            Maximum Likelihood   BIC:                           5873.12\n",
+      "                                        No. Observations:                 1000\n",
+      "Date:                Sat, Jan 07 2023   Df Residuals:                      999\n",
+      "Time:                        23:33:58   Df Model:                            1\n",
+      "                               Mean Model                               \n",
+      "========================================================================\n",
+      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
+      "------------------------------------------------------------------------\n",
+      "mu             1.8666      0.123     15.134  9.642e-52 [  1.625,  2.108]\n",
+      "                            Volatility Model                            \n",
+      "========================================================================\n",
+      "                 coef    std err          t      P>|t|  95.0% Conf. Int.\n",
+      "------------------------------------------------------------------------\n",
+      "omega          9.2230      3.434      2.686  7.241e-03 [  2.492, 15.954]\n",
+      "alpha[1]       0.8143  5.184e-02     15.707  1.347e-55 [  0.713,  0.916]\n",
+      "alpha[2]       0.0000      0.397      0.000      1.000 [ -0.778,  0.778]\n",
+      "beta[1]    4.1327e-15      0.545  7.588e-15      1.000 [ -1.067,  1.067]\n",
+      "beta[2]    1.1124e-13      0.145  7.665e-13      1.000 [ -0.284,  0.284]\n",
+      "========================================================================\n",
+      "\n",
+      "Covariance estimator: robust\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = arch_model(y2, vol = 'GARCH', p = 2, q = 2)\n",
+    "results = model.fit()\n",
+    "print(results.summary())"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -501,15 +801,160 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "%%capture\n",
+    "import numpy as np\n",
+    "import quandl\n",
+    "import my_secrets\n",
+    "from arch import arch_model\n",
+    "from statsmodels.tsa import stattools\n",
+    "from statsmodels import api as sms\n",
+    "\n",
+    "# qs 1\n",
+    "# download data\n",
+    "quandl.ApiConfig.api_key = my_secrets.QUANDL_API_KEY\n",
+    "price = quandl.get(\"BITFINEX/XRPUSD\")\n",
+    "price = price[[\"Last\"]]\n",
+    "\n",
+    "# qs 2\n",
+    "# log returns\n",
+    "price[\"log_return\"] = np.log(price/price.shift(1)).dropna()\n",
+    "\n",
+    "# train test split, limit the number of samples as run too long (200 takes 10 min)\n",
+    "price = price.iloc[-200:]\n",
+    "\n",
+    "# qs 3\n",
+    "price_in, price_out, buy_sell = [], [], []\n",
+    "in_buy_sell = 0 # -1 for sell, 1 for buy, 0 for not in trade\n",
+    "for i in range(10, price.shape[0]):\n",
+    "\n",
+    "    # get prices\n",
+    "    price_i = price[\"log_return\"].iloc[:i]\n",
+    "\n",
+    "    # armia\n",
+    "    pq = stattools.arma_order_select_ic(price_i);\n",
+    "    p, q = pq.bic_min_order\n",
+    "    model = sms.tsa.ARIMA(price_i, order=(p, 1, q));\n",
+    "    out = model.fit()\n",
+    "    \n",
+    "    # garch model\n",
+    "    model2 = arch_model(out.resid, vol = 'GARCH', p = 2, q = 2);\n",
+    "    out2 = model2.fit()\n",
+    "\n",
+    "    # make prediction\n",
+    "    prediction = out2.forecast(horizon = 1, start = None);\n",
+    "    prediction = prediction.mean.iloc[-1]['h.1']\n",
+    "    \n",
+    "    if in_buy_sell == 0: # not in trade\n",
+    "        \n",
+    "        # place trade according to strategy\n",
+    "        if prediction >= 0:\n",
+    "            buy_sell.append(1)\n",
+    "            in_buy_sell = 1\n",
+    "        elif prediction < 0:\n",
+    "            buy_sell.append(-1)\n",
+    "            in_buy_sell = -1\n",
+    "\n",
+    "        price_in.append(price[\"Last\"].iloc[i])\n",
+    "\n",
+    "    elif in_buy_sell == 1: # currently long\n",
+    "        \n",
+    "        # so can only sell\n",
+    "        if prediction < 0:\n",
+    "            price_out.append(price[\"Last\"].iloc[i])\n",
+    "            in_buy_sell = 0\n",
+    "\n",
+    "    elif in_buy_sell == -1: # currently short\n",
+    "\n",
+    "        # so can only buy\n",
+    "        if prediction >= 0:\n",
+    "            price_out.append(price[\"Last\"].iloc[i])\n",
+    "            in_buy_sell = 0\n",
+    "\n",
+    "# store outputs\n",
+    "price_in, price_out, buy_sell = np.array(price_in), np.array(price_out), np.array(buy_sell)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Strategy return: 46.02854718166256%\n",
+      "Market return: 6.317416164425904%\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# qs 4\n",
+    "# discard active trades, now all 3 arrays should be same length\n",
+    "price_in = price_in[:len(price_out)]\n",
+    "buy_sell = buy_sell[:len(price_out)]\n",
+    "\n",
+    "# compute startegy return\n",
+    "trade_returns = (price_out - price_in)/price_in * buy_sell * -1\n",
+    "trade_returns = (np.cumprod(trade_returns + 1) - 1) * 100\n",
+    "strategy_return = trade_returns[-1]\n",
+    "\n",
+    "# compute market return\n",
+    "market_return = (price[\"Last\"].iloc[-1] - price[\"Last\"].iloc[0])/price[\"Last\"].iloc[0] * 100\n",
+    "\n",
+    "# summary\n",
+    "print(\"Strategy return: \" + str(strategy_return) + \"%\")\n",
+    "print(\"Market return: \" + str(market_return) + \"%\")\n",
+    "\n",
+    "# plot\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.plot(trade_returns)\n",
+    "plt.show()\n",
+    "plt.plot(price[\"Last\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- cannot run the above code for too many samples as takes way too long to run\n",
+    "- best can do is 200 samples which takes 10 min"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -523,7 +968,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 2.3.1 - Statistical Significance.ipynb b/notebooks/Module 2.3.1 - Statistical Significance.ipynb
index ce7d3c3..9f98667 100644
--- a/notebooks/Module 2.3.1 - Statistical Significance.ipynb	
+++ b/notebooks/Module 2.3.1 - Statistical Significance.ipynb	
@@ -30,7 +30,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -53,7 +53,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -76,7 +76,7 @@
        "(1.2251849250000002, 1.1445464953333333)"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -94,12 +94,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -134,7 +134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -143,7 +143,7 @@
        "0.0806384296666669"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -166,16 +166,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function RandomState.shuffle>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "np.random.shuffle?"
+    "np.random.shuffle"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -184,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -199,7 +210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -208,7 +219,7 @@
        "(60,)"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -220,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -229,7 +240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -243,7 +254,7 @@
        "       0.97622673, 1.30603338, 1.61753557, 0.69778236, 1.06337962])"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -254,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -263,7 +274,7 @@
        "0.04210193966666664"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -287,7 +298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -300,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -338,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -348,16 +359,16 @@
        " array([-0.26254333, -0.21219628, -0.16184923, -0.11150218, -0.06115513,\n",
        "        -0.01080808,  0.03953897,  0.08988602,  0.14023307,  0.19058012,\n",
        "         0.24092717]),\n",
-       " <a list of 10 Patch objects>)"
+       " <BarContainer object of 10 artists>)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -569,11 +580,68 @@
     "\n",
     "Note: There is no solution to this exercise"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.0806384296666669\n",
+      "mean:-0.00028032517400000987 lower CI:-0.16971068268333359 upper CI:0.17192527244999956\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# qs 1\n",
+    "\n",
+    "# qs 2\n",
+    "# - Oberservation shows NEW has a higher average performance than OLD by 8%\n",
+    "# - Simulation shows there was a 36% probability that this higher average of NEW happened by chance\n",
+    "# - We can say that 64% of the time, given this set of observations for NEW and OLD, we \n",
+    "#       expect to see a 8% or greater performance difference between the two methods\n",
+    "# - In terms of statistical significance, we cannot conclude this from the limited 30-sample, as more samples \n",
+    "#       generated using the same mechanisms produces more statistically significant results, however\n",
+    "#       it is difficult to say if they are actually significant\n",
+    "# - If we observed a larger sample, we can draw more concrete conclusions to drive our decision making (NEW vs OLD)\n",
+    "#       as we saw in the 2nd experiment where more data was used\n",
+    "# - From the 95% confidence interval shown below, we can see that 8% falls well inside the range of -17% to +17%\n",
+    "# - This means that for our simulation, if we performed 100 samples and computed the CI for each, then 95 of 100 would include\n",
+    "#       the observed performance difference between NEW and OLD\n",
+    "# - From this perspective, we can see that the observed 8% is not statistically significant as we expect to see this from random simulation \n",
+    "\n",
+    "import numpy as np\n",
+    "\n",
+    "print(experiment_difference_in_means)\n",
+    "\n",
+    "# compute 95% confidence interval\n",
+    "l, u = np.quantile(differences, [0.025, 0.975])\n",
+    "plt.hist(differences)\n",
+    "plt.axvline(l, color = \"red\")\n",
+    "plt.axvline(u, color = \"red\")\n",
+    "\n",
+    "print(\"mean:\" + str(np.mean(differences)) + \" lower CI:\" + str(l) + \" upper CI:\" + str(u))"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -587,7 +655,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 2.3.2 - Confidence Intervals.ipynb b/notebooks/Module 2.3.2 - Confidence Intervals.ipynb
index 9423215..7da0220 100644
--- a/notebooks/Module 2.3.2 - Confidence Intervals.ipynb	
+++ b/notebooks/Module 2.3.2 - Confidence Intervals.ipynb	
@@ -119,10 +119,10 @@
        "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.929</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Date:</th>             <td>Sun, 20 Feb 2022</td> <th>  Prob (F-statistic):</th>  <td>0.00141</td>\n",
+       "  <th>Date:</th>             <td>Sat, 07 Jan 2023</td> <th>  Prob (F-statistic):</th>  <td>0.00141</td>\n",
        "</tr>\n",
        "<tr>\n",
-       "  <th>Time:</th>                 <td>13:39:53</td>     <th>  Log-Likelihood:    </th> <td> -100.11</td>\n",
+       "  <th>Time:</th>                 <td>16:36:46</td>     <th>  Log-Likelihood:    </th> <td> -100.11</td>\n",
        "</tr>\n",
        "<tr>\n",
        "  <th>No. Observations:</th>      <td>   125</td>      <th>  AIC:               </th> <td>   206.2</td>\n",
@@ -164,7 +164,7 @@
        "<tr>\n",
        "  <th>Kurtosis:</th>      <td>12.136</td> <th>  Cond. No.          </th> <td>    56.6</td>\n",
        "</tr>\n",
-       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
+       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
       ],
       "text/plain": [
        "<class 'statsmodels.iolib.summary.Summary'>\n",
@@ -174,8 +174,8 @@
        "Dep. Variable:              Inflation   R-squared:                       0.102\n",
        "Model:                            OLS   Adj. R-squared:                  0.087\n",
        "Method:                 Least Squares   F-statistic:                     6.929\n",
-       "Date:                Sun, 20 Feb 2022   Prob (F-statistic):            0.00141\n",
-       "Time:                        13:39:53   Log-Likelihood:                -100.11\n",
+       "Date:                Sat, 07 Jan 2023   Prob (F-statistic):            0.00141\n",
+       "Time:                        16:36:46   Log-Likelihood:                -100.11\n",
        "No. Observations:                 125   AIC:                             206.2\n",
        "Df Residuals:                     122   BIC:                             214.7\n",
        "Df Model:                           2                                         \n",
@@ -193,7 +193,7 @@
        "Kurtosis:                      12.136   Cond. No.                         56.6\n",
        "==============================================================================\n",
        "\n",
-       "Warnings:\n",
+       "Notes:\n",
        "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
        "\"\"\""
       ]
@@ -261,29 +261,39 @@
     "5. Compare the results from (3) and (4)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From the solutions"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(125, 6)\n",
+      "   index       Date  InterestRate  Inflation  AUDUSD       std\n",
+      "0      0 1990-03-31          16.5        1.6  0.7542  2.810048\n",
+      "1      1 1990-06-30          15.0        1.8  0.7890  2.810048\n",
+      "2      2 1990-09-30          14.0        0.9  0.8265  2.810048\n",
+      "3      3 1990-12-31          12.0        2.4  0.7733  2.810048\n",
+      "4      4 1991-03-31          12.0       -0.2  0.7752  2.810048\n",
+      "CI with norm distribution\n",
+      "(3.2342254549056957, 6.403778711760971)\n",
+      "CI with bootstrap\n",
+      "[3.3541666666666665, 6.5625]\n"
+     ]
+    },
     {
      "data": {
       "text/html": [
        "\n",
-       "<div id=\"altair-viz-4d3acf9208184f4facff33c7180b960f\"></div>\n",
+       "<div id=\"altair-viz-9f5d26ab510b4d0f9a901960d9c644a3\"></div>\n",
        "<script type=\"text/javascript\">\n",
        "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
        "  (function(spec, embedOpt){\n",
        "    let outputDiv = document.currentScript.previousElementSibling;\n",
-       "    if (outputDiv.id !== \"altair-viz-4d3acf9208184f4facff33c7180b960f\") {\n",
-       "      outputDiv = document.getElementById(\"altair-viz-4d3acf9208184f4facff33c7180b960f\");\n",
+       "    if (outputDiv.id !== \"altair-viz-9f5d26ab510b4d0f9a901960d9c644a3\") {\n",
+       "      outputDiv = document.getElementById(\"altair-viz-9f5d26ab510b4d0f9a901960d9c644a3\");\n",
        "    }\n",
        "    const paths = {\n",
        "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
@@ -329,112 +339,57 @@
        "        .catch(showError)\n",
        "        .then(() => displayChart(vegaEmbed));\n",
        "    }\n",
-       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": \"line\", \"encoding\": {\"x\": {\"field\": \"Date\", \"type\": \"temporal\"}, \"y\": {\"field\": \"InterestRate\", \"type\": \"quantitative\"}}, \"transform\": [{\"calculate\": \"datum.InterestRate-datum.std\", \"as\": \"ymin\"}, {\"calculate\": \"datum.InterestRate+datum.std\", \"as\": \"ymax\"}]}, {\"mark\": \"errorbar\", \"encoding\": {\"x\": {\"field\": \"Date\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ymin\", \"type\": \"quantitative\"}, \"y2\": {\"field\": \"ymax\"}}, \"transform\": [{\"calculate\": \"datum.InterestRate-datum.std\", \"as\": \"ymin\"}, {\"calculate\": \"datum.InterestRate+datum.std\", \"as\": \"ymax\"}]}], \"data\": {\"url\": \"altair-data-cdaa144340b5d81477c4e61852014b5a.json\", \"format\": {\"type\": \"json\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
+       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"mark\": \"line\", \"encoding\": {\"x\": {\"field\": \"Date\", \"type\": \"temporal\"}, \"y\": {\"field\": \"InterestRate\", \"type\": \"quantitative\"}}, \"transform\": [{\"calculate\": \"datum.InterestRate + datum.std\", \"as\": \"ymax\"}, {\"calculate\": \"datum.InterestRate - datum.std\", \"as\": \"ymin\"}]}, {\"mark\": \"errorbar\", \"encoding\": {\"x\": {\"field\": \"Date\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ymin\", \"type\": \"quantitative\"}, \"y2\": {\"field\": \"ymax\"}}, \"transform\": [{\"calculate\": \"datum.InterestRate + datum.std\", \"as\": \"ymax\"}, {\"calculate\": \"datum.InterestRate - datum.std\", \"as\": \"ymin\"}]}], \"data\": {\"url\": \"altair-data-47e422924d39b43dea6fc292584bf969.json\", \"format\": {\"type\": \"json\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\"}, {\"mode\": \"vega-lite\"});\n",
        "</script>"
       ],
       "text/plain": [
        "alt.LayerChart(...)"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# 1 & 2\n",
-    "d = data.reset_index().dropna()\n",
-    "d[\"std\"] = np.std(d[\"InterestRate\"]) # Add a column for std\n",
-    "\n",
     "import altair as alt\n",
     "\n",
-    "# the base chart\n",
-    "base = alt.Chart(d).transform_calculate(\n",
-    "    ymin=\"datum.InterestRate-datum.std\",\n",
-    "    ymax=\"datum.InterestRate+datum.std\"\n",
-    ")\n",
-    "# Data Line\n",
-    "line = base.mark_line().encode(x=\"Date\", y=\"InterestRate\")\n",
-    "# Error Bars\n",
-    "bar = base.mark_errorbar().encode(x=\"Date\", y=\"ymin:Q\", y2=\"ymax:Q\")\n",
-    "line + bar"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4.145879803992343, 5.461082862674323)"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 3\n",
-    "from math import sqrt\n",
-    "pool = data[\"InterestRate\"].dropna()\n",
+    "# remove nans\n",
+    "data = data.dropna()\n",
     "\n",
-    "sample_size = int(len(pool)/5)\n",
-    "iters = range(1000)\n",
-    "means = []\n",
+    "# compute sd for error bars\n",
+    "data[\"std\"] = np.std(data[\"InterestRate\"]) # Add a column for std\n",
     "\n",
-    "for i in iters:\n",
-    "    sample = np.random.choice(pool, sample_size)\n",
-    "    means.append(np.mean(sample))\n",
+    "print(data.shape)\n",
+    "print(data.head())\n",
     "\n",
-    "mu = np.mean(means)\n",
-    "std = np.std(means)\n",
-    "stats.norm.interval(0.95, loc=mu, scale=std)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(4.173333333333333, 5.474666666666667)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# 4 \n",
-    "pool = data[\"InterestRate\"].dropna()\n",
+    "# qs 1 \n",
+    "plot = alt.Chart(data).transform_calculate(ymax=\"datum.InterestRate+datum.std\", ymin=\"datum.InterestRate-datum.std\")\n",
+    "plot_line = base.mark_line().encode(x = \"Date\", y = \"InterestRate\")\n",
     "\n",
-    "sample_size = int(len(pool)/5)\n",
-    "iters = range(1000)\n",
-    "means = []\n",
+    "# qs 2\n",
+    "plot_err_bar = base.mark_errorbar().encode(x = \"Date\", y = \"ymin:Q\", y2 = \"ymax:Q\")\n",
+    "plot = plot_line + plot_err_bar\n",
     "\n",
-    "for i in iters:\n",
-    "    sample = np.random.choice(pool, sample_size)\n",
-    "    means.append(np.mean(sample))\n",
+    "# qs 3\n",
+    "n = data.shape[0]// 10\n",
+    "sample_means = [np.mean(np.random.choice(data[\"InterestRate\"], n)) for _ in range(10000)]\n",
+    "mean, sd = np.mean(sample_means), np.std(sample_means)\n",
+    "print(\"CI with norm distribution\")\n",
+    "print(stats.norm.interval(0.95, loc = mean, scale = sd))\n",
     "\n",
-    "idx_lower = int(len(means)*.025)\n",
-    "idx_upper = int(len(means)*.975)\n",
-    "sorted_means = np.sort(means)\n",
-    "lower = sorted_means[idx_lower]\n",
-    "upper = sorted_means[idx_upper]\n",
-    "lower, upper"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Very similar results, as expected from 1000 samples."
+    "# qs 4\n",
+    "sorted_means = np.sort(sample_means)\n",
+    "lower = sorted_means[int(len(sorted_means)*.025)]\n",
+    "upper = sorted_means[int(len(sorted_means)*.975)]\n",
+    "print(\"CI with bootstrap\")\n",
+    "print(\"[\" + str(lower) + \", \" + str(upper) + \"]\")\n",
+    "\n",
+    "# qs 5\n",
+    "# numbers fairly close\n",
+    "\n",
+    "# show the plot\n",
+    "plot"
    ]
   },
   {
@@ -461,7 +416,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -470,7 +425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -479,7 +434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
@@ -494,7 +449,7 @@
        "Name: Close, dtype: float64"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -505,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -527,7 +482,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -537,7 +492,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -546,7 +501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
@@ -585,35 +540,35 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>1986-03-13</td>\n",
+       "      <th>1986-03-13</th>\n",
        "      <td>150.50</td>\n",
        "      <td>28.00</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1986-03-14</td>\n",
+       "      <th>1986-03-14</th>\n",
        "      <td>150.38</td>\n",
        "      <td>29.00</td>\n",
        "      <td>False</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1986-03-17</td>\n",
+       "      <th>1986-03-17</th>\n",
        "      <td>150.88</td>\n",
        "      <td>29.50</td>\n",
        "      <td>True</td>\n",
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1986-03-18</td>\n",
+       "      <th>1986-03-18</th>\n",
        "      <td>152.38</td>\n",
        "      <td>28.75</td>\n",
        "      <td>True</td>\n",
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1986-03-19</td>\n",
+       "      <th>1986-03-19</th>\n",
        "      <td>151.63</td>\n",
        "      <td>28.25</td>\n",
        "      <td>False</td>\n",
@@ -633,7 +588,7 @@
        "1986-03-19  151.63  28.25   False    False"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -644,7 +599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -654,7 +609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
@@ -695,7 +650,7 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>1986-03-13</td>\n",
+       "      <th>1986-03-13</th>\n",
        "      <td>150.50</td>\n",
        "      <td>28.00</td>\n",
        "      <td>True</td>\n",
@@ -703,7 +658,7 @@
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1986-03-14</td>\n",
+       "      <th>1986-03-14</th>\n",
        "      <td>150.38</td>\n",
        "      <td>29.00</td>\n",
        "      <td>False</td>\n",
@@ -711,7 +666,7 @@
        "      <td>True</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1986-03-17</td>\n",
+       "      <th>1986-03-17</th>\n",
        "      <td>150.88</td>\n",
        "      <td>29.50</td>\n",
        "      <td>True</td>\n",
@@ -719,7 +674,7 @@
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1986-03-18</td>\n",
+       "      <th>1986-03-18</th>\n",
        "      <td>152.38</td>\n",
        "      <td>28.75</td>\n",
        "      <td>True</td>\n",
@@ -727,7 +682,7 @@
        "      <td>False</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1986-03-19</td>\n",
+       "      <th>1986-03-19</th>\n",
        "      <td>151.63</td>\n",
        "      <td>28.25</td>\n",
        "      <td>False</td>\n",
@@ -748,7 +703,7 @@
        "1986-03-19  151.63  28.25   False    False            False"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -759,7 +714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -769,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [
     {
@@ -778,7 +733,7 @@
        "0.49712858926342074"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -799,7 +754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -811,16 +766,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.5031210986267166"
+       "0.49887640449438203"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -831,7 +786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -842,7 +797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -859,7 +814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -868,7 +823,7 @@
        "(0.48414481897627965, 0.49637952559300874)"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -890,7 +845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -944,11 +899,79 @@
     "\n",
     "    If IBM drops by more than 5% over a given week, MSFT will increase the following week."
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "If IBM drops by more than 5% on a given day, MSFT will increase the following day.\n",
+      "Estimate for conclusion being true\n",
+      "54.54545454545454 %\n",
+      "Confidence Interval\n",
+      "(0.42857142857142855, 0.5454545454545454)\n",
+      "If IBM drops by more than 5% over a given week, MSFT will increase the following week.\n",
+      "Estimate for conclusion being true\n",
+      "48.09917355371901 %\n",
+      "Confidence Interval\n",
+      "(0.466996699669967, 0.504950495049505)\n",
+      "               ibm   msft  ibm_up  msft_up msft_up_tomorrow  ibm_down_5  \\\n",
+      "Date                                                                      \n",
+      "1986-03-13  150.50  28.00    True    False             True       False   \n",
+      "1986-03-14  150.38  29.00   False     True             True       False   \n",
+      "1986-03-17  150.88  29.50    True     True            False       False   \n",
+      "1986-03-18  152.38  28.75    True    False            False       False   \n",
+      "1986-03-19  151.63  28.25   False    False            False       False   \n",
+      "\n",
+      "           msft_up_next_week  ibm_down_5_week  \n",
+      "Date                                           \n",
+      "1986-03-13             False            False  \n",
+      "1986-03-14             False            False  \n",
+      "1986-03-17             False            False  \n",
+      "1986-03-18              True            False  \n",
+      "1986-03-19              True            False  \n"
+     ]
+    }
+   ],
+   "source": [
+    "# exercise\n",
+    "print(\"If IBM drops by more than 5% on a given day, MSFT will increase the following day.\")\n",
+    "stocks['ibm_down_5'] = (stocks[\"ibm\"].diff() / stocks[\"ibm\"] * 100) < -5\n",
+    "premise_true = stocks[stocks['ibm_down_5']]\n",
+    "\n",
+    "print(\"Estimate for conclusion being true\")\n",
+    "print(str(premise_true['msft_up_tomorrow'].mean() * 100) + \" %\")\n",
+    "\n",
+    "values = np.array([compute_msft_follows_ibm_statistic(premise_true) for i in range(10000)])\n",
+    "print(\"Confidence Interval\")\n",
+    "print(compute_confidence_interval(values, ci = 0.95))\n",
+    "    \n",
+    "# extended exercise\n",
+    "print(\"If IBM drops by more than 5% over a given week, MSFT will increase the following week.\")\n",
+    "\n",
+    "stocks['msft_up_next_week'] = stocks['msft_up'].shift(-5)\n",
+    "stocks['ibm_down_5_week'] = (stocks[\"ibm\"].diff(5) / stocks[\"ibm\"] * 100) < -5\n",
+    "\n",
+    "premise_true = stocks[stocks['ibm_down_5_week']]\n",
+    "\n",
+    "print(\"Estimate for conclusion being true\")\n",
+    "print(str(premise_true['msft_up_next_week'].mean() * 100) + \" %\")\n",
+    "\n",
+    "values = np.array([compute_msft_follows_ibm_statistic(premise_true) for i in range(10000)])\n",
+    "print(\"Confidence Interval\")\n",
+    "print(compute_confidence_interval(values, ci = 0.95))\n",
+    "\n",
+    "print(stocks.head())"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -962,7 +985,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 2.4.1 - Residual Analysis.ipynb b/notebooks/Module 2.4.1 - Residual Analysis.ipynb
index 6d962f1..8a8483e 100644
--- a/notebooks/Module 2.4.1 - Residual Analysis.ipynb	
+++ b/notebooks/Module 2.4.1 - Residual Analysis.ipynb	
@@ -36,6 +36,18 @@
     "Let's start with a model that is accurate with regards to the data it is modelling:"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# qs\n",
+    "# - Anscombe's Quartet shows that our regression model is as good as our assumptions about the data\n",
+    "# - therfore for linear regression, we have to assume the data has a linear relationship before performing the analysis\n",
+    "# - if we graphically visualize the data and see theres not a linear trend, we have to choose a different model"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,
@@ -64,7 +76,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x23c32436d48>]"
+       "[<matplotlib.lines.Line2D at 0x1e2a32f24d0>]"
       ]
      },
      "execution_count": 3,
@@ -73,7 +85,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -116,7 +128,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x23c410cbc48>]"
+       "[<matplotlib.lines.Line2D at 0x1e2a7273c70>]"
       ]
      },
      "execution_count": 5,
@@ -125,7 +137,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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Y7t/PyI2L+X7PLaw51ZXFbe6h1YiBfCfFpmbBTxdlZX4EP2zY+W4GdR1xB9NAQ4b4TwgtW/r9RuIlkQ25z0wUFORFckTYXSKTTUzedhssXJhkwvKKk76kZtUqaNaMgq/N4OZx47i5adO0zx/8dBHWxHAwDdSjB1x7rS/xNPP3NYaSyHRpMZRIDohiR6JkC48efzzB/Z0c7zy5HubNgz/9yUfKhx/2UbQOAT4ozEVI1Y/VsqXfIfCZZ3xFjwL8xRTkRXJAFCtBk61eLS298P72h0u5afuv6fneyz7R/c1vwuzZ0K5d3U8ekPaOUVk6VmOhdI1IDohiJWiy+vnevf3XgrZHGbB9OT3L13HobDvKx38RHrgqkn1Vw5wYbowLmjKhkbxIDujXzwfeoEwrRZKlSR76zlku+fgdrlw5j+7lRXzU+XoW9nuY6/5iVNY3zpboKciL5IAommclSm389e1bmVvxFN8asIRTPfrxSq/vsO+qaXzvBy01Os5T5vfhzg1jx451q1evru/LEKkXke7B+tlnsHgxfPwxdOnid2caPDikg0t9M7M1zrmxiR5TTl4ky5IF83RzzSm9KZw6BW+95ZeFNm0KU6fChAnQTP/rNxYayYtkUbB2PdhnJt0KkZqOA7DgD46zGzZyw7EljLr8CAXTRsK0adC+fTS/mNQrjeRFckRYG2onO86TT0Lz/Xu49tAiep4uYW+zXvxk3+3cP7AvIxXfGyUFeZEsCqtUMtFxurY5xr7/9zo3d19D0/Zt+GTIbezpMRp3yNSZsRFTkBfJorB6vwePY66KXqUf0KN4BbuPn6Li8gmUDJjImWatAHVmbOwyDvJm1hf4T6AAcMB859y/mFkX4AWgP7ATuN05dzDT84k0BMkmRcPq/R4/TpfKHYzZt4hmB/ZR0uwyts+YxamO3egc+D9bnRkbtzDq5M8A33fODQMmAN81s2HAo8By59wgYHnstkjeq6kPTVjL8kf2O8TfX/kiE3c+w9EDp9h2zR1c++S93Pu/ujXKzaoluYxH8s65PcCe2PdHzKwY6A3MBibFnvYMsBL4QabnE8l1tU2uZrQs//Rp30Ds7be51IxLf3yTbyLWvPm5pzTGzaoluVBz8mbWHxgNvAcUxN4AAMrx6ZxEP/Mg8CBAP32mlDwQRR8anIOPPoIlS3z+ZcQIXxJZvQMZ2entEunCLQlVaG0NzKwd8Afge865w8HHnC/GT1iQ75yb75wb65wb261bt7AuR6TehN6HZu9e30v397+H1q3hG9+AuXMTBvhsiKItskQnlJG8mTXHB/hnnXPx5qh7zaync26PmfUE9oVxLpFcF9rG2sePw4oV8MEHfo+7W26Bq6/2vYjrUVi1/pIdYVTXGPAboNg594vAQwuB+4Cfxr6+kum5RBqCjDfWrqqCtWvh9dd9oL/mGpg82Y/ic0Ak6SiJTBgj+euBe4EPzWx97L6/xgf3F83sAWAXcHsI5xIJXRT55TrnxXftgkWLoLwc+veHWbPOb4wakXR//7Bq/SU71LtGGrWweslkrLISli6FjRv9hcyYAUOHRt7fvS6/f868ZnKOeteIJFHv+eUzZ3yHyLfe8hU0kybB9ddfUBIZpbr8/hmnoySrFOSlUQszv5xW2sM539t98WI/DB461I/eO3VK/8QZqOvvry34Gg4FeWnUwsovB1MYwbLChCmMigp47TXYtg26d4f77oMBA2o8dlQ16cqv5z9t/yeNWljb7gXTHk2anP9+wYLAk06c8MH9qaegtBRmzaLoum9T+MwA7r8fCgsvrjWPuiY9im0HJbcoyEujFlYvmZKSi9cmnUt7OOdLIufNg/fegzFj4OGHKWo9nsd+0aTGAJ7Sm0cGwvr9JXcpXSONXhj55WRpjxEdP4VfLYKyMv+ke+6Bnj2B1CY9s1GTrvx6ftNIXiQE1dMex/Ye4bINL3H3id/A0aPw5S/7dgSxAA+1jP5jQm+RII2OgrxICOJpj0s6nqHVmj8x45N5fHX4RgrmfgEeegiuvPKimvdUArhy5pIppWtEQjKy1SeM7PoaXPMZDBkC06dDly5Jn59KjxvVpEumtOJVJFMHDviqmS1boGtXmDkTBg5M6UeTlUeme780bjWteFWQl7wWRVCMH7Nsx0luqHqT6R1W0aNPM79addw4aNo04+Mnahtw222wcKHaCcjF1NZAGpV4EF6/Hnbs8PtrXH45fPIJ3HuvX3c0alTdAn5RETz2c8fws0V8uWIpVYeP8nyb0Uy9dwojJrQL5fqTVd08/jhcdZVa/Ep6NPEqeSW4eOjgQT/XuXEjFBfDpk3+dvyxuiwqWvofpcwq+w3jy17iZOtOfHT9N9kxcjb//Vo4AR6SV92UltZejSNSnUbykleCo+DDh30QPHHC77tRUOD33jh8GE6e9K1j7rsPZs9OYVR/9CgsX87AFeto1bUdxYO+xN6CkWBGx6raA206aaNkNfe9e/uvakEg6dBIXvJKcBQcD/CtWsGRI/7riRM+Zf7uu34hqnMXjuqLinx7gXNtBtad9U+eNw82bODwiOtYPPhh9va46lxJZG2BNt3WBMnKJh96SOWUkj6N5CXnZDJZGhwFDxni4/PJk9C+vQ/Gzvkuvq1a+ed36nR+ZPzkk3Ds2PkmY012bGP9txZRcPV+Cq4fCDNnclVZV5Y+Bmebpb61X7rtfGsqmxw8WOWUkh5V10hOSDZZmm4FSfXKlK1bfS6+Sxf47DN/3E2boEULH/yvu86ncaqqfOXKxInQs9VBBm5bTNf9mzngulA2ciYP/+ugcyP3dN+E7r8/9qYR+NxcVeV7xTz9dGavmwioukZyXDAwBydLO3Q4v/NdqhUk1UfBgwfDo49eWGMen4CNB3jwbybN3SlGf/YW/UrfxVkTtl82lZJeEygpawZ24TnSGT2rna/UJwV5qXfJJkuLi316pbjY9/eC1FI3yYJw/P74StMWLfyIuvKQo+2Ojfyg7VLafnKYiv4j2XbZNE61bM/Bg5kH41RWtopEJZSJVzN72sz2mdnGwH1dzGypmW2Jfe1c0zGk8Uo2Wbpnj8+pV1ZCr17h9VIPttc98skebtr17/xlnz8wYUpbFvd5gHd6zOFE8/ahTWyqna/Up1By8mZ2I3AU+E/n3IjYff8IfOac+6mZPQp0ds79oKbjKCffuMTTJy+/DC1b+jbrzvnAbgaHDvmJUefOp1biaY/CwgxPfuwYvP46rFkDrVvD1KkwahRFG5uobYA0OJHn5J1zb5pZ/2p3zwYmxb5/BlgJ1BjkpfEI5uHHj4c334SVK+HGG2H4cJ83r6ryI/thw87nzjNe/FNV5YvmV6yAU6f8ySdNOlduo97qkm+izMkXOOf2xL4vBwoSPcnMHgQeBOinmahGo3pZ4cSJsG4dvP++X5z06KP+OaFOWO7YAYsWwb59cNllMGsWdOuW8e8iksuyMvHqnHNmljAv5JybD8wHn67JxvVI/au+41GPHjBjhs9ZB1MxoUxYHjoES5bARx/5/M9Xv+qL6Kv1d68LdYWUXBflite9ZtYTIPZ1X4TnkgYmlQ0zMp6wPH3a54Aef9y3Ab7pJvjud2Ho0NACfJSbbIuEIcqR/ELgPuCnsa+vRHguaWBSLSusU448Xne5eHFso9URMG3axd29MpTuSlaR+hBKkDez3+EnWbua2W7g7/DB/UUzewDYBdwexrkkP0S249HevT7vvnOnn6390pegf/8Qrvhi2dhkWyRTYVXX3JnkoSlhHF/yU6iVLMeP+4qZDz7wlTK33AJXX31hL4GQaSWrNARa8SqRi3RysqoK1q71Ne/Hj8PYsT733rp1SCdITitZpSFQgzJJWzpBO9lWdqGs+CwpgVdfhfJyn5KZNet8QX2WqLpGcoEalElogkE7WFGSLGhHMjl5+DAsXQoffujfOb7yFb9iKoSKmXRp8ZTkOgV5SUu6Qbv65GR5efoNx845cwbeeQfeestX0EycCDfcAM2bR7phd0mJb2Zm5tsTa8QuDYl2hpK0JNt/NFlFSbAevry8jg3HnIPNm+GJJ3zufeBAv03S5MnnAnzY9erBYzZvDm+84UvumzdXPbw0LArykpZUFjEFBbeyKy72o2HnfHYl/olgwYIaTlhRAb/9LTz/PDRrBl/7ml+x2qnTuacEP100aZLicWsRPObHH/ve9h06+O/DOL5ItijIS1qS7T+arB1vcNVqWZkf9Qc360j6KeDECb+Y6amnoLQUZs6Eb3/b95ypJt1PF6kIHrOy0ldltmp1/g1O9fDSUCgnL2mpyyKm4ORkrXXlzvk9AJct8+2Ax4zxJZFt2ybNu0dRrx48ZseOvjoTLgz8qoeXhkBBXtJW14qSWuvKd+/2JZFlZdC3L9xzD/TsCVxc1fPJJ3DvvTBggG9uVlrqB/lh1asHr/WKK3wrZIBRo85/elE9vDQEqpOXpKKuWDl3zAFH/Mh9wwZo3973mbnyygtKIgsLz4+s4xO4Zj6ojxoF27b594Uwq19UXSMNherkJW3p1sPHf6a2N4ULPgWcOQPvvQfz3oCzZ3055Be+4LeJqiZYirl58/kc+eHD/hovvzykHaOSXatIA6UgLwmlWw+f9pvCli3w2mtw4IDPh8yYAV26JL2eYI68stJXupw4ceHesJoIFbmYqmskoXQrVlIuYzxwAJ57Dp591uc/7rkH7ryzxgAPF1b1dOjgA/2JE741PGgiVCQZjeQloXQrVmptu3vypJ+9XLXK17tPn+73V23aNKXrCVb1dO7sN3saMcLv3qeJUJHkFOQloXQ7LCZ9U+jrYEOR7zVz9CiMHg1TpkC7dmlfUzBHXj3/H0ovepE8pOoaSSrTbpOutIy/GvEqfdgNvXv7LpHVh/sikjFV10idpFNdEkyn7N12lBtPL2dy9/X0aNcWpn4RrrqqXrpEijR2CvISmpHDzzLy8/d9J68zZ2D8tb5TZIKSSBHJDgV5Cce2bX5v1f37fZfImTOha9f6viqRRk9BvhEKdSXrwYO+kdjmzb4M8q67YNAgpWZEckTkQd7MZgL/AjQFfu2c+2nU55TkauoBM2pUGgH/1Cl4+22/iUeTJr5i5tprfXmkiOSMSP+PNLOmwBPANGA38IGZLXTOfRTleSW54KKl8nLYtMkPuuO15rW1LsA52LjRl0QePuyfOG2a7zkjIjkn6mHXOGCrc247gJk9D8wGFOTrSW09YKCG/Vf37PF595IS3x1y7lyKDvVjwT9pI2uRXBV1W4PewKeB27tj951jZg+a2WozW11RURHx5UhwZ6f4Zhi19oA5dgz++EeYP99PrN52G3zzmxQd6hf6tnsiEq56T6A65+YD88Evhqrny8l7wZWs8R4wzvm9OaBa64KqKli92u+reuoUjBsHkyZB69ZA+k3MRCT7og7ypUDfwO0+sfuknqTcA2bHDp+a2bfP78YxcyZ0737BsWrtVyMi9S7qIP8BMMjMBuCD+x3AXRGfU2pRUw+Yb37lECM2L4GPPvKbZX/1qzBkSMKSyCi23RORcEUa5J1zZ8zsIWAxvoTyaefcpijPKek5F/BPn4Y//QlWvu0D+k03+ZLI5s2T/my6TcxEJPsiz8k7514FXo36PPkqii34LuAcFBf7BU2VlT53M23axc3kE6jLpt4ikl3qQpnDEnV2PHiwljr2dOzb5/PuO3ZAQYHvEtm/fwgHFpFsUhfKBiqy6pXjx30TsQ8+8M3DbrkFrr7ar1wVkbyiIJ/DQq9eqaqCtWt9SeTx4zB2LEyeDG3aZHyt1UWeZhKRlCjI56B4gFy71rcdGDPGZ1PKy2HdOl+yXliYZuAsKYFXX/UHufRSn5rp0SOy609rU28RiYw+n+eYeIA8eNBvgXr4sM+sbNwIb7zhb48bl8bq0sOH4Q9/gKef9itX586Fr389sgAPaWzqLSKR00g+x1TPw0+c6Efvb7/tB+DxUX3w+QlHx2fOwLvv+s2znfMHuv56aNEi8t9Bi6REcoeCfI6pHiB79IAZM+B3v/Nfg3OjCQOnc75/8Guv+eH+0KEwffqFK5bqIJ0cuxZJieQOpWtyTLCBWFxlpd8HO9H9FwTOigr47W/9O0KzZvC1r/kVqyEE+HQakc2Zc75FQlXV+e/nzMnoMkSkDlQnX48SjY4hcW38bbfBwoXn79+61U/KDhgAY0ec4K7eb3BZxXs+HTN5sq+cado0lOssLLx4ZB6/XViY+u+mSVeRaKhOPgfVVIGSbBXp4MH+/vXr/fqlEcMd17ddT++Vy1h/5BhNHhhN/wemQNu2oV5rXXLswf44IlJ/FOSTiHokWtNCp8LCxOeKB87CQhjZZTdjKxbRoayUyi59WXfF3RR91os528K/buXYRRou5eQTSDcHXRclJRe3h0mpAuXIES5562Umb/s1LU8epnjoHNaNvh/r3Yv16zO77qIi/wZy//3+a/znlGMXabgU5BPIRp13sgnWpKPjM2d8l8h58xhe9SEfdbmB98c9xN6CkWBGZaXvDV/X667pjS3eiKxzZ9i923/VwiaRhkHpmgSyUeedVpveLVt8SeSBA3DFFRT8eAb/8asudD5y4c926nThp4Pyct9gsqzs/DmTBeba+uQoxy7SMGkkn0Dao+w6SGl0fOAAPPccPPusv3333XDnnQz/QpeEPztq1PnrLi/3a6EqK6FXr9pTN3VOH4lITtNIPoFsbYaRdHR88iS89ZaP0s2a+cVM48dfUBKZ7Gfj111c7Pf+cA6GDau9g6UmV0Xyk4J8AvW2GYZzfqi9bBkcOeKH5lOnQrt2Kf148LrLyvwIftiw883NakrdaJcnkfykxVC5oqzMb+Dx6ad+eeusWRdPDKQhuIApnrox8wF81KjEm49oAZNIw9RoF0OFFbQiDX6ffw7Ll/suZG3awOzZPgon2Dg7HcGReaqpG02uiuSfjCZezewrZrbJzKrMbGy1x35oZlvN7GMzm5HZZaavLrXuierEI6uZP3sWVq2CefP8EtZrr4WHH4bRozMO8HDhxG5ZmR/BX3fd+Q6WmlQVaRwyHclvBOYA/xa808yGAXcAw4FewDIzG+ycO5vh+VKW7tZ5ydoMtG0bwRZ827b5ksiKChg4EGbOhK5d63iw5IIjc02qijROGQV551wxgF088pwNPO+cOwnsMLOtwDjg3UzOl450a92TvSm8+Sb82Z+lfpwaHTwIixfD5s3QpQvceadvSGMWaUpIk6oijVdUdfK9gU8Dt3fH7ruImT1oZqvNbHVFRUVoF5BurXuyOnHnQqiZP3UKXn+d8r95guW/2s5P3p/Cj/f/BUUnrzgX4KNso6AVqyKNV60jeTNbBiTaK+5HzrlXMr0A59x8YD746ppMjxeX7ug1WZ34hAn+/lSPcwHnfD/gJUvYu+UwLxaPZPeQqbTq3oHKyvNdJ9NNLdWFJlVFGqdag7xzbmodjlsK9A3c7hO7L2vSrXVP9qbwyCP+vrRr5svLfUnkrl3QsyfPtZrLzqv6JQzk2i5PRKISVQnlQuA5M/sFfuJ1EPB+ROdKKp3Ra21vCimPgo8dgxUrYPVqaN3aJ/RHj+bDP2+SNJBnutpU9e0ikkxGQd7MvgTMA7oB/2Nm651zM5xzm8zsReAj4Azw3WxW1tRVRimNqiof2Fes8G0Jxo/3m2e3bg3UHMgzmRitafMRBXoR0YrXMOzc6VMze/f6/fhmzYLu3S94SjAYV08HjRxZ99F4XbbmE5H80mhXvEbu0CFYutRPrnbq5DfNHjIk4WKmVNJBdRl5K58vIjVRkK+L06fhnXfg7bf97cmT/XLS5s1r/LEoKlzUPVJEaqIgnw7nfCOYJUv8KH74cN8GuHqBfRZpoZOI1ERBPlX79vm8+44dvgHM178O/fvX+mNRV77UW1tkEWkQNPFam+PHYeVK+OADaNkSbroJrr7ab6Jai9omW0VEwqCJ17qoqvLtf5cv94F+7Fife2/TJuVDZGMlq4hITRTkEykp8amZPXvg0kt9SWSPRJ0daj+MKl9EpD4pyAcdPuxLIj/8EDp0gLlz/eRqHfu7q/JFROqbgjzAmTN+f7w33/QVNDfeCDfcAC1aZHRYVb6ISH1r3EHeOfjkE7+Bx8GDMHSoL4kMDr0zoMoXEalveRXk0ypX3L/fB/etW6FbN7j3Xrj88tDPpxa/IlKf8qaEMuVyxRMnfFpm1Sqfjpk0Ca65Bpo2De18oK6QIpI9jaKEstZyRef8htnLlvl2wKNHw5QpfhPXEM/35JP+8OoKKSK5IG+CfI3lirt3+5LI0lLo2xfuvht69YrkfAsX+g7Dqo0XkVzQ4IN8PC++dq1vBjlmjO86AHC84ijTjy2DX6+H9u193uTKK+tcElnb+Sor/aET7RWr2ngRqQ9RbeSdFcENsMeP92XuK1dCeelZOn70DtesmsekSz705ZAPPeSH0hkG+ETn27PHF+msXOkXxy5e7FvLx6k2XkTqS4MeyVfPi0+cCBXvbmXAokWMHniAy746mB7fnAGXXBLZ+dat88G9VSsYMQLatfPzuitX+nL7Vq1UGy8i9adBB/lgXrz1sQNMq1hMl06fUNb6Eib96m4YNCiy84HvdDBjxsV5+Hjwf/99mD1btfEiUn8adJCPtw0YfHoTQ4sX4Jo0ZX3BdA4PGQ+D0iuJTOd81dsUVM/Dx4P/7t3agk9E6leDzsnPmeOD7i7Xjz0Fo1g8+GHWtbqOL81NPcAXFflAfP/9/mtRUe3nO3jQN6mMfz9hgg/2QcrDi0guyCjIm9nPzWyzmRWZ2Utm1inw2A/NbKuZfWxmMzK+0gTibQPaFLRneZs/o01B+7Tq0YMTqcGa9mSBPn6+zp39KL1zZ3/7O99JHPznzAnvdxURqYuMVrya2XTgdefcGTP7GYBz7gdmNgz4HTAO6AUsAwY7587WdLxsbxpSWHhx+iV+O900S9Q7QImIJBPZilfn3JLAzVXA3Nj3s4HnnXMngR1mthUf8N/N5HyZSBSEw+z3rh41IpKLwszJ3w8sin3fG/g08Nju2H0XMbMHzWy1ma2uqKgI8XLOS5aWadlSuXQRyW+1juTNbBmQaFukHznnXok950fAGeDZdC/AOTcfmA8+XZPuz6ciWZ+Zkyd9wAf1exeR/FRrkHfOTa3pcTP7OnArMMWdT/CXAn0DT+sTu69eJEvL7N6tfu8ikt8yysmb2Uzgr4CJzrljgYcWAs+Z2S/wE6+DgPczOVcmatqGT7l0EclnmebkHwfaA0vNbL2Z/RLAObcJeBH4CHgN+G5tlTVRql7fHu8zs3597bXxIiINWd5sGlKbeHXN+vWwY4fvM3P55TVsLiIi0kA0ik1DahNPyxQWwqWXqt+7iDQODbqtQV2UlKjfu4g0Ho0uyPfrp9p4EWk8Gl2QT9ZkTH1mRCQfNbogn6zJmPLxIpKPGs3Ea5Bq40WksWh0I3kRkcZEQV5EJI8pyIuI5DEFeRGRPKYgLyKSx3Kqd42ZVQC76vjjXYH9IV5OWHL1uiB3r03XlR5dV3ry8boudc51S/RATgX5TJjZ6mQNeupTrl4X5O616brSo+tKT2O7LqVrRETymIK8iEgey6cgP7++LyCJXL0uyN1r03WlR9eVnkZ1XXmTkxcRkYvl00heRESqUZAXEcljDSrIm9lXzGyTmVWZ2dhqj/3QzLaa2cdmNiPJzw8ws/diz3vBzFpEcI0vxDY1X29mO81sfZLn7TSzD2PPi2Zj2wvPV2hmpYFruznJ82bGXsOtZvZoFq7r52a22cyKzOwlM+uU5HlZeb1q+/3NrGXs33hr7G+pf1TXEjhnXzNbYWYfxf7+/zLBcyaZWWXg3/dvo76uwLlr/Lcx719jr1mRmY3JwjVdEXgt1pvZYTP7XrXnZOU1M7OnzWyfmW0M3NfFzJaa2ZbY185Jfva+2HO2mNl9dboA51yD+Q8YClwBrATGBu4fBmwAWgIDgG1A0wQ//yJwR+z7XwLfifh6/wn42ySP7QS6ZvG1KwQeqeU5TWOv3WVAi9hrOizi65oONIt9/zPgZ/X1eqXy+wN/Afwy9v0dwAtZ+LfrCYyJfd8e+CTBdU0C/pitv6d0/m2Am4FFgAETgPeyfH1NgXL8gqGsv2bAjcAYYGPgvn8EHo19/2iiv3ugC7A99rVz7PvO6Z6/QY3knXPFzrmPEzw0G3jeOXfSObcD2AqMCz7BzAy4Cfjv2F3PAF+M6lpj57sd+F1U54jAOGCrc267c+4U8Dz+tY2Mc26Jc+5M7OYqoE+U56tFKr//bPzfDvi/pSmxf+vIOOf2OOfWxr4/AhQDvaM8Z8hmA//pvFVAJzPrmcXzTwG2Oefqupo+I865N4HPqt0d/DtKFotmAEudc5855w4CS4GZ6Z6/QQX5GvQGPg3c3s3F/xNcAhwKBJREzwnTF4C9zrktSR53wBIzW2NmD0Z4HUEPxT4uP53k42Eqr2OU7seP+BLJxuuVyu9/7jmxv6VK/N9WVsTSQ6OB9xI8fK2ZbTCzRWY2PFvXRO3/NvX9d3UHyQdb9fWaFTjn9sS+LwcKEjwnlNct53aGMrNlQI8ED/3IOfdKtq8nkRSv8U5qHsXf4JwrNbPuwFIz2xx7x4/kuoCngL/H/w/59/hU0v2ZnC+M64q/Xmb2I+AM8GySw4T+ejU0ZtYO+APwPefc4WoPr8WnI47G5lteBgZl6dJy9t8mNu92G/DDBA/X52t2jnPOmVlktew5F+Sdc1Pr8GOlQN/A7T6x+4IO4D8mNouNwBI9J5RrNLNmwBzg6hqOURr7us/MXsKnCjL6HyPV187MfgX8McFDqbyOoV+XmX0duBWY4mLJyATHCP31SiCV3z/+nN2xf+eO+L+tSJlZc3yAf9Y5t6D648Gg75x71cyeNLOuzrnIG3Gl8G8Tyd9VimYBa51ze6s/UJ+vGbDXzHo65/bEUlf7EjynFD9vENcHPx+ZlnxJ1ywE7ohVPgzAvxu/H3xCLHisAObG7roPiOqTwVRgs3Nud6IHzaytmbWPf4+ffNyY6LlhqZYD/VKS830ADDJfhdQC/zF3YcTXNRP4K+A259yxJM/J1uuVyu+/EP+3A/5v6fVkb0xhieX8fwMUO+d+keQ5PeJzA2Y2Dv//djbefFL5t1kIfC1WZTMBqAykKqKW9BN1fb1mMcG/o2SxaDEw3cw6x9Kr02P3pSfqmeUw/8MHp93ASWAvsDjw2I/wlREfA7MC978K9Ip9fxk++G8Ffg+0jOg6/wP4drX7egGvBq5jQ+y/Tfi0RdSv3X8BHwJFsT+wntWvK3b7Znz1xrYsXddWfN5xfey/X1a/rmy+Xol+f+DH+DchgFaxv52tsb+ly7LwGt2AT7MVBV6nm4Fvx//OgIdir80G/AT2dVFfV03/NtWuzYAnYq/phwQq4yK+trb4oN0xcF/WXzP8m8we4HQsfj2An8dZDmwBlgFdYs8dC/w68LP3x/7WtgLfqMv51dZARCSP5Uu6RkREElCQFxHJYwryIiJ5TEFeRCSPKciLiOQxBXkRkTymIC8iksf+P3oD9rUZ8fZlAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -159,7 +171,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.LineCollection at 0x23c41189f88>"
+       "<matplotlib.collections.LineCollection at 0x1e2a72ce800>"
       ]
      },
      "execution_count": 7,
@@ -168,7 +180,7 @@
     },
     {
      "data": {
-      "image/png": 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7SPq6pCslXSHpxDISZgnoe0sBs5YoI0d+P/DWiHgKcDDwXyTtX8Jyu6HL4z+4XDNdXT4ue2jqQB4RN0XE9/L/7wauBB437XI7IbVBh5bDnYDSk9px6YvOkkqt7JS0CvhX4ICIuGvBd+uAdQCzs7OrNxeN8d01fkiwtVFKx6WbJW6l8ocvS3oU8A3glIj43GLT9qbVilt1WBuldFymdNGpQaWtViRtB5wDbFgqiPeKW3VYG6V0XHqc+7GU0WpFwEeBKyPib6dPUoe4VYe1UUrHZUoXnQaVkSN/LnAs8EJJl+SvI0pYbvrcqsPaKKXjMqWLToPcs9PM2m3DhmzYh+uuy3Lip5zSzotODUaVkW/bRGLMzMa2dm1vA/e43EXfzCxxDuRmZolzIDczGyWRXqUuIzczK7KwV+lgKANoXZm9c+RmZkUWe3BKy3LqDuRmNrmWBbJKjOo9OsiZt2jQMQdy66c+BKKqpDZ64nKN6j26YkVxTv11r2vsWHIgt/7pSyCqSl+e1TqqV+kDD4yep6FjyYHc+qcvgagqfRnIatRQBnNzi8/XwLHkQD4p35Knry+BqCp1DGTVlvOs6MEpRTn1hWo+lhzIJ9H0LXlbDu7UeUS96VQ9kFXT59lShnPqo9R9LEVE7a/Vq1dHkubmIrJDa+vX3Fz1656fj5iZ2Xq9MzPZ5zYZ78vpzc9nx72U/S1z3zV5nk2q5mMJ2BgFMdWBfBJS8QEmVb/ulA7uFIwTiKoMVnWuIzVNnmfLUeNvOCqQexjbSTT52KmUHs/VBVU9K3J4SNadd4a774b77it3Hanz491GqvRRb73R5CD3LtetVxUtWxaW/d5++9ZBvIx1dIEfJjExB/JJNPlkFR/c9aqiZUvRxaHsdXRBSk8wagkXraTET0qpTxW396OKx8pch3VapUUrkl4q6WpJmySdVMYyrUBRm1arRhV3QOMUg02yDjdHtdzUgVzSCuBDwOHA/sBrJe0/7XLNGlXF7X3RxWG77WCXXSZfR9vbWlutpi5akfRs4OSIOCx//w6AiPjrUfNMU7RyyCGHLGs+szY49JZb+OOf/pTd772XW1eu5CP77stX99hj4uWcddFF7HnvvQ/7/OaVKznm4IPLSKpV5MILL1z2vFU+fPlxwPVD728AnlWQgHXAOoDZhltalHUydYH3Rb2+uscepezf3QuC+GKfW7eVkSN/NXBYRPxR/v5Y4KCIePOoeRqt7KyqfXCKvC/S5bbWvVRlZecNwD5D7/cGbixhudXwyHcP8b5Il5uj2pAyAvl3gSdK2lfS9sAxwLklLLcaHvnuId4X6XJbaxsydRl5RNwv6QTgfGAF8LGIuGLqlFVldrb4lrSPPSS9L9K2dq0DtwEltSOPiPMi4kkR8VsR0e57O9+SPsT7wqwT+tdFv6+3pEWdR/q6L8w6xl30+8CtU8w6waMf9plbp5h1mgN5H7h1ilmn9SeQ93mAIY9lbtZp/QjkfR9gyK1TzKbX4sxgPwJ538uI3TrFbDotzwz2o9WKn3dpZtNoydg2/W614jJiM5tGyxsM9COQu4zYzKbR8sxgPwK5y4jNbBotzwz2I5BDWs+7bHHteC36vv3WPi3PDPajsjMlfe9O3/ftN1tEvys7U9L3ppJ9334rXw/u8BzI26aNteN1nght3H5LV8vbf5elW4G8C1fettWO130itG37l9KFY67L+nKHFxG1v1avXh2lm5+PmJmJyMJN9pqZyT5PSdu2Y25u67QMXnNz1ayvbdu/mJTS2ldS8fErNZ2yZQE2RkFM7U4grzvgVGl+Pku3lP1tMjA0cSK0afsX06Vjrqs69huNCuRTtVqR9H7g5cB9wDXAGyLijqXmq6TVirvhV6MlXZNbycdc+3WsFVRVrVYuAA6IiN8GfgS8Y8rlLV9qZaupaHlHiEb5mGu/lrf/LstUgTwivhwR9+dvLwL2nj5Jy+SAU42enAjL4mMuDSl1BlymMlutvBH40qgvJa2TtFHSxi1btpS42pwDTnV6cCIsi485a4kly8glfQXYs+Cr9RHxz/k064E1wKtijEJ39+y0Sm3YkDUvu+66rJjjlFMcXK0Tll1GHhEviogDCl6DIH4ccCSwdpwgblaponbvxx6b5Zjdzts6aqqiFUkvBd4OHBUR9yw1vVnlijqADPIXHe3Vt6i2dFhqSzo6atrmh5uAlcDt+UcXRcSblprPRStWmVFNAof1pelkW5retSUdHVBJ88OIeEJE7BMRB+avJYO4dUCbc1fjNP3ry7gtbeme3pZ0dFi3xlqx6rV9EKKiJoEL9aWdd1sGIGtLOjrMgdwmU1Xuqqxc/nCTQMgqOYf1qZ13WzostSUdHeZAbpOpIndVdi5/0O49As48s7/tvNvSYakt6eiyogFYqn5VMmiW1aOKQYg6NrBRq7RlALK2pKNqFW8nVQyatVxutZKwKlogePAp64IaWuf4UW9Wjiq6pbsMtTva3KKpag22znEgt8mVPfaKy1C7oe0tmqo2qp5o8+bKL2oO5NY8Dz7VDSm3Fy/jTmKxO8iKL2ouIzezcqRa11FW2XbRchaaslexy8jNrFqp1nWUdSexsA9DkYo6QTmQm9lDpiliSLWuo8y+EYP6o1HBvKKLmgN5F/S5pYBlyjgGpq2sTLWuo4o7ibovakWNy6t+uUNQiebnI2Zmtu5IMzPT3Q4X9nBlHQN97ZhV1TlUQecgRnQIciBvSlk/cl9PPntIWceAVLwcqYpUt0siPU9HBXK3WmlCmT3AUm0pYOUp6xhYtSorTlmoL+O3J8CtVtqkzPa2qbYUsPKUdQykWllpDuSNKLOWvE8nnyt1i5V1DKRaWWkuI29E2eXak5bvJVIeuBVX6i4uxd/UJkaVZeSS3ga8H9gtIm5banqXkTf4DMNUn5/o8luz6srIJe0DvBjwc5vG1eQtbKrjYfhxYWYjlVFG/nfAnwL1N39JWdkjCI4r1YDoSl2zkaYK5JKOAn4WEZeOMe06SRslbdyyZcs0q7VppBoQ+1SpazahJQO5pK9IurzgdTSwHnjnOCuKiNMiYk1ErNltt92mTbctV6oB0S0qzEZadmWnpKcBXwUGBa57AzcCB0XEzYvN2/vKzqZt2JCViV93XZYTP+UUB0SzBIyq7CytZ6eka4E1brViZlYN9+w0M+uo0gJ5RKwaJzfeOu4taGaJ27bpBDRqYeeYwfjL4DJjM0tGv4tWyuwc45y9mTWk3znysjrHOGdvZg3qd468rM4xqXZ7N7NO6HcgL6tzTKrd3s2sE/odyMvqLZhqt3cz64R+B3IoZ/CqVLu9W7uUVWHuivfe6XdlZ1kGwd/d3m25yqowd8V7L/nhy6N4PBKrU1kPzvADODrNXfQnMcjVbN6cPVRskKvxLapVpawK8+UsJ6WimJTSWiMH8iJuTmh1K6vCfNLlpJRpSSmtNXMgL+LmhFa3sirMJ11OSpmWlNJaMwfyIm5OaNOatAigrKawky4npUxLSmmtW0TU/lq9enW02vx8xMxMRHYDl71mZrLPzZaS0vEzN7d1OgevubmmU/ZwZad1fj6bV8r+tvH3WQDYGAUx1TnyIn6smE0jpSKAlPpAlJnWrpW3F0X3ql+tz5GbTUMqzjlKzaZrVA40pZxpWWlN6U5kCCNy5G5Hbla2NrblXthRCLLcbF/vNLfZJgvdC0lZL++Wcjtys7q0sbgipeKeOnSsQcPUgVzSmyVdLekKSe8rI1FmSWtjHYtbfGytjRfbKUw11oqk3wOOBn47Iu6VtHs5yTJL3Nq17SqymJ0tLu5JNAc6tY6NjzRtjvx44D0RcS9ARNw6fZLMrHQdy4GWooyRT1ti2kD+JOD5kr4t6RuSnjlqQknrJG2UtHHLli1TrtbMJtLG4h4rzZKtViR9Bdiz4Kv1wCnA14ATgWcCZwOPjyUW6lYrZmaTG9VqZcky8oh40SILPR74XB64vyPpQWBXwFluM7OaTFu08nnghQCSngRsD9w2baLMzGx80z4h6GPAxyRdDtwHHLdUsYqZmZVrqhx5RNwXEa+LiAMi4hkR8bWyEmY5D6RvZkvwMzvbzM9fNLMxuIt+m7lbtZmNwYG8zdyt2szG4EDeZh0b2MfMquFA3mbuVm1mY3Agn0bVLUoW61bt1ixmlnMgX666HhVVNLBPVx5T5YuRWSn8hKDlavIpMG18As2k/MQas4n5CUFla7JFSRdas6TWtHKauwffeVjFHMiXa7EWJcs5cSeZpwutWVK6GE1TlNWVYjBrt6InMlf9Wr16dXmPlW7K/HzEzMzWT+CemYk4/vjizxd72veoZY2aZ9Lp2yilp5hPk9aUttNaD9gYBTHVgXwa8/PZCSllfwfvJz1xlzNP0bpTktLFSCr+faRq57XyJX7eOJDXZTknbl9P9lROqnEvtGVd2K0aKWUeRhgVyF1GXrbllF93ocx7OVJ5ZuI4HbNGlYUfcYQ7dbVFahXsE3AgL9tyemO6B2e7jfO8y1FB4rzzuv+szFRa5aRUwT6pomx61a9OF61ELK/IIJViBivW5+KxVIorOlDMxYiiFXcIMitDFzppLUdK292BTmjuEGRWpb4Wj6VUXDFOEVmipgrkkg6UdJGkSyRtlHRQWQkzS0qHg8SiUquoT6WCfULT5sjfB7wrIg4E3pm/N+unjgaJRfX1TqRlpg3kATwm/38H4MYpl2dmKenrnUjLTFXZKekpwPmAyC4Kz4mIgpoPkLQOWAcwOzu7enNRBYmZmY00qrJz2zFm/AqwZ8FX64FDgf8WEedIeg3wUeBFRcuJiNOA0yBrtTJB2s3MbBFLBvKIKAzMAJI+AZyYv/0McHpJ6TIzszFNW0Z+I/C7+f8vBH485fLMzGxCS+bIl/DHwKmStgV+TV4GbmZm9ZkqkEfEN4HVJaXFzMyWoZEu+pK2AMtttrIrcFuJySmL0zUZp2syTtdk2poumC5tcxGx28IPGwnk05C0saj5TdOcrsk4XZNxuibT1nRBNWnzWCtmZolzIDczS1yKgfy0phMwgtM1GadrMk7XZNqaLqggbcmVkZuZ2dZSzJGbmdkQB3Izs8S1MpBLerWkKyQ9KGnNgu/eIWmTpKslHTZi/n0lfVvSjyWdLWn7CtJ4dv5AjUskXSvpkhHTXSvpB4OHb5SdjoL1nSzpZ0NpO2LEdC/N9+EmSSfVkK73S7pK0mWS/knSjiOmq2V/LbX9klbmv/Gm/FhaVVVahta5j6SvS7oyP/5PLJjmEEl3Dv2+76w6Xfl6F/1dlPn7fH9dJukZNaRpv6H9cImkuyS9ZcE0te0vSR+TdKuky4c+21nSBXksukDSTiPmPS6f5seSjpt45UUP8mz6BTwF2A+4EFgz9Pn+wKXASmBf4BpgRcH8nwaOyf//MHB8xen9APDOEd9dC+xa4747GXjbEtOsyPfd44Ht8326f8Xpegmwbf7/e4H3NrW/xtl+4E+AD+f/HwOcXcNvtxfwjPz/RwM/KkjXIcAX6jqexv1dgCOAL5ENaX0w8O2a07cCuJmsw0wj+wt4AfAM4PKhz94HnJT/f1LRcQ/sDPwk/7tT/v9Ok6y7lTnyiLgyIq4u+Opo4KyIuDcifgpsArZ6vJwkkQ3g9dn8ozOAV1SV1nx9rwE+VdU6KnAQsCkifhIR9wFnke3bykTElyPi/vztRcDeVa5vCeNs/9Fkxw5kx9Kh+W9dmYi4KSK+l/9/N3Al8Lgq11mio4FPROYiYEdJe9W4/kOBa2LE8xDqEBH/Cvx8wcfDx9GoWHQYcEFE/Dwi/g24AHjpJOtuZSBfxOOA64fe38DDD/RdgDuGgkbRNGV6PnBLRIwa+TGAL0u6OH+4Rh1OyG9vPzbiVm6c/VilN5Ll3orUsb/G2f5/nyY/lu4kO7ZqkRfl/A7w7YKvny3pUklfkvTUmpK01O/S9DF1DKMzU03sr4E9IuImyC7UwO4F00y976Yd/XDZtMgDKyLin0fNVvDZwvaT40wzljHT+FoWz40/NyJulLQ7cIGkq/Ir97Itli7gfwHvJtvmd5MV+7xx4SIK5p26Heo4+0vSeuB+YMOIxZS+v4qSWvBZZcfRpCQ9CjgHeEtE3LXg6++RFR/8Iq//+DzwxBqStdTv0uT+2gDXD3kAAAJxSURBVB44CnhHwddN7a9JTL3vGgvkscgDKxZxA7DP0Pu9efhzQm8ju63bNs9JFU1TShqVDd/7KhYZATIibsz/3irpn8hu66cKTOPuO0kfAb5Q8NU4+7H0dOWVOEcCh0ZeOFiwjNL3V4Fxtn8wzQ3577wDD79tLp2k7ciC+IaI+NzC74cDe0ScJ+kfJe0aEZUOEDXG71LJMTWmw4HvRcQtC79oan8NuUXSXhFxU17UdGvBNDeQleUP7E1WPzi21IpWzgWOyVsU7Et2Zf3O8AR5gPg68Pv5R8cBo3L403oRcFVE3FD0paRHSnr04H+yCr/Li6Yty4JyyVeOWN93gScqa92zPdlt6bkVp+ulwNuBoyLinhHT1LW/xtn+c8mOHciOpa+NuviUJS+D/yhwZUT87Yhp9hyU1Us6iOwcvr3idI3zu5wLvD5vvXIwcOegSKEGI++Km9hfCwwfR6Ni0fnASyTtlBeFviT/bHx11OYuo/b3lWRXqXuBW4Dzh75bT9bi4Grg8KHPzwMem///eLIAv4nsEXQrK0rnx4E3LfjsscB5Q+m4NH9dQVbEUPW+OxP4AXBZfhDttTBd+fsjyFpFXFNTujaRlQNekr8+vDBdde6vou0H/oLsQgPwiPzY2ZQfS4+vYR89j+yW+rKh/XQE8KbBcQackO+bS8kqjZ9TQ7oKf5cF6RLwoXx//oCh1mYVp22GLDDvMPRZI/uL7GJyE/CbPH79IVm9ylfJnp72VWDnfNo1wOlD874xP9Y2AW+YdN3uom9mlrjUilbMzGwBB3Izs8Q5kJuZJc6B3MwscQ7kZmaJcyA3M0ucA7mZWeL+P/Nqu2s9qoaHAAAAAElFTkSuQmCC\n",
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qcQtqJ7N09NGm1LtHrpIDuaWrxy2onczS0YdVzoHc0tbTFtROZunowyrnQG6Wglk6+rDKOZCbpaJvRx/uTtmYym6+bGb2S8vdDL3rO6gEuUVuZtVzd8pGOZCbWTnjUijuTtkoB/ImOWdYjOupOyZdkbrvvuOnd3fKWjhH3hTnDItxPXXLpBTK/e+fdZ8cHfDM3SlrUbpFLulQSV+U9G1JV0t6TRUF6x3nDItxPXXLpFTJHXe4O2WDqkit3Av8WUQcCRwD/FdJR1Yw335xzrCY5erJKZf0LHdFat+6UyasdCCPiFsj4hv5/z8GrgEeUna+vVP1Jdh9DWqT6mPffT06YIp8RWoSKj3ZKWk98Fjg0jHvbZK0IGlhcXGxysV2Q5UbfJ+HPJ1UT+CUS4p8RWoaIqKSB/AA4DLgBStNe/TRR8dMmp+PmJuLkLK/8/Orm8/cXEQWwpc+5uaqK+uwqspdZnnS+HWW6i2LWUKAhRgXf8e9OO0DuB9wIfCnRaaf2UBelSaD2vx8xNq1S5ezdm39wXxU0zuvKjW9I0yd62PVJgXyKnqtCPhn4JqI+Luy87MCmhzyNJVeJF3NxfY5DbYaro96jIvu0zyApwABXAlcnj9OXO4zbpGX1GQrOaWURhdbcl0+kqiD66MU6mqRR8RXIkIR8RsRcVT+uKDsfDut7h4lTZ5gSumGB13szuZup0u5PmrhS/Sr1tShY1NBraspjVSktCNMgeujFg7kVUslp1wVdy8rxzvCpVwftVCWdmnWhg0bYmFhofHlNmLNmqwlPkrKWs82e7ZsyXbk27dnLc8zzpjtHaHrY9UkXRYRG3Z63YG8YuvXZ+mUUXNzWQrEzGyVJgVyp1aq5kNHM2uYA3nVnFM2s4Z5PPI6bNzowG1mjXGL3Mys4xzIzcw6zoHczKzjHMjNzDrOgdxskr7ehcl6x4HcbJwuDLfqHY3lHMjNxkl9zJwu7GisMQ7kqXOrqx2pD7ea+o7GGuVAnjK3utqT+nCrqe9orFGVBHJJz5Z0naStkk6rYp6GW11tSn3MnNR3NNaoKu7ZuQvwHuAE4EjgxZKOLDtfw62uNqU+Zk7qOxprVOlhbCU9CTg9Ip6VP38DQET890mfKTOM7e/+z6+u6nOddOkl8LN7dn59j93hicc0X54+uf12+O4NWf3usTscdjgccEDbpZpOH9ZhBp37B09a9WcnDWNbxaBZDwF2DD2/CXjimAJsAjYBrPPhXzGHHQ7XXbf0hhRr1mSv2+rdfvvSev3ZPdlz6FYgPOCAbpXXatPY6IcRcSZwJmQt8tXOp8zerJPG3k3l5LZL1W2++Yf1TBWB/Gbg0KHnh+SvWRU8JG71fO7BeqaKXitfBx4m6TBJuwGnAOdXMF+zerjHh/VM6UAeEfcCpwIXAtcAH42Iq8vO16w27vFhy+ngRXiV5Mgj4gLggirmZVa7QarKd3K3UYOL8AbXbwwuwoOkt4/uXNnZwb2kJWzjxuzE5n33ZX8T/pGW4t/NdDp6EV437tnZ0b2kWav8u5leR0+Ed6NF3tG9pFmriv5u3Gr/lY6eCO9GIO/oXtKsVUV+Nx6YbamOngjvRiDv6F7SrFVFfjfTHO3OQss99TF2JuhGIO/oXtKsVUV+N0WPdmep5d7BE+HdCOQd3UuatarI76bo0a7PUyWtG4Eclu4lzzgj24D6fIhnNlAmpbFS63Jcq13KWtzDy/J5qqR1o/vhMHepsllS9/Y+fHHUjTdmQXwwtPXwstatGz/QmM9TJaH0eOSrUWY8co9cZzOlye19uWWdccbSHQpkLXmnOBs1aTzy7qRWBnyIZ7Okye19uWX5PFXSuhfI3RXR2tZkN7wmt/eVltXB3hyzonuB3F0RrU1Nd8Nrcnv3b6u7IqLxx9FHHx2lzM9HzM1FSNnf+fly8zMram4uIgvhSx9zc/Uts8nt3b+tpAELMSamdu9kp1mb1qz5Va+OYdLSe6ua1aA/JzvNplF1PtvnaCxBDuQ2va6MuVFHPtt5ZEtQqUAu6W8lXSvpSkmflLR3ReWyqlUVfLs05kYdl5Wn2g2vKztXq0WpHLmk44EvRMS9kt4GEBF/sdLnnCNv2OjVgbD6izm6dEHWrOSzq/x+LWm15Mgj4vOR3XwZ4BLgkDLzs1zVrasqW6ZduiBrVvLZHtBq5lWZI38l8NlJb0raJGlB0sLi4mKFi+2ZOlIXVQbfLgXHWclnd2nn2jVdSVmN65M4/AAuBq4a8zh5aJrNwCfJUzUrPUr3I++zOvopVznP+fmItWuXzmft2nT7G89Cv+g2+rbPggS3dSb0Iy99cQ/wcuCrwNqin3EgX4Y0/kcprX6eVW+QsxAcuyTBgNMLCe4gJwXysr1Wng28HnhuRNy90vRWQB2pi6p7WhQZc6Mrh6R9kGpPmq7rUMqqbK+VrcDuwA/yly6JiFet9Dn3WllGH3og9GEdzBLsoVVXr5WHRsShEXFU/lgxiNsK+tC6ci8K64MOnSz3lZ0p6vpwoR06JLWWpZyCK9uoanDdunerN0ufbwtmRXThto0bN66uLA2vm1vkVr0OHZJai/qcgmt43RzIrXp9yPP3XQopjT6n4BpeNwfyPmvzx9r1PH+fpTLwWZeuEp5Ww+vmQN5XqfxY25BCazNlqaQ0+pyCa3rdxl0lVPdjpq7sbOsqyASvSmuEr3JcWR1XD69Wn68SrmHd8K3eWtDmhTGzMoTrqAQv4kiO66izfKu3NrR5CNvn/ONy+nwCrSp9TmnMKAfyOrUZVGb1xzqrO7BpuFdR7ziQ16nNoDKrP9ZZ3YFNy72KesWBvE7LBZUmelbM4o91VndgTXPPoKT4ZGfdtmzJcuLbt2ct8UHL0KMDWld5dMvW+GRnW8a1ilPpx2u2Gqluv2WOEjp+hOFAPqypL9M9K6zLUtx+y1wA14OL5xzIB5r8Mt2zwrosxe13uaOElRpoqR5hTKGSQC7pzySFpP2qmF8rmvwy3bOiWh0/LO6cFLffSUcDgwbZcg20FI8wpjXucs9pHsChwIXAjcB+RT6T5CX6TV+23OdLk5vkS/Lbkdr2O2lIil12WXmoig4NZ0Fdl+hLOg94C/CvwIaI+P5Kn0my14ovW+4mf28Gk3vSjB5lDwwPVdGhXji19FqRdDJwc0RcUWDaTZIWJC0sLi6WWWw9Ujxc7Jo2Uhx9OCy28iZdPzA3N3764Xx+ldcetJXmG9dMH34AFwNXjXmcDFwK7JVPt40up1Yi0jtc7JKyKY7V1n2HDoutBU2m3hpYFhNSK2Vy448Gbs8D+DbgXmA7cNBKn002kLep6zuRMgG1zA/AOXJbSVO/rQYaFZUH8p1m1IcWeVv6EIzKnCwu+wNocyfY9R2wVaeBDhOTArn7kaegzX6sVeX0yvQtXk2ee7jcmzdn5zKaHlOmBxeSWIXa7F8/LrrX/XCLfERbd2yp8kigzLymbZGncgTj/LwN62KOvMzDgXxEWwGh6uWuNs0w7Q8glQCa0i3TrFmTtvWaU20O5Clrq4WZUiCa5geQSrlT2aFYs1o8IpwUyJ0jT0FbY2inNGbGNGOnp1JuX3vQvBSGY0hxbJZx0b3uh1vkiUgl1zytlMrd0iF251RRH6l87y0eEeLUyhD/yH6lq3WRcrlTCTipqKo+UklltViOSYF89u4Q1KFxFayjPP7LUlXVx5o1WcgcNTxuShNajCG+Q9BAivkt6xeP/7JUVfWRyrmRBO8LO3uB3D8yq1sqAScVVdVHSieXE7ux+ewFcv/IrG4pBZwUVFUfCbaEUzF7gdw/MqubA85SVdZHYi3hVMxeIPePzJrggLNUkfqYto94Cn3KEzF7vVbMLD3T9gSZ0d5nk3qtOJCbWfum7aI4o1083f3Q2uNDYFvJtL3J3PtsCQdyq5fH7LYipu1N5t5nSziQW718AZYVMW1vMvc+W6J0IJf0R5KulXS1pLdXUSjrER8CN6+Lqaxpe5O599lS4wZgKfoAngZcDOyePz+gyOdaHzTLmpPKQEd9sdJgYR6wq9eoaTzyVwNvjYh78p3C7SXnZ33jQ+DqFDnf4FTWTCobyB8OPFXSpZK+LOnxVRTKemS5Q+AupgDaVCRIO5U1k1YM5JIulnTVmMfJwK7AvsAxwJ8DH5WkCfPZJGlB0sLi4mKlK2E1qDLIjruqb1Z7s5Sp1yJB2r05ZtO4fEvRB/A54GlDz68H9l/pc86RJ67OPOsgxzsub9733HnZei1yvsE58l6jjjsEAa8C3pz//3BgB/nVoss9HMgTV9cJynFBpsnbZbV9V6Gy9Vo0SLe9nlabSYG81CX6knYD3g8cBfw78LqI+MJKn/Ml+omr604sky6rHlbXJdYpjM1RRb1u2ZLlxLdvz9IlZ5wxu13uZpDHWrHi6hrHYlIgG6gzsKYwNkcKZbBO81grVlxdXQaXO+FW9wUdKfTmcFdMq4kDue2srqvmJgWy+fn6x+xOoTeHr0a0mji1Ys1qK8ebQo7crKRJqZVd2yiMzbCNG9sJnINl+kSh9ZADuc2OtnYiZjVzjtzMrOMcyM3MOs6B3Mys4xzIzcw6zoHczKzjHMjNzDrOgdzMrOMcyH2XGjPruNm+IGj0su3BXWrAF46YWWd0v0VepkXtG9WaWQ90u0VetkWdwtCmZmYldbtFXrZFncLQpmZmJZUK5JKOknSJpMslLUh6QlUFK6Rsi9oD/ZtZD5Rtkb8deFNEHAW8MX/enLItag/0b2Y9UDaQB/Br+f97AbeUnN90qmhRb9yY3Z3mvvvqv0uNmVkNyp7sfC1woaR3kO0UnjxpQkmbgE0A66rKQftmAWZmK9/qTdLFwEFj3toMHAd8OSI+LulFwKaIeMZKC/Wt3szMprfqW70tF5glfQh4Tf70Y8BZqy6hmZmtStkc+S3Ab+f/Px34Tsn5mZnZlMrmyP8L8G5JuwI/I8+Bm5lZc0oF8oj4CnB0RWUxM7NV6PaVnWZmtnKvlVoWKi0CN67y4/sB36+wOFVxuabjck3H5ZpOquWCcmWbi4j9R19sJZCXIWlhXPebtrlc03G5puNyTSfVckE9ZXNqxcys4xzIzcw6rouB/My2CzCByzUdl2s6Ltd0Ui0X1FC2zuXIzcxsqS62yM3MbIgDuZlZxyUZyCW9UNLVku6TtGHkvTdI2irpOknPmvD5wyRdmk93rqTdaijjufmdkS6XtE3S5ROm2ybpW4O7KFVdjjHLO13SzUNlO3HCdM/O63CrpNMaKNffSrpW0pWSPilp7wnTNVJfK62/pN3z73hrvi2tr6ssQ8s8VNIXJX073/5fM2aaYyXdNfT9vrHucuXLXfZ7Uebv8/q6UtLjGijTEUP1cLmkH0l67cg0jdWXpPdLul3SVUOv7SvpIknfyf/uM+GzL8un+Y6kl0298IhI7gE8EjgC+BKwYej1I4ErgN2Bw4DrgV3GfP6jwCn5/+8DXl1zed8JvHHCe9uA/Rqsu9OB160wzS553R0O7JbX6ZE1l+t4YNf8/7cBb2urvoqsP/CHwPvy/08Bzm3guzsYeFz+/wOBfxtTrmOBTze1PRX9XoATgc8CAo4BLm24fLsA3yO7YKaV+gJ+C3gccNXQa28HTsv/P23cdg/sC9yQ/90n/3+faZadZIs8Iq6JiOvGvHUycE5E3BMR3wW2AkvuEypJZCMxnpe/9EHgeXWVNV/ei4CP1LWMGjwB2BoRN0TEvwPnkNVtbSLi8xFxb/70EuCQOpe3giLrfzLZtgPZtnRc/l3XJiJujYhv5P//GLgGeEidy6zQycCHInMJsLekgxtc/nHA9RGx2ivGS4uI/w3cMfLy8HY0KRY9C7goIu6IiB8CFwHPnmbZSQbyZTwE2DH0/CZ23tAfBNw5FDTGTVOlpwK3RcSkIXwD+Lyky/K7JDXh1Pzw9v0TDuWK1GOdXknWehunifoqsv6/nCbflu4i27YakadyHgtcOubtJ0m6QtJnJf16Q0Va6Xtpe5s6hcmNqTbqa+DAiLg1//97wIFjpildd2WHsV01LXPnoYj416bLM07BMr6Y5VvjT4mImyUdAFwk6dp8z11LuYB/BN5C9sN7C1na55VllldFuQb1JWkzcC+wZcJsKq+vrpH0AODjwGsj4kcjb3+DLH3wk/z8x6eAhzVQrGS/l/wc2HOBN4x5u6362klEhKRa+nu3FsijwC3hxrgZOHTo+SH5a8N+QHZYt2vekho3TSVlVDYO+wtYZijfiLg5/3u7pE+SHdaX+gEUrTtJ/wR8esxbReqx8nJJejlwEnBc5MnBMfOovL7GKLL+g2luyr/nvci2rVpJuh9ZEN8SEZ8YfX84sEfEBZLeK2m/iKh1gKgC30st21RBJwDfiIjbRt9oq76G3Cbp4Ii4NU813T5mmpvJcvkDh5CdHyysa6mV84FT8h4Fh5HtWb82PEEeIL4I/Mf8pZcBdbXwnwFcGxE3jXtT0p6SHjj4n+yE31Xjpq3KSF7y+ROW93XgYcp69+xGdlh6fs3lejbweuC5EXH3hGmaqq8i638+2bYD2bb0hUk7n6rkOfh/Bq6JiL+bMM1Bg1y9pCeQ/YZr3cEU/F7OB16a9145BrhrKKVQt4lHxW3U14jh7WhSLLoQOF7SPnkq9Pj8teKaOJu7irO/zyfLE90D3AZcOPTeZrIeB9cBJwy9fgHw4Pz/w8kC/Faye4nuXlM5PwC8auS1BwMXDJXjivxxNVmKoe66Oxv4FnBlvhEdPFqu/PmJZL0irm+oXFvJ8oCX54/3jZaryfoat/7Am8l2NAB75NvO1nxbOryBOnoKWUrsyqF6OhF41WA7A07N6+YKspPGT26gXGO/l5FyCXhPXp/fYqi3Wc1l25MsMO819For9UW2M7kV+Hkev36f7LzK/yK7DebFwL75tBuAs4Y++8p8W9sKvGLaZfsSfTOzjutaasXMzEY4kJuZdZwDuZlZxzmQm5l1nAO5mVnHOZCbmXWcA7mZWcf9f9uow6D4rjNGAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -209,7 +221,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x23c41209908>]"
+       "[<matplotlib.lines.Line2D at 0x1e2a73a5f00>]"
       ]
      },
      "execution_count": 8,
@@ -218,7 +230,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -242,7 +254,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x23c41289508>]"
+       "[<matplotlib.lines.Line2D at 0x1e2a7427220>]"
       ]
      },
      "execution_count": 9,
@@ -251,7 +263,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -288,7 +300,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x23c412f6488>]"
+       "[<matplotlib.lines.Line2D at 0x1e2a748ae30>]"
       ]
      },
      "execution_count": 10,
@@ -297,7 +309,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -335,20 +347,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# solution already here so just adding comments\n",
     "import statsmodels.api as sm\n",
-    "def fit_linear_plot_residuals(x,y):\n",
+    "def fit_linear_plot_residuals(x, y):\n",
+    "\n",
+    "    # add columns of 1s to data\n",
     "    X = sm.add_constant(x)\n",
+    "\n",
+    "    # fit orindary least squares regression\n",
     "    model = sm.OLS(y, X)\n",
     "    results = model.fit()\n",
-    "    y_pred = results.predict(X)\n",
-    "    e_linear = y_pred - y\n",
+    "\n",
+    "    # predict our original data to get line\n",
+    "    y_lobf = results.predict(X)\n",
+    "\n",
+    "    # compute residuals\n",
+    "    e_linear = y_lobf - y\n",
+    "\n",
+    "    # produce plot\n",
     "    plt.title(\"Residual analysis - Linear\")\n",
     "    plt.plot(x, e_linear, 'ro')\n",
-    "    plt.hlines(0, xmin=x.min(), xmax=x.max())   "
+    "    plt.axhline(0)\n",
+    "    plt.show()\n",
+    "\n",
+    "    return(e_linear)"
    ]
   },
   {
@@ -360,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -389,12 +415,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -406,7 +432,7 @@
     }
    ],
    "source": [
-    "fit_linear_plot_residuals(x, y_linear)"
+    "_ = fit_linear_plot_residuals(x, y_linear)"
    ]
   },
   {
@@ -420,7 +446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -430,22 +456,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x23c41149b88>]"
+       "[<matplotlib.lines.Line2D at 0x1e2a77557e0>]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -462,12 +488,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -479,7 +505,7 @@
     }
    ],
    "source": [
-    "fit_linear_plot_residuals(x, y_quad)"
+    "res = fit_linear_plot_residuals(x, y_quad)"
    ]
   },
   {
@@ -501,6 +527,35 @@
     "2. Using a different test from a previous notebook, test again for autocorrelation"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(array(29.05801892), array(1.21865019e-185))\n",
+      "[1.         0.90357375 0.8381482  0.77871947 0.74242302 0.70562805\n",
+      " 0.65544125 0.61557215 0.5698876  0.51048711 0.47029858 0.41620032\n",
+      " 0.38660638 0.34656985 0.30823534 0.27563039 0.22236123 0.19193002\n",
+      " 0.14887232 0.12534876 0.07452717]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import statsmodels\n",
+    "\n",
+    "# qs 1\n",
+    "print(statsmodels.tsa.stattools.bds(res))\n",
+    "\n",
+    "# qs 2\n",
+    "print(statsmodels.tsa.stattools.acf(res))\n",
+    "\n",
+    "# shows data is not random"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -517,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -529,22 +584,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x23c426cbdc8>]"
+       "[<matplotlib.lines.Line2D at 0x1e2a77a5ff0>]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -568,12 +623,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -585,7 +640,7 @@
     }
    ],
    "source": [
-    "fit_linear_plot_residuals(x, y_seasonal)"
+    "_ = fit_linear_plot_residuals(x, y_seasonal)"
    ]
   },
   {
@@ -599,6 +654,30 @@
     "Investigate the `statsmodels.tsa.seasonal.seasonal_decompose` method for removing a known seasonal trend from data. Remove the \"seasonal\" trend from the data above (assume that the x value is \"number of months\") and fit a linear model to the non-seasonal data. Check the residuals of this fit."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Apply seasonal decomposition to our data\n",
+    "decomp = statsmodels.tsa.seasonal.seasonal_decompose(y_seasonal, period = 12)\n",
+    "_ = fit_linear_plot_residuals(x, y_seasonal - decomp.seasonal)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -612,7 +691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -624,22 +703,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x23c427bca48>]"
+       "[<matplotlib.lines.Line2D at 0x1e2a95cc5b0>]"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -656,12 +735,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -673,7 +752,7 @@
     }
    ],
    "source": [
-    "fit_linear_plot_residuals(x, y_variation)"
+    "_ = fit_linear_plot_residuals(x, y_variation)"
    ]
   },
   {
@@ -700,12 +779,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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lXGkXjxEZc23/HNqoKLrPsgAbWKVGDmwCloHlhYWFOg5e/Zbi9G+WGtE0NfIUtasqTnP7VrPtknFNc/4cVkWVTSsxQSAfXNy0UlKqQDRL00XRtkcdENrQ3jlKi9tBa1fne7FaRWDeP4cJOJD3Saq241kPCEU/fNdyu6nuz83t4qU4kPdJyh9DytqYa7ndU3fmSFszVTpiVCBP0tkp6RLgi8DRknZIemOK9doIKTvrlpayoce7d2d/y3ScpVxXKm0bcNI2dY9k9CRilUiVtfLaiDg8IvaJiCMj4oMp1jtXpgk4/jFMxkPax6s7g6cvmTdtU1RNr3px08qQWdop+9CMUfU++DR+PPdtdAojmlY8+2EbtHzGtUoMX8sSsrOKlLWzNWuKc5WlrPnHMp5RsTNGzX7oQN4G8xhw6jh4zeMB0nrN09i2WVdHGpZRRyeb+xJsTjiQV2HaTIl5DDh1HLzcsWZzwoE8tVkyJVYLOH1Nn6vr4NXGlEiz1Ip6QKteep21kjJTou8ZBX3IvOkqv/edRJUDgmxAyrbfPl4RZpBry81IkV9f55liVdvq09luUXSvenGNfEKel6Kdul6bLfsdrfNMsaptdfRsl6rnWplm6XUgT/kFmXV2wi4HmbbraADYQ9kKQp0DraqaLbGjg8UcyEepar7rFOucNmj0Ici0XUcDwB7K7kOdZ4qrTZFc5vvd0bNdB/IiXQh80xwU+hBk2q6jAWAPZb/3bamRl9luR38rDuRFOvphjtSHINN2ffnOlDlrbLqNPMX3uwuVuAIO5EX6Fvj6EmTarKMBILk6+2JWtpWyRj643g71JzmQF+lb4HOQqUcHA0Av+Ps9MpCnurDEKZK+IelWSeekWGct+jY03kPS6+H892b4+z1S6dkPJe0FfBN4CbADuBZ4bUTcNOo1rZr90FN4mllHjJr9cO8E6z4RuDUibs83dClwOjAykN++60F+9b99McGmU3gynHvJozcfAIbLtnMnfOt2+NGPYd/HwpOeDIccUmspzcxGSRHIjwC2D9zeATx3+EmSNgGbAA44/CkJNpsbDLL75Lvz8CN7/j8cfKcJzDt3wje+8ei84D/6cXYbHMzNrB2KGs6nWYB/BVw4cPsM4P2rvSZZZ+ckqUnDnSKTdphU1VNuNoo7UW0MKuzsvBM4auD2kfl9aRVNcFM0qdQoDz0EZ54Jr3vd+ImoBicVGqWqq4z30eBnt25dtvRhoqKUfKFoKyFFZ+feZJ2dJ5MF8GuBX4uIG0e9ZurOzqLrO0rZFz6VwcuqjbpE2CBfLmwyRZ/doNTX6ewqX5bOJlDZpd4i4hHgLOAq4GbgY6sF8ZkU1bxTBnHY88o042rbXU5RrNu4s6Y+TctbRh2XvrPeSpJHHhFXRsTPRMRTIiJ9hKv6yzwcmFe73JhzV6czyWfnYDWf1221ZLpxYYlJv8wHH5wt0p7/77XX6NcUBeZRA4U2b56fASCpJt2f5LNzsOrf4DSrV1EPaNXL1Fkrk2SnjJuje5pMFSni4IOzZbUMgr5mGaQcCj3us5uzIdar6uv3yZKh83OtDKYDDk92NUkwGPcjSTH390q5uv4jTD0HzbQHSDMr1P1APqiKmsu0wWvcPMltrmmOe//6NiukWU+MCuSl0w9n0aq5VlasWVOcCTOYljjJ8we1MXWsKB1wOAXQqXBmrVRZ+mFvTJs1MEkH3XA2Rhuu2l2UDjicAuiON7NOcSBfMW3wKnr+sMFg35aRe5PkK3u6ULNOcSBfMW3wGnw+ZK8ZNHwQmKQmPKtpavqTnmF4zm2z7ihqOK96ac0VgmYxqqNw3P3TXG9wms7cFNk2beiYdeqd2Vj0KmulKSmC5qjMmMHAP802ZkkVbFvQbOvBpe3a9jla5UYFcmetTGPabI5JJt9asZI5ct55021j2mybNnKWzPQmyT6y3nHWSgrTTmw0zRwiK+3l066rD3N0tHXCqDZkGY1SZZ+LdY4D+TSqSFEctHLd0GnW1YdUwTYejNqSZTTKpAe/Nh+MLJ2i9paql7luI1+7Nhuivlpb+bTtxV1vK21jG/m4q0M1/R5P0jfSxvfVSsGdnYlMGzSLnj/uB9b1wDyLtu3zqGkK2hIQJwnSqefMscY5kLdN2wLXarpU1lTGzaXThoDoOXPmzqhAXiprRdKrgHcCzwBOjIiJUlE6m7Uyj+Y1O2LcJeqg/ZlBzgbqnaqyVm4AfgX4fMn12CSm7bhK0dE1r9kRwyN3i7Q9M6hNHeHudK1WUTV92gW4Btg46fPdtDKDOkZwFp2q+/S8252GbWgW6/L71zJU2UY+SSAHNgHLwPLCwkItO90rKedLn+bCGqtl2MyTugNiGwJwKu50TWZUIB/bRi7pM8BhBQ+dFxGfzJ9zDfCWcBt5dVLPlz7pHOQHHww//OH8tZE3qW/9En0YfdwSM7eRR8SLI+LYguWT1RTVCqUejDTczj1qgMk993hK27r1rV+ijQO+esYjO7uiivnSB4P3aj82T2lbr7ZOWTCrNnW69lSpQC7plZJ2AD8HXCHpqjTFsp9Sdr70IoPB2z+29uhbDdYXKqleUcN51YuzVmo2adZAnzrYusxZHjYCIzo73bQyDyatEbkJpR1cg7UpeT5yM5sfW7Y8Ol30wkLWdNihA6TnIzerc3ShRzK2T9unJi7Bgdy6b5KgWeePuMcBo9P6ltY5wE0r1m2TDp6pcwIpT1bVTj0YmOSmFeunSWtZdeZm9y0PvC/6ltY5wIHcum3SoFnnj7jHAaPTejxWwoHcum3SoFnnj7jHAaPTepzW6UBu3TZp0KzzR9zjgNF5PR0r4c5O676O5wabTcqdndZfPa1lWcu0eGyAA7l1R4t/SNZzLR8b4EBu3dDyH5L1XMsHEzmQWze0/IfUOJ+tVKvlYwMcyK0bWv5DapTPVqrX8rEBZS8s8YeSbpF0vaS/lnRQonKZ7anlP6RG+Wylei0fG1C2Rn41cGxEPAv4JnBu+SKZFajyh5SqWaKp5g2frVSv7WMDiq42McsCvBLYMslzfYUgm0kVVzBKdTWeJq/qs7i453ZXlsXF6rdttWLEFYKSDQiS9D+Aj0bE5hGPbwI2ASwsLJywrWh2OLO6pZqpsMkZDyedAdI6b+YBQZI+I+mGguX0geecBzwCjDyXjIgLImJjRGxcv379rPthllaqZokmmzeGT/sPPhj22w/OOKO7GSwpm6nmIaOnqJo+zQK8HvgisHbS17hpxVojVbNEW5o3mr5wc4rmr5T70PT7kRgjmlbKBvFTgJuA9dO8zoHcWqMPbeSDmjygpHoPUu5DWw6wiVQVyG8FtgPX5cufT/I6B3JrlVSdqFV0xk5LKg5cUvXbThU0U+zDymdRtJ663o8KjArknv3QrE+a7HRNdSm1svtQ1Pk767rKqGBWTs9+aDYPmhy4kmrQVtl9KBogNeu6ZlX3aNuianrVi5tWzCrUVBNP6k7KWfdhVNPMSjNPh3P7cdOKWQv0/SIYbdi/JpuXVqRqZvqpl7tpxaxZ8zC5VRsu8tGGeVFqnhvIgdysLp7cqh5tmBel5oOJm1bM6lLR6ba1VI1ZK3uXWquZTW5hobjt1lPx9tPSUm1nAW5aMatLG9puLb0WzOXiQG5Wlza03VpaLenAdhu5mdmsak51dPqhmVlqLbk6kwO5mdmsWnItWQdyM7NZtaQD24HczGxWLenAdh65mVkZNeaLj1KqRi7p9yVdL+k6SZ+W9MRUBTMzs8mUbVr5w4h4VkQcB3wKeHv5IpmZdVRDg4NKNa1ExP0DN/cH6k9KNzNrg+ErE60MDoLKm15Kd3ZKerek7cASrpGb2bxqcHbLsSM7JX0GOKzgofMi4pMDzzsX2Dci3jFiPZuATQALCwsnbCsaDWVm1lU1zG45amRnsiH6khaAKyPi2HHP9RB9M+udGobrVzJEX9LTBm6eDtxSZn1mZp3V4OCgsnnk75F0NLAb2Ab8RvkimZl10EqHZgPXLPXsh2ZmHeHZD83MesqB3Mys4xzIzWwyLbikmRXzpFlmNl6DoxZtPNfIzWy8Bkct2ngO5GY2XksuaVaZjjcbOZCb2XgtuaRZJVaajbZty4bYrzQbdSiYO5Cb2XgtuaRZJWZpNmpZDd6B3MzGa8klzSoxbbNRC2vwHtlpZvNt2smuapgcaxSP7DQzKzJts1ELO34dyM1svk3bbNTCjl8HcjOzpaWsWWT37uzvam3/Lez4dSA3M5tGCzt+PUTfzGxaS0utythxjdzMrOOSBHJJvy0pJK1LsT4zM5tc6UAu6SjgpUBPJl0wM+uWFDXyPwbeCtQ/ssjMzMoFckmnA3dGxNcmeO4mScuSlnft2lVms2ZmNmDsEH1JnwEOK3joPOBtwEsj4j5JdwAbI+J7Yzcq7QIKxrhOZB0wdhs9432eD97n+VBmnxcjYv3wnTPPtSLpmcDfASvThh0J3AWcGBHfmbGQk2x3uWiugT7zPs8H7/N8qGKfZ84jj4ivA4es3J6mRm5mZuk4j9zMrOOSjeyMiA2p1jXGBTVtp028z/PB+zwfku9zI/ORm5lZOm5aMTPrOAdyM7OO61Qgl3SKpG9IulXSOU2Xp2qSjpL0WUk3SbpR0tlNl6kOkvaS9FVJn2q6LHWQdJCkj0u6RdLNkn6u6TJVTdJv5d/pGyRdImnfpsuUmqQPSdop6YaB+54g6WpJ/5D/fXyKbXUmkEvaC/gAcCpwDPBaScc0W6rKPQL8dkQcAzwP+HdzsM8AZwM3N12IGv0J8LcR8XTg2fR83yUdAfwmWbryscBewGuaLVUlPgycMnTfOcDfRcTTyMbhJKmQdiaQAycCt0bE7RHxj8ClwOkNl6lSEXF3RHwl//8HZD/wI5otVbUkHQm8DLiw6bLUQdKBwAuBDwJExD9GxL2NFqoeewP7SdobWEs2mLBXIuLzwD1Dd58OXJT/fxHwihTb6lIgPwLYPnB7Bz0PaoMkbQCOB77UcFGqdj7ZJGy7Gy5HXZ4E7AL+Im9OulDS/k0XqkoRcSfwXrIZU+8G7ouITzdbqtocGhF35/9/Bzg0xUq7FMjnlqQDgMuAN0fE/U2XpyqSTgN2RsTWpstSo72B5wB/FhHHAw+S6HS7rfJ24dPJDmJPBPaX9LpmS1W/yHK/k+R/dymQ3wkcNXD7yPy+XpO0D1kQ3xIRn2i6PBV7PvDyfLqHS4EXSdrcbJEqtwPYERErZ1ofJwvsffZi4FsRsSsiHgY+Afx8w2Wqy3clHQ6Q/92ZYqVdCuTXAk+T9CRJjyHrHLm84TJVSpLI2k5vjog/aro8VYuIcyPiyHyU8GuAv4+IXtfU8gnmtks6Or/rZOCmBotUh28Dz5O0Nv+On0zPO3gHXA6cmf9/JvDJFCvtzMWXI+IRSWcBV5H1cn8oIm5suFhVez5wBvB1Sdfl970tIq5srkhWgX8PbMkrKLcDb2i4PJWKiC9J+jjwFbLMrK/Sw6H6ki4BTgLWSdoBvAN4D/AxSW8km8r71Um25SH6Zmbd1qWmFTMzK+BAbmbWcQ7kZmYd50BuZtZxDuRmZh3nQG5m1nEO5GZmHff/ARAa46iQyNKrAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -717,12 +808,13 @@
     }
    ],
    "source": [
-    "#from solutions\n",
-    "y_increasing = np.random.random(len(x)) * np.arange(len(x)) * 25  # Your equation may vary\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y = np.random.random(len(x)) * x\n",
     "\n",
-    "plt.plot(x, y_increasing, 'bo')\n",
+    "plt.plot(x, y, 'bo')\n",
+    "plt.show()\n",
     "\n",
-    "fit_linear_plot_residuals(x, y_increasing)"
+    "_ = fit_linear_plot_residuals(x, y)"
    ]
   },
   {
@@ -743,7 +835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -752,12 +844,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -784,10 +876,29 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 66,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "Came from a uniform distribution"
+    "# qs\n",
+    "plt.hist(e_linear)\n",
+    "plt.show()\n",
+    "\n",
+    "# data came from uniform distribution, not normal. qqplots is for normal, hence the graph above is not a straight line"
    ]
   },
   {
@@ -806,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -815,12 +926,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -836,6 +947,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -843,8 +955,182 @@
     "\n",
     "#### Exercise\n",
     "\n",
-    "Plot the QQ plots (both against a normal distribution and a 2 sampled QQ plot) for the other datasets we generated in this module. What do their patterns show?\n",
+    "Plot the QQ plots (both against a normal distribution and a 2 sampled QQ plot) for the other datasets we generated in this module. What do their patterns show?\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def return_linear_plot_residuals(x, y):\n",
+    "\n",
+    "    # add columns of 1s to data\n",
+    "    X = sm.add_constant(x)\n",
+    "\n",
+    "    # fit orindary least squares regression\n",
+    "    model = sm.OLS(y, X)\n",
+    "    results = model.fit()\n",
+    "\n",
+    "    # predict our original data to get line\n",
+    "    y_lobf = results.predict(X)\n",
+    "\n",
+    "    # compute residuals\n",
+    "    e_linear = y_lobf - y\n",
     "\n",
+    "    return(e_linear)\n",
+    "\n",
+    "# exercise\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y_linear = x * 3 + 5 + np.random.random(len(x)) * 15\n",
+    "y_diverg = np.random.random(len(x)) * x\n",
+    "y_quad   = 3 * x ** 2 - 4 * x + 10 + np.random.random(len(x)) * 90\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "qqplot(return_linear_plot_residuals(x, y_linear), line='s');\n",
+    "qqplot_2samples(x, return_linear_plot_residuals(x, y_linear), line='r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "qqplot(return_linear_plot_residuals(x, y_diverg), line='s');\n",
+    "qqplot_2samples(x, return_linear_plot_residuals(x, y_diverg), line='r')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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QWAiPPQZ33w277x7nqCVdxappPJm0KEQkYcLMvwi1bMf06XDzzTB/Plx4IfTvDw0axDdYSXslJg3n3CfJDERE4m/U7NxS52DsXzOTHhfF2B97/Xq45x4YOhQOOQTeeQfattWci2oqVvPUCOfcn8xsPlGaqZxzxyU0MhEptzCjpDLMeOpPx5ecLAoLYdgw6NIF8vKgc2ffLLX33okJWiqFWM1TdwSPbZIRiIhUXNghtaU2Ry1YALfcAtOmwWmnwcCBcJz+TpTYzVNFy453dM7dE3nMzPrgt2AVkRQrz1IgJSaMzZvh4YehXz/Ybz/fJHX99ZpzIb8KM0/jXIoniPOjlIlIEpVnoh740VFRE8bo0XDbbX44bfv20KcP1K4dp2ilqojVp3EL0BE43MzmRRzaB/g00YGJSHTlTRZQwvyLFSt8shg7Fo45Bl57DVq2jFO0UtXEqmm8BowDHge6RZRvcs79lNCoRCSq+0fNZ/jn35drb4tio6S2bfPNUI884l8/8YSfsJcZlz3WpIqK1aexEdgIXGVmGcCBwfl7m9nezrnvkxSjSLVXkdpF1CG1U6b4ju5Fi6BdO3jmGahfP34BS5UVZj+NTvjl0dfy2+q2DtBQCpEkKO8S5gZc07w+Pdsd+1vhunXQtSu8/DIcdhiMGQMXXVTSLUSKCdMRfieQ45xbn+BYRGQXZV0zqmi58mJLgRQWwpAh0K0bbNrkHx94AGrWTGD0UhWFSRor8c1UIpIE5WmKijmre+5c3xT12Wd+UcHnn4cmTeIYsVQnYZLGt8DHZvYefnl0AJxzTycsKpFqqDzJImoTVJFNm/wM7mefhVq14JVX4M9/1vIfUiFhksb3wc/uwY+IxFF5O7lLrF0455csv+MOWL0aOnTwq9HWqhXHqKW6CrOfxsPJCESkOipPJ3fMNaO+/dZvijRuHBx/PPz739C8eRwjluouzOipOkBX4Ghgz6Jy51wpW3qJSGnCLFseqcQ1o7Zu9bvm9eoFu+3m51906uSfi8RRmAVlhgNfAw2Bh4HlwFcJjEmk2lhdhvWi9q+ZGT1hTJrkaxUPPOCHz379tZ+kp4QhCRDm/6rfOeeGmtkdwR4bn5iZkoZIHNTNzir/fhdr1vhd8157DQ4/3DdJnXdeAqMVCZc0inrnfjCzC4HVgHrUROKgS+ucqH0aMYfQFhTACy/AvfdCfj7cf79/npWVpKilOguTNHqa2X7A3cBzwL7A3xMalUgVt+uIqRIn5e1q1iy/5epXX8HZZ/s5Fzk50c8VSYAwo6feDZ5uBM5KbDgiVVPknhdG8a0wCx1kZljJCWPjRt9nMWAA1KkDw4fDVVdpzoUkXZjRUy8RfbvX9gmJSKSKKGlzpJIWBNle4Og7fsnOScM5eOMNuOsuWLsWOnaEnj0hOzthcYvEEqZ56t2I53sCl+D7NUQkioqsSLvTaKpvvoFbb4WJE+Gkk/x+F82axTFSkbIL0zz1duRrM3sdmJawiEQqoYokikh1s7Pgl1+gd294/HHYc0/o39/3Y2RkxClakfIrz0DuRsAB8Q5EpLIaNTuXLm/NZXthebZG+k1mhvHEvmv87nnLlvk+i6eegoMPjlOkIhUXpk9jE74Ztqj/bg3aH1wEKPvS5SX5/Y7/8eKs1znksbHQqJFvkjrnnDhFKRI/YZqn9klGIGGZ2XnAM0AGMMQ51zvFIUk1FI/mqHrZWXRtdQRtPxvt51ps2+a3Xu3SxTdLiaShmEnDzLKAa4CixfdnAP92zm1LdGAlxJMBDADOBVYBX5nZGOfcolTEI9VTeRYZjDoP48sv4W+XwOzZ0Lq177s48sgERi5ScSUmDTM7FhgDTAFmBsWtgb+b2blAZ+fc/YkPcScnA0udc98GMb4BtAWUNCQpytocFXVmd16eHzo7aJDvr3jzTbj8cs25kEohVk3jWaCDc25iZKGZnQMsABYmMrAS1MPvJFhkFXBK5Alm1gHoAFC/fv3kRSZVXlENI0zCiJosnPOT8u6+G3780e938fDDsO++CYxaJL5iJY2Dd00YAM65D81sO36+Rtpxzg0GBgM0a9asYr2TIhEeHruw1CapEpcuX7zY1y4+/hhOOQU++ACaNk1csCIJEmtp9BpmtseuhWa2J7DdObclcWGVKBc4NOL1IUGZSEKNmp3Lhi2xO72jLl2+ZQvcd59funzOHN8kNX26EoZUWrFqGq8Ab5vZrc65FQBm1gDfbPWvJMQWzVdAIzNriE8WVwJXpygWqeJKWgZkVyXupPfee34jpOXL4dpr4ckn4QBNcZLKrcSk4ZzraWadgKlmVjMo3gw86Zx7LinRFY9pRxDTePyQ2xedc6noW5EqriwjpIoljFWrfH/FyJFw1FEweTKceWbighVJophDbp1z/YH+ZrZP8HpTUqKKwTn3PvB+quOQqidszSJSdlbmbwlj+3Z47jno0cPvefHYY77Te/fdExSxSPKFWkYkHZKFSCKVZ+5FVmYGD118tH8xfTrccgvMmwcXXuiTR8OGCYpWJHXC7BEuUuWFGRkVKcPMd3rX3xNuuglatIANG+Cdd/xqtEoYUkVp53mp9sKMjIqUlZnB4+2Opt2cCXBuFz9Zr3Nn3yy1996JC1QkDYRZsLAmfqvX+s65m8ysEZATsaOfSKVVNMM7rHrZWTxyhKPVnVfDtGm+hjFwIBx7bAKjFEkfYWoaL+GXETk1eJ0LvMXOmzOJVCplWXDw1wl7v8/2M7gf6OdncQ8dCtdfDzXUyivVR5ikcYRz7gozuwrAObfFTIvkSOVVlk7vXxcY/H4GtL0NVq6E9u2hTx+oXTsJ0YqklzBJY1uw2q0DMLMjgK0JjUokgfqOXxJ+OZBaO+D2jjBmjN8c6bXXoGXLJEUqkn7C1Kt7AB8Ah5rZcOAjoGtCoxJJoNUhZnj3viiHdhNehSZN4MMP4YknYNYsJQyp9sJswjTRzGYBzfG7993hnPsx4ZGJJEjd7KwSJ/BlZWYw+LDNnP7XNrBoEbRrB888A1oxWQSIUdMwsxOLfoDDgB+A1UD9oEykUurSOoeszIxi5Ye7zXww9yVOv+ky2LzZN0m9844ShkiEWDWNp2Icc8DZcY5FJOGKlgrJ315AhhkFznHIvnvwXP4smg7oDZs2QffufvvVmjVLv6FINRNrwcKzkhmISKLtOmqqwDlO+GkFQ8e9yO/mzYQ//MHPuWjSpJQ7iVRfYSb37Ql0BFriaxhTgUHOuV8SHJtIXEWOmtpr6xb+Pm04188cy6aa+8KwYX75co0mF4kpzJDbV4BNQNFy6Ffj99O4PFFBicTbqNm5vvPbOc77z3R6fDiYA3/+iTeOb80TZ/yFOdddmeoQRSqFMEnjGOdcZH19spktSlRAIvEUOfP70Lw1PDJxIGd9O5NFBzSkY7vuzK7XmHrZWakOU6TSCJM0ZplZc+fc5wBmdgowI7FhiVRcUR9GQf4vdPrybTp9NoIdNTJ45OybGHZSGwpqZJCVmUGX1jmpDlWk0giTNE4CppvZ98Hr+sASM5sPOOfccQmLTqQC+o5fwglLZ9FzwkCO+GkV7+a05NFWN7J2n9+W/yi2p7eIxBQmaZyX8ChE4mzch3Po/K9HuWTRxyzPPpi/XP4wnxx+0k7n1MvOUsIQKaMwM8JXmNn+wKGR5zvnZiUyMJHyGDXjexY+1JfbPnyJPXZs5ZnTruT55pezNXOPnc5Ts5RI+YQZcvsocD2wjGDRQjS5T9LQ5OHjOLzrnbRb/R8+Pew4Hji3I9/+7pBi5+1fM5MeFx2tWoZIOYRpnvoTfnn0bYkORqRcNm6EBx7gD/0H8FPNfbn9os6MOeqMqHMu/nHFCUoWIhUQJmksALKB/yY2FJEycg5GjIA778StXcurTS/gqdOv5X97Rt9yVX0YIhUXJmk8Dsw2swVE7KPhnLs4YVGJlOabb+DWW2HiRBYe3Iju13Zl3sG/L/F09WGIxEeYpDEM6APMBwoTG45IKX75BXr3puDx3myxDPqe8zdebXoBhTWKr1pbRH0YIvETJmlscc49m/BIREozYQI/3/g39l65nHePOoOeZ9/Aur1rxbxEfRgi8RUmaUw1s8eBMezcPKUht5Icq1fDXXfBm2+yrlY9/nZFTz5tcEKpl6kPQyT+wiSNpsFj84gyDbmVhBv91Qq+faQvN054kd0LdjCg5TUMOuUytu2WWeq16sMQSYwwk/u0r4Yk3SevjKXRPX+n7ZplfNLwRB4892ZW7F831LXqwxBJnDA1DczsQuBoYM+iMufcI4kKSqqxDRvg3ns5/YUX+O9e+9OxbTfez2kRap8LJQuRxAszI3wQUBM4CxgCXAZ8meC4pLpxDl59FTp3hh9/5KWTLqZfy2v4eY/St1xVshBJnjA1jdOcc8eZ2Tzn3MNm9hQwLtGBSTWyeDF07AgffwwnnwwffMCL4zfwc15+zMvqZWfRpXWOkoVIEoVJGkX/creYWV1gPXBw4kKSamPLFujVC/r2hb32gkGD4KaboEYNurDzft6RsjIztKS5SIqESRrvmlk20BeYhR859c9EBiXVwPvvQ6dO8N13cN11PnEccMCvh4sSQt/xS8jNyyfDjALnVLsQSbEwo6ceDZ6+bWbvAns65zYmNiypslatgjvugJEjoXFjmDwZzjxzp1NGzc6l7/glrM7Lp252liboiaSRGiUdMLP/Z2YHRby+DhgBPGpmsafhlsLMLjezhWZWaGbNdjnW3cyWmtkSM2sdUX5eULbUzLpV5P0lBXbsgKef9oli3Dh47DGYOzdqwug+cj65efk4IDcvn+4j5zNqdm5KwhaRnZWYNIAXgG0AZvYHoDfwCrARGFzB910AXApMiSw0sybAlfjhvecBz5tZhpllAAOA84EmwFXBuVIZfPYZnHQS3H23TxILF0L37rD77sVO7Tt+SbF+jPztBfQdvyRJwYpILLGapzKccz8Fz68ABjvn3sY3U82pyJs65xYDWPGx922BN5xzW4HvzGwpcHJwbKlz7tvgujeCcxdVJA5JsPXroVs3GDIEDjkE3nkH2raNOueiqEkqt4QRU6tLGUklIskRq6aRYWZFSaUVMCniWKhJgeVQD1gZ8XpVUFZSeTFm1sHMZpjZjHXr1iUoTImpsBBeeglycvxj585+WG27diUmjKImqZLUzc5KYMAiElasL//XgU/M7Ef8sNupAGZ2JL6JKiYz+xA4KMqh+5xzo8sRayjOucEEzWfNmjVzpZwu8bZgAdxyC0ybBi1awMCBcOyxMS+J1iQVSetIiaSPEpOGc66XmX2En5MxwTlX9AVcA7ittBs7584pRzy5wKERrw8JyohRLulg82Z45BHf2b3ffjB0KFx/PdSIVZn1tYxYNQwNsRVJLzGbmZxzn0cp+0/iwmEM8JqZPQ3UBRrhlywxoJGZNcQniyuBqxMYh5TF6NFw++3w/ffQvj306QO1a5d6WVGzVEnqZWfxaTctpiySThLVNxGTmV0CPAfUAd4zsznOudbOuYVmNgLfwb0DuNU5VxBc0wkYD2QALzrnFqYidomwYgXcdhuMHQvHHANTp0LLlqEvj9UspSYpkfRkv7U6VT3NmjVzM2bMSHUYVc/27b4Z6pFgoeOHHoI774TM0ve5gNJHSoF23BNJJTOb6ZxrFu1YSmoaUolNnQo33wyLFvnRUM88A/Xrh768qEkqVse3dtwTSV9KGhLOunXQtSu8/DIcdphvkmrTJvTlYWoXoGYpkXSnpCGxFRb6kVD33AObNvmZ3PffDzVL3+eiSJjaBWiklEhloKQhJZs718+5+OwzOOMMeP55aFL66i2RCw7ul5XJ/37ZTmEpXWcaKSVSOcQeRC/V06ZNcNddfr2opUth2DC/Gm3IhBG54GBefukJQ01SIpWHahryG+fg7bf90uU//AAdOvjVaGuVvqhx2D6LXalJSqRyUdIQb9kyvynSBx/A8cf75NG8+U6n7LrPxVmN6zD563Xk5uVj+N25wtLueyKVk5JGdbd1q981r1cv2G036NfPJ4/ddv5fY9fO7Ny8fF79/Ptfj5clYWSYKWGIVFJKGtXZpEnQsSMsWQKXXQb/+AfUi/5FXtqigmGphiFSuakjvDpaswauuQZatfKzu8eNg7feipowRs3OpUXvSWXuqyiSnZXJ/jUzMXz/hRKGSOWmmkZ1UlAAL7wA994L+fnwwAN+3kVW9L0qws6viEY1CpGqSUmjupg508+5+OorOPtsP+ciJ6fEzu3VefnUMKOgDGuTFXWGa0SUSNWlpFHVbdzoaxQDBkCdOjB8OFx1FZiV2rkdK2HU2yXB1FWiEKkWlDSqKufgzTf9JL01a3yHd8+ekJ396ynl7dzW7G2R6ktJoyr65hufJD780M/qHjMGmhVf5Xh1OTq3NXtbpHrT6Kmq5JdfoEcPvyHSl19C//7wxRdREwZA3ezoHeC7yjDT6CcRAVTTqDomTPC1i2XLfJ/FU0/BwQfHvKRL65xSR0dpFJSIRFJNo7JbvRquuAJat4aMDJg4EV57LWrCKJpz0bDbe7ToPQmAxy89lnrZWb/WJP7cvP5Or5UwRCSSahqV1Y4dftjs/ffDtm1+69WuXWGPPaKeHm2kVPeR83n80mPVqS0ioammURl9+SWcfLJfjfa002DBAj+stoSEAdFHSuVvL6Dv+CWJjlZEqhAljcpkwwY/Qa95c1i7FkaM8EuAHHlkqZeWNFKqPCOoRKT6UtKoDJyDV1+Fxo1h8GC4/XZYvBguvxzMQt2ipJFSYUdQiYiAkkb6+/prv7DgtddCgwYwY4ZfjXbffct0my6tc8jKzNipTHMuRKSslDTS1ZYtcN99cNxxMHs2DBwI06dD06blul27pvWKjZTSyCgRKSuNnkpH773nN0JavtzXMPr2hQMPLPNtdl2MsEvrHI2UEpEKUdJIJytXwp13wsiRvv9i8mQ488xy3aqkIbaAahciUm5qnkoHO3b4GdxHHQXvv++3Xp07t9wJAzTEVkQSQzWNVJs+HW6+GebPhwsvhOeeg4YNK3xbDbEVkURQTSNV1q+Hm26CFi38/IuRI2Hs2LgkDNAQWxFJDCWNZHMOXnrJ91m89BJ07uznXFxySeg5F2FoiK2IJIKap5JpwQI/o3vaNL/8x8CBfkhtAhR1du86ekqd4CJSEUoaybB5s19Q8Omn/aS8IUPgr3+FGomt6LVrWk9JQkTiSkkj0UaP9st+fP89tG8PffpA7dpxfYto8zGULEQkEZQ0EmXFCrjtNt+5fcwxMHUqtGwZ97fRfAwRSaaUdISbWV8z+9rM5pnZO2aWHXGsu5ktNbMlZtY6ovy8oGypmXVLRdyhbNvmaxNHHQUffQRPPAGzZiUkYYDmY4hIcqVq9NRE4Bjn3HHAf4DuAGbWBLgSOBo4D3jezDLMLAMYAJwPNAGuCs5NL1Om+LWhunXzO+ktXgxdukBmZsLeUvMxRCSZUtI85ZybEPHyc+Cy4Hlb4A3n3FbgOzNbCpwcHFvqnPsWwMzeCM5dlKSQY1u3zieHYcPgsMN8k1SbNnF/m2h9F3Wzs8iNkiA0H0NEEiEd5mm0B8YFz+sBKyOOrQrKSipPrcJC+Oc/IScHhg+H7t1h0aKEJYzuI+eTm5eP47e+i7Ma19F8DBFJmoQlDTP70MwWRPlpG3HOfcAOYHgc37eDmc0wsxnr1q2L122LmzvX91N06ODnWsydC489BjVrJuTtSuq7mPz1Oi15LiJJk7DmKefcObGOm9n1QBuglXPOBcW5wKERpx0SlBGjfNf3HQwMBmjWrJmLdk6FbNoEPXrAs89CrVq+Seraa+M2m7uk4bOx+i40H0NEkiUlfRpmdh7QFTjDObcl4tAY4DUzexqoCzQCvgQMaGRmDfHJ4krg6qQG7Ry8/bZfunz1al/DeOwxnzjiJNbwWfVdiEg6SFWfRn9gH2Cimc0xs0EAzrmFwAh8B/cHwK3OuQLn3A6gEzAeWAyMCM5NjmXL4IIL/J7cder4lWkHDSpzwhg1O5cWvSfRsNt7tOg9iVGzd64sxRo+q7WkRCQdpGr01JExjvUCekUpfx94P5FxFbN1q981r1cv2G036NfP76i3m//YyjITO8wkvNKaoEBrSYlIamlGeEkmTYKOHWHJEl/D6NcP6v32BV3WmdixahFF55fWBKW+CxFJtXQYcptUpTURsWYNXHMNtGoF27fDuHEwYsROCQPKPhM7zCQ8NUGJSLqrVjWNmLWD4w7y/RT33Qf5+fDAA37eRVb0juayzsQO05GtJigRSXfVKmmUVDsY8+JY2n0+FGbM8DWMAQP8hL0YyjqaqUvrnJ0SFkSvRagJSkTSWbVqntq1FrDP1s08NHEQ/xxwK6xc6Wd1T5xYasKAsjcltWtaT5PwRKTSq1Y1jV9rB85x8eIp3D9pCLU35/FO84v547iXITs79L3K05SkWoSIVHbVKml0aZ3Dg2/Nov/rPfjD8tnMO+hIOl7xEH/u9McyJYwiSgIiUt1Uq6RR9AW/bnJ9Hmx0CpPPvJS7z2+iL34RkZCqVdKAIHF8Noo/pjoQEZFKqFp1hIuISMUoaYiISGhKGiIiEpqShoiIhKakISIioSlpiIhIaEoaIiISmpKGiIiEZs65VMeQMGa2DliRgreuDfyYgvdNZ/pMitNnUpw+k+JS8Zkc5pyrE+1AlU4aqWJmM5xzzVIdRzrRZ1KcPpPi9JkUl26fiZqnREQkNCUNEREJTUkjMQanOoA0pM+kOH0mxekzKS6tPhP1aYiISGiqaYiISGhKGiIiEpqSRgKYWV8z+9rM5pnZO2aWneqY0oGZXW5mC82s0MzSZghhspnZeWa2xMyWmlm3VMeTDszsRTP7r5ktSHUs6cLMDjWzyWa2KPh3c0eqYwIljUSZCBzjnDsO+A/QPcXxpIsFwKXAlFQHkipmlgEMAM4HmgBXmVmT1EaVFl4Gzkt1EGlmB3C3c64J0By4NR3+X1HSSADn3ATn3I7g5efAIamMJ1045xY755akOo4UOxlY6pz71jm3DXgDaJvimFLOOTcF+CnVcaQT59wPzrlZwfNNwGKgXmqjUtJIhvbAuFQHIWmjHrAy4vUq0uCLQNKbmTUAmgJfpDgUdkt1AJWVmX0IHBTl0H3OudHBOffhq5jDkxlbKoX5XEQkPDPbG3gbuNM5979Ux6OkUU7OuXNiHTez64E2QCtXjSbDlPa5CLnAoRGvDwnKRIoxs0x8whjunBuZ6nhAzVMJYWbnAV2Bi51zW1Idj6SVr4BGZtbQzHYHrgTGpDgmSUNmZsBQYLFz7ulUx1NESSMx+gP7ABPNbI6ZDUp1QOnAzC4xs1XAqcB7ZjY+1TElWzBAohMwHt+xOcI5tzC1UaWemb0OfAbkmNkqM7sh1TGlgRbAtcDZwffIHDO7INVBaRkREREJTTUNEREJTUlDRERCU9IQEZHQlDRERCQ0JQ0REQlNSUMqBTP7XcSwwzVmlhs8zzOzRUmOpV3kwnFm9oiZlXlSo5k1KGlVVzM72swmBavhLjOzh80s7v9eY/0uZvZxdV6NWKJT0pBKwTm33jl3gnPuBGAQ0C94fgJQGO/3M7NYqyW0w69QWxTbg865D+P43ln4CX+9nXM5wLH4hQ4TsTR2OxL4u0jVo6QhVUGGmf0z2HNgQvCli5kdYWYfmNlMM5tqZo2D8gbBX/HzzOwjM6sflL9sZoPM7AvgiWjXm9lpwMVA36Cmc0Rw3WXBPf6fmU03s7lm9qWZ7RO831QzmxX8nFbK73M18KlzbgJAsKpAJ6BL8B4PmVnnopPNbEGwoB1mNiqId6GZdYg452cz6xXE9bmZHVja7xLJzP7PzD4L4n8rWA8JM+sd7Pcwz8yeLPt/OqlslDSkKmgEDHDOHQ3kAX8MygcDtznnTgI6A88H5c8Bw4L9ToYDz0bc6xDgNOfcXdGud85Nx9cCugQ1n2VFFwbLgrwJ3OGcOx44B8gH/guc65w7Ebhil/eL5mhgZmRB8D5ZVvqGXu2DeJsBt5vZ74LyvYDPg7imADfF+l0imVlt4H7gnOB3mAHcFdz7EuDo4LPsWUpsUgVowUKpCr5zzs0Jns8EGgR/CZ8GvOWX8AFgj+DxVPxmUAD/Ap6IuNdbzrmCUq4vSQ7wg3PuK4CiFUnNbC+gv5mdABQAvy/rL1gGt5vZJcHzQ/EJdT2wDXg3KJ8JnFuGezbHN2F9GnwWu+OX/NgI/AIMNbN3I+4vVZiShlQFWyOeFwBZ+Fp0XtDvURabg8fyXh/N34G1wPHBfX8p5fxFwB8iC8zscGC9cy7PzHawcyvBnsE5Z+JrN6c657aY2cdFx4DtEastF1C2f/sGTHTOXVXsgNnJQCvgMnwT2tlluK9UQmqekiop+Cv/OzO7HPyKoWZ2fHB4On51WYBrgKllvH4TfkHKXS0BDjaz/xdcs0/Qob4fvgZSiF+ALqOU8IcDLSNGMWXhm7R6BMeXAycGx04EGgbl+wEbgoTRGF9DKE1Jv0ukz4EWZnZk8J57mdnvg9rYfs659/GJ8fhYN5GqQUlDqrJrgBvMbC6wkN+2Vb0N+KuZzcN/iZc0Kqmk698AupjZbDM7oujkYPvWK4Dngmsm4v/Sfx74S1DWmN9qM1E55/LxHdT3mdl/gB/xHeNFm3m9DdQys4X4v+7/E5R/AOxmZouB3vgv+9JE/V12iWcdcD3wevCZfRb8HvsA7wZl04C7QryfVHJa5VYkzZlZO+Bp4Czn3IoUhyPVnJKGiIiEpuYpEREJTUlDRERCU9IQEZHQlDRERCQ0JQ0REQlNSUNEREL7/58TK6DQYK8fAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "qqplot(return_linear_plot_residuals(x, y_quad), line='s');\n",
+    "qqplot_2samples(x, return_linear_plot_residuals(x, y_quad), line='r')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "#### Extended Exercise\n",
     "\n",
     "When fitting a linear model, a common visualisation is to output the following four graphs in a subplot with two columns and two rows:\n",
@@ -858,10 +1144,101 @@
    ]
   },
   {
-   "cell_type": "markdown",
+   "cell_type": "code",
+   "execution_count": 97,
    "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "*For solutions see `solutions/residual_analysis_two.py`*"
+    "def four_plots(x, y):\n",
+    "        \n",
+    "    # add columns of 1s to data\n",
+    "    X = sm.add_constant(x)\n",
+    "\n",
+    "    # fit orindary least squares regression\n",
+    "    model = sm.OLS(y, X)\n",
+    "    results = model.fit()\n",
+    "\n",
+    "    # predict our original data to get line\n",
+    "    y_lobf = results.predict(X)\n",
+    "\n",
+    "    # compute residuals\n",
+    "    res = y_lobf - y\n",
+    "\n",
+    "    # extended qs 1\n",
+    "    def abline(slope, intercept):\n",
+    "        \"\"\"Plot a line from slope and intercept\"\"\"\n",
+    "        axes = plt.gca()\n",
+    "        x_vals = np.array(axes.get_xlim())\n",
+    "        y_vals = intercept + slope * x_vals\n",
+    "        plt.plot(x_vals, y_vals, '--')\n",
+    "    \n",
+    "    plt.plot(y, y_lobf, 'bo')\n",
+    "    abline(1, 0)\n",
+    "    plt.show()\n",
+    "\n",
+    "    # extended qs 2\n",
+    "    plt.plot(x, res, 'ro')\n",
+    "    plt.axhline(0)\n",
+    "    plt.show()\n",
+    "\n",
+    "    # extended qs 3\n",
+    "    qqplot(res, line = 's')\n",
+    "\n",
+    "    # extended qs 4\n",
+    "    qqplot_2samples(x, res, line='r')\n",
+    "    \n",
+    "    plt.show()\n",
+    "\n",
+    "four_plots(x, y_linear)"
    ]
   },
   {
@@ -877,7 +1254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -890,7 +1267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -900,22 +1277,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x23c42a7b288>]"
+       "[<matplotlib.lines.Line2D at 0x1e2a953e5f0>]"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -932,12 +1309,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -949,7 +1326,7 @@
     }
    ],
    "source": [
-    "fit_linear_plot_residuals(x, y_corr)"
+    "_ = fit_linear_plot_residuals(x, y_corr)"
    ]
   },
   {
@@ -963,22 +1340,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.LineCollection at 0x23c42b16348>"
+       "<matplotlib.collections.LineCollection at 0x1e2a9565240>"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1002,7 +1379,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1011,14 +1388,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Object `autocorrelation_plot` not found.\n"
+     "ename": "NameError",
+     "evalue": "name 'autocorrelation_plot' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\86155\\OneDrive\\Job Stuff\\Past Jobs + Applications\\Tibra\\QuantFinance\\notebooks\\Module 2.4.1 - Residual Analysis.ipynb Cell 65\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/86155/OneDrive/Job%20Stuff/Past%20Jobs%20%2B%20Applications/Tibra/QuantFinance/notebooks/Module%202.4.1%20-%20Residual%20Analysis.ipynb#Y114sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m autocorrelation_plot\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'autocorrelation_plot' is not defined"
      ]
     }
    ],
@@ -1028,12 +1409,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1060,7 +1441,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1074,7 +1455,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 2.5.1 - Model and Estimate Instability.ipynb b/notebooks/Module 2.5.1 - Model and Estimate Instability.ipynb
index a9d5ccc..587cc34 100644
--- a/notebooks/Module 2.5.1 - Model and Estimate Instability.ipynb	
+++ b/notebooks/Module 2.5.1 - Model and Estimate Instability.ipynb	
@@ -77,7 +77,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x22db4223f48>]"
+       "[<matplotlib.lines.Line2D at 0x1ca6d8c6500>]"
       ]
      },
      "execution_count": 4,
@@ -86,7 +86,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -147,7 +147,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x22db428f4c8>]"
+       "[<matplotlib.lines.Line2D at 0x1ca6f9c35b0>]"
       ]
      },
      "execution_count": 7,
@@ -156,7 +156,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -183,7 +183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -256,12 +256,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -319,58 +319,24 @@
     "2. Test again using a polyfit with degree 2. How does the SSE different change?"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "From solutions"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "11149281.771428553"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#1\n",
-    "train_err = y_pred_low[0:50] - y_low\n",
-    "train_SSE = np.sum(train_err **2)\n",
-    "train_SSE\n",
-    "\n",
-    "test_err = y_pred_low[50:100] - y_high\n",
-    "test_SSE = np.sum(test_err **2)\n",
-    "test_SSE"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9.721046160338954e-25"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "training sse: 46175.0658323055\n",
+      "testing sse: 11149281.771428553\n",
+      "new training sse: 1.162654756550936e-25\n",
+      "new testing sse: 9.721046160338954e-25\n"
+     ]
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -382,29 +348,38 @@
     }
    ],
    "source": [
-    "#2\n",
+    "# qs 1\n",
     "\n",
-    "# Fit model to each\n",
+    "# compute training and testing sse\n",
+    "train_sse = np.sum((y_pred_low[:len(y_low)] - y_low)**2)\n",
+    "print(\"training sse: \" + str(train_sse))\n",
+    "\n",
+    "test_sse = np.sum((y_pred_low[len(y_high):] - y_high)**2)\n",
+    "print(\"testing sse: \" + str(test_sse))\n",
+    "\n",
+    "# qs 2\n",
+    "# Fit model to each (copied from code above, change input to order of 2)\n",
     "params_low = np.polyfit(x_low, y_low, 2)\n",
     "params_high = np.polyfit(x_high, y_high, 2)\n",
     "\n",
-    "plt.plot(x, y, 'r-')  # Actual data in red\n",
-    "\n",
-    "y_pred_low = np.poly1d(params_low)(x)\n",
-    "plt.plot(x, y_pred_low, 'g--')  # Predictions from \"low\" model in green\n",
+    "# make prediction using parameters learned from low and high\n",
+    "y_pred_low2 = np.poly1d(params_low)(x)\n",
+    "y_pred_high2 = np.poly1d(params_high)(x)\n",
     "\n",
-    "y_pred_high = np.poly1d(params_high)(x)\n",
-    "plt.plot(x, y_pred_high, 'k--')  # Predictions from \"high\" model in black\n",
+    "# produce plot\n",
+    "plt.plot(x, y, 'r')\n",
+    "plt.plot(x, y_pred_low2, 'g')\n",
+    "plt.plot(x, y_pred_high2, 'b')\n",
+    "plt.plot()\n",
     "\n",
-    "# Rerun our SSE calculations\n",
+    "# compute training and testing sse\n",
+    "train_sse = np.sum((y_pred_low2[:len(y_low)] - y_low)**2)\n",
+    "print(\"new training sse: \" + str(train_sse))\n",
     "\n",
-    "train_err = y_pred_low[0:50] - y_low\n",
-    "train_SSE = np.sum(train_err **2)\n",
-    "train_SSE\n",
+    "test_sse = np.sum((y_pred_low2[len(y_high):] - y_high)**2)\n",
+    "print(\"new testing sse: \" + str(test_sse))\n",
     "\n",
-    "test_err = y_pred_low[50:100] - y_high\n",
-    "test_SSE = np.sum(test_err **2)\n",
-    "test_SSE\n"
+    "# much lower error when we us polynomial rather than linear fit"
    ]
   },
   {
@@ -449,6 +424,4121 @@
     "Note for this exercise, that the process may take a long time to run, especially for large datasets."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "               Last\n",
+      "Date               \n",
+      "2023-01-03  0.34410\n",
+      "2023-01-04  0.34802\n",
+      "2023-01-05  0.33845\n",
+      "2023-01-06  0.34501\n",
+      "2023-01-07  0.34399\n"
+     ]
+    },
+    {
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+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42],\n",
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+       "   2,\n",
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+       "   6,\n",
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+       "   14,\n",
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+       "   30,\n",
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+       "   2,\n",
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+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
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+       "   6,\n",
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+       "   10,\n",
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+       "   13,\n",
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+       "   15,\n",
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+       "   50,\n",
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+       "   6,\n",
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+       "   2,\n",
+       "   3,\n",
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+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
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+       "   30,\n",
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+       "   51,\n",
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+       "   3,\n",
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+       "   5,\n",
+       "   6,\n",
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+       "   21,\n",
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+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
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+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
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+       "   6,\n",
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+       "   10,\n",
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+       "   14,\n",
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+       "   18,\n",
+       "   19,\n",
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+       "   21,\n",
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+       "   27,\n",
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+       "   30,\n",
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+       "   35,\n",
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+       "   1,\n",
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+       "   3,\n",
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+       "   6,\n",
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+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
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+       "   25,\n",
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+       "   31,\n",
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+       "   50,\n",
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+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
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+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
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+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
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+       "   25,\n",
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+       "   27,\n",
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+       "   31,\n",
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+       "   33,\n",
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+       "   38,\n",
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+       "   40,\n",
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+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
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+       "   25,\n",
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+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
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+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
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+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
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+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
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+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
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+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
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+       "   30,\n",
+       "   31,\n",
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+       "   35,\n",
+       "   36,\n",
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+       "   38,\n",
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+       "   2,\n",
+       "   3,\n",
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+       "   5,\n",
+       "   6,\n",
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+       "   11,\n",
+       "   12,\n",
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+       "   14,\n",
+       "   15,\n",
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+       "   30,\n",
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+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
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+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
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+       "   25,\n",
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+       "   30,\n",
+       "   31,\n",
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+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
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+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
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+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
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+       "   14,\n",
+       "   15,\n",
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+       "   25,\n",
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+       "   30,\n",
+       "   31,\n",
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+       "   35,\n",
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+       "   38,\n",
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+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
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+       "   11,\n",
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+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
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+       "   19,\n",
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+       "   21,\n",
+       "   22,\n",
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+       "   24,\n",
+       "   25,\n",
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+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
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+       "   34,\n",
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+       "   69],\n",
+       "  79],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
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+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
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+       "   18,\n",
+       "   19,\n",
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+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
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+       "   35,\n",
+       "   36,\n",
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+       "   38,\n",
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+       "   40,\n",
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+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
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+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
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+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
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+       "   60,\n",
+       "   61,\n",
+       "   62,\n",
+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
+       "   67,\n",
+       "   68,\n",
+       "   69,\n",
+       "   70],\n",
+       "  80],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
+       "   59,\n",
+       "   60,\n",
+       "   61,\n",
+       "   62,\n",
+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
+       "   67,\n",
+       "   68,\n",
+       "   69,\n",
+       "   70,\n",
+       "   71],\n",
+       "  81],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
+       "   59,\n",
+       "   60,\n",
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+       "   63,\n",
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+       "   65,\n",
+       "   66,\n",
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+       "   70,\n",
+       "   71,\n",
+       "   72],\n",
+       "  82],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
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+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
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+       "   60,\n",
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+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
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+       "   69,\n",
+       "   70,\n",
+       "   71,\n",
+       "   72,\n",
+       "   73],\n",
+       "  83],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
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+       "   55,\n",
+       "   56,\n",
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+       "   58,\n",
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+       "   60,\n",
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+       "   64,\n",
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+       "   71,\n",
+       "   72,\n",
+       "   73,\n",
+       "   74],\n",
+       "  84],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
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+       "   71,\n",
+       "   72,\n",
+       "   73,\n",
+       "   74,\n",
+       "   75],\n",
+       "  85],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
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+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
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+       "   55,\n",
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+       "   57,\n",
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+       "   60,\n",
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+       "   72,\n",
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+       "   74,\n",
+       "   75,\n",
+       "   76],\n",
+       "  86],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
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+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
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+       "   71,\n",
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+       "   73,\n",
+       "   74,\n",
+       "   75,\n",
+       "   76,\n",
+       "   77],\n",
+       "  87],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
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+       "   74,\n",
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+       "   76,\n",
+       "   77,\n",
+       "   78],\n",
+       "  88],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
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+       "   60,\n",
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+       "   74,\n",
+       "   75,\n",
+       "   76,\n",
+       "   77,\n",
+       "   78,\n",
+       "   79],\n",
+       "  89],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
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+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
+       "   59,\n",
+       "   60,\n",
+       "   61,\n",
+       "   62,\n",
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+       "   64,\n",
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+       "   66,\n",
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+       "   69,\n",
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+       "   71,\n",
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+       "   74,\n",
+       "   75,\n",
+       "   76,\n",
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+       "   78,\n",
+       "   79,\n",
+       "   80],\n",
+       "  90],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
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+       "   59,\n",
+       "   60,\n",
+       "   61,\n",
+       "   62,\n",
+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
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+       "   68,\n",
+       "   69,\n",
+       "   70,\n",
+       "   71,\n",
+       "   72,\n",
+       "   73,\n",
+       "   74,\n",
+       "   75,\n",
+       "   76,\n",
+       "   77,\n",
+       "   78,\n",
+       "   79,\n",
+       "   80,\n",
+       "   81],\n",
+       "  91],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
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+       "   60,\n",
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+       "   62,\n",
+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
+       "   67,\n",
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+       "   70,\n",
+       "   71,\n",
+       "   72,\n",
+       "   73,\n",
+       "   74,\n",
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+       "   76,\n",
+       "   77,\n",
+       "   78,\n",
+       "   79,\n",
+       "   80,\n",
+       "   81,\n",
+       "   82],\n",
+       "  92],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
+       "   59,\n",
+       "   60,\n",
+       "   61,\n",
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+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
+       "   67,\n",
+       "   68,\n",
+       "   69,\n",
+       "   70,\n",
+       "   71,\n",
+       "   72,\n",
+       "   73,\n",
+       "   74,\n",
+       "   75,\n",
+       "   76,\n",
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+       "   78,\n",
+       "   79,\n",
+       "   80,\n",
+       "   81,\n",
+       "   82,\n",
+       "   83],\n",
+       "  93],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
+       "   59,\n",
+       "   60,\n",
+       "   61,\n",
+       "   62,\n",
+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
+       "   67,\n",
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+       "   70,\n",
+       "   71,\n",
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+       "   73,\n",
+       "   74,\n",
+       "   75,\n",
+       "   76,\n",
+       "   77,\n",
+       "   78,\n",
+       "   79,\n",
+       "   80,\n",
+       "   81,\n",
+       "   82,\n",
+       "   83,\n",
+       "   84],\n",
+       "  94],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
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+       "   60,\n",
+       "   61,\n",
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+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
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+       "   69,\n",
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+       "   71,\n",
+       "   72,\n",
+       "   73,\n",
+       "   74,\n",
+       "   75,\n",
+       "   76,\n",
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+       "   78,\n",
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+       "   80,\n",
+       "   81,\n",
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+       "   84,\n",
+       "   85],\n",
+       "  95],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
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+       "   60,\n",
+       "   61,\n",
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+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
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+       "   85,\n",
+       "   86],\n",
+       "  96],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
+       "   59,\n",
+       "   60,\n",
+       "   61,\n",
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+       "   63,\n",
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+       "   71,\n",
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+       "   74,\n",
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+       "   81,\n",
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+       "   85,\n",
+       "   86,\n",
+       "   87],\n",
+       "  97],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
+       "   59,\n",
+       "   60,\n",
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+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
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+       "   71,\n",
+       "   72,\n",
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+       "   74,\n",
+       "   75,\n",
+       "   76,\n",
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+       "   80,\n",
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+       "   84,\n",
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+       "   87,\n",
+       "   88],\n",
+       "  98],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
+       "   59,\n",
+       "   60,\n",
+       "   61,\n",
+       "   62,\n",
+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
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+       "   71,\n",
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+       "   75,\n",
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+       "   81,\n",
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+       "   84,\n",
+       "   85,\n",
+       "   86,\n",
+       "   87,\n",
+       "   88,\n",
+       "   89],\n",
+       "  99],\n",
+       " [[0,\n",
+       "   1,\n",
+       "   2,\n",
+       "   3,\n",
+       "   4,\n",
+       "   5,\n",
+       "   6,\n",
+       "   7,\n",
+       "   8,\n",
+       "   9,\n",
+       "   10,\n",
+       "   11,\n",
+       "   12,\n",
+       "   13,\n",
+       "   14,\n",
+       "   15,\n",
+       "   16,\n",
+       "   17,\n",
+       "   18,\n",
+       "   19,\n",
+       "   20,\n",
+       "   21,\n",
+       "   22,\n",
+       "   23,\n",
+       "   24,\n",
+       "   25,\n",
+       "   26,\n",
+       "   27,\n",
+       "   28,\n",
+       "   29,\n",
+       "   30,\n",
+       "   31,\n",
+       "   32,\n",
+       "   33,\n",
+       "   34,\n",
+       "   35,\n",
+       "   36,\n",
+       "   37,\n",
+       "   38,\n",
+       "   39,\n",
+       "   40,\n",
+       "   41,\n",
+       "   42,\n",
+       "   43,\n",
+       "   44,\n",
+       "   45,\n",
+       "   46,\n",
+       "   47,\n",
+       "   48,\n",
+       "   49,\n",
+       "   50,\n",
+       "   51,\n",
+       "   52,\n",
+       "   53,\n",
+       "   54,\n",
+       "   55,\n",
+       "   56,\n",
+       "   57,\n",
+       "   58,\n",
+       "   59,\n",
+       "   60,\n",
+       "   61,\n",
+       "   62,\n",
+       "   63,\n",
+       "   64,\n",
+       "   65,\n",
+       "   66,\n",
+       "   67,\n",
+       "   68,\n",
+       "   69,\n",
+       "   70,\n",
+       "   71,\n",
+       "   72,\n",
+       "   73,\n",
+       "   74,\n",
+       "   75,\n",
+       "   76,\n",
+       "   77,\n",
+       "   78,\n",
+       "   79,\n",
+       "   80,\n",
+       "   81,\n",
+       "   82,\n",
+       "   83,\n",
+       "   84,\n",
+       "   85,\n",
+       "   86,\n",
+       "   87,\n",
+       "   88,\n",
+       "   89,\n",
+       "   90],\n",
+       "  100]]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import quandl\n",
+    "import my_secrets\n",
+    "\n",
+    "# download data\n",
+    "quandl.ApiConfig.api_key = my_secrets.QUANDL_API_KEY\n",
+    "price = quandl.get(\"BITFINEX/XRPUSD\")\n",
+    "\n",
+    "# Rolling Forecast Origin Function\n",
+    "def rolling_forecasting_origin(time_series, m=1):\n",
+    "    \"\"\"Performs a rolling forecasting origin with steps of m\n",
+    "\n",
+    "    parameters:\n",
+    "        time_series: pandas data frame with a time series index\n",
+    "        m: positive non-zero integer. 1 by default \n",
+    "\n",
+    "    returns:\n",
+    "        folds: a list of each of the training and testing folds for pandas.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    folds = []\n",
+    "\n",
+    "    if len(time_series) == 0:\n",
+    "        return(folds)\n",
+    "\n",
+    "    for i in range(0, len(time_series) - m + 1):\n",
+    "        \n",
+    "        # train is everything up to i, test is m steps after i\n",
+    "        train = [i for i in range(i + 1)]\n",
+    "        test = i + m\n",
+    "        folds.append([train, test])\n",
+    "    \n",
+    "    return folds\n",
+    "\n",
+    "# crop only last n datapoints\n",
+    "price = price[[\"Last\"]].tail(100)\n",
+    "print(price.tail())\n",
+    "rolling_forecasting_origin(price, 10)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -505,7 +4595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -516,7 +4606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -525,24 +4615,85 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 37,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "Cannot interpret '<attribute 'dtype' of 'numpy.generic' objects>' as a data type",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-17-949e6d6c682c>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mchanges\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdescribe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mdescribe\u001b[1;34m(self, percentiles, include, exclude)\u001b[0m\n\u001b[0;32m  10263\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0minclude\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mexclude\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m  10264\u001b[0m             \u001b[1;31m# when some numerics are found, keep only numerics\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m> 10265\u001b[1;33m             \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mselect_dtypes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m  10266\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m  10267\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mselect_dtypes\u001b[1;34m(self, include, exclude)\u001b[0m\n\u001b[0;32m   3425\u001b[0m         \u001b[1;31m# the \"union\" of the logic of case 1 and case 2:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3426\u001b[0m         \u001b[1;31m# we get the included and excluded, and return their logical and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3427\u001b[1;33m         \u001b[0minclude_these\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mnot\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3428\u001b[0m         \u001b[0mexclude_these\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mnot\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3429\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 311\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_cast_failure\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36msanitize_array\u001b[1;34m(data, index, dtype, copy, raise_cast_failure)\u001b[0m\n\u001b[0;32m    710\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_cast_to_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    711\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 712\u001b[1;33m             \u001b[0msubarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconstruct_1d_arraylike_from_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    713\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mconstruct_1d_arraylike_from_scalar\u001b[1;34m(value, length, dtype)\u001b[0m\n\u001b[0;32m   1231\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1233\u001b[1;33m         \u001b[0msubarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1234\u001b[0m         \u001b[0msubarr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: Cannot interpret '<attribute 'dtype' of 'numpy.generic' objects>' as a data type"
-     ]
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>9481.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>0.000003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>0.007190</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>-0.074449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>-0.003668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>0.000149</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>0.003920</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>0.074184</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Value\n",
+       "count  9481.000000\n",
+       "mean      0.000003\n",
+       "std       0.007190\n",
+       "min      -0.074449\n",
+       "25%      -0.003668\n",
+       "50%       0.000149\n",
+       "75%       0.003920\n",
+       "max       0.074184"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -551,7 +4702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -561,31 +4712,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
-     "ename": "TypeError",
-     "evalue": "Cannot interpret '<attribute 'dtype' of 'numpy.generic' objects>' as a data type",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-19-95d395e442de>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mchanges\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhlines\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mucl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlcl\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxmin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchanges\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mxmax\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mchanges\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_core.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    792\u001b[0m                     \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabel_name\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    793\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 794\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mplot_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    796\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\__init__.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(data, kind, **kwargs)\u001b[0m\n\u001b[0;32m     60\u001b[0m             \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ax\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left_ax\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     61\u001b[0m     \u001b[0mplot_obj\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPLOT_CLASSES\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 62\u001b[1;33m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     63\u001b[0m     \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdraw\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     64\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplot_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36mgenerate\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    277\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgenerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    278\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_args_adjust\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 279\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_compute_plot_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    280\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_setup_subplots\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    281\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\plotting\\_matplotlib\\core.py\u001b[0m in \u001b[0;36m_compute_plot_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    402\u001b[0m         \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimedelta\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    403\u001b[0m         numeric_data = data.select_dtypes(\n\u001b[1;32m--> 404\u001b[1;33m             \u001b[0minclude\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"datetime\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"datetimetz\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"timedelta\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    405\u001b[0m         )\n\u001b[0;32m    406\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mselect_dtypes\u001b[1;34m(self, include, exclude)\u001b[0m\n\u001b[0;32m   3425\u001b[0m         \u001b[1;31m# the \"union\" of the logic of case 1 and case 2:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3426\u001b[0m         \u001b[1;31m# we get the included and excluded, and return their logical and\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3427\u001b[1;33m         \u001b[0minclude_these\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mnot\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3428\u001b[0m         \u001b[0mexclude_these\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mnot\u001b[0m \u001b[0mbool\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mexclude\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3429\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    309\u001b[0m                     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    310\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 311\u001b[1;33m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msanitize_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_cast_failure\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    312\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    313\u001b[0m                 \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mSingleBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\construction.py\u001b[0m in \u001b[0;36msanitize_array\u001b[1;34m(data, index, dtype, copy, raise_cast_failure)\u001b[0m\n\u001b[0;32m    710\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmaybe_cast_to_datetime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    711\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 712\u001b[1;33m             \u001b[0msubarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mconstruct_1d_arraylike_from_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    713\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    714\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mconstruct_1d_arraylike_from_scalar\u001b[1;34m(value, length, dtype)\u001b[0m\n\u001b[0;32m   1231\u001b[0m                 \u001b[0mvalue\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mensure_str\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1233\u001b[1;33m         \u001b[0msubarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlength\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1234\u001b[0m         \u001b[0msubarr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1235\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mTypeError\u001b[0m: Cannot interpret '<attribute 'dtype' of 'numpy.generic' objects>' as a data type"
-     ]
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.LineCollection at 0x1ca6fba7a30>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 864x864 with 1 Axes>"
       ]
@@ -631,6 +4773,50 @@
     "If you do not, choose another commodity from Quandl. You can search Free dataset as an option in the Quandl search: https://www.quandl.com/search - the option for \"Free\" is on the left hand set of filters."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.collections.LineCollection at 0x1ca710e2b00>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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RUw6qEgoAcjOMFeLDGYzFoJOiJPPA59+yBW/9u1+17GYwGAwGg9EKb7v7Ofh/fvN+eNmpjZUZx/7aT34CLt4YwZOXduE7vvpV8G1/7BUL2/df+C8fgc+9sA2vv/Mk/NJ3fOXC9stgULzt75V/xswyg8FgMBgdYiUT/GCJRUlW6DwxjiY4WGYwGAwGo0OspHUcreC3aJ9lfF2h08U4WuBgmcFgMBiMDrGSRUmAFCVZuBnG6rmHMI4WOFhmMBgMBqNDIDGLQbOUCv7eL90Dn3rqyhJbVQ2laFGS5cgwmFlm9BUcLDMYDAaD0SF8zfJBmsF7PncO7n6mx8EywPLKXcPqabwZRwscLDMYDAaD0SFQ8+szpn0OBpVSEAtM8Fv0vvNXlmEwegoOlhkMBoPB6BB+gp9NYOtvMLhMNwy5gu4hjKMFDpYZDAaDwegQyn9dgWCQapaX5oax0L0yGM3BwTKDwWAwGB3CD44xCFy0vKENHM3yoqN6jpYZPQcHywwGg8FgdAjpWaEpCc7fvYRSEC1Ls5y/Llr+wWA0BQfLDAaDwWB0CGsdp18xSO47sywEQCQWL8NYRV9qxtECB8sMBoPBYHQIP2FNWfHychrUAEoBCNBSjEXLMOx56u/5YRxtcLDMYDAYDEaHsDGfy5j2m1lWIISASIiFyyEUMLPM6Dc4WGYwGAwGo0OgjMGv5NdnzTIyy5EQi5dhoKa7v6eHccTBwTKDwWAwGB2i4LPsaZj7CKW0ZjmOxNLa2ePTwzji4GCZwWAwGIwO4csKVqGcs26aACGWUO56FUocMo40OFhmMBgMBqNDGPmFtcPQLz0OBpVShlledDvZZpnRd3CwzGAwGAxGhyiUuSavj5+/Ad//jgcXrgtuAtQsL9oNg8tdH22cubAD3/Tmj8GNg8mym1IKDpYZDAaDwegQ0ivdh39LqeAjj1+CX7rrGbi236/AADXL2g1j8fsG6HcCJGN+ePjFbbjvuWvwwrWDZTelFBwsMxgMBoPRIQouGCR27mu1OgUKBIilFCVZARtqxhyxCm4xHCwzGAwGg9Eh/Ip0NED2HTL6AtcNY1lFSRa6W0ZPYNxi5HLbUQUOlhkMBoPB6BB+8EcD5L5Wq7PlrgVkCw5afI0342jByJR69kxQcLDMYDAYDEaHsEVJ/LLXigQGS2laKZTKZRjR4oMWK8Po2UlhLAR9exZC4GCZwWD0Bt/z1nvh+97xwLKbwWDMBM8xztEs+3rmvkABAAiAeBnlrnsqTWEsBmoFmOVk2Q1gMBgMxJOXduHYGndLjNWGSVTyXB6kUs6/ewVS7nrRRUmkd54YRwurUOGSmWUGg9EbKKUWPlAzGF1Deewx1TD3NZlNa5YFRJFYeNuYWT7asM9Jf28ADpYZDEZvoJep+9thMhhNUO6G0d8lZ61ZBoiWUe7ae2UcLUhmlhkMBqM5tLXWslvBYMwGnymzjJnqbWBA3TAWHsj31CGEsRgws8xgdIBPP30F3vSrn+lleVhGt5Cyf4wbg9EWfkBsXsn93bf7XBHN8qLb5jPxjKOFVbAO5GCZ0Xt86qkr8K77X4Txos0/GQuHAoCsZ0EEg9EWRYbMVijrr8+yAiFEXpRk0fv2/8E4SrCTyf7eABwsM3oPfIB6NrYw5gDtQ7vsVjAY7XF++wC2DyYA4FYiU0Ra5GqWF93CalhmeQmaZc9qj3G00MZ7/Nc+9Szc/fSVObeoCA6WGb1HX31JGd2DlgNmMFYJf/stn4b/9N5HAcDtq6Ry/ZZtsl+/7nOlAEAARMsod43Me8/OCWMxaGMd+J/f9xj8xt1n59yiIjhYZvQefdX4MbqHVHyd+4RMKvjte5/v9fJoX3B9fwLX9jWzTM8WZZYVkAp+PVSVCRBL0izrV77LjibaWAfqyefi7xQOlhm9R1+XLRndQ/ssL7sVDMQnnrgM/+jX74X7zl5bdlN6D6UUpLI4sad2iEr1d6VMKQUir+C3cK/znnpPMxaDNoTYshyTOFhm9B6m3+aO9NBDKV6K7RN2RpopHac8g6mDVOH8Cjq4Oyxzz27zXIUBUbR4YkKRBEjG0YNs8UzoKpiLBwfLjN6DZRhHB1Ipvs49wsFEB8m8qlMPWcYsk2XjfhclIT7LSyp33UdpCmP+8CteViGTzCwzGEH0ddmS0T30kvWyW8FAHEwyAGC2vwkos+zfw9TtobcJfqAgMtZxi3bD6Ne5YCwWfln4KtDJ5yLBwTKj92DN8tGBVIqTyXqE/TxYZu/rJlDmPPnMMoLe3327zZFZFkJAtiSfZQ6ajyZsUZJmzPIydBgcLDN6D9niQWKsNriCX7/AMozmkMr6E9Ogz5EWKarP7NdJ1a0REIvFt419lo822shwWLPMYJSgjfifsfrgwKw/QGZ5mgnMm371M/C2Tz/XdZN6C6mUCZadoiTgajKpfrlPoJrlRbphqBIWnnF00Kbc+bK8+DlYZvQenOB3dMAJfv3CCIPlKYKnjz9xGe49QpZzUpJg2QkAFZEZtEtmWiSUUrkbxmLLXdPTwKuHRxNt8pKWpMKAZAn7ZDBagZaKZRxuSKVAcEZ8b3BgmOX2vz1q1RgVEBkGed93wOitGwYgszzd5GiW/Zp/9+uUMBYEW5Skv24YHCwzeg/Z4kFirDaUAuBYuT+YRYZBWdSjAKVsIqRy6VLLLEN/ZWWaWV68G4Yjw1jYXhl9QlM3DJsIuHiwDIPRe7SxlWGsNvpc7vrXPvUs/JPfuG/ZzVgIfvLDT8CHHrlgE/ymYBqPmqTG0Sx70gLKnPVVVobMsnbDWFzbnHPVr1PCWBCsDKP6e6EE2kWBmWVG78E+y0cHSimQIJbdjCDufvoqfPyJS8tuxkLwsx99Cv7Y57+EMMvtt3HUmGU3WKZuGMTtgbDMfZOVKaUr+MVCLPS6uTrlnp0UxkLQdAJpVmXm3aAAOFhm9B7ss3x0oJ0D+nmhFQmGDjvGmYSdUQqTDK3jppFhqCP1zCoFYWaZJPj1WrOsFIi8KMli3TDC/2YcHTS1DpRNvzgHcLDM6D3Qhqlvgwuje/T5GssjFPyNUx0s46A0zXWhiW1HATRY9nW4TgU/wzL369xga4SA5QXLC9sro0+QDeUVy6y5wMEyo/dgzfLRQZ+r9/VZT901xqmEnYMU4khLYqYLlo+WEZhU4Qp+VLtN76EmBRgWCqUDZS3DWKQbBplYHJHni+GCrrxUYZnJsRwsM3qPvla8YnSPPl/io5KwJqWCVCrYHacwjKP8vfbb0c4Ph/98IcqKkoBy2eS+5mAoABAgdFGShbphuG1gHD00JcTsys28W1QEu2Eweg/WLB8dKICFDtRtQJfZDzPGuU555yA1CX7TXJOjpll2yl1TtpT87RYlWXQLq6E1y0soSkL+3eeVJcb80PSZsNZxLMNgMAroq9USo3v0mb3VyVnLbsX8MUrzYHmUghBahjHNqs5R0iybCX0gwU8SCwzfRq5P0Mzy4ouSONUOF7ZXRp/QdIxfJrPMwTKj9+iriT+je/Q5ia7PgXyXGOfB8iiVIEYTAADIppFhHJHJBYDtm9JQgp+NlUHKnlvHCVhCURL6x8J2y+gRmrpcLPOZYRkGo/dgZvnoQPWYjaTL7IcZozQz/zZFSaZklo/KM+v3UW5REjeLv6/9mQJtHReJxVrHYYAkBMfKRxVNdfz2OVo8OFhm9B7U0J9xuKFU/xg3xFFhSpFZpmg7gbFSg06a1HvgPZvKYiAspXI0mb1N8FMow1hOUZJowS4cjP6gaV7SMp2xOFhm9B59ZWIY3YM6CvQNUvU3+bBLjAOai7bXRPU0IJwXMOALFSXRn9t/9HUioQAAhNYsL6PcdSwEM8tHFOgeU5e4Z/uhxd8pHCwzeo9lLr0wFgvTFfYtkoCjp1mmaDt/CckRDjPM5KBMs0wm/L2dSChtHbd4zbLelxD9m0AwFoOm/cUyV5k5WGb0Hn1dtmR0jz4HWdrdoZ+BfJcIB8vtjtlev8N9rhB4foIyDFKcRYFrI9cnaM1ybh23wIIpeBoiIZZiCcZYPprWUrDWjIsHB8uM3qOvVkuM7tFb1g2Ojt93F8EyBj2H/Vwh/Am9X2iDMssYiPbtHrea5UXLMFCz3L8JBGMxaCpNkkuMBThYZvQediBabjsY8wXtAPuoW8aOuo9t6xKjgGa57SH3edIzDxSZZfsZTQzVCax9ZZZtueuFXrd8V1HEmuWjiqZ5SeyGwWBUoK+DC6NbuAHG8tpRhr4ygl2jGxnG0XpmaTCsg2Mqw3DLXSvzfr9OjlIKBAgQuRvGotg73EscLcY77q4nL8NkGuNwxtxgc1Wqv7fMmgscLDN6D9YsHw34Os++4agEgKNQsMxuGJXwV0Xc4/Y0yz2V8xhmOdJVG7F9k0zCV//wB+E9D56bz36RWV6AZvmFa/vwLT91F7z/ofNz3Q+jHZqO8axZZjAqQPV+jMMLenn7eK2xSYfdPq5LN4xDfqoM6PlJpSqskqyCGwbVLAPYwGR/ksHZq/vw5KWduezXapbn7++8N07z16zmm4xFoml/wZplBqMCR23gPapwCzkssSElOCp+3126YRwVdwN/VUQq7Syh/3aXj5c54NdCCIgMs5zf73nj02w+7cWtRmL+z1bIrYSxfJgEv5r+YpnjAgfLjN7DGJZz/3ao0Xdm2Q8eDivGebnrkxsD817rY0b2tIeTnnmA3q5pXrEvyYNOlRvGAfgV/BbcyAoYr2PQDC9AMaE1nZPOV1FmeS57sDDL+D069wyaD1LzvSUSZxwsM3qPo8LoHXX0X7Psvh5WYAW/m7aG5r3pi5Ic8pOVw0noyzXLGHSqOSf43f30FThzYTaJBDYF3TAA7DVH2dF4Xsyy0SzPPwjCYPmwS6lWDU37i6whAz0PcLDM6D36qvFjdAt6dfsYkCqPaTusQBmGGyy3lWEsLxFnGaC3BCb4YaKcUm62f9cJfv/s7Q/Amz90ZqZtYFO0G4b+t8/Czo9Z1q8o/5gnMpZh9BJNXS6WWSqeg2VG78Ga5aOBVWGWe6k17RDjVIIQAKdyGcYwjlozcTY4PNznCkHv1yyXYcRE+2uYZVAOy9wFRqk0qwHTgpactkG+J8OY0yQRWULLxM/vnrHB8tx2wZgCTQuPsXUcg1EBy1JxD3eYoch4389g+WgMtKNMwjCO4Nh6AsMkgjhq71JwVM4Vgh5mptxgGcA9H11LVHQwPtu2LLNs220kC/nrvLyJqQyD/j0PpLJZUMZYLJr2F9Y6zn7x009fgcs7o7m1DcHBMqP3MFrRI5IsdFRBO8A+Blm+hvOwYpxKGCYRnN4cwvG1RLsUTOmzfFSCEnp+0iyXYYiQDEN1PpFQava+kWqWRd7uzAvq5+WGQa3jAOYr3cHrdNiTdFcNTRnj0Crzt//cp+AXP/HMnFpmwcEyo/dgn+WjATp+9XEwM/dhD9vWJUaphLUkgr/7tZ8PP/FtfxSiSLSXYZg8gzk0sIfwnVykUo4FG9Vadp2DkUk18wQOJ6pCCCfIByDFSebEVmDLo2j+MgxklucU9zOmRONy1+iMRd7bn2RwkM7fNzuZ+x4YjBlxVFwIjjr6r1k+GpO2caplGC89uQEvPbkxVbGIXnsJzwG+Zlkq6ypBz4ACtzR2V/ueWYZBfu4XJbHWcXPSLPsyjLnsRSNjGUYv0XQlyjxn5Pu0nPw8wcwyo/ewAxF3cIcZLju3vHaU4ahM2lCGgYgj0doB5Oi5YbjBsnLcMNyqfcjidrVCQb2bZ4UQUCxKgjKMuengPBnGHG8adsPoJ/CZqLsqvnWcea4W0ClzsMzoPY5KkHLU4XjV9nAw84s0HFb4wfI0ldWOmt0jPcosZ7usG4artey6P0PZxywwmmUQtihJHhvj/T5O56VZ1q9Wszx/GcYhf4RXDrYoSfWF8a3jrK5+bk0z4GCZ0XuwZvlogHZ4fVwmPSpJa+PMDZaFEK0HI8v4dNiwHoPeEybBz7hhuMzyPNwwZg0WrGYZII7sdgGoddx83TCoL/W8wMxyP9HcDUO/KvP34q4nB8uM3uOo2VAdVVBGaU4uVTPBBA+HfKBFzTIiFqL1MufRk2HQf+vgmFqhGSGZ6n7ShbKPWWCZZcvwmiXv/LO5aZaNDGMum3fgHxOjH2iq4/eD4kUSGBwsM3qPo1IM4qjDDzj6BjNp62Eg3yXGqYS1JDZ/TyXDwNceXsd5oJjgpyCJovwzt+hC1xX8lJr9ecFfC1EsDoIB5rx9loVwtdLzQJY/vIddSrVqaJoQjJN2/95kGQaDAVzB76iAspe9DJYb6upWHaMOZBhHxTkEQQ8TK/hRKzQzeQAbmHYpw5g1+DMV/IhmOfM0y/Oq4IfnYREyDGTHj8p9uSpobB2HxFn+N8swGAyCo5YsxOjnxOioaOdHk6zghtE+we9oTXBDzDIqWRS4907XsjK0qpsFlFn2Ncu2KMmCKvjNZS8aLOnrJ5oWJfFlNF2v0lSBg2VG78Ed3NGAH3D0DUfFlcVP8GM3jHo4zLJyK/ihhhm/ZworTHFufu9z5+D6/qSw7840y4Iyyyg7QhnGfK+lEIsrSnJU5EGrgqZBr5mE538v0jebg2VG73HUChwcJnzg4fPwXb/6mUbfXRXNch8D+S4xTiWsxTRYnkaGoV97eBnnglBRkohQpVZ6Mb0M4/r+BL7zl+6Bd977fGHfM9+SGCyD1Sz7Ca3z0izbctdOU+YCdsPoJywRUX1dMq9j8UuyzxMcLDN6D8vELLcdjPb41FNX4D0Pnmv0XbeC37xaND2OSqJpwWc5YjeMOtDTY4qSkAp+9paZPsEPZRBjj+FFJnsWuNZx1h8awPa/89IsY9MXax03v30w2qPp/etrlu0KVvdt8sHBMqP3OCpa0cOIcSYbXzf6tT4GpOqoMMssw2gNer/6zLKvU5723IQma1gdcGbNMmGWhV/uet5uGPkrMtrznGH50pJVwNs/cxZ+21tNOGxoyixbNwz9d+b9PU9wsMzoPY6KVvQwYpLJxppKt4LfPFs1HY6Kdt73WdYyjOmY5aMSLPvMsraOs0ypZcJs4Nz21ISSJrta7cA2CSGcMt0ANkCZm88y2TfAoir4rc59+SuffBZ+9ZPPLrsZc0XThGC7YuUSF3X3/8985El49wMvztRGDpYZvYetG786HRxDY5I2Dwz8gKNvOLIyDCFaF4mhCW1HAX6pdqWIrEB/If9s+omEXYJ29zXNtnzgr6nPsq/vnVcFPzyumBRxmRdWUYaRSQXjPlZp6hBNV1t8N4ym9/+vfPJZ+J/3vTBTGzlYZvQezCyvLrCTbzKY0yCgjwHpUajgl0kFqVSeZrn99TAT3MN7qhzQvgldI2iiHGWWlfnNtGx98b1ZYykqw7DthnzbKMOY18V0z9c8b5lVTPBTSsE4PdzBctNVOz9xuGlsME7lzJp7DpYZvQetfsVYLdhguf67lLjq48RokckkywIOym4Fv2lkGPh6iE8WAe2bkIFNqBsGGeSnvY9C8o1ZbOgoTAgvhHGlWLTP8mKt4+a2i86RHaFguW6qVGYdV9fPpFLOvFrJwTKj97BZ2SvUwzEAAGCSd/JNJDSuG0b/rrUtd92/tnUFHJQLMowOArvDDIdZzqVHpoIfEJ0yTD/5Dy1Vd6YNJ8xyHHnWcXmcNpmXG0b+Sou4zAv47PZR5lUGKeHoyDBqDtPXKDftZyaZmjlBlYNlRu9x1GyoDhOwg2o7lvdR6nAUktZGWQYAfrDcTWB3mEHPzyQf8U1REumeD9+SrSlC57KrpFOqWRYlmuW5+Szn+1lIuesVlGHII8QsNy133fZ3k5SZZcYRgJTdDAiMxQN1jk0GJ/qdPkpufA3nYYSRYcTdlLs+xKfKAT1OdI2gCX5Ws2xXWaaWtpCdYcA9c4KfYZZFIWil7N087n0bqM9fhrGKjjZ1wfKF7QPYH2cLbFH38P2TS79XYh1Xd/+PM9YsM44ArN5vhXo4BgDY4KuRZjmgxewTjkIAGJJhCCFaB0n264f4ZBFQmREysBGxYKPLxjbAbbeP0ApbV3IXpyiJxyzTaz8PdhnbvogEP5zIrNJYksnqYPkr/t0H4P/4mbsW2KLu0ZxZxmcAJXH4fvX2Jxkzy4wjgKOw/H1Y0coNo+ea5aaenqsMvF7FoiTttnMUJhYUITeMWBQ/m02zXAyMuyr3S90whJfgR4OMeVTxw+NajHVc8/6oL1AKYFQzSfnMs9cW05g5gSbAVqHMOq7qWcIiQbMmqHKwzOg9fLsYxurAaJYb9FMhS6w+wcgweti2roDMm3FygFyGMSWz3MfrOA/Q48RBOY5weFXOoK4Cv2m2j+LvQgHtNLBSiGKCX+jYugRuPVpAUZJsBSdxmVJ5cadiow/LxL1J0Ks/z1/zv5tcTxyDWIbBOPRYRZ0ZQ2MyNbM8tyZNhT63rUvgdTKlh2E667imFbkOC9wEv5wpRXcH5TJn0yYsh37XlJGrg6miB4IUJcH92u/Nw2vZsNpi/gl+q1juGovchIK9w+KS0XSML7hhNNAs4xjEMgzGocdRy6w/TGiT4BfSYvYFrp66X23rEqaaGmGWhRDtZRhme6t9ri7eGMH3vO1eOJhUJ1BRjb1llosJfhj46H+3a0vIU7mrIhvm58IG+WEZxjyYZZyg4d/zQ9qiP+oL8JSHdMujQ+KS0XQlyp8wNilKgmMQM8uMQ4+mSzSM/qFVgh/N8u/Zpe67B3RXwMCIEMsQi2kkA9566Yrinmeuwts/8zycubBT+T16mBMvWKYBsoLpJSrBoiQd53NozXKVDKP7C+pP0ObZz6+iDAPPfzBYnhyOYNkvNlIGM0bkL03ySFJmlhlHBUetwMFhwtj4LNdfvD6ztzLA5h1GmGSraDYZxmHRLLfN0gegCX5WVkDLf3dZlGRalrps20II6w8dYJbnsexvJCCLlGGs0H2JbQ6d+1Ga+6LHqx3KWTlR3XOWf8/8XX898bxxURLGoUfT+u+M/mHSotx1n90waHN61rROgQMz1SwLIVpb+R0WN4yQfVoIjmbZt46DEjZ4yqIktCVdObQY6ziASs3yPJhl3CKZn80Nq1juupJZNuXpFx/K/eWf+Dj85Ief6GRbTZ8JP7m6ye9w8srMMuNQo88BFKMek7RFgh/5d9+CLIdZPsT3IZ53GizHUftnz8oOVvtcNU08ChYlIUU2utAZh1i0zir4GWYZIPI0y3SVZy5V/Lx7bp6PV5OEsL4BT39In4wyjOESguUnL+7AM1f2OtmWbNhf+JLMJs9Sr9wwhBBfL4R4VAhxRgjxvYHP/6QQ4jNCiFQI8Ve62CfjaMDxKF2hDo6h0cZnuc+64L7b2nUF64Zh35tOhnE4mOWmMgz6sSl3TSrhucyy+9q8Lbiz9u2rA/5aCBu0YmBJJ4fz8FnGttuEyPndNGnDlYI+wcgwgsxysTx9CL9x93Pwuw+82Hm7uhqTG09KcWXA+11VM/C8LZ1ZFkLEAPBmAPgGAHgdAPx1IcTrvK89CwDfDgC/Ouv+GEcLjj5vie1gtIdSyiyBNelTV0Wz3LOmdQo/cAHAYLnddppqEPsOJFLrBtpQElxEgj/6a3SUmLYoSehenNlnGXXDpNx1aNtz8VkmrDb9ex6wTOT89tE1jAwjqFluxiy/5WNPw699+rlO26VUeylR1bb0a91z5n6/Sbl3nCDNuiqSzPRrja8AgDNKqScBAIQQvw4A3wQAD+EXlFJP558djtRNxsLQZ7aRUQ3qybryPsuk5+pbIN8lrBsGCZanKkrSfJLUZ4SkCFXfA7DBsEmUkx6z3GCAD+9Dv4b0z7Oe5xCznAWOfS4+y/lrvIBy16tYhVNWMst5sFyT4DdKs84nOpnyp4HTo+l9bK4fuKseq+KzfAcA0CnL2fy91hBCfKcQ4m4hxN0XL17soGmMVUdo+ZKxGqAz+WYJfvbffZsYHZVJGx6ayyxPH9j19Vz97gMvwnserF+Wlg2ZSHqY49SXFYQ17+1lGMXfde6zDMTvOLC/efgs+6sZ87xnVtENA89/iBkdTZrJMMaZ7Dw5E8tId4Hmcic3qG7yfGLeTC80y11BKfVTSqk3KqXeeMsttyy7OYwe4KgEKYcRbrBcf+1cS6x+XeujYh1n3TDse9PIMJAf7Oup+tmPPgVv+djTtd/DwLYuqZMybGlBs6y85NXpAjbrhkGfE/ez6WFXFLDdxgkk4PTRJawMY/4Jfnht+npfhoDnv5JZrgmWRxPZue2fUt2NyU11/JkfLDeQb4x7xCw/DwAvJ3/fmb/HYMwM5/5eoQ6O4XbuTYJf+pW+BaRuouny2jFvlJW7bns9+p6Ym0rV6Doa5qpOhuFU8POYZQXOTWP1mc3bW/a7zt0wwAatodLQ85BhYMduJ2jzu1+mlcAsE/j8BN0wGsswZOcTnUw1e4aaoHFREu8LTVZpqHXcLH1RF8HypwHgNUKIVwkhhgDwLQDwzg62y2Aws7zCGLeUYfQ5ic6x/jrE92E4WG4f8IYKZ/QJmVSNrmPbMrwA9r6PSxL8Qr9pglAl0+58ljWE8IJ88Nww5qFZzje5COs4yyz38KYsQaOiJHUyjHQ+Mozu3DD0a22Cn3e/N7ECpFrtWaQYMwfLSqkUAN4EAO8FgIcB4G1Kqc8JIf61EOIvAgAIIb5cCHEWAP4qAPykEOJzs+6XcTTgJFatTv/GgPYJfn22Z+tz27oEHmdXbhh6m/07X5NMOu36wMPn4d7nrhW+17woif03Ds5OBb/Az6f1rnbvxeJ708Ayy8IwvKEEv/lolvUrLeIyL1i2fI476Rh4fqrKXdcVJRmlWafMctdFh5om+PmFeZqsrNBJxiwrlp1olpVS71ZKfaFS6guUUv82f+8HlFLvzP/9aaXUnUqpLaXUS5RSf6iL/TIOP5hZXl3QzrnZpesvG9l3prQrBDXLkWjNpvfd8jGTygkC/927H4af/oMnC99rW+46joSZJCI5L1X499Mm+Lma5Wbtq4Op4Ed9lgPbno8bhnvPzfP5auKe0CfQe3RazXKaSZDK+n93ga4TJUPa+4NJBt/05o/BfWQS6zvCNFlZoffsUpllBmOeOCpBymEE7dzbMst9kzoclQQ/I8OIZpNh9J1Z9jP5J5kKsqbY9jpSDjeVRMIMyEmNDGPacxpyw9Ds9fTnmWqWC0VJHD32/BL8jAxjnkVJWvi+9wG0HxznkgsKlGEMKjTLGFBP0u4O2k+0mwVlVXov3hjBfc9dg8+9sG0/96zjmkxmKWlzfX8C//ht98H1vUnrdnKwzOg1+p4oxCjHuCWz3OdVhL4Hf10hpFmOoylkGD1eJQDQLJtbSEQGA+KsYUIYfp5EwsowSir42d+0a3NoqTokyZgGJlgWUChK4jDLc5go4hYXwiyvmHVcSAtPgYGwKHxiMTbWaV3KMPLXDiY2ZcnTVgJVJF0ss1zchg8aLN/33DX4rc+chXvPXmvdTg6WGb1GnwtVMKoxacksq5JOsw9wAvlDfCPiuBTP6oZBxuW+XUsAgCxTzjFNpCqRSjSVYejXQRKZwTlyrOPKt90UoSSoriaYtn1Es0z02ng7zIdZdlczFiHD6NvKVRnoc1SlWa4CBtSh30+LLrXfZavHGNzT5xQVFfhOM2bZfrY/zvL9tL/+HCwzeo2jklh1GOEm+NV/v88BaVcMXt+BQQSJlUFMVZSkv6sEADo49pPyQhOC5gl+yCxH5r5PaMLa3JjljoJlwiwLIUAQ6Y1UyliTzdNneREyjFUrd+0wy0HNch78VWxj3FFRDooutd9l9zA+R7Td/jPQJDGQ3rP7k/rzVQYOlhm9Rp/txBjVaFuUpGx5uQ8IVWA7jMAAibphxKJ9uWv67T4Gy751XFrLLFdvD386jIVhxKgVWmjb7TXLxQCla/9vvOqxEE5AhG4LK5/g15HV3qLgaJYD5x5Z46rDwYC6UzeMBvKHxtui9zB5PySZsf2QKnxnksmgow1d4TyYMLPMOKRwB5nV6OAYGq5muf7a9TkgLVv6PmzAS+b4LE+jWaZLq100rGOkma9ZVkEf2sZFSZBZji2zTMs3hzXL7pv3PHOl8jkxMoxA+0LbawPLLOs2U7vATCpYH8QAMB+fZRm45+aFVdMsqzoZBgbLFdswCX5Zd77Itn+eJ7NcZMSLzLJ9/79+8Ax8s+eeQbcDALBnZBjt28nBMqPXcNjGFfLGZPhuGPXfdzXL/RrMHBlG32jvDmHdMOx708kw7L9VD5/b1LOOS6UMTtCaalxxU0kkzODsJPiFfkPOy2Pnb8Bf/olPwF1PXqnYR4hZ7mblzVjH5X9HkeuGMYgjEGI+PsvYbL8YyjyQmsnP/PbRJVxmOeCG0YAppZX/upJidClnKZO4hSpIYtyL70hyPZ+7ugcAAI+eu+FsnzLyGCxP024Olhm9Rt+1j0cJB5MMru6OG3/fkWE06J36fK2Pihwo6IYhROvrUaar7QtSYh2nlIJJpoL3KGVXq4DByiCOisEy1Bcl2R2lzmsI3gq0+16DNlaBapYB3KROqRREEcCA6LG7hPLuuXlqlptUfPO/36bP6xr1muV6Zpn+rquVga78vQvbqNEsm7LY3iRWKQW3Hl8HAIALNw6c7dOkVJZhMA4tjkpi1Srgv/3+E/C//8THG3/f1SzXf7/XmmWnkmTPGtchMJjw3TCmTUYDsAP5O+97Ac7m7M+ykUlVYMdC7LFfXrcMVoZhg0yrWQ7roUP3e9W9FQpQskAgMQ3wl3jZY3LNpVIQCQFJLObjhpG/omZ5ns9+2jJYft/D5+GrfugDsH3Q3pe3C9QXJcnZ5orDGRF/5pD93DSwevbZt1VWpTfILGNwbL5v/77l+BoAAFy4MXK27yT4MbPMOKxwB93DG6SsAl64tg/nrh/UfzEHXf5qMpDT69s3qUOfnTq6BF4yR7M8hQzD13grpeAf/fpn4TfuPttJO2eBUjpQxjbiYBq6rm3LXYc0y2WnLpSPUbUbv9Sv3nY3Kx64HZELMaj0JpMKYiGcgitdomgdN7/nq6184ML2ARxMZCXjP0/Qdlb5LFeNjS6z3FGw3GGiZKlmWYY0y5DvN/+bTH6wiuFFL1h2ZBgTnDgws8w4ZHAShQ5JjDJOJfzyXc/MhaWZJ/bHGYzSrHEH2Vaz3GebwD6z3l3CBi72vSgSrSvE0W9KE5x2x2zNgtQLmPDvILNsWOfqbRqfZc9FBEAHMqFzF5L2VCb4BRwIspJAg+Lq7ri2rzG/RGY5stIbLcMQMIijuVw/bLaVYcwPbRP8cOKzrGfereBX7rNcdThUs9yVjEYp93UWlPksZ1nxufRXemhREnyvmllOzffbgoNlRq/R5wBqWvzo+x6D73vHg/Dez51fdlNaYW+cglTNO9y21nF91gX32amjS/gSAvrvNtfEYWmVPWd9mCBmXnCMbQo1zWbfVx88fo+WHa5L8AtJNRsxyyXPSegZy6SCP/2ffh/eevdzle33A1aqWTbM8oJkGPN8vGh58CbAhMZlJRzT52hU5bNc0byxEyx3yyx3o1mm/7Z/mKIkjl+/uz/6TGCbfM1y0GeZg2XGqqJseb/PAdS0+PBjFwEA4Nh6suSWtANmElMNXBVmqeDXt4mRy370q21dAgNI6rNstaTNj9vPNUBWdB4JYm2BgydeR+uQEA426WsZTFGS2J43tFvTrFfxN6F7qmoiFmLz6lbeDiYZXNubwKUbdUlq+sfWDYNqlvXfSRTNxToO2y3MBG1+9wgGYE3v5bRlcN01mif4lTdwNI9gWXV3Xsru4dCKj5ns5H9TH3QTLG+7zHIaqOA3zfjCwTJj6bjnmSvwVT/0AXjm8m7hM7ds7vIH2i7w8IvbADDd8Xz08Uvw1KXieZoGH3rkArzye38Hzm830yHjrPygQYlVALdjbnKoDmPWs5lRV64DXeDHP/A4/NPfvH8u27aBi30PtaRtjpt+UyplgpR5WI+1hR8Ap4HlXvPdBkEsgD1vlFm+8/SG/gzKCp4U/13ts4yBQjF4oJ9TYIBVFxz4150WopFSQSQAhkkEkznc+9g2I1uZ0+OlEy31v5vey6mRYSznmaftrNQsVzLLltzoSnPepRuGcv5NmOXMfU71ft0fUYYbPxul0hl7xg6zXO8eUgYOlhlLx8UbY1AK4HLAoqdMz7Sq2CGJItMEXf/kN++Dn/7Ik5205Yff8wgAADx3pZlDQVtmeVyxfBZCn51Pukqk6gL3nb0erFTVBfCe9N0wANo9f35REoyRlz3RAChqlqsS/ExQUNNu/BhLXA+TyGTnl8kwQsnLVc+JYXpL3ANCTcRAoTZYzl8xwY8mdWZKQRzlCX7zlGFE7t9dIxh01WD5zLL9d4gVHjUo30yZ5RA7PQ2M33EH56Vs9Tj1JrUAITeM/FW6FTmpbnmSSbM6hpplto5jrCTwYZgEHuQ+L81Pg3ufvWb+Pc0sf5LJzjq8R3LzdlwursO+CZab7b99UZI8UIva+/rOG33Sziul5qabDvksTyfDcFcJsL19kGEYthCZ5YoEv6buCb5m+c5TG451XOjUBTXLFY9WiFmuc2mZllmOIlvuOpNoHTcfn2Xw7rl5PV6hSnC1v2k42ZgXmsowqjCea1GS2bdXqlk2uQTF6+Yz20q53zt3fd/8e5JJ2MjHOK7gx1hp4NJsqCPuc6GKaXDPM1fNv6fR/0nVDTtHZ9ZNt7eXz8oPJg01y60T/PRrPEV55XmjT9ZxmQov63cBPLYoKjLLbQJ0PxC0koflyzB83WqGEpHA85g1ZMSVUiCE1XrfcXrDS1ir7ttMsFypWS4G7nXlrjGYqjvt1jpOIxLCaVMcCRjEYi4yGjwEOrmYFb/w8afhB9/1kLefKYJlTyO7aGA7h0lUCJaVUo1kGPPQLIdsDKfelpcMjAgxy9K7Hq4MgwbLhFlOFWwMdbBsEvzYOo6xijDMcqAjPmwyjHPbdsY7zcCj9Z+zn4hnLlvpRdOBY68ls+xqlpsEy/o7SSSWHpD66FOiaSbD1ea6gFSWSUaYIKbF7eqOfza4n4fmtS18jfKkQpfaVJupQAeaGCzfeXrDJKwpaJDgB5YhK4PVNRff8/+NGJtgqr79AOBYx9miEGB9lhdSwW92fPyJSyaRGhHy660DHu+y8mXwGqwHgmVXw1zePvq9roPlTjTLdcxyoO/1J5eaRLLboY4YEynN6ikSPdPM+ThYZiwd2ImFZBh91rFOg+2D1Ayo0zDEUirDhM2C+85es9ts0OFl0rIYTZnlscMsN29bH2UYtDnLto6TJFGpa6A+lQL/nlqGQZjlrA8yDE9aEUokQmQNgwKscocBzZ2nNw1LK0tWAkJ9W7VmuRi41fksN9Us4+e0KAlNbhRCS0y6koBRYMu6tI7LZPGYs5ZFkgDoKsTsbZoG2Mz1QVxI8KOkRSWzTPrrrmQ0TeVJ//ht98G//O0HK78TqvYJUKJZNswyTuTs80mfBZ9NRxkGHv80Z4GDZcbSYZjlwIPs2sosf6CdFTsHKZzeHADAdPoxpbrp8O577rr5d5Nm7JMOtzmz3G5VwGGWe3ap+2QdJ+X8EuWkUsTCSwODmGknCViUBKBfbhimgl+FnZh1zqjepmbkBZzLnWXuOLVh9L9KFe//SIT7tqrLalg18l5d/zhuKMPAjVI3DJrcGEcC7ji9AU9e2un8/sfNmQp+HXDLmZSFcx6yIKuDCa6W9MgbZnkQF/rdEXElqmoe/V1XMqgm7i0A2vnpzMWdmm3hyoLHLIdkGMq9HtTCjraFHieVYfjbaQMOlhlLh2GWAw9ynxKrusCNgwmc2hwCwLSaZdVJoHR512q6mizp7xEXj1FD67hxmhl3gHaa5WjpAakPeoqW7eiQqXBFuC4gpXKcMACs/20rZtlzHqiaEC8aEy9xqIpZbrrcLHP2Ff3iCzIM7/u+Lt8f/EMISUJczXLxN40T/PJXqlmmxVviSMAb7jwFl3bG8EJ+jP/lA4/Db9QUO2kC3zquCx1GpgLMcn48wzhqPBnPWvoydw3c78YgLrD61JWoqj+YR1ESP2gtw8Ekq5U8WCeZyPluKMEP96e83+K4iBN72s9MMllMYp/icnKwzFg6sgwT/Go0ywtr0fywM7LMciYlPPj8dfjQIxca/16qbjKaaQfahDFEvTJAi6IkmYK1RHcxbSr4JSQTvy/ok2ZZyvnJMEKaZVqJrs12ENS9o0/MspFhyOKgjGhqeadyZvnFPAv/jtxjWYiwG0YcCZcVNprl8v2EAhQ69wjLMDD7v779ur25dZxflEQIeMPLTwEAwP25beE773sB3v9wd1VI0Tqui3s7k7JwPrDfHMTNZV7pkpllbOf6QJcap9fRkWFUbGMe5a6bumEcTLIGHuU5s+xFo1XWcXjAVIYhFUASRxAJt58ZB4JlZpYZKwnLLJczO/rfC2vS3HDjIIWTGzmzLBX81B88CT/wzmpNF4WeQc8ecIwb6t0QTrDcoijJWt5JNdkHfqePbhh9Kpji6/O6RCaV44QBQGQYLY7bf25lxTO+aDiJXlKRwhPF7zbVLKMbRpJbx916fB0ArKuELy1IosibUNj2lCGka3bvy+JvjAyjllnWnxvrOEECkZyxe+1Lj8MgFnDf2etm390480C+7y5lGKpwPvB4BknUOPidNAwK5wU8veuDGJRHlDgyjIrmjVIJW0PU7Had4Ff9vf1J1sKjPPIS/IrBsm+fSCe+UmFZdrfSZJop2Bi4oe40V3O16u0yDiXsEm21z3LfluangaNZzhSM0gyu700a/16p6eQbPmiySJMBb39CZBhNi5KkshWzTEsG901y4xaCWG7bMjW/Z0Ep5XgsA0wnw1DOv5VhQJctYQFw9YzaXaaKWVbOaxmQfX3nm/44PHD2umHjBejjD2mWQ0RAtWa5jkwIMcvNghoTsOZ/0yTbLNcsryUxfPHtJ+D+PDnYD96mBQY+XfosS1l8RiyzHME4TUM/KwCJiT5olgH0GIle3o4Mo2IbozSDrbUEdsdZ6djx3JU9uGlrCFtrzUJC7A/r+qH9SdY4udRP7A75nxvruMDzkmZ6UhcJ4dyXk0zC5tA9LmaWGSuJas1y9WCwSpBSwc44hdNblllOMwU7o7Rx8NOVZnmUShi2CGQps9y03PU4o8Fy/feNZlmI3k2MbNGJ5bPe2hFlTsyyCrhhYLDcgpTyGc8++Sz7y7rIdofYVxMwNtQsf/HtJ+CvvvHl5n0h9H3t389JHAWJgGqfZSh8J6uZxBnNcp2MhLQXINcsk/1hIPv6O0/CA2ev51Kgbu5D+tzTtsyCVMrCc0o1y60T/JYkAqQ+ywDu5GR/3FyzfCwPgkMlswEAvvnNH4Of//jTjdtFE+vKoJSCg4mEOm6HBst0e+GiJPm2vd/q70mIImSWq2UY0wwvHCwzlo6q5B+b9CWWNrvvCrvjFJQCOEU0y5Ncf7o7bsbWSqU68aodp9ZOp22w3FyzLGEtab4PKsPoAwNJQe/DZU/a5mkdF9IsWy1p8536k9w+JfjRgEMpspQbOKmWEa/eJmqWfQgjw3ARCQEhZrnqFPusmv5dcRsUzSv4mXA5b599D5llAIBX3bwFN0Yp3DhIQUE3q1y4a2sdN/s2qxL8krj5WNLUIm1ewAnqMGeT6T16NV+R3BpWV2AdpdIwxmWT1at7Y9jeb77C2STxddTU49vpW+37ldZxqvjZRNriOXSMpNZxZp9eG375rmfg6Uu7le3kYJmxdDRhlvsQpMyKGwd66e9UrlmeZMp0Xk06KpUHSV1pltcH2AHXf39/Cmb5YGL30aYoSZ81ywNPV7cMZHK+Ffz8oC+aRoZRwur1IcGPtiGTyrpjhJhlb3AuA2qWfZTJMJJCf1YffIQ0y3UV/Mb5xLZWhoHtDRUlUVbHHhFdcVfaeauX7o5ZDiXB4nUfxM2fYbw3llaUhKxoAbgTvWv7YwAAOLU5rAz+x6mErTXXZ9jZR36upnG7qfoNjhlNkmMBiiuKeL3cyS0y/XnbHY2z1GXZI5dZnmTKjEP+drB93/eOB+Htn32+sp0cLDOWDgz+wprlnA3oYQDVFju5/dqJjcQMRsjMYCBdBXy+u2Bz6Gx7Hm4YmVTw3JU9uPOmTQBoxswoEiz3T4ahX+N48az3uesHcF/uQKDbMs8Kft0Ey7R5tChHFxrXWUGfH6mU43jgn1eq260CapZ9aDeM5tZxTTTLLrNMtxEIlrNmMgzjs2zabYN5emzI/mppSTcadMos0rbMglQW7RVxjtQmWK5K/lwErPyryCxfy5nl01uDSpnIKM0qZRg47rY5Rsvwln8HvfnrtuvIMAL7oMfsyz/c4Fr3XUlsK02iZG2YRI68zJF7VMQfFCsZLL/7gRfhT//H3+/dUi1jOlS6YeT3bx8DqLa4caA7t2NrOlhOpTIFEfCzKjQduJuA6rianNe9sQ7m40g0Kkry/NV9GKUSvvDW4wDQUIYBYBI0+vZoW1u75h6tXeG/f/gJ+Lu/dA9py/wG70xCQbNsg+Xm23E0y8rKGOZRLrktXDcMj2kuWbpvoln25SsA6IZRXAnw+7MQa+zDZ9X8788kw/DY3VgIxzYvRi2zsRFU3SX45W2zsfLs20RNNQVe52GLvAN/2X/RoA4eAB6zvDeGjUEM60m1DGOcURlG8ThsRcv2k+GqX5hguXaimfetXmI3xgNVZcrpWJhmCuJITyxQhjEhqwm0Xwu5btTlU6xksPzkxR146tJu47K7jH4Dy5DWyTBWPFY27PHx9QEkkYA0k+ZB3W4ULOvXToLl1AbLTXKucEnt9Oag0XN35uINAAD4otuPAUDTBD/NDESiW9eEzz57FZ6/tj/TNqgH9KKt4/bGqXN/SDk/67iQnMCyic33Sb9JLcb6kODnDr7K+du/75oWX5AKCpUPAXIZhoJCVOGvlPnle8v2odvitt/8O/Bbax1X3X7cjClKErmTcwySbQlv1KLPfj3Nvjt0w6hK8EOWtkkAjMHWsoYePAbULGdOsDwx+S9VhzKa6FVEIewYe31vYvrxidEWt2lXfYBtZBi1E039qidoxWex7B7X/YrdTipRhiFMP4MB9zCOTIEs/Vv6u3KyjmIlg+XUdLwrHj0xAMA+TFUV/Ioav9WDDZYJs5xJ57MqdLmU7WiWmzDLE12N79ha0ohZPnNBlzh9zW2aWW6mWdZMXNTxtf4Hv/ZZ+G8fOjPTNrA5y7C1S6WC/Ulmk1rU/Cr4hdwwMFBq5bPssUF4zrpITp0VNMCjMgz82/2ucl7LoJSCALGcyxmKbKl/j1tmuXwfYRkGCS4C98Qoa8gsm4A1b58QZMnbVnW0Aa3qzMYSt2AT/GbeZFCD6wfLTe5nupy/DBgHj6TY5qt7uhosSn3KgK5EmnHV98O3/sxd8KPvewwAyISgxYlvUpRkZPTy9c8OQFGGgWNj2fOplCfDyDTZEkfCBL44EUhiUcos47GUOYUgVjJYlg0PjrEaqMqUpw9SD8bZmYCa5ePrCQxy+yIMfLdbaZa7SvBr7lSxP85gYxjDWhI3Y5Yv7MDNx9bgdF7au8lgoxQACFvIoSuMUtk4KbEMVDu46PswkzowGZEl9Xm1IaS9ncb/1tfSWoJj+X027We0dVy557hZzWkQbAY1y1Ce4EcH+ybWcSGpRkj3TDFu6khg2msT+fCZzZQimmWbhNeddZzt42lbZgE+M/57ADpw0vut3w7et8saekxicSDB7/r+GE5tDECAqNYsT7RN6DCOYJLq7128MYKLN0Z6m1Posg2zXPE474+b6eUNs+ytHocCcqf0NXgJfhLdMKKCDnkQR2aS5MMkEh7GYLlPpVMZs6PaDUO/JlF0CDXLspUbRpfM8iiTRuvWzDouha1hAuuDqBGz/PiFHXj1rVtOQlAdlEJT+XZL/k22O+tyMe3Ql8EsA4CZpFDf4q6B1dooppNhuIMaDph9SPBzBlxV1DBT2KCgbsAPa5ZNgp/387IS4lV9XFCz7ElKfBgZRgNmHNuL7bOyL7u6IMi9oBpstwmw2RFhrWeFDpbd7eB1NjZsDfaTNmTm5wXrhlFss5FhVDDLUqqcWY514hspwOOPu2204o0IlpYJfr6dYkizTHfrV5CcZBIiAU6C34Rc8zpm+VDKMPqULMKYHU00y1G0vNl9V9g5SEEIgK1hkuuqbEGENjKMWQcopZT2WR421yzvjTPYzJnlunLXSik4c2EHXn3rsVbV31CzLES3AalUswdpjmZ5wQMnPh97nhXTPCaPQTeMKWQYjlRAUs3y8p9iX6PsFymhaLLcrD8v0SxXJPjR7VLnifJ9FL/TPMGvsvkBH2j32JGUc90wVCeTH8Nqd2eGYezQnPfyc4WShiaPj2GWl3Tb4jFgsEyfHyPDgPA5e99D5+Hz//m7jRvEII6stEGqgq63zaXEMaNSszxpZh1HE/xcZrnITPvPp89ER5GAQWSPk8owSjXL+fEfThmGanZwjNVAE59lv278KmL7IIVjwySvMpRbx7Vyw9Cvsw5Q2Dm20SwbGcYggoMa67hLO2O4cZDCq2851kqHKJVeto5E9fJeW3SxXExLcS+628EBwjI13UyagvuSAc3yFG4YfhDXp9VAv9x1IxlGbVESZYq3UAihAxn/1NlgGX9v21MG07SSADnILDe0jgtplul9FtIsS9UVs6yTSlEC0k1RkuIEBSedSRtmecnlrvG6+Wy4UkrLMDYH+poF2vexM5fMv9eSCAZEy0uZ5XQKzXKTBL+DfHLfriiJ/a5tH73fCcsMRTcMtI7zfd2TArNs999UIraSwXKfWArG7LA+y8Xr6TxI+b38ngfPwfe87d4Fta477IxSOL6uLXySSNvbWDeMJprlboIkDA5Mdb0G22vDLKM2+9TmsJVHL+o+u5Y6dFEeeplyIGw7Zpc3YSGnRYghjadYHneWS8H6Qk+y+SUnNoVTPleWJxDpz5sxywpssEeB+vuCDMN7LmywXL6P0HV3JSWBYLmhdRxGW3gM9BmkRUnwCJVhlt2+4JFz2/CeB8/V7Mvbcz5JFi0m1nUIaZYxKBrEzfskq+ddzj1rcyVczfLuOINJpio1y7ccXzP/nmQSBglllqWVH+Q65jbzWPtclH8HSZUmtosAWJTEvm+KBZXIpJRytz2REmKhy11PSH8DADDItczmt+R8VcUfFCsdLNeZSDNWA82YZdt53/XkZXj3Ay8uroEdYecghWN5sKyLkkjihtGGWZ7tvscBFGUYTYKuvUkGG8ME1gZRbVES7HziSLRiJWXOMPnatVmhOmDA6H24aH93X7PcVBowDeiSOwIJmVZuGJQB8ga1eZ+/H3nvI/CeB8v7B5+pmshyZjlTzc51qWbZ/Nb9va9ZbsLU4UeOHpyyzIFuobEMI8gs43aLCX6YZOqfr5//2NPwr975ueqd+fsGBUKIOcgwvIkPyjBMgY/67Sw7wc84eBg3DN3oa3u6et/pCjcMWnV1fRAb6R9u10gVjD1e86P0i4OEYCv4VW+L5oO4QWyRHMIxAtvrV+LTMgxrHZeS1YTyoiTN4smVDJaNDREHy4cCVZMffE6ojjKVsnYW2EfcGE3g+Lr2xcSOCx/UVtZxMx47Ls22SfDbH6ewOYhhLYlqnSXwmJJIOAlBddDLscLYbXUF30t3um3o12Uk+OEA6SfMzCtY9jXLosWEx27H/tufrMw7ye+tn34O3v/whdLP6XJrppRZngcIyDACA3YIUlVU8INiUJF47CZ+XO2zXGQA6SSkUobRgBnH9gLk1nZ47MpKc1BqolRY3pTK9s+aXlGiMoxWPw8iFCybfqkVs7zcBD/crfVZ1n9j9b6TuQwj1LqDiV4NfPc//Br4G1/1ChjEkbkf0kAewTTPd9UqEfZXTUrFA6Atp33fSEZoQKwUWelyn8tJpgIJfmWaZdIfVeRMUaxksBzSsjBWF5UV/JwHyQ5cfmLOKmDnIDVlR3VmMpFhtHTDmGUp2zLLLXyWc83y+iCudcPAY6LMchMowAp+3Q5OXWgrrYXT4q3jUi/Br8kS6LTIZDFYtixomx26Qdwig+WDiawc+Nzs+mZFSZowy6FbXeRLy/7vfXmStY6r3geA54ahip9TjJpW8ENm2VjHucE5thc/l0oBqOK1lFP0TTpXQTiM4awIJviRam56v02CZff6LBp+UmJqmGU9XhgZRqB9+5MMNgYxvO5lJ2AtibWlWibza1QMEtv0uU3kSSbBr+G9568o+swyentHJOilIUMmJcQRyjBcZnkQNWGWq9u5ksGy1b8xs3wYUMUsh3yWJw1ngn3DDUeGEXnlrpv7LAPMFijhAGp8lhtsbJJJGMYRrCURjGp8lqmfqcmeb7APZDXjzt0wOtAsS3ofLnbgxLYfeIPPPCaLyPJRGBlGq8GU/NsPluf83I7SrHL1xV3WdfuRghtG03Otyiv4hcI/ZLksQ4ftKd9PiM0LlcymMDKMugRFU+5a/x2ToiRUmkOlEqHnSqr21SUVKADh6qFnBS2oYt7LzwEGnk0en2W7YfiFVPA6XtvPZRhbwwpm2Xrp623oBD8/sc/c/y2O0UrByr9zMG5Z7jqq1iyb1T3CLIeKkgyI3AT7miQWJrHTb3fmn4cSrGSwPI8Ev4NJBld3x51tj9EczXyWbZBiLW9WK1gepdbbeBAJGKeZ6RyaaZYpOzf9seMAim1p8hhlEiCOhU7wq2OWTWAZtdQs68FYCNGpG4a/XDcN8OeDJVTwM24YXoLfPNiukBvGdDIMEtB5f89TQqW1mKrSKWniyS6cBL8CU5q/1jS5VLOcO7soBc559dl63Hwjn2VnoKfBcvG3ttx1U2YZ222fwWBREqXDa59ZzlSzibG7cy/Br92vgwgFc4ZZjpqXu8Z+dlmLmHRFi7bnKmGW9feKvz2YZMbxCEDrdieZLDC20yQxNumDUK5X/+zoV/+Z8NtJg2oAPclyZBgSfZYjGyOYyYYnwyB3mWXYDyGzXFUeeVq8+UNn4K/89493tj1Gc5iZXVq8WalhOd7ffqbrqiCV0jywcSRMZzJMItgdZ7WMm9vxT3/sGEisDZoPGlne9vWB1r1VDYjYtli01SzrQbp7GUYxa3+abQC4riyLgnHDIEVJdJu635eWE4RlGG2uCf2mLh5g/56nfAqDw6pnqVDumsowvGO0UoR6diwkOYqETlpS4DL2vnVcI5/lgLetK8Mo/gaf9cYV/ExREr0fs/RtrOPsvoLMckD+UAd9bgRAl9ZxAZmAdcPI9b8NtLSTZcswSLAHYI/nep7gpzXLYambDpYtszzMg2XLKLtx1DST4arT4ltd1m3LT3pNS4LliHzP8UjPsIKfMDGCYZajcuu4poYRKxksyzkES5d2RnB5Bmb5+97xAPzuCjo09AG+GJ/CMMtUs7yiMgxkZwH08eCy+ku2dElotFwrAx2wZ7n3jWZ5gEVJmjAsuiNCu7kqdhk74zgSJsu9yWBjK/j1T4ahzH24eL9vfD58DeA8gk6pbAINoo2Uhm7H/Ft6Fk9zfG7xmap6PiaZ2zY6kSq4YeCAXTvgl8swcLmYBtNJ5PvmgvN3eB/5dwPv6W1UMMs1187+1jLINC/EJPiRJXDMBXClIMXEujpImfssN09vqN0ebY///iBptlJSNxFZBHC/Q68oyfZBCmtJZPrjUPNQs4zAPBmfsZ14wWgTdFmUxCT4ec8ErTYIYCeLllkuao/RetQvuOIn+NEfHmo3DOzrujS4TzM3K7ot3nnvC/Dhxy521p6jhGaaZZtYhdd93KDscp+QEWY5iSIzsJ/e1MFynW65bPm1LfA8G81yg01lUhlmGQAq7eOoZhnAtaGqglQKBAhHn94FpOq2gt+iE0vLfJbnU8EPCsU1pilKQpumACAjz/Y8E/xwElc18PnShdQLnimaelorpQIuy2CcXSg7C2DZsXYJfm6bdHvDgSGiqXWcba9tn1R2kmCDZbKvfJsOuyenCJbzc9OVZrmsPHKh3HXNSaH30LKYZeOzbKzjkDFVhiEXAMGT5jPLgziCcSoLmmUMLNscYZPn4qAxs6xfI2+1xdjcmX0p53tKufp4DJaTKHIs8gD0eFumWbbnobqdKxkszyPBj1a0mQZS2Wx1RjuYmV1IhmFudss2rmqCH7KzAPp4UIZxc24ejxnOZehas2x9luvve912y2RU2cdZzXIxs74KOqDIdZ4dDU5dFXIxKxzREtww0DpunJllcYB2CXdNEXLDwL/blbt2gzg6DnWZ4PfY+RtOP4CTuKZuGJnyKvh559SyWnXsWHGSAYDWcfp/dBk48ZaccetV+1H+l0Hfl0bSEfJZbirDyD/GFuIzi+/be4L6LGPQRa91uB1VSKWEJBa2OuCMqmXp3XsIP1mu7vFxCr7M1KLpYYJlTzpC3VfKEvz2Awl+jmWcF0e1quCX/7bqWh14Vpel28IJmeeGQmUYNDCmzLL2ANe/SzNJZBhokVdiHRfQLB/KcteWiezuFqY30TTIpDLLDox2aOqzbIKEOVz/RYCWjY0jYe6XW/Ng+cpetQwo1PFPg5GX4NdkeR2Z5bWknlmmExwAy67VAZey6bWeFdK7Z6bfTnHStihQzbLv5NA1fLkAAPXWbTGYkq8qpTqTEFFc2R3DN/zYR5yKcXhvVxEfqccYVlvHhd/3UaZZxlLEOBFEFK3jwPm7bB/+dzKlzHMWmjw1TvAzbhh5/5RLofC4se20fD218qRtbPt8TDIFSRTNhVmml81f8aprZ1Vlx0UBb1XULFP9Lk6SUOrj42CSGUJEbwM1y3kw6iW2tZnk2KC9/DtNZRj4cRy5kxi3LD2Ayv90pUDKyDdSqYuS6AQ/99h867jQfXEoZRj+xe4CmlmenvHIlHIq5jCao2kFPxysTeWhVWSWA5plDJbr3FicpaNZNMuZ67PcJNEFXRKQqajWLOdMAWGWG2uWI/39rqQOoQF9Ghg5UEs3jBeu7cMnnrg8077xfO5PMldHOYdomRagQEwjw3C1tO491tW1vXEwgUwqR76EpdirJFqubKBY0Y+iablrnOj5ECAMQxtiln1pRRMZBv2KIsFyZbnrOus4he3ViHK5UVGGQWy78u/656/tikeaSRjGwtn2LChzCMG3B542tgw0h2ZJsbJpoy1KQoJlYcmIEMN7MMlgPbEh3iAPIjNPojBN4RVzL1b8hsZDTVxeUCURWrFIpbTMsqWg9WQRS4HnRUkGkTC1CBzruBqf5cMpw/CCpi6QSpkvIU33VEipYG9c75XLKKIqYRNv6phU98Gbu27ZpG+QUjmaZQw4DbNcEyz7pT0RZ6/uwePnbzRuBw6ga0kzzbLVfVlm+aBiFYXqxABswFAHlX836pC9DXW8021Hvw5IZbMmeMvHnoL/61fumW3f+f4OJlnpEnNX0AUo3PeMDKPF/ug3pXLv11Ai7zQwjBhp10FLGYYkAypAIMEvwOaGUGYdFwlrnUeZZ5w0m+XsBsFHiFmWEowW078tlVLNK/hhsGwY5LyYimGWfTcMIgfymHml2q1CTDIJSRy1cs6pAn0+FbkNfNeFuv3U2fItAiYpMbbsKYCe5OHkrIxZ3i8wywLGmSxIFIzjxxTtquoKqVSvaoJsr4vPLLv3uZVrWLmOlHbimUoJsbB+yplUJnnR91kOldU+5DKMbjXLANPrAKVSrFkOYJxKeObybuV3CuboBKHlbxzcVinBD5d7sUOgs9ybjq1BJACu1sow7L/pKsgP/e4j8H//xn2N24LnbRBHWptYE/wZpjgWxtC/LbOMu7jrycvwa596Nvg7DDi6lGGEBvRpQDv0NpsapbLWl7oOeD73xvOXYYTkBHbpvUWw7AX19B7rakUwpLVEZrlShuFbx2U20C34LLdYmShL8EMWllYeiz0WNRQI+7ABtX2PyjD8344z9zirgJ9iml0cVbthlEnCmliK+ZhIZWQGtC3TomylQJLASX9W0y5HrjNjo6aEmaQnblKiUm6hmDIZhp/glxKfZbOiK9trlkNFX3xQWSoex5XdMex6rk++ywV95vC+SKW1K6UWc1IpZ7KoZRj4GzsRHkRRLbN8KGUYXbFFFL6nXxsopb0lq9i2o4p3fPZ5+HP/+Q8qJSr04fUfPqNnEqKw7LdKMgw8DuqzjBjGEZzeHNYyyyHPUACA3VHaaqI2ztm3YRI1qkhHmWWjj6x4TtDHNiEDrFQKdkYpfMtP3QX/7O0PBH8nlf5ulz7LXckwzPVrKcPowraOumF0pVsvQzhYbsbEOduRbiKOs6Ta0XPrZ7wDkAS/qsmcx1hNpDSrLAWf5Yae1qWa5fwzUOBY8pWxm9UyjGIgqohsxv8tJRPq2o/9Lk0ak8p6JkfkfYAim2z2k++yDek0SSUMCLM8a7Rctvpi++BmMgx6n3RRgnsa4HkckAAQwE/EFYXWKaUKFfySKNIV/Ex1O/d1GplVNbNMg2X9xW9/y6fgP7znEbet+at/H9PnUjPL7vcU2FwahJZh6Os7yaQ5tiQWXrnrYn9Ud8uuZLDsa2063eYUAxD+hJnlIq7ujXN2rfzc0HPuSzHo0hne4H5N+1UA9R4GAGdJaBALOL01bMAsh9m5SdZOJ4iM0zCJQJCytuVtx2sQkcz7CuYucxkAZD5+/IOPV+5HKgUgrMdrF/ClO9MCPaBFSz21L0GYBtj2g0nmaE8XVcEvNhOk5ttRoByLp7KJ3iywEgP7nrGOq5zMucFUJpVZMSlWpFPme1XQ8pUAt5zLMHw3DD9Y9hnm4D4CbJ5U1N3B/a0TLNecc/9TTPDz7bpw6Z+eQ8enuuH5okil6tQNo2z1xddf16mBHIZ6SUONzJ/H2DvvtKpiyMfeJHGTCn6DRDgV/PwV3WncMMp+p5Q2PFjzLO8u3RjBFc/1yZfHgNLbV8q2n2qWY69fSbznimqYkTUfxC6z7K7SNjvulQ6Wx5mC56/tw/X9+lLBdTAsxRRLhL4PKsMCb8Sq+9HRM3ojsjEsJ5plfADGAau5vsJfzqQPbhJHcNPmEK7u1ljHkVNzaWcE//Q374f9cVZbUc8HDqLDXIZR10dSZhnbX9XBFHyWc/b6f3zmeQAAOLaWON+/vj+BX//UswAqZ5Y79Fm2zPJsox0yh3FLiYiU7SYyH3/iEjz4/HXnPccNI8CUdQmlXLkAAEylJZWESfXZ9a788UOJSU2s42ggra3jlBnUp5VhUCsvCqw8qpRbdKMswa/qFNvv2vfo5MZvexsZBsanVLNMJ3p4LfHWKEvYNH7RLS7xJMuZZbON5r8NIdQe/HckmmuW6X26rJEGpWnIhuOx6edUfyck/8FYhBYlGURuBT/fVapNf0Ifr9DvRqkEpQC28r7e5JlJVeiLbVESe11wjKcrPgUZRn4MMZHwRESzPJGEWSZjlz/mNSUzVjJYptrVP/FDH4Q3/L+/N/M28eGfpiPH9uxNsqWZl/cVNHu3DC6z7J5/uvRir7sKfrcPUErBjYNi0EsDTgBXhjGIBJzeGrRilu968gq89e7n4KEXt7UOrRWzrL87iHXwV69Z1uc5ioQJpKr2lxYGWH3tkO3wEyne+7lz8L1vfwDOXtvPNcvdsaaY4NNFgl+U29q1OdeYCFV2js9dP4AXru2bv7/1pz8J3/jjH3W+Y3yWJ/OXYWSqmKhmXQqa7891aXDvl66s42wpYvseJhVV6aIzKc2yNmbMrweqWVJP6ybMbAmxbBjaELOM5xS3XnVN7XfphEmVanBbyTDybeK1Nm4Y+Q+jSDifu5Of4j3ZZmI1ySQMIivDmPXuKGOWccJr9OktZBhLs47DNueRGq2+FzvMsvs7THT1NctS2ftikrmOEW3lZYhQv4ASjM2hlVFgu/1n00h9SLCM36HMtM9AK5WvrBCD80gIGCChkymg+TO4AjNM3CqsR4JZpgd599NXOtnmNAMQ/e2qOTTMG2mg87y+N4G/8bOfhHPXDwBAnzfsKEtlGEGf5f6d6/c9dB6+4t9+ALa9gNlnlmlCSxJHcNNWO80yOq9MMqllGC0CkHEqjQSjSfAX1CxXBiPusWKCX1kxIewYR5NMu2F0WO7aaOtmDpb1PdrWqQN3W3aOv/ft98M//a37K7fhaJY9CUHXoJZUiKlkGMqtyDWXBD8ZYJbzQbqqH55kilhx6T7KMMslk5Em9oplPstK5Ql+Qc0yOPttkuDnB4BlVmjW9SaqHdfwp5ZZ1q8+s4xHUKZZzrzjaYI0UzBIhEkunPW2Lkvwy6Sd8OrPmm9nWSSYyifphlkm94mRYUDROi7ELOOk6sCbRE0aanYpQlpwit18/8fXB873J5kM6PRdIkmBPfdDJ1jW33fdMOxkEUA/VzENljNdNVcI+/4wdsPepquOqxks5ydtnEpz4D/+wTMzbdOWgJwiWCYX/2DcvwBumcAbkT4fD5/bho88fgnuP3sNAPQKARbIKGOWaZDSZ5/lZ6/swf4kg2uepMJ3iIjJbDiJBZze1Jrlauso++/dkV1untQwy09c3IEL2wfm73EqYS1/bqKoXlZANchNbMRSE1zn1nFCOMUflAoHIuNU6oB0Bs3yx89cCiaWzK5ZtsmHSjUfPDFILDue56/uw3agzPnlnZH5Nw2WXRnGHIJlYkmFmE6GoZzlbvqodiXDwCQ+emqblrsekuA4lZIMyuR7LSQvuPLgI8o9cPX9Y98v1yxX7cP9LoC+XmWyghEJlmsr+OWvxg1DoNwKV5XyzwPJnk4i3JQyjMRhlmef2Ib+jT7uTe9nt3jNTE2aGiiz8ZllqRTRkRd/hyssVLOMsRLtH1MpjSypzXmvs9Xbyfu0E+tahmFW7rNipWQzxpN7C8/9Glnx8UkY9FmOfWaZyjBIML2WRLA+iAqER9OVrtUMlolPIJ6nj565NNOyZObpeNqAsiZ7E/Zapggxy2gpgzPcTCrjBxnSLPt2YtZneUk9WAVwRu8nNFZplgeRZpYnmXaMKAMd8FxmWVayfm/61c/CD7/nUfP3OMtMcBA1SFhzmGXPHzb8/TyZETXLQg+eZdp03NYolYb5mWZwurB9AN/6M5+Edz/wot02MruzMsvS3od0u7W/q2EMr+yOg4nKj53fMfvFfS2igp8Oct33rM1Zm2DZG9RI9NQVsxzqWzBA9Cdk/u+GRKOcEs1y5gRX9jd1KxNlmmX9mQ5GHRlGicNIk8myb8s3KJNhmOJDcQNmXL8aZjlvKwYSRp6Rf06vYYhlbifD0McwH82y+35EGMa6+7msEuAigfeVqVJHqu3RFSD/UHCMXQ8xyzRYzogMo8UEh56P0GncGWmy6OSGyyynUhaeS1+zTJ/dNeKZbIuXWAaa3v8A+v50EvxyiQ8AwP/xx14Bb/7WP1IYXw61ZhnHlkmq7VFesjWETCq4eGNU/cMKGBuVGWQYAOyI4QOX6+lpxYASH9pUKrNcFJJh+HZi+HBX2UMtC7smWHbb5muW6dLRINHMMgBUJvk5zHK+n3GqE5SqBqe9cQrX963EA2UYAK4WvAw0C9kyTlXBsn71reMypQzTEUo+GmfILE/HmmKi716gclTIlrANkDlsmhxEf4f795FJBVf2xk7/gcHIY3mRGTz3m8MYpHKN/ufBLLuWVNimok61Dgq8BD9HI9gRs2wGebttGgiUsctpnlCm2wZ5gl+x9HsbGYZUJRX8cGD2ZBoRCQzofhu5YXjvld2TY+OIEDfWLGMLsa14Dq2zTYBZdnyr9WsbXX8q8+sxhWaZrsAgylhPfIYbyzB6YB2Hbhh+YmVGJmd5DqmDUSBYxnueGhGkUgWL+zRpF4Kemw89cgHe/pmzpqqmCZal7osnWdFK0yTuxfaZQLZ7bWCDZd8NA7dbcMPwrONwrL3j1Ab8L6+9zdo5knPQBCsZLOPJ3c3ZtVfdvAUAAC9c3y/9TR2s7rh9R047BnbEcGHYn8CEYkRqx+NDEZJhRELYQYdss48yjP38nixnlotFSZKcWQYAuFKR5OdolkeWWaZG82W/owbx49QGC6KBPtgG+lGjYNEwy06wnK8g5J13iJkaI7M8pRsGTiDK9HSzsEPI8IR8ZqtgNIaB72vZjdtZ37Slqzk+isFy/hk6iFBD/3mUu5aq6IYxTblr3/+XPqpdJ/iFZBgAui/+nftfLFyrkAyDDsrme/Q+qo02yyv4AaiCTKMgw8D9VOzGSjXc+zvxKp8hsH9cT+J6GQZ+LLB9uI283/LdMKhcRRbPU1tmOYkjIwFpSi0/d2UPvvzfvh/ueeaq8351gh+RYdRc00lgErBo0PFPF4qxE0QzgQFRuL7Y3ztuGAFmOZPKapZbtKtMovTLdz0Db/7QGbNCeiIPljNlg+QyGYYzufYT/JQyzDdN8NNFeYgMIxLmODOpn+3EWyoTwvWlPuSa5TxYHrnB8ovXDkp/U7vNkgvZBPRc73NhEgfU6gZhZBgTabSslln2g2UbpKyCG4adCHhJbB6zTHVW6LMMAHC1IsmPDkAYGE4yCeNMVXb8SrmTuHFGmGUh6v1GiWaZJk+Ufh8nBiRbGzuk9cB1NsxyKq2X8RSsKUpTypilWaQYqBH0K6/VAQex0L4xodMtt6z/jeXL8Vwey7V/SBDoNhX39Ut3PVOokNUGMhD04a06rWYZE3GMl3FHz61hlh0Zhr3P3/XAi/Bdv/oZePOH3HyWVNoEPxyYTcIflWHkzcQE1Sr4wTBCCJRhuG4Yic8s18h16GeOPESpUmmUYZaHcX2CH7YX7AQXwF4rU1rZ0zLrfxeftzaPr7aOE63dMC7cOACpoLCiXKbrx2e4afl2msS8rAQ/6qesg2X9Pk3wQy9vCqtZDjDLZGxKSeGOdjKr8DlOpYLr+6nRLFtm2WqVC8yyxxgHreMk8fymCX7KTfDTMgzrzTzJlHHHQPjuIUeCWcbZyysxWJ6BWTYJftP4LFPGj5llB0HNcj7gH0wyMwiVBctK2aUz3ITxWe6hZnmvVIZh7dcAAm4YuQyjyhGDPuAFzXJFR6eU20HSxNioQWAa8lmu+o1eynftppChws47ZGs1ziSAudZTBMsju1Jht91hsCyaD7Tmd8QyycelfAnZqW6X//vRczmznJ+344ZZdlkhiicu7sL3v+NB+MAjFxq1LdheVXTDmKaCn1Ku40OmlNEfdlWUxFbeIsEyuc+z/POf//jThd9R94uJlCaRyJFh5NtN4no3idAkA8Cyfkp55a5LEvyqTnEooKYMo99E7EvXkgYl2vNtUp9lgGJicig5LsTGt3nWUFcatZyIjkjOC0WZzzLe20334/gsL2moUeS+ioVlljNyP+WLFw5CzHISTPBTZNLZvF2ODMNbZdjen1hmed36LJeZKOC5dRhjlGEQNwxfyiiV/i5ljmNiHTdBNwyPWfatSZvGfCsZLOMDibqYW4+vweYwhhc6YJZnTfDbH3OCH4V9uIsTioM0Mx2STfDzZp0YeOXMMlb2AXADruv7E/ivH3x85kSuWbFXIsMoMstEsxwJOLWlZ+BVXssOs5x3RuO8Q6iTYdAOcpxZlq+JFZqpPkhKhlbtL5Xe0piwGmXs/CYOm2qfvUhg9bDKJgWxNykGyy6DMD2jqZlD1wu0CajG0MflHX2tQwlS6JCBv0NmGRNnAIpMEF7j0QyrW9rGcXYZBg0eVf7crg26DZZxskzPLZ2k4urLld0xPHN517xPE/wyCaUJfngthnE0tWY5ylk/pQDI/LgQ4OLWq59jcL8MKMMI35P4/UFc76WOH1vNsn7F5xbjDatft7+dVYaBulLcd9Pf4ljhP9flMgzI7TL13+euH8DHz1yq3X6bNnUNdMMA0NcZzzsNon1ZAYDtC1w3jKIMQ1e5a3/N6DBNNct6pVPCpbxvs9ZxpIhQDbOsFJnoBTTL/kqKU+46skVJ8Ngo8wyAE1hyLIeZWcaDw9nL+iCGl53acMz92wIfOE7w6xahJR4qw7AzyHLruEhoD066PON/9/cfvQD/8fcegzMXduZzIA1Rl+BXVsEPB2v/dxT01jTWcamsTfCTSrkyjJS6YdQPpA6z3CDZi3bwuA/sKHFSVOZh6idztgFOVMuWYWeZSCmlnIG2qV64iQyDDsr+ecF+aXOog+V9Yk3pbxLvnVmCUcoII6aTYbhaWu1ljHr1bmQYaYARo4HADWLJ9677rUOKo1nOl4f9srx6u3Zwrtf8qmAlNcgnfgo8NwzPkcFYrlWuEBW/Y6zFAs+M9a+Naq8dbhsDfl9uZT19cb/Fya7eZ/7a0jpuEBPruIa32dhYB7o/KE3w81a8fv7jT8P/7xfvLt3+vJ1nmoDKeyKiWc6kcryvyybO68OiDMO3jrMrNG3aFT43eM6ev7YPm8PYBKpaG42TG3dH+JdrNenGBZmkFfzcSXfijTW4z0l+bInXnwnvWWlKoqxksGwS/PJgeS2J4KUn12eSYczCLDsJfiuoWf6/f+M++J/3vTCXbYfKXVM3DPy8zDoONcvI0NAlE/pd6ju8TOyXaJaLbhiuz7JJbqhhiM1+SPGFcS2zDIUEvzXHDaP6mOhSbCNmOVNOByWEDQjRT5uuCtBLpjXLopWXMQLvAVkyyM0kw5AwpXVc+b4xk78s8KBLj3i9xmTFwmc7cTVjlmAU2X2KqMG9CQC5hWH+HeWy8JlSZFWhm8gjrFm2x05Z+O39CfmdMoGDXfEoVvCjwWat5pcENRQYyOBkCxF791EzGUb+Hec9nMQVV4jw+0mDZxw/xhYabbLnhhFilmev4IfWcahFbQbsQ/xldJfptu+jjh6P4cbBpKZ4DZVhLCdallKZyWoSCUfWSH2W/dYdVMgw9ssS/NpMhksmJNi+56/uwbG1xAmAbZnt4hiPxwegjwX7iCoZBv49oDKMSBiruDR33qCrnADofW5xqJllvCAOs3xyA1643kWCX/uBxpVhrF6w/D/vewF+hzAvXSLUeSL7vj+xFck2BuGBVOWdAgZQ5cEyyhKWGyzv1sgwbFESKsOIGmmBq8qKyorgUinPDSOzmuUmyXQhN4xqZlk6+kytWcaEk4AMg+wfK/jhMbXBPrEiRHSuWW4rw6iYhF8yCX6WXcyk9Q3NFC37mk8yAomRCFuQY8bjnNIN4//8+U/DD77rIbOd2DDSVlsbR6JVH/vIue3SpNdQuWv63O0QZtmdhNj731S5C7lh5P8exqLy+QLA81Z830pRXF9cX7PcJsGPfge3i44zFNSXtq3PMrYPl+gNs5x/7spVikHlTNZxDX87zoqyK3/f7vNvV64AsHx8Vbvc87wMZMoyyNTmEyfvADghc3+3P8mcEs8ANk+G5q9MMmod16JdpcGy3vYL1w7g2HriOFxgX1b0WdavNDeiiWbZX63FbVifZWkmYj6cNh9mzTIeKJ19vPTUOlzaGcE4lfDclT144Oz1Vtu0FmfTtMf+e9VkGGkmYZRKOHNxPvIFfHhCjiGjiTTnPeSSAGCXofBBosHCOLUnHoPUZXsv708lwxBmib+SIQ4cGr3fyn4qlQ4KqEUbdcNoas7fmFmWqrA0NmkowxACSJDVboTCCZMs2fYs8gR7H0JhH9W/Kw+CLnsJflQjq/dBEl0Ck8ky94PZtNkhn2Vw2leG56/umxL2OlhGGYYycgHKjjXBt/3Mp+CnP/Jk8LOQz7LLLOv7YdMrykE1y76W3l2216/IyFWzvkWtN4C1Ziy4YcRusIybrg6WodCOTNmqdP5v8c9GMhJsr3HD0H9jf+oHy075cjrB8GQldUDv3YTIMJoC73c/MHddLNx9RZE9lv1xVtuPmd9O6bP8lo89Bf/9w09M9VsAV4YRR8IWJVHEOk4Uy10fTKTDKgOEfZbRXg2geIy/c/+L8M6SFWfntJF/Y/vO3ziA42uJeSYog11ww8AgmPgsFyr4qaJmGdvtu2EMjAwjbB0XRV6bDzOz7J9sZJaVAji/fQA//sHH4bvf+tmptjnNQLNozfLTl3bhbXc/18m2cOn66Uu7c5EwhJhlKsPAz8uDZdcbk35O/43nvavkoWlR5oZB7dfoK4Cb9FeXqFfcX5g9C/0OWWjqs9yktDQyR23cMOjxhWQYE0eGYbeFnqJVx1MGPPdlHqCza5aLy+d1MHaHgR+gZtm3rRySZDN8L6Rp9zfZDbMMBTcMIURut1Q/qcpI8IdjmFI2CB/EUSvHod1RWmqFZyYZVIYxsRKjGwcpDGK9Tye4I1ZxKJeyMgy7fekNznWOM6FYD1k/paCw2oK/o69V95XVN7ttRBmG3zybONVERqJsg0n7THKvJ8MISS/0v+uPgwK3M4jaV/Abl7lhlDDLeA/i7Y32amX3dZqVP2tN8XufOw+/+8D0q7ZahkHGB8LcUx15iFmmyX0ANlimqy+plDDJSSc/9PnFTzwNP/+xp8LtquljldJJySGHi1KfZTK59vu9VBbLXWN7B2RJRxM61qJy4kkCAcDkP9lzUPxOCIciWF4bRHD7yXUAADi3fQD7E+lUumqCMg/AJgjZos0Tv3nPWfh/fvP+TpwfkJFNpYJnr+yZ93/9U8/CT/9BmNFpg5Q8PAgM8LQbhhcsp8UHiSZWUa1rH2UYxg3D065T/SOA7biSSDhJNVUDcuhy740osxz+Lb69T4JlDMjQB7YKIZ/laZllZArGZTIMEdZ+NsEeuZcRIT3dNJiHdRy6YWDSqh8s0+VIfD7oQOdf77EJlrvVLAPYwjJVSDNJktCKCTtJ7qbSRlOtk3rDO8bjpafhIM3geO4ccuMghfUkLrDZNMEPz9UwwCzjNTYezBUnQEH4vsUgVn9u38d+wFrHua8hWBbafsfKMIorDfhnE80ywreOG5tn333fn3zY9rQbR/G3gyQy/WBTFndUolku09Nmyq3CiX1QWVPLpAZtkClVmbRdB+oqQ8kUnaxodRh+8w7GmeOxDGBJmUIFP1z99Rl6qUrH03IZhv331jBxVqWM3KNwn+b3GCEicJK2RhJx/TGUujQhIiHMcWIp70HIOs45liILH8JKBsv+zb2exCQBRuYZzu1u0LJZT5vfAiwmwQ9v4J0Zig8gKGtDnSR+54EX4bfve37m7QeZ5XwiczCRZsnM+Cx71w0tcrAjrWWWMwX3PHMFPv5EuSXQvDDOnSkAAsyyNys2cgzyoMdCOEuIPkID6W4LZhk7SacoSdTCDSNu5oYhpXI7sMheq1AFP3pYlPlpO0DhPeAMluQyzKZZ9qzj2sowAt0R+iwrpbeH1546NfjaPTqZ9NtgE/ymO068v3zNMkB+bzZglq1UgBYl0Z9pZlmUBr/hNkHpM5EGBvnRRJpqhzujFNYGUcEecSJlqQwjZINmS2NXB7JBckqAKZ7gapbxd/lxgvt3cB8B1hYnN3WaZfp3CB6xTNww9E5tUZJ8vzQIDcgwmj67eP6TaZjlkmV9d7IM5N/2XFGUtXVS0ke1QSZnC5YzEhQ7wbKimuXijXeQFoNlvOcPKLOcKZIz4W4jlaqQqI5wdPPOb+z3j60nTo5HmeMYrtrh8fzE75+Bf/E/HgQAu+KTStvOQoKfR8zg8zqRWuZZsI7znpU0U45rSBlWMlgOMcuU8dL/Nd8eaupC227UHnLjNJFhZFLBmz90BrYPJrXfDWHSYbC8UxIsTzJZYHmnAdVYIWhREnyATOKX17H4jB7teGhRkl1S/vn/e//j8CPvfXTmtrcFnbGXFSXxNcsDr1Rna2aZ6s9qmGXjw0vcMEJZ9D5oRb7pfJYFWUEo91kGAKfTbDtA4fkPLaXXtbkO9j4sbrfudwB2EHnzh87Ad/3KZ2CSSdg+SG1VOzLBp0ymb6GESU162+6+cGDzJ5xNgfsqr0TXJFi2Ay9lllFjmURRK2aZSlF8mOVjGiynknhSp7CWxHkxB8tqKQVEhqHP5zBoHadfrb64vJ0hrTeADj6lAgDl+jDHHrMsG4w/Pgtt9huJoF86/ukHFyEgm4ttjEyw7LJ+If26E5ya81y6KwcpYfaNdVyzn5Zqlt0ESLdt9BkOfT/UNn87bZBJNZPvuQ6K7ZhB4xTf4pFif1yUYYSYZccNwzvzVYG+b8lnfkPG5ONriZO0h5OPgmZZuSTJO+59AV7Mcx8wV4Myy3Hs3s9OUZIICnZ1vhuGLynLpDq8zLL/cKwlsXm4UQjetN43QPmD3xRt3TA+8cRl+JH3Pgrfl8+e2gI7MJrtPS1oRbAnSJKfNvSeXdJAZ8KIvYBm2Vbwq36QHGaZPMi4zUkmYTSRjlxjUdib2OtRcMPwZsX4gNNZL+0MQwhrlknwVMcsTzLtjDHOYCv37Y0aLNFSrViTRMSiZlmYa7UekmE4wbJwOtg22DXlrukgZz+fJfFNKUw+bKtZxlf9j4de3Ib7n79mJi4nctP+VNoEzGFik1r85UjHcs+XYWThZemmwLaGBuGQJtbHJJPO8VJdbiY1s5rE7RL8ZEVfbpeP7Xuj1N7bNw4mhkjB282XuuA5G+YJZqHJFU5oa5NvS+QroNBn2b5vy13nATA53jIoc27d98q8ya1muf6e9ZllI3vzrONC+QT0+vj3ex0mpl+MrHVcw9ujTLNM7/8isyzAT8Qs218TN4z3P3Qevv8d5eP4rMwyTeSjpAN9vkL5BKlUBfmB9VkmY6i0q6H+/ZFKVRjHEFnJuaGrRtQNQxf/KZ/c0NVjClezrN/zJ3+JN9bg8zoxMgxfs1xs86ENlv2gYH0QOT61dPmyCdKSB78p2ib4ba7pC/PY+Rut9wVgB33qIzotMMC4aWsIT1Bmmcw4Z4HJtHWYZVuUpOCzHPBgpGxjqWbZlH/WOqtpA4ZZQCce/vKVP3CF/JanSfCjMpomMoxxps853oOhQdaHNX+30o0qBjyVrhG8ALsKgGxHSOeI7bHtrmxWAfvBBL9i8DMN/IG2LbNsEp+kgklqWZaNIWWW3UAu5IZB739/gDTM8pTPLbY1JBmvuzcBdFupZpkGhFJqOU6bBD8sE51KBXvjFH75rmecYw5pIEcTaTTLk0zBehLnbcclYP068BL8UGIU0mIOkmbSmzJGXoFyZCkAVM6Dx+q+hhC651wZRpFoACiv8EeBn+Ah4HhaXpQkTDA1scCjwHtVOwJhW5r9ttRnueT5l0qfd/86ld3XqZREEhZuwx88fhF+6zNnS9vYRbCMt01CKjE6wTIU2Xjf6x4gXJQky+yEvKhZlhXMsttG+xsSLK8NnIJGJi8sQIhRy1AKXFGTyiYPF4qSUClj5FrHpQEGPvLcQzIpD68Mw2cm1vIOEUBfLJoY0wTOgz9FkGUSQZIIruyO4Z33vdBo2WbaioM4SNzohFnW2/iCW7bg4o2ReT/tSIYRkrcY67jUMstrgWIVAJQ50deXMs+OZpkUJRmnshNWvC2qZBhl5a59vVUTZsnZ56RehoGnfn+SmYAe2be4gQzDVE6Ki9q5EHxmORI2SAm5ntB202vd1q1pd5EyjIbbwu9ZQ349CcXj3xzo65Blts8aElux1H8+KnyWUaIxrRuGWUGYVoaRURmGchjNTLX3WcZTnGYKfv/Ri/B973jQkYrZCn42aB5nErZyzTJATqREwpTnxf5l6LmLDOKoIIOyzFWxFHaxrWHNMibQKnCDaT+AbRJkhuQFyDz6OkwAG3QmDTTXlll2GeQyN4yy5yqUo4Lt/dH3PQbPkSRyvX285yPy3dJmOhiXMZWB4B3fj0Tx/i6XYahavfokKw8o8Xdl7GwTZJK4YTjMMmX7i+csJNNAhtWv4GdWaAPbKFuhdew5yftUunJsncgwCBlQplkOiUrWCLlCXZkA7HWnMgtcvcL9GA9vAv980XL3VVi5YNkfpAZ5hrVjaK2aZ6sDlNvgNG+Tft0axvDo+RvwD3/ts/DIuXLWGPexPWWwizdklwl+JzcGznmgA/os8Cv40QIZlFleSyI4vpbAtT2XLfeXaDAg2BjETidlXD3ydi+DWaY2bn4H6dveYMflVPKLRGW7w8wylWGEf4cD68EkM9d7c4jMcj1jWAj0a34TdMNIK4Jl0m4Bll1uK8PYDyX4kU3MmuAnyEDbWoZhkrN0QIeD0DpZUSlYx8lQBb/weQOwLOm0FfzwfIcYnjiql2GkUjpMqV9kADPVmwbzNOkan6dRYGUJzzEGT8dIsLyWxI5TxBMXdwEA4FU3b+Xb09sdILPsLOEr8xn9O4RyzbL2OPY/Lyv00nSyTOUOKI3yCZois1y6aaJZ1n+boiSGWXbbHQqQafv9fV3aGcN/+cDj8N7PnXPeDzHLTWFlGGFiAqBos6cnFu52ys5LKpUhM8pO3TjV93bZM5dKvYo0bd9DnyO3KIkyxyGg6LOsV/c8zXKggp92wwhPcKpY8TL2np77404FvzoZhggW9VkjkjQjw0BNsidtBND3r5VhyCDD7lvHZVLlFpPVN+BKBcvv+OzzcNmr5oQn0zLL0FqGMatmGS/+VRLoVckxZg1C8ebuRLOct/PExqDAsHcRLOM2bcAmTfLP/iRzEt9edmoDnvfYdpl3FngbYyGSzWEcdMMY5+2epeTvtMA2xJFo4LNc1CxPl+BHZBglv8W39yeZaSOyb1FUH/j5yYntmWXrgGA1y0WWDAA9fdtJHRC7Aeu4roqSKE+G0bR/MTIMI8fwmWVbZtkkOxE2q2gdV84sGyutKY8TY46QG0bdpEon4LgMqe+xmkQ6U731uZN28utO6N1BHicLmOAHYJllZEgfenEbAAC+5I6T+jcpslXIQIeCZSuL8THJJFzeGZnJlI9csuwkPALQAgzuMTSVYVC5i5FheO3z3TAqE/y8j7CpeJ/S0sr+tkLuE/6+8G86uafbH8RR0JauCkaj731fBq4htk0IUbi/q3yWkzgKTkT89pfKFfK2Tcsuo9YfwC1KkilVyyz7LhC2X3G/l3qTTgR6G4fGUvrdsj52a41Yx6nyBD+V38MhzfLWmu0fTfESTD7Fvz0po5baYAyjCkVJCsyy1AWUfAbax8oEy1d3x/Ddb70Xfvte184MNZB4nFk+wNCLdjDJnKUHH2Wz5KbAi/gv/vxr4ev/0O0AUP1w0H3cmMIRo2tmORJ6FugMRET4Pw3ufe4a7I7SArOMs9rTm0PIpDLJBkkk4GWn1gvSFF+zTCvB0fYhY5oaGcb0bZ8WGIie2hgUNMs+sxxyw6izcQsFj5QlqE3wG0sTUFJmuW5wCklIKpnlLFSUJGdSMWGjJMEvIte67RU01nHeABnaT1tIpScWTZwFKKwMyQ5ItMSsqWiYFSUXdcxyWQW/WTXLITlBnbbd91NXQAtvkAp+sWjcPuskQvSOJCI0Wfz5PrHPPe4xy7ooh/77oRe24eTGAO48vaF/M7HBmv/84W9wEA1NRt/66efgT//H34c0kxUV/GyCKMJPusNNV7PXxX9LpazPckH6oF/9ZMIqYBsxoMR7CvupULDcRIaBk5Vdrw4BPgeDmFjH1bYSnLb5z0HI0QTbRPuX0HectuWMo6hIbsV7sEyugPct3mf/5l0Pwc+UVKQMQbud6H+71nFegl9gvz6j6gfPADrIlyX3Hu4rNBFosnp3zHPDoFI05fXRlBADAPjrX/F58IF//LVw09bQ/MbXLPsrP/ozu2KrV+sCCX6eZCmVEgaRKJwvHysTLOMsEgdEDJJxYBG0Y1bKeJcCAPzztz8A/+DXPlu67ZmZ5fw3X/n5N8Hf+1NfAADlM00AVxf9rKfhagL8fRea5Z1RClvDBBKP8ZmFWX7w+evwzW/+GPyXDzxe6DyRCT29OTD7B9A3+R2nNwrBMjJ6fue9MbDMMpbsBsg1yx2x4m2BA8HprWFRhuHpqxIjw3Af9Kr7LzTYqZKOioJOVFDbjcwyXdorgx/ox1HUqoIfdVKo0ywLwjC0YZZxkkTb62+ji6IkfqnVOuDubWUzlS9vWjkRts26YdjBwK9k5WqW3X3hNqe99+1gFGaWq06fGQgJU4qTXGScozxTvalECveHxQUAXD126jFVOPEuapbtffDQC9fhdS89YaQLI8NsilJm2VjHBU7A+e0D2D5IYXechTXLAOYmoDKM2LvHcRm96hw7zDLYY8fVGP+3shBclG8b+xZb7lq/4j2F5yBUlMdOlEj7vH1ZZtkds/C6JhEpStLwMR2XrKSUPf92YiFKv++8n0/6qyaKdcyyH3B++LGL8LEzzWsA0KA4If2ulPR+KgbzIc1yKBikJKK/jdRru799/3c4Kcb9HPc1yxUuJUK4z8fGIIYvuOWY4+vvkzZ+7QIAO3YMchY+DVjH6a+7/UgcCdPvlmFlguXUdIj64mKQjAJwelKNZ2V+FV+8fgAXtg8qtk1ZrvYDTUY6Q7+MatWxAEAh4aEJjAyjI2Z5ay3JtYSUtdE35zT+kj/30acAwC45A1B2M2eW8xkjdp5JrGUYV/cmjrQAOwWrWc6Xr4kMY5dIXiZGhjF9YDQtzLFtDmqLkgTdMGqS7fC2KZsAhwJYev0OJlmBWRbCJj+VoeiGUcxodr/v6uVoXxWWYdjPqU1gm1tvjyY6lg2WMzLLQghSPappwOcGdPiKk35kljOiWR6Q5VLsj0zlw7lax+UBU4AhrVuBoJZWAG5irgLLLLdJ8KPnzCQHkWMbe30LBnbHnGDZ+iynmYRHzt2A173shDkm9MBNchmAkxyK18NjsijweoxTGdQsRznrJ5Vy9PjUg1q/usccAt29DVBsZc1SNwzPl7Zq20azjP0tylRiV4bhMvDFSaq/L5zk+MGyZZYjwiw3u3/LipI4f3tkAu1fzFfKAuG8HwtNRGwb3HvPh39vpmQ1tQlo0BtFvnWc/o7wgj/cT9Ff2MYneA7cYDl8HkPHFpK6YNtwbD9GNMs0FtDftf9WShVcSjBwNcm1JK6z9zPem+4YCoCSFS13KzLLRTY8icXhkWFk5qbMB4/EZZZ9Nwz8N4C+MFWD26zMMl7ESAgTvFfJMOgS9DTMMiZLdaVZ3lyLwV9ax5u5rRTj8s4I3nX/iwCgGR6rWdafo2zgpk03WI6jCO44pZdGX7hmJzY468TbncowsCOnwbVxw1gms7w5LBYlIZWqACzbQ90wmmiBAaB0BhwKZuhb++PMnCvrhlG/PFtglkW1tjpTUGCWEUks8kTGMhmG7TTbBMu+0T7C16ZNC6UUxAKILVGzbWWlwbK+Di6znGuWSYJfgVluYh035XHi+BVyw6iVYXherSbDPR+YsnxA1DKMZu1TZhC2k1/qcmNtKfXf+Mwdz72rAfR5w+fqqUu7MEolvO6lebAcCSsziIVmoGnAh8u8SXmwSZ/zsHWcDmJRhkFL29O2U3auDK5m2b6H0oKQA4W7r4pgGdtrjkW/YmCHE4YqZrks6QuAMMvjMs1y+0lyKbNc0g6p9MS96IYR3j4mfgkoD+BxLC5jln12NpXSqaBXB5ykA+Rly0k/YjTL0MwNA8D2X+t53LQ/Ll+pwj46RPzRYN2Pt77utbfCt3z5y+HO0xtOImt5UigUJjGmaFZkv4+/oeWsAfwxVL8O4ggmef/py0/0SqfbljiKgjIV53eVn/YI+ABgEIoMFcoxaFlF7ItpNnVd9r7ZzwzWcXEkzEWulGGQ/T1zeQoZhvFZ7oZZPramZRiUSTaDU8ug80OPXjQz/kkmC4zTnmGWUYaRMzt5gh+Aa6mHDxI+dNhBbg4TwlZQZlk6+10k9scZCKGTJf2qTdgevE8ts9w8WMZrUzYDDgWwdLCg1nHWZ7mBG4aXdRzHdUG957PsLT8P4si5r5yBVUznhuH4TZcMltOsGtHtoJsDwBQyDOU+B3gdHM1y5gbGdIDwi2gAFCdHZkCeh2Y5aibDcBPPhAkWpVTm2rfXe4c9Wv0Kfjg2bA5jM/CuDywR8Gjua/9aDJaFax3nTwLt4FzBLNecawxkFKjcSxbybXoyDI9hDiHE5iFbGi537e6raoJrmWXLYgIQt5CkQrMcKJdcqln2ZRjE99rIMEpb6aJUs1yy3G8kfYVgObxHXJ6vKshTp1k2CX7Gqaa8hHTw9/kkHcAvSuImXfrN873uETh2IKlHc178CQGeu9A9rtlYvQ08N9i2z7/5GPzQX369Xq0hPst0Eu97c0fC1fQXmGVFKvh5z2NIhpHE2oEpCzHs4N4XqO8+PMxy/lDhTNcyy74MAwoyjLTGuqUrzXIcWaa7Oli2n00T8Bqf5a5kGMOkYC9EA942eP6qDnRR1pGRhxuAShU0s4wMW0yCZeqIoZdoihWlNogMgzLL41SaydG0JUqnxd44g81BDOuDKFDu2g04MUgeeJm8lY4D+UfDsmA5yCy7wbLPLDer4KfN+anfZ5sEP9pnx5G26JlkbmdpvzudG8ZeCbPsd4rTAuVAOEC0lWH45YzDzLIbGEulCHuiGVJXhuHuq6xIQ1Pg/svcMJoxy/bVJOYqOyAdW0tgu2FSM57iVFrNMu07bQU//UU6NpigIMnlFUqZe+TERmKOyfdZllLBtT3tuISHa63jim0cN2CWlcplKZHVBM+a4If/VHnQJALMstFcR+Xtt9vTH+IRYPvG3ooYtj80ZjrPnTds4P3hM8vobuSweg2f+zI3jKqiRG2CZb2EHwUr5NHvAJSvJBv5qMknUC2ZZapZ9ouS6O+gPSFFloWZ5YHHLKMMA3ML3LaXM8tK0cTR/Pv5uaD7pdr8tGSij+w5TfELMcu+ZtlPwKX7S6IIDlL33kUIIQre0EkkSsdVxMoEywXNcj4zQobZnFRlBx2cYdYyy5n7QLUF3iyODKPCfQP3R5PU2sAwy1M4afjYHWWwRbRFNGlO/93ufLx4fR9uOb4GW2uJKTcJQDTLKMPIdU00we+242sQR8JjllWYWR7EkErNWFFmec/TLy8Se+MUNtcSWEviYrDszYJ97TK+VxWE+VZWW17VoRDZSfvQg7FlljFIi0S9ltfPrG4iF3Es8YT722HiMsu+GwZ+u5UMo4FmeTY3DB38+UuAdaCrW3o7+n2fWc6kdXDBTtvJAM/lK5UyjBTtE6djlmk/5qNOhmEmxdJuS+T/Q2Y5igTcdmINLmyPnLY/cPY6/MvffjDgE2zJDjx/oaJEeE7x+NcGsVmaXcuZZWonSuVEJoEt1/0+fmEHvuwH3wefe+G6+f6AXA8fbrBcPC8YxGIoajTBPrPcIMFPBYLAzMgwigETbsvsq3LVyraXHgt1C6Hv01ss5IBRZJbzYLmCWcb9N31Ky8pduzIsNyiLIgHCi3rKbmus/laV3Go0yyVssc8sZ1K2YpYzSUgKovfPpLWUCzyuQfkBQJFZxnhqmEStNMvUus7XLFONMLXapH2mb++JUiJESLPsP79Ws+yOL9iGA5KPQOFPfvBcHRoZBp5oyx7kCX4es6z9Pt2Ll9Ysy3fHLLeTYWjdbfv94bnoRIYxTmFrLXYssdAzFWAKZvnaPrzs5DoM4gjGhFnGmxOD2Zu2fM2yZu1uP7HuMMs468QIyvjTkmIOlFl2g+XF6pb3xhlsDmNYS6KiG4YnZbCuGO0T/FBDSf1kAcoS/Oy/kVneHMZOB9zEDcNhCxoEyzFN8CN9UBLpJLlJJuEH3/UQ3PPMVWcgmlazjPeRz7D52rRpoRQyyxgst5NhUEYIoMgsT7KiZlkq0reIYrDsHw/VRU4DO6Erfla3AuEn8tIBUKp8cBUCbjuxDvuTzCnI9L6Hz8MvfOIZZ8JDt5XmFlAAXl/tlbseUWY5sWQKTkL9JFsRkGE8e2UPlAK4eGNkNctNg+VAtGwnflbDDUAmJHh/KPu9MjjMsvkdkWF4lx0DcH/ZOgT8xMgwBMow9KqSX8GPSprMRJDsv1SzXHDDcM8xylaaoEmwTD+y+m5R+n2Kg0kGG4O4snplnRuG1SznCX6ZqrSx9YHPEUDROo7K2/zW1WmWMU7ZJ4YJRWYZ2x6WYfjVDW0c5K6W6uNw8w18QoM6XtH24VtIjAEU7Tv9oiT6OCNznv0EP19Wg+fqEMkw3Jtu3WeWCd1PlwLxt9WaZXoR2w80QTeMJsHylMwy/qaTBD90wyDLy/Smbtu+F67tw0tPbsAw1wzZJTr9+T5JgtP7t8wOAMDLTq0bKQdAsYOz/rSJ+Zsu7VEfz0U7YuyOdOe6lsSFqk0FNwwjwyAMbEPNMt5jNOsfoJkMY3ecwebQ/k7UJOth252SolFUOan0mWhHsxwJGCQCdscZ/OxHn4Lff/SCw3gJAEfn1hQo7zlGkkoB3AF8Zus44rPcdFtmlcsbUHYLbhiWeQlZx6HjwYguZXpNmFWGIUk/5qOpG4YN4ogbhrIM2a0n1gEAHHei7f2J036Eo1kO5FD4ATr2ueuD2NyvmOAnlSJJtujq4ib4RZEwvvc6Kc9lykK3I2XxQ7wUHr9m2u25LTDLJPgtgz8J1P/Z5Khigp9+TSpkJIUvk3YD6PGWBhFGs0yD0CCz7G4eJ5d+sFyQeYhiNboyjMqC5RKGG4OypuWu98YZbAzjygC+1jrOuze1G0bzYJkGvUlk+2opqxP8Qm4YAHZSgn1MGbMspSr4l1NQGYZPrLnVW+1xlK3gY19Br8rQaOSFXRnyVmd9lyb6WRIRZtmXYYCrz8ZzdWiCZX+Ws+4xyxGZbfhLnxMpK4OQrtwwdClNzS6XCf4BbMexPogqWaCf+oMn4DfvOVt4uLCN3WiWM53gR85fGljqbAKlFLx4/QBedmoDBvlSu79EZ4qS5MzyDcIsA+gg+vr+hGwTB139N/VZBtDZyHsjy9I5zPIMCV3TYJRmsD6Ig44oWi/nZjbrV/sIJrWaZZeFOUay/unnZe/tjzPYG6VwbM3KN6qSV2jbXWa5fkm+WrMcwfW82iWVGgC42rVWCX6oR113y7Z3KcPQCX44qWx2b/mrXEazPKrQLFMZBhmABrHbr+C2n7i4Y5weAKZbUXnv587BRx67CADTyTB8zbKCvByvsARGHAHcngfL57dH5rfoF+/LR3B3lBUOV/DTfxsZRhLBELWZ1DrOm7C6Li1RvrKj/5bSTvCRSAhNKun1CBclsTIMskBmnR/MsbrHEgKdrChlv1uW4FfGxIWA7UMYzXIqHbcBk08QGDNDyZH+d3bHmbsE7k0QWzHLAR07QHmCn7UhdbdTdlr285XCKCpKXBDWDaPEOs5nlqU0WtomoAxyFNly7DrA1N8RAQlOGbOMriZJFEESCdjPV2OGcVSa3xGSjWQOIeL2bSEJXqbcQnEhqRx9foaxHaPiPLExk64fc2h/OBEaxJGJNUIyDHrL4LHUlbtOKj/tEQyzXKJZpjN12+HlFzFTtUyYv59WbfNmPKFl+NCxbA4Tk9HtY2eUwr979yMAAPC+h87BT/6NN9r2EhmG8pZjWrVbKtif6A7BzNYy6cy8m+p+f+9z5+BlpzZgb5zBy05ZGUapG0ZelMT4LJNiHUU2wF0WBLAyjHEmTaB0cmPgMBeLZpZHqYS1JLJSnImEnECHTPnsrMsw43sHk6pgWb/iwHK8EbNs/30wyWBn5DLLsai/51MpvWC52tEgle495GuWB1EEV/Mkqkwqp42RKAYSTYArFltrcWl1qdmYZctyADS/t/y+yLhhILOMwTJ5VgyzTAO8WBQGP9zm97/jQaAl1qc5zh97/+NwZVdfkzJmucnqHPUNFoZZVkZjeduJNQAAOEeYZWRzfYKBrgyaBL8KZpkm+OEASa3j/GVbuuyLzDIiU6q9DCPQDaMGlzox0P4Muzrccp2uGI+FrqDGUVhOhX81so5TLjOOt8AolU6wYZfFi6ux7nNXDN7wdZTKQnEiyyxPo1l236dBu6/zDskwys7LPsowoLxN1me5RoYxkaCUrdyJpbTrIBVNWrPMMloxmmMo7LfEDSOx24ojAQdjO8HcPghfv6AMQykjdcLLHioSQu89+uy6mmVwkvixPXQbUinIpD4XdnUDn2e68mHH1e19PSaEKvhRZnmSSYgb+CyvTLBsMjONz3JYs5zJYlLNRFo9YAihWXIbUJ9lAIBhIMGLwkgJBnFpMg5NEHzxultQhZZ53RtnTsWqNkDJAmWWU6kgainDUErBm371s/DKmzcBADSzHEfOjBSfvYOJNgnH5eddj1n2l/hxZo0PCPVZBtAdJrJ0JzcGU2uWz1zYgev7E/ijrzjd+Dc+xqmE4+tJ0BHFn+njDH/gaHurC4QUmGXvuocGWjoQoGZ5y2OWW2uWawLsTCqIKxL8BomAq7uWWXZkGE4g0fxZtJOopHywnCFYRu3ggMiVmsDX8xXcMIblbhg0WVlrvf3lY8i3lcHBJLMV/FowV4iDNCPPYvHz+gp+NkgGcDXLCqyO/dbjyCwTGUYeLPsTczz2SRZmlnHC4i8Xrw1im/VPrOOKzLLdly53bf9GmYP+rHylg8piyt0wcmYZ9P8JsuRctI6rmiwr51gu7YzMPkLMv/SCiarrp1cCikHOKJUlCVv2t/61Dx0H7Yt3R2kxWDaa5fqVLn+b/theFrTj+fNjyLLzomUYSWUfWSXDoO2gZaUBAA5SCccaBctWs0xletr33U4w/Gi5VLNMJEiDODLOHMMkcrZBn7PQKrlSyhbrwWczc+83AMoCl5OSB5MM1pLYZZa9YDnNlHFkMsEy7i+Y4BcRiWe1dRwyy1/32tvgFwpHarEyMgw8uShJKGiWic7RDkxW01TNipCLOI3PstcJryVRdQU/lGEMyzXL9OHzb9aUPAizJPnh4Eg1y5lUQV1gFfbGGYwzCY+d3wEAgJeeXIdBLJyEHZvZru148LrtGGaZzJ49ZpDKFwoJfjmzPEwi2BjGTrJfm4nPj7z3EfgX/+OBxt8PYewzy2R1QZfdJAFk/tDTB10fe/n5xv4aB68mCX70FIQ0y3qJsfq4/LbXJfgVNcv2M+yk0Z7Ld6qhy6RtYlta2bEswWdWZjmizPKUwTL+DDtyfA5oMIcyDKlctib2GBKqg76+PzH9xDRFSUYTaSbPwQp+UfXkpSDDULYIB/bJcaQnByfWE0ezbGQYBWY5P05SlMQGyKpQwQ373HViHbc+0JZwmSomCDkyjEg4iUmUdEm8oIDClWEUzwvKCqRUhmmPBBgaF7doViAqJ8uWFPp3734Yvurff9AcR7jctX7FmKw6H8Jlls1K3sTVLEfm2SwGPo5W2Gd7yb4poeHf85pZbnb/lhUlccgWJ+nQTSC274f3t58nQ1dV8KvyWabtOJhkzljaVLecScsg07FRy/ryYBlEgFlWQWYZz3OSr1RhrscgjtxrSuKgoBuGpG4YuE+c+NBxDvLvuHEFvR+u7U3g9OYg6IYBYJll9Go39oXKfZ71d/XrqY0BXN4dFdoDkN/bZFKf5hP5b/tjrygcp/O7yk97BGsdV8cs0wp++rdUOxsC/awu2Sn4e+yU8jasDaplGDbBL2oWLHvfmWTSyBhuzJDkhwP2lscsOyVlG7h1UI0xAMAdObPsBstgtp9EwmjODZsVI7MsnP3b5Utsj3v9J5nSbOlQs0l7IxqgNmfYLtwYOR3Yv3/3w/BDv/tI498D6E5lLaGaZXep0s+W943QNXNQvn0crIf5sbdO8BtrFt5llutlGNQmCKCBG0bBZ5kGJREMosjIEOhyMoBdugdo54aB98X6wA+W6aA+vYYdGR6cqDS9t3zPd6Pd92QYuo/K9YNEhmHyIYRwViEAbPCaSQVX98a1RUk+9OgF+JJ/9V4jt6CgzFeogl9d1caiDMM+t0q5y8a3n1wPapb9vpDqvTH/YCLdAJl+b5RK46pjfZa1ZtmRtHjBcpLnmtAxlZIueM3DmmXbZ5SWCVeKuE1AXpwEo+U8+PeOJQSpbAD0oUcumPfjfAnbn8ygAwdNfi+DAjfYN1admQxqUB2GHyeCJc+d/31K8OAqCC2n3eS5p9fT74vKLOz0hKU4qQn1ZUopK8OoaFOVz7IvZfCD5yZQZIKEri5KaemaU5TEO04t2SmGd4kZZyMYxMKMvxtD1w2DymzK3DBMURJfsxzo+7XPMr1n7Dav7U/g5MbQGSd8GQZWYU4iYWZ1ZkU/oKm/aWvolFKnwDwC3S4otLkMKxMs23LXVmMDUNQs02CZ2g3NU7NsLxrkbauWYaAGdJjEpdpHPM6NQVxkljMFpzZdn+K2ePtnzsJf+8lPAID266Wa5bIa7mWgwfIgFnDzsTXNLI+LzDIavQ9iPZD6bhhhZtn1WR7EwgQUk0zCzkEKm8NEL73QAiUtguUru2PnPvj001fgM89ebfx73N8wiawMY0LPY3Gmf2wtcQJenYhUIRfKm4fJS8c9ZrkqwW+YRLA/TnN7O7LPgNbRRyYDzHLFbzLlseiUWRbC6OZw226wHGav6jDJjeX9QJ5uo0vNctN+wrKj7oCy68kw9IqOy7L50oGCZpkEywf0XivpU77j5z8NNw5SePryLjx7eQ/OXNgxn1HZV2CMrWTX6D7x9sXnFn+HrBAAwG0n1oOaZb/PDLkZWVcMOqHOjyHNzLhgyvoOIqP1RDbMJkzp3+FgSs8vncQZi6zAo0n7mNB4a54V0o8JO94TRlk5xxKCIgHSzcfWyH4FhKQCyus767ZN/QjwWCaZKkzSANzA2E/i9v8N4I4jdPUPV0Fo0ZMm8qtxCUsJAB7ZQtqs7PWn1yq0O5w8bgzj4ERE/84+s2VJcHZ7mcPWHjT0WqYMMiaqYlPwGAS4KgzjElHhs4x95eV84nx6cxBcLdBtD8kwikVJ8FwEi5JI5dwD9Fm6vjeGU5tusrrPLGOxuSiyd6nJFRPF/aE1rd8egDypPf93iA0vw8oEy0VmOXJeTalMpQw7hxckzVTlwBvyjGyDcIJfVbCMvn6iQrOs3z+2ngRkGJRZnq4wyYPPb8PVvTF88e3H4bUvPeEyy+QcNJFhoPXT+iCCl53agChnTOk5oGW0k1h3WOtEs02LdVDG4Pmr+7C1FpsHZJzJ3ObFMh9PXd6Fl9+0AYmf0dtCUnN5Z+y5gFRLd0IYpxKGcViGgR01xS99x1fC//nVrzJ/15WR9jWIRWY58KN8c3ee2oDdcQYXbhw4xUxEYJD1QWU/AM2s4+KAbg0ACokUvkRK76aeCfMxTvVExZ9s0U1MI7Gy23E1y00TXwsyjPx1b5w5GdhUjoIrE36A598/eL3pIBRHIugC88K1fUdP/IO/8xB872/dbz6nz2qZG0ZVEEO1lPRV5P+mqxO3Hl83MgyllPFc9vsaGiAbGUb+Hj1GmuCHz56VYcTGRaBwH5NkIP+4aaK4tV4rHn9dBT9te2WZWwF2EkHPk/Vbrn7+sZ9+yTEbDOBqjH/ZTUIbyjAqmWXl6DDoeQq6GwSY5TL5E4DbF++M3LySQWzPR1Nm2c8HoQgVzMD3sf3+tfaBRM/mMC7VUdM+IDSO+44S9J5tLMMgKzI4PvhBon/OfFkoxYCs4CaRHaNPbQyDAbdue7OiJHYlxl0t1d+HSmb51MagnFnOSSTcp6+bj4mOGdtEg+Vigh+VhbqTtSqsTLBsdGkkiYO+AthZvM8sT2R1URKXWW6/VIttw4utNcvVFfwGkV5WrZNhHF9LnAcRZ7MnN3SwTPVfbZBKCSc2BvCe7/6T8LJTG6Wa5TYyjB/5K2+A//ItXwYAmh1zmWX9OpHSBHuU4TRuGES3+4GHz8O57QP4S192h6NZTmLrZ30wyeCxczfgi247bhhXeoxNoF0iUuf747RauhPCKJWwNqDBssv2+Q/kH77jpDsDrmHvcFkVB6+CZjkow9Cvr33pCQDQHfymx2bXHWaWeU4eQgd9P/LeR+Dv/MKni9+XFT7LQjjbKrphWNanrQxjmFh9KsIfLKeFtY6zKzBNfwdQ1HXujVMYxJG1ossCmmXpBnh0EEoiy8DR6741DK9Wve3u58y/R6mEGwcTsyqVSeUxpOGgr+p58KvpUVZTexbb7d52Yg0u3BiBlHqpG7frkwJ4uWhRElNZNGChhzIoAHsO15LISEgyzyEA2xNilqlm2bhhkPvnvueuwSjNat0wknwJWSkFAsAkK5t7HI/VVPBTcObCDXj/Q+cL25IkcHrJlmWWY1EsxqO/70o+Kiefbqzs3AMhzXIWGDPp7VEIYMnfrmORdPoDnyUtAz3v/tgupQqWKEfrOH0cNcHyxK7sRoFzC+BO7kLMsp/gR5/LKjKNwilKIqwTCgCVYbjuDoYtDQbLdpylk6CTm4PSAk7lRUncc4z7DdmGUtkM3f4ozWBvnMGpzYGzooXPMW5PP4+uHzO9JhGZOADYOg76WH0ZhiB9C04s6kPhlQmWU++BRK3fOg2WhXA6ORSFK2WTLEIwjE4STWU3Rn2WAXQAX/UwoN5nkIhShgonBcfXE2dgwLYezz122xicU0y84K1Ms9wk4ERm6A13noI3vPwUAOiHki630ZkcPmSvyt0zAOxDRVnLX/zEM/DSk+vwda+9zdEsJ5GAV968BQBau7c7zuALbz9eeCiasn+o4fQZ9amZ5QG6Ybg+y36Clg9azjQEXNrGJbQNcu/rz0PBsn7vdS87Yd6jzHITzXIZs3zmwo6zlA+AKzvVPsvDKhkGFG21mmCcKVOFrcw6bhqJFd3OVAl+RpaAga3+e2+UwYCUWM2k1SxTSyYa4PlsXyixamstMbpGiicu7pp/j1MJo/w//JsixEjVrUD4S/HWpqvI3tx2Yh1SqeDK3tjJuShjlqWyz3LIFQMfc21J5sswYpML4N/HGGxQtg1BZRh08gIAcG1vDH/pv30MfvveF2p9lq3VGyY84oDvBhpUvvKWjz0N/zyQbCyVPYd4nHgcQRkGeJrlintWgadZdu61ojWXtdWzOSZ0//79R68XDZYnZDzA/TZ5TunkLqSPNk4NXpuMdIEca2h3e6RoUJkEyQmWA5plh51NM1cH3CLBzy9KgpuxCX5tmGV8PiLz+dYwLvgsN5Fh+JaEIZaWss+hBD8k2k5uDh0ZUFGGIU1fiNeOupDRCQWAyyz7EgtB2hzSWZdhZYJl/wH6otuPwz/5X78I/tQX3WLeiyIbIAPoi+eUWCzp7PEiryXV/rGlbVP2ouF2qq3jpEnuKmOWsRM+vj5wOgYcNHAJftpg2Z/RU80yfajL2ve9v3U//NJdzwCAveFPbFjGcpBEji6Lzj5xX6+57bjZN/VHzKSC7YMJfPTMJfirb3w5JHHkJZxouccdpzbgN+85CwAAX3TbcRNk2GNsdi0v74wL3x9PGyx7PsumLVIFE6cocMm2DBiAJLnUw09cqErwu+XYmtE5+m4Y9ZplN8knjnRHNclUYUJSleSB7zsyDOXKMKhNYGsZRmz1qYjuNMuYHCQMW9j0d3Tf+PfuODWyEfzcZ5bRyJ8OlogksoMblZegjaR/Xfaplj+V5j+AYh8Suk3rViAm5vj03xh8CSHMZxiAYRv3x5kjIyvzWaZtxGeUfhcHvtFEGkZqQJjlJLKOHCEtPfaDDtsoiXuGmdDgtctAKoCru2PnPIfOW5LLytCaLRLWQo62nTKDB5Ni36O8MYbeyziJ868Psvm+a0EImvl2V4AQg8A5o6x7WLPsbp+uxLjBsnT6g9ObQ+PBXoWxt2pHIZUy18xhS6eSYSSlDh1jJ1gOMMtkuz6zfFBhAOBug7DhkWZEse8xl0WUaJYDASCeF2pFeWJjUEgSdAP9ALOs7Goj/sz0/c5KhJ1chSr4YXEqLcOw2x/GXrCsNNEQR0U3jCiykzhT3MyRYbjjZETO16HWLCOSSMB3/elXwwlSxQyXKailkrtcZP99eWcEHz9zyXl/bRDP6LOs/25SlASlBLUyjPUEJpntuDH4xyX4pokCZW1A0EGbSi9CRVOUUvDO+16An/voUwBgNcvHybXw9dj4IE4yW1P+C289ZtqCQM0ymqVjEQPs2yapMp33G195Gq7mD9trbjte0CY1reCHFjOu/KRdsIzJWGtJHJRh+GxrCHj/lkGzU7qTG5JgucoHFt8SAuC1L9WTk4LPcktmOYkiyHKmoJBcg6xGwD5I/9uVYaQyJMPIB7rKVrkYZ1qvSqtcAUDp0mJbUBkB1dXXwTCt0vZJ+n309sVJqjLtxvsHAza/gA0ytn4gDmBXDfxgfm+cwYm8zxhn0rDLAMXBMDSpi6JqZhJZceoXjMlsGChZOYl+nWQSru+XJ+T6wQY9rlCVxoM0M3pv32cZi76EtPRhZtky1r5PsSndnFtm+tujiKMIskxZKz1wZRh0f/ZYs6CkAsDeAzTwKCt37bs/VLphKLdN9N90nDAV/AyzbFcCy8ZaAI9ZptZxZDwAALjl+BpcvDGCOowDq630b5OUST6Syk7YnGsduK+ND/oghrIqp45muYF1HH0mm47bUtoAH8doP5HOLRRNmeVieIdBaEz64ZMbgwJ77icn+qDxg98PhYgSGuTT717LY4dTmwNnsrk28IJlKXMZElQyy8YNw5FheMwyuZ5HglkOBR64hGOWPJXLfNGb9xc/8Qx8289+EnZHqXl/FmY5EvZCDWt8lieZrUVO23fu+oFhUKgMA4CU9sy/f3x9NmZ5It1OimqWHe1uIJi/MdKuCk9d2oUnLupiHsfXE+eaDEtYz5RYEX1hzixTYMCIbNTAY30mpPLRl7/yJgDQvs4nNwaFrO22zDK99pNMttK4Ymc5TMplGL5MxEdUE4ThMuLXvOYW+OYvvcPIGdCGr4pZjoSAL75dn++iG0b1sRUYufw5G6fFXIBw5+N29lSGQRk8AGg8uPsYp5mRYbjMsv3OLBUdaXGAQdxMroUWTwBhx4ABsThzmOWEPov23NOCAnRFgG4TJ9EFZnmSGQedcSphnElzf/qDoW9xCGAt0MqA+7MWeTbxzAzunkZ4kqkaZtn+G/s53Bad2GJ3NQok+K0lEWBBFW1p6B4T/S4NdqkvM96v1tFHv257lpmh8TaOIGeWUT8Muce6u016aieZLDyT+D08hy6zDOYY3d+4k896GYb7jCNC1lt4/w9IxVV6DP69UqZZnmTuqtUtx9oFy8M4KkwM3WCZ9AWyuQzDaJaHcYF1NW0n92vQOs5jZ6exjqP9Dga/eO87kzPazzVllmPLLONXjaSiwjoOv4Pb9yeRIQkedfvBvwG0xzKADdgRDrOMJKhEn+V8G2Zsc4kMAICbjpUzywKKCX6HTLPs3qyhDt2aV9vfOLNdcrGu7I5BKoDHL+wYVmQtKT54TYDLAwi0jtsfZ0FrN1zWRskBPlT/23/9KPz3Dz8BAMQNY02ztTZYtol/AOB4GbdBmvkljG0n7GiWA8HyeVJR8AMPn4ft/YnD8AMUb1DzQJGg8TWhYDkPxPwHD5+jUWo1nBgs43aoJRlA8wp+yCxTrWdbN4wRqYR0amMAwziCh1+8YT732dkQkqg6IEG27i+8/qXwr/7iH7IBwcAGV8Xf6NcoAvji27VumTLLQtQnvhVdBGwiqJ8Qi88p7cjxp1Yj6jPL7sBfcApogEmmYJhEeca4fb/KzqoN8NwD1Ce7IVRgAKMD6CCmVnTSSJTo0iVdkbDPgshlES6jAwCwlU+E/Od2P0+iAQjJMNzvlpW7rjpkax2Xf0nZiQ/eI7S4AoB+Pl3NcnmAZWUY0vwWEUrwS6IIhkmkV2JiQSYeVOurXxPDttl9S2lXKPE32B7ct+8v7zN8epuRsY5DWYoAm0xn7xGXpSwyyy6bSMcp62ft/gY1y81kGM0S/PAzh1nGiVKA7UfgPbo+iNwqqzLALO80CJYzG8z6xxVKPsM2hWQYoX6GumGU3fuuZjmcBGc+n0yX4EddJ/A04XNLA38qE8k85pmCJrPi5yfWrROFP7nHtoeOq6BZRoIrUFEPZVCUCAAAU5zqFPFZRqkh3YYJlo2OiV5b22fiZBJ12AABzbIoJvgdLmbZ6/yDS4X5Q2zYDenWI6dBAXZ0j52/QRL84lYBEkKRh1BvJ4JxmsE//x8PwLf/3KcK359ItI7LZ4pSextfvDGC56/uA4ArwwCws1hkXIdJBMM4mlqGUZrg5/ksh5Lk0CN1EAt4/8MXYPtgYtw5EH4HS5MA8GG6mcz+/Hbg8fu2TpSJeM2tx+DO0xvwFa88nf/WT/CT8KFHLziuHCFcJkUa8OFpq1m2xVIi2FpL4Ov/8O3w9s+cNfv2db8h1AVhyBQhaOEF/Xnxt1Tr+CdefTN86ctPGWcMAP0c1QWlPiseR5FhCnyGtUqz7DOkAG5Qgt+1LEdlsxygXtz3qnaY5VmKkkgbxA9i0WgiFgrU6bEO4shZWsVJSUQGIRrgmUQ/oZlJbEITzfLeODPPqGaVNbuslCoyy4HblMo+QrAyDHu8yPgYaQ4y88Qjfdthlt12uJplme/HZXfXkogEyzbBb5gIWE8sY6wHW48gqJRh2ElcYbk533chWA6cN7QylArdMDy215wv+xt9Xdzt4N8hn+9r++Nwgp9yn6dW1nHkYPxAIhJUnxpONPW7MTxnJzcGhaIkNLi69fgaXNubVMoYAWh5+7jwXKeSFMzw2mQL0oDzvo89UjQoJHEB8DTLgXG4mOA3DbNM9biRs18rw/An5uU6XOqGMaAyjPxzP/DFtlOYSaQndQnJPzAHRebkCq784PZtgp9tA3XC0G0WhryimmUqf8Xb1b4KOL01yI+1uDJiE66LbHgZViZY9pnloAxDuG4Yvmcwfaiwk37s3A0rwxhU+8eWwdej6gp+Ep65vAv3PHvViNjN93Oj96GzHKk7ELx5ymUY+YMQRbA+iKaWYWTSTaxwmGVyDkIyjHM5s/xnvvhWuPfZa3Bld+wk9wEUWV5q54c3bzB7PH/AsfOx39WfT0hiYhQJeP/3fC38X3/q1QDgZtACALxw7QD+9ls+Db/zwIvhk5ADZRgAulNXSrXWLI+IDAMA4Fu/8vNg+yCFd93/gt6udCdUIdQHy8oZkIvMcug3+lUIAbefXId3fNefgJee3DCfYyBRhaIbhmWW/efF2AcF7KaMZpXcG6n0ZRjFQKIJMMHPP4fY+Tdlg8vgWDg13BYNHowMg1wjZD4Nc5JbStpqpF42PAnq4ig8sOGqgR/M709IsJyzykrpvsefcJf1rU1kGK4bhh4osS14T+AgnUpVySzTczXyyALsB4eJTXQcpTbB70++5hb4S192R348NlkyZGkYlGGQ+9KWH88D9bxh17xEtCrLPQWWaadSI2QEaVA3SYt1AXxmmV7fizdGWnPqPf/YX9RZx33m2ata30zeE6QrLS5j2/t/QGQQbmDqT6IlCKHzWhzrOOkmmt9yXOeoXNqpTvLDe2VjGBf806kftS/DwPPe1DpO+yyH8yfqfJaLCX7TaZbx9MdkDASw969mli2q3TBs4G2Y5Y3ETNDN5E26bafAw8LcIbsai2O2P7kS5vlb8ySD1/cnEAm9Uo7XxB/Hk/wey0ifQrdBJ5/0mNE+LjTZQ4TY8DKsTLDs39ChG0EP4lTLFs7ABCDM8oUd8/4wnl6zTGfiKMO4tjcBpQA+9fQV5/u43GoSXXLfU9ouDBZR3oDMJT6gSayLekzthiHdBD/jJSpdi5eQNvN8zix/9WtugXEm4ZFzNwrMsq9ZxtM6ydz9/qUvuwNeslUU42O2sGUC9Os4dRlatIaiv0XgYEadAEK44jDLNgCcJljGmfNXvuomeMVLNuG9nzsHAG4HXoa6IAyZIgSeY6NZrmCWy/asGcPKZoHvT4vWcaEJRYhZ9jOV6b0hlZvgJwStbtb8/I8zCQOiTzXbJ53hbJplypBHwRUXH7T5IfaNLoniJFUzy2C+G9IsYwAUYquRWfYnMVSGMSISDKpdRgRlGFG1UwtNXATINbD5f0YX6DG5tN/DtlA4wYYnw8DvriWxTfCb2Ap+3/AlL4X/95v+sNmvs4ybw3cZofesUnbyaftGMO0GsMlJiNDjjdvMpDKDOnV8sUy8/c0oC8kw3DbT5+7ijVGQ+bfMshvUUHzwkfPwv/+3j8OvfPLZgh+6OYZAUQe85+iY6cifvPtvkt/HW2uJyyxnynExwmC5TreM9+/GIC70e3S53+kLyDhNjzXUb+4T67ioZPUN27A+CCf0p0QOMUql02e00yzn/SeSa6kbDPtVDys1y7hKRWwrT27Y5Dpf2jWMo0Iugc8g+8yyf7+gk06a2ZUfPOfX9vSqtNbx6+8XguVIGJMDzSyDsw3HOo4cM9rHFa3jiITtKGiWg8GycH2L9QAUzprF5AyXWS4vP10FLMOIwA77Qv7A3/Xk5cKxDGLhLEcWmWUdUGM5XLxh8XgGcTRTsDzJpJMQV6ZZDi03n98ewanNAfyh3LtXZ9rXaZYts0w/+89/7Uvhnu//s6QduZPEBI/Tzp4BsIJfOPTz94n+z+Oaa3qZaOTSzE4WpknwoxUlX3Zyw1zPNKvXLNexvDTZA8AytNgBhRJ48K0yVtswChX79dseR7ot40wWHEdCrEaVDCPNihX8rGa5tEkFGOu42LV1w00P4mj2oiRG41pdlhwRcgeg7+G9nUTarSHLl49piVgqHaCvuIrmbzOkWVZKwd44NRNalGAA6PPWTLNcDMZ+7P2Pw4++7zEAsGyrVHplRimS4GfuCf07XL6d5MwybZdz/qgMIw9GjM8ykWHg17AokA+aGBVKPjLMMvmMFreKvWfELCHnK4Y4+QutlNFtCuc/N4ClZxZZf4ois6xgaxjDl778FLzpz7w6yPxTKQxAeOXpk09dMcdEm1+lWRbCDYx8W0T9b3c/OFE5XgiWpWNN1zZY3hwWpZM0Idlvk3XDsN8PBcK+DCPUdeA4cWwtcWQYDz5/Hb76hz9oyJrNYazLXcvi/VwFTBD29bj47OJpK2eWi88CBqLaDYMEy+D2ubiNzbVizQir5c9/A+5z4Y9zVoahTF0M7D+v7U9M4jHecmtesKx18dIQB0K4zyNdPaG7Rvu4guY+CtjdHSYZhr/UUsZ++MbXoaoxAGAsi85tH8DVnFmcxQ3DTfDTpxU7hU8+5QXLefAxIBqkbZ9ZTjPHS9dYJxHj741BPLVm2Q+AyjXLARnG9gHcdnwdvuCWY+a9es2y3W/VjWmY5UmYWdbMdPi29WekeP7rqq3RJb+JlGbm3kqzTJaFEVtrsel0qdVOGXyPYB+UZQAoapar3TDC22xS3ctn5JIoMveJUm6gHdYsu/uiMgwMSujyKH6/TYKfsY7zlqNpIlLo/EwyCd/44x+BDz92sXL7UtlgKIlsAFj9G3JelCpMSKh+kDLLlD0MyjCEyDt8ZYrAIEKa5VGq3RU2hwkkkXCWwUdpFnDDKB5LKBj72JlL1n4z359S9llHVjM1g7u7sjBJpU4O3kiCNpqONAGLkngJfo5meZIV9I4ANjAaeatSBes4b2ke78vYC7xw39eMZWaSb6+wa7evy88HXUrGQ6THOk6zomY5PzVWoqPg1OYQ3vFdfwJefevxoKuN/tuuVISe8Wcv7wGADvho8+k94C9RU7vJJCI+yyVjLUCeqxJFcHw9caQ3qbfS2DhYNgl+SdCRx2qWyfuqRIYRGB72J1k++Y6C975uAwmWSUD5+IUbcPbqPpy9pvOPNod6nKbEQpVblmlXvkt/BcR3wxDecZbJIeh7urCVPkc6wU9/7ge+W8Ok0D/gvWi1/PpvKhGlQOOFVFpmGeOYa3tjEzuUyzCESQSnDDTuVxCChV5XXLEuWMdRZvmoapbjULAcsI5TSsH2/gS+8DYd7D18bhsAym1o/uGvfRbue+5aadsy6V4kWoL71OYAPvfCtrPciPZnGDSkQc2yHvzXCPtMXzWzHE3vhuEFb5RZpoFASIt1fvsAbsvt2rBzO1EIlt3rY3yiPRN6H9gO7Hzwu3RrZfoi/6HAcx7Sof/3Dz8B3/zmjwGAlmHggJdJBaPMBrhNgcvEdLDeGCYmWG6iWUZLtrIgkQZsAESGgcxy4Hf4Xoj1AmiWKe8Hyyh18Kuq0X873/c6eyrDwOQnw8iTtraZt+r7Shh9KsLo6+KwLd/VvTE8+Pw2fPTx6mCZapZxslAHujs8TgpaTQsT0BzmRHnBMi675gFX5klYAACOBTTLB6R07zCJYOfALVDiD9xhn+ViMIY6QgBalMTm5aM2119tMF7BUq+oHV8baF923zoucIpT04/o1yEhOMqYZbz/xqmEkM+ydcOgAZS1qjKTF+XuG/eLfV9ZBT+EyP+Psr14ruitMQ7KMHxmWToBrRCh599llkP9yjN5sKyUKpdheMEP3nsAOGbm58N57rxgWUqIYwHH1hL3/vPGAyycdOHGAVTByjCKk+BMFasuYptiElT50gOK/XFqVnVLK/jlbTi2njj3Lt4fOCZsDRMYTTLPDaN+3PaJDl+vbu4t776r1iyHmeWiG0Z+fodx0Q3DZ5Y96YZPCuEqGNUs47Fd358YeZg/mUag7A2fx+L5CcswjGY5sDKCVyJUdbAMKxMs+w9EqEP3ZRhZiQwDlyHRFeCFawfGd9Dfz+XdEbzzvhfgozmDEgIV4QO4ywhfeNtxUMrNnMZlIus3amUYe+MMJpk01ahwlmVlGPaGXJtJs+x2UlSzjIGAEGHN8rnrB3B7Xizk1Tm7XNAse7NDM/usYVitG4bHLDsygPBtS71VAcCc0xA7fu+z1+CB56/DziiF/UkGt51Y1+0jVelmZpaHsWHx6NJgGeKaIJH6hALY47VLW8UfWpYvvM2qAQPhJ0ah1AEHi5DcgA6wuI+YyA7o9zNpJxmCdHyhqlllMG4YOdNFi2MA5Mxy4F7eG+n77Ok8aCiDdjOwwV6Te8Nn2vxVA+rCgFp5yixLT7OM930cER2gkWXp71jNsr3n94gN1jCJYMer5ucvCQflBIRNpMdki61YGQYd5IUQhcEd2zrOSYLj6wkMkyKzHFpl8UkDlGFoVw8ZZpbJ8rXrF65fcXCmfYxUdtne9yn2J0onDLNczuQB2OVitI/T+8kJnJbWcX7gECqcIaWrWQ7N7565rMugT6Sb4EePJVQuOOSGQW+P4sRK38fH1wcOcRQah27aGsJ9z12DH/jtB51VEAorw0jM/i/tjODv//I9cHV3XGA9AaxuHEBfi3UTuBW3vzfOYCPvV33mFkGr6dLgF8dMXPXdGGopA5VuNVkRNglsngwrxCwDBILWYLBs+2CjWd4MaJYzZJYDMgxz7V2SpkzSgBKuNFMFN4xrexM4ZSab+vuUbMQ2o0wtiorPDr3H6X37Na+5Gf7c624z1Y4RwaIkFQSeOY7ab/QERZ/l4neEcAMjvygJzcAEAHjZKe0KcG1vbAYpfz8YcFGLIwCAf/s7D8FP/P4TZj9ugp9t3K0580rbMcmXibCTGGduosv1fW2dszaIbLDsDRJJFGkZRkO/Rh++HCKkWd4cxIUBLM0kXNoZwe15cPkFt24BQL0Mgz6EVcU5sB0HnmbZCRJLIj/87mbOCOxUBMvnbxxAJhU8dVEPFlgpcJKFg8A6UJN8xOYwIdZxqjTIR/hldX1IT+6Dx4v3Wyi4oB1KCDZALz9Wv+0odTD3JK3MFPD49DV3NKHHZ5YxmNBtKm1SAdQ6jv4WXykDRoGB5LM1wbIrh2iW4Odbx/lMqanAGOnkQ3wmy9wwqFwAmUS8V/70F90K3/j6l8KpDc2m0PbtkWSlYewyy6MQsxx4vnDAo5hkdgJgfJaVu5oRieJqgynEkulAfWMYwyCQSBRiQq3TkZvg5yfYho5nnPqaZZfppn043peRKK6++BKcE2YZubBrzxVGGPs4X4ZB741JVlwx8JfkU+nmyYSuj22//ZsizaSppjfJpKtZJtv2mT66WoB6Un0MZKz19pXl/f7x9QR2x1a/S61EEbccW4MPPXoRfvETz8DPf/xpCAGv98bQVt2977lr8LsPnoNnr+yRqov0GbTHFQmbDxT0WZ5kZhxByRMAwCPntuE//d6joJTNbfFlGBgUYwCtpQzS5M4I0SzBj8qZANz7WLfLBv70+82YZWtbeWLdFgTxt7GR663ddvnMsn6/TLNsZBiZLBA7VIZhgmWfWc77biSc8HvWDYNI/ci+3/jKm+Cn/uYbC+2JRHFicahkGH5CTRMZRqEoSf7vbS9Y3j5IAWul+4EKBstUZwUA8NMfeQp++D2PwNXdcWmCH4DVYLnyEM1whKzjADBY1jIM/I5hlknlpPVBZMpCt4VfOcnRLJslmKQgw7i4MwKpAG7Ng2VklgvWcSU+y7hcXgZsk2WWi8kzZcw07hMZAatZLnaGF7a1Ju6x87pwyG3H9fFQN5Cp3DAGNFiOYXecGl1pHbNcpx+Wyku8SVxmOZSkZzrckifdsk7VwbI7scKJaH6eAhPSkGbZD5bw+1Ipc94E6fhauWGkEoZxbAIDbAcydkkc1h3uT/Q98syV3cokR3ruQ/2ED12y2P6dZkVm2ZSejV23BqoxpZNLk/2eMylS2pyMr3jVTfBfv/WPmMk1vecLMoyRGyz7zHIw6BNFPT2uCgC4ZAZ21RgU+m4YJsEvkyYxc5hEhb4mxISaCn55XsHaIGocLNOCRvo48XoWZRj4zMZkpQOv+cQL6lHCFZqPOswy2OV/E5zg/jxmGdtA20PbmEpZYJZDDhpCuLIeiudyT3+9fWyhhZEd+cvqEbWOo8yyOzmkmOSJqniudkzytSywejhmHltL4Kc/8qRDJCHwXtkY2AQ/OtnCoMokUAYkDThOlLlhGBkGCHN13vvgefjxD55xkmT9YHniMcubuTQKXZmODZNGwTK2K/aD5cw9FpOcl/+uTA6h37N5Eji2ngxU8MN9Y6AfbJc3iTSxSUi2I7W80xA70p6jjaG7MuOvSg/yuM7mhlkyIT8B1nGpRuqYf92cq0OZ4FfQLJcsFVJGxS9K4hth33HKBkhxfvMUmWX93Z2D8HLQL37imUCCn11GuDUPwuiDjAyS0e5l0hnAbLAckmHkzDK6YTTQPoWgAyB7+WPCahpmeRgXGDT0JEZt2Zd93mmIBMDn3bTpfM8PiDMyk6uSYeADfGB8lvMAgXynbMkE3x8mEQxiYa5d0b9VGU3cYxd0sIzB/ySzA29Vst23v+VT8CuffAYeemEb3vhv3meKyTjM8pquLqVLnUpzjsuPXb+WeX37Psu4L9sBhX8DUK5Z9v01Q0ilctqOUgfTSZIfW5/lALOc78u6B1h2FJ+ZiHR8bRP8BokdAMzydr6JQQ2zfDCRxr0mBN1H6H8nUbEoya988hnjqnJ++wC+5F/+HtxNLCMpC4ywzHIEE6nMkjRN4gpplnEVTLPVbmdvbNmCMgwtd/BLTCOzjMFDaBUi5OObShmUJuA9gMujZtnYb2OmzIpAyKIqNLnBbePxDeMof8b0Ma4PQgl+YWbZ3I+JZRsRWD0xigR5RlxWG4FOQEHNMnkPJw9UCqC8+xTbqfdn38N/W1LDHXNC18dU8CsJlp+8uOP87Tff3nceswwus4z9JL2/Q5KdJBbmXOFKbZp7i1N8/i1bcMepDfiZv/VGuLY3gXfc+wL4cKzjZO6NT+5BdIzBZlgW0p57m+tR2Lwjw6CsfWpYY5sIf2w9ceSLNhC08icAgN1c8nVsPWkkw7B9tz0mADtZi8mxAJBA1yStFcdJKv3Cf4c1y3kMsJbk7iwBQqQgw9CrE351Zcx38JllKZVJzAawY3wwwS+3jnOTY+01xUNtEPPmGnRL3uH5qEMnwbIQ4uuFEI8KIc4IIb438PmaEOKt+eefFEK8su0+CprlEmbZD45Dbhj4oJ7eHJoHJomjGmbZnd3iDPmXP/lMQT9Wxyxr67gGMozE/Y7ejh0c15NZrOM8n2VSKADP4fqgmMjkM6hvePkp+Oz3/zl49a1u6eqycte0qEgIvmbZr+AHUFwWtPu0M9MksgyaH9hc3Rub8/j4eT1goAwjldJhlssCtruevAyfeeYaPHp+Gy7tjA1DTR/0zbxj2MuXHetmvdi5lbGWymeWfc1yUIahX8v2bFjcSmbZ81n2jiOUF+AGCdhB47Om/z62lpiiJOuGWW5fwQ8HyrU4Kkw4rM9yVFidAgCn9O7TuX4zhEzZ1SNfs3zxxgj+xf94EN51vy5+c+76AYwzCWcJc0f1vQhM8NX+2tIyyyS4ccvdWgYUK/iVSRwo+7mXM1ohGcY4l0IMk4gsO4eIiFDSliok+OGx4m+EoBX88jYSK7dxZpll/zkNBsv5c7uduxmd2BhovfKkmQwjtOJhmWX7vlR5Qpgji8EB1m3XCS+bP7RvAMvyhlwMnGAZZQ0BphZ/6ycMh2QY2F9YzbXbticvuve733pasZKCJvhRl5lQcI+wmmU9buK4Sp0rEN/3F14H7/nur4Evf+VNAADGrYpilFf+G8Q24KUMqH6O7DnB9uD1+NI7T8EbXn4KAMplGKEEP5O8l2Y2wW/NVsYEsBM5W2VQHzPqr7fWig4TIeAKOHXN0W3AnKIikQRQnbRGS0BjwLw+iCo1y/TYAOy96if4oZe2j0ggcelqlo1X+sBK8ACKzzAWJcGcDtyD67OcxwkNgl49ScXjxTYuIFgWQsQA8GYA+AYAeB0A/HUhxOu8r30HAFxVSr0aAP4zAPxw2/00dcMYO0vCMjiQmxKLGwMj/jaaZa/DxiDWl2Fg53x5Z2QsTRB0KR5LOjv+zxkWJbEyjO2D1BzT9b2JTvAbWDcMW5QEtbwRbAxnsI6TYZ/lTCoY5zqyQcDOySTWkA7u5KarV8b2UdCHsEqG4bth4MNHb+Yvuv148YdgO4JB7rdrmU/3GM5vWwYRg1zUYKfS1bmHYshM6qpnNw4mZtC+bOwHLbO1uWY7ST9JLgQ8LWWBq++zHEcCbtoawi3H14yXpQ/lDbKFfXqsWQh+BT9/ZSDkOBMOStyA7uTGAGTOUFO2uW0Fv1RqX99hbh0HAIUBfBhHQTkOLYX+7OU9GKcS/slv3Adnr7oaZkkmO0nOBB9MMtgbp+aZwMkZOtSMvQlymRuG7ndsMh/1vqbnnnqOx5GVCuhtuBORUGndzWEMa0kEN6gMY5LlycSRmXQFk6ejogwD2R6AsBQHC9OmHnszIFIRZJZDmuUws6zfe/7aHpza1P13Jm3Jbj85iB7POCtzw9CvLz25AXec2oBBHJky7E6wif2J1yceX6uwjgskx+E7kRC2gl8gmTUULNPVyKJDjSr8nuqj/et31atAWGCWhfu82u9ZFrvcZ9ljlo1mWY8VdtWv6Js/TPT34sitAEmBCfDUWWXsBcs0yPVZ2h/9a18K//DPvCbYVgDdL2yaYJloXPO2jEmREXSgwZjAJvi5zDIm1m6tNWOWf+1Tz4IQAF/7hbcAgA0Ey9hQPIoqzTJ1fnn9nSfhT77mFkeqE9Is62MJECJR8TchIiwWaB3nyjDw/Kwn9jwDlMkwSLlrr4+PyJjRJOjVRVzwr3wbDSLhLpjlrwCAM0qpJ5VSYwD4dQD4Ju873wQAv5D/+zcB4H8RZevCJSj4LAdnML51nD+Q688wwKHBchvNcpbPipJ8eWGcukEMBkzH1xOzlOMzy0ksHL/RGwepCdiCMozMfRC1G8YM1nGZu7ROZ61pzv4O4qhQ0MMksgUYHAo/ILZJAEWNGoVhlkvKXQMAvPEVp8O/JcEy7eB9Jug8sSU6e3UfNgax6cRxAEf4gTaADYZuHKSm08cleNcNIzHfl1IFOy8KmrwTglTFzuA93/018De+6hVBTSn+BqC8EzEdT0Ww7GuW/W2F8gJCiVT4zH7VF7wE/u7Xfj684eWnDDtpZRj2d001y3QCGZNAk26jTLO8S5whnr68C89d3YPfuOcs3PXkFed7lOFNcib4+9/xIPz9X/6MOWZkcE2wTO4jZIkp6OQO8yssiyxyKYANJsxnQpjgyNfcJYS1RdACC75mWVfw08ujODCGbtOQfRZ1+JgESAk98bH3s28hOEZmOUFm2QuwAvEEBitnr+7Dnac3DAt8UMEsR6ZPyRzXIt+q6tv/+CvhA//4a43EBS1BfZ/iMmY59Ij5z0EU2e8JUUxEpaC3i/Ke4zTzEvyi4vVBZtkGNe4X9saZYQ4BrPbVtle/hqy3qHWcUmCs9hD+xB2r1hrNMnEJquoXByXVMg/SDNYH9Hl3nze0E/NdcVw23v7Wx94kdWRJdvxCZtnVLON79DuYfL9hZBj6mI+vJbXyyd1RCr9817Pwv77udnjlzTqJ3j43NkgEKCb4NXXD+Gtf/nnws9/+5c62/MS3kNOSXVXwJ5FhQkgILHftyjD8VWrhPY+IJE8ilUoTFrgHKjE0UpUmDLFzX+RtLF17Jb+r/UY97gCA58jfZ/P3gt9RSqUAcB0AXuJvSAjxnUKIu4UQd1+86PqeNtEsF32WpfM7n1k+sTEwVktxJCAOeLGGZBg4I8JO8mCSOQ8hdtinN4eGRXFYJl+zLDVLeefpDdM+lGH4CX4mMIgiWE9ibTPUlIIj8Csnodl3lrNZGMz7LErTYLlQ7jqXNEwCGjUK44bhWcfRy/2H7zgZ/K2RYcSR89D6zMSFbdfD8yXHhg5jQ78f7EjzoOjGaGLuDyxsQgfrTdJJ1mm1ASw7WJ7gpwoD8q3H12EtifNyxOXsVNkpx+eoKi7V7KY9Lr9DpBOKUHKJzyyfWB/AP/uG18J6Eht5wrpZirODWF2sPMkk/Pkf+wi86z4tf9DyGzf4V8oWlghNQpBZvvnYEJ65vBfU4GJFOspEppmCF67vw/ntA7Ndo38mDgMIfZzuvgvlrmkyH8ossrBmWQ9ARXupgbmP7bH61nG+PnaUl4k2wUFw1S7snYv7p4OpCY4FFiVR5pjocaeZgkkq88ltwGc5cANg4HT26j7ceWozDxLtwBvULBNmOQmsphmpVyRgfRCbc69lGFCUYfia5Y3m1nECLDNGGa6QFIAev7/0PZESaHdCAwD6e7pS408+9sapIQmwfRT+PUX3RTXLAMWVk5AMYxALOObJMFCSWIYkLq72AhBm2RAM0pFhJJGdUNL2+Bpy/VmoX7CJZwIs84/P9JholjGGsEn4+vWA+CwDWM3y1lrRu9jHex48B9f3J/B3vuZV5j1su58DYJNF3Wexzg2Dosxn2cgmvP4QwE7MqZ47lJcT52PTxJNhWOmUO0n3vdKTWBeBsswytpFOyvEZLuy+ADpJtZPQ+t/1KsFPKfVTSqk3KqXeeMsttzifNXHDiISr1aOaPv23/vf2/gQ2c7siR4YRWMoywTJhY0ywvG6Zw1CC36nNgcMeI1LpW8dpN4ybtoawOYxdN4wSn+VBYq1v/GzVJghpxQZ5giMWDkniYiJTyE84hIH3uSQDezWzrD+z5a5dPRNAeECk38VlXYS//I4yjDtyN5SXbA1Jp6u85fMAszy2zPK2CZZzZjkuBstGs1zLLOM+w1GiUuWFTfQAXz7glib4eR1PCAVmuRAsFxkl2hmXZSrjSo5UytxPglQcq0vwe/7qPjz04jbc88xVAMhlGEROBGDZ+DIHCwwkX/mSLbi4MyJFL8qXHbVJvi4TrZPw9HfRsxknev42ij7LVouIkjHrK26TAjGYw+dGCAFxHhz5LFJighe7733POo5ilBJmuSLBLwrcX6g3B3CfMTqICWGDS8qaR4JolpMIhklccMMITf4wj+Ds1T244/SGYf2MDCPQL+H5mxSS4vLP/QQ2Yc89MrORKDoSISoT/OhzADgZtPuxMowi6OGbSS+5v4syDPf3RrPsMeOIvVxqYAgJb/++W4j/PoBrd+lXrKTA9lrNMib4ycp+MSQFBNDP2NrAlV3RyRYGUDb4s/ckokqCtj9OrYafyE7w2mOCXxLRcdgtZmU1yy6zfGxtUJtr9NnnrsLxtQT+yOedLrQXj9PvT4vMcijBD1ez3N/iX37AjWOtm5OgX/HaUzY6xCzHka14muSrf1IqkpTrM8uez3JeBAoTbpEFpqumuNtG2mMif/LlOZW/q/9KLZ4HgJeTv+/M3wt+RwiRAMBJALgMLVDwWS7p0N2LKp0ZEWWWsYOjwTKWnaUwbhij1HTe+wFm2QmW84t/anPo6JLtsXjWcalmlo+vJ3ByY6CD5XzmTAuXANgZXhJFsJ4PDI+c24aPPq6LpnziictGElCFSYDpjPOAAnXFlTKMioAXIKxZrrK0oW0AKDLLePwvv2mjdp+67eXM8vntA7hpawgvPallLy85tuYEGXXMMjIEVIaBCSc0kMTEjr1xptnZmifSMMslgSsWGQj/NizDqJs5+5n+iDd/6Az8+qeetRZaZAP+5avXLNsgyd+3ZqRoURIodIY+fvR9j8Hf+rlPwTNXtK4Y9eK+kwQeVyT0uQ1plvfGuqzt2iBylpIdf3blBct5gt8ozcxKDICVdOyPLftktiHLE/z0pNSu6OA5M1XkIhtU63bY4DXzAlFa8AOx71nHUYxTCQc5s7w+LNcsh4KxjPosB2QY6JmNtxa9/hgETTKlE/yCzHKhGZBKCVd2x3AwkUSGUW0d5wR3gfvSPydGhqGUOYbTm0Oj8fX7E2RnQ88Y7QaF0P/numGUHyudLBo5kekP6xP8dCEdulITCJbXLDvrB/v+PWWOg/x7QPpN2lf6+8LEbuuGYZnlqlyOQRwuLe9rlrU00gag1/cnZtJD2xOShxUnGUon+A1sn2QkOJi8N8n0KmlsV39HZvVXf9cwy5i7gtZxa/WJ+Q+cvQ5/+I6TXjEuj1km9xGF6ROC1nElfbF3LowMI8AsFzTL+H5JDQUhiKwytvUsDkqY5ZAbhlS5KQFhlqXpZ8rHmBAcZtm81yDIrv1GPT4NAK8RQrxKCDEEgG8BgHd633knAPyt/N9/BQA+qNr4QsF0bhiZct0wqHUcGmHjshD6DqL+CoHMslL2ZseLjA/+fokM49TGgNgkkXblNxUOlljB7/j6wAbL+czZDH7eg6izWPVN9qPvewy++62fhUwq+Js/90n4mY8+VXEm83MRSKxIIr1kOpFWs+x7ik6jWY4j4Zi4+z6MThvQZ9krSoJ67n/2Da+t3admxalmuZjgd+vxNXhJnnx5E2GW0c4KEdYsW2kO1bL7Ewj019wbp7lmufqc+R7BPkIyDEQkyhL88s9LOpGQbu/+s9fgR977KPzYBx4PBr+xd5yuZrmYfGJkGHHgfvOW+yhLUFbB70OPXICPnrkEZy5oJxMMYtYSq2GkzLIQeRnsoAxDl7WNo8jYhQFAcMJEO+RJpjt7WsQHWWqb4EcD7uL+TWZ6hOWuSbU+o/NTZnLiaJYjq6vFcwlAXG08zfIwfyaGXoW7scMs5wxP4DYNJZBNpDT7D02YKIuq22//PYgjc76sZtl91sIV/JRxGbnz9KaZNITKzdv9FgMO+u9ixTHre4337OktGiwrZxubxI+3uG/KLAvnfGgZBo7aoVWh4r/LjkWIogzLMMveBBKxN05hc5CUBqv4u1CCHyIhzxu9Xv6+8FyuGVvP1Hm/DGWl5UeeZtlnli/tjBytMWUh7fHhZ8W26gm8zZfBb+C1H2c6oRAr6WKbAIi9nJ/gl5Msm2tF72KKcSrh4RdvwOvvPOm8b5hlI8PQ7xuf5QKzXDyvxtvdG0j8ALTALDtJ73kM4pE7ExleJYiFMOdmEKHrmCysBpW5YVBHMN/eEX+H92SjBD9RlD81IJYhqf9KNZRSqRDiTQDwXgCIAeDnlFKfE0L8awC4Wyn1TgD4WQD4JSHEGQC4AjqgboUis1z8Dg5i9De0E8eLun1gg2WrWY6cTPJhvoMbI6tVxoDWapZzYf9EQrxpG4SB5OnNQcH6DUDfVAmxvdFZ9RkcX0/ghAmWtQxDCAHDJIIRMsvEZxmXf56+vAtX9yawvT+BSabguSs1pXvzzsCfBWrNtiTMsigEi9iOOmaZfj6I0WexObPsl7u+9cQ6PPXv/3zlDJDKMGhH4d87F24cwO0n1+GmLW0X52qWlTuxCSWFjXCZXRn5BUAxE3/LY5brNMvUFioElBSEUMYsy5rOwB8wlFLwb37nYQAAePH6ATz4/HW9fTr58doQ1CwHGBy/7XEkzGRsjWiWqyr4pZmER8/fgEwq+NAjFwDAWksNiY0VvqqcWU6iKHh+cCkaZRpBGYZhpfTfgzy4HaUZSGn3hcus2D/4+RO4nbUkylciipplDK5Qgx5iltFeTyp77qmtnT5PhFnOJwR4jihGaWaY5Y0KN4zQZIwuvYfkbiCEE0D6y/dIPgxjTCZ2+xqfT1kf6POOwfIdpzbgHiPDcO8jijInFyPD8M4JJstRu8ebNodwJb/PaPA2jCN49a3H4Ju+9GXwRwOJx0XNMn0emjPLIWbUZ0n92xsn135VS8T+OIPTW8NgXgi2DyBUlMT+29EsE6mDv69UKlgf6KAGS16jjKiKRBgkIrgihA4uMekz3WB57LDt1DkBUdbP+AmpevLkruyOJnoFchhHJgcHYw+aBAhg/cv3RtrxamMQG4vWkCTx0XM3YJxJeP2dp5z3TfJuaoNEfRz68yaa5S+87Th8x1e/Cr7qC9yUMd8Nw0/wo328X3odz1/ZxCcSdtUI5a6pLK4GJZGAY2uJsdu1x623eZAn6PqBvd6H+1oFAUV/8yZB9szBst6hejcAvNt77wfIvw8A4K/Osg86U6X2IRSREE6Hm2V2mRLA3gDX9mwy3fE1yiwXZ+CUOcR/W80ykWE4zLK2aLr1xHqwohYySDgzu7KnA3Jklp+9vGfcMAC0TZvPLCeRMJ+/cE2Xbn7+2n7+t/V3DcHongtMn9Yso2OFXip1e5LmzHLk/FsqZZawpnHDAKhfKjHBcsENwx2Ez10/gNfefsLY+mnNsl1OHHvXygf15qXn2g9EaBZ0E81yqEQrhVKqNIFBS2iK79dpls09n3/vwo0RfOqpK/C3/8Qr4S0fexre99D5vG1FNgkR0quGtaFFBg/vr+PrgzwBaFAwnad46tKuuQfvelIruVCGQavA4fFIZXWnZddyYxibRLSQ4wGVFQDYSWU2ya3RULNsCpwU3TAwYQxAD0A0WB7kSUwOsxwJw3T7emStobW/oefWyLaoZpmU7g3JMEap1M49wwrNclSUYdAKqe6ECZeJ3cDKl2Hg5AKLkvirWP71Wh/EsD/O4Plrmgy44/QGxLm2G/uLqgQ/vw34/jCQwCbziQAGTKc2B/BMXhKd9ifD3HLvx77lywr79feHHsvYHOowEraOK/7br9pH2+xPBnFVBVcK/H5ld5zBHaet1M+/7FSjT0EnQHg/IRsLoCeTxdLa9j4+vp7AjYO0cO+GMIiKkygALdO7iQT66FCF0pZLO6O8YI2dNAO4K2xRST/jS16oXIYyy5jbUyha4yf4EQvRJBJGo3uQSjgWGAvvf/4aAECBWcbL4FvH4RE9dn4Hvuet98I3vv6lTvsphkkE3/+NvrMvORcFzXI4fgGwkyg6YQ76LEc2LhvE2khBSmIdlz+zSRzB+7/na+GmraHze+z3RnlRobBm2fabddCaZXDavijN8kKgTdj1v8u0nwU3DKWCg97l3TG8JGcVtzzNst6X3caNg9Sw0Ds5y4wyDHx/f5IVBoXf+vt/HP7mV72ioDlGR4gkEkaGcWVXs5PH1xO4+dganNvWwS/OuOgSpS13HZkbGY/r2ZxRfvG66/bgIy0JWpNIQJYpGKd5+wLep42DZfI5Vtmy5TCbM8t1bKzTfkeGYX/ne1xf2hnBbSfWzEP5kq01x0XATRINBVh2AnV1z648+OeEJvj55WlDaCLDKJsBo8bVR51m2a+Wh8lgX3LHSXj9nSdNsOz403obc5wQzGSOfB+ZNO93SWw70ZMbA3jXP/ga+N/e8FKisSy296EXt+2+8v1eJ+b9Res4m+CH9/3Zq3vmOPfGKWwNE8ssYznlCo3eIGeCR5PM+Y3VLLvMMgZfuEl8bmk1rSyfpCKDr9ksN8OcyjCsvZnbNlrhDUGrkflLnKNM5kvasRm0QhMyX4ahE+3seQ4NpnQQA7D5IXgOcXIxiCMYBMpd+7czMnJnr+7DiTy/A9tVleDnMMtBn2VvhU3Yc4vn86atIVxBGQaZ+Nb1g365ayEIIwgkwS9wr4d8i+lz6DDLUcgaMdfrGwbV/Xx/nMHGICFBV3Eyq4+xOJnwj48WqEniYl9EGeTj6wns5A5B+P0yoPOMj4NJBuueZnmUSrg1ZyU3BrEzGaFL9v7xhRJXAez5pfe+reCXa5YTUeivjHUcVsbMx4EbJlhG7+IMvuet98LbPk3NxLRe+fTmwBB65lx4tpB4HfCQHj9/A568tAtPXtrNz13z8K5Ms4z3N+0Px4YRdiemaDlb3Lb9DRZ/c5hlshp0+8n1wjNFpaghN4xI4IpkM+2xnvy0Z5ZXJljOiEdfGbvmL0XhAITApaIru2O4+bgOlI41YJZflrsmYFICahIxs9dP8APQ9mbIlgHYm406QmAgjct7J9YTuOPUuhn818ygGhE3DGmssHwWBYPl89sHQZ0XgrLTFGY5WFo7p4IbRuMEPzsrT2KtzbMyjCpmGaUpyCw3D5axTcMkcnTR///2/jvuluWq74R/1b3DE08O99ykI92gG6R7r6QryRJCWUKIIIGQwLJBGsYWBpMz9tjjF4cPvALG8ILtYWxAmBkb40R4bTJjPCYYAZJAEiDAQghd6cZzT3jC3rt3zx/Vq2pVdXXv6t75edb3fM7nSbu7q1PVqlW/tRa/Fk/cGGCca1nH2R0rw0hNpz92Bu1w6rhwcEZIa9VLE9wYjDDOJ896Q533nz523XivyVMUgjyR/+y//IlZYdDblAcJZztzTP3zIesIX3n3eXyk0AXz8cy/J6HqbW656/B2vmfs2Tft6jR4ym0758OPXEM3VXhWkXuU0/OWZWkfSmljIisS27/xe/8rfui//Q8A1rOcpsrRLIdkBTbATwcLHozGjnfVz4ZB74rOozxm3prU/F5fFz0Z5sZZouzEmiaX5m8J5S4tt40GE/7M7w9sNTI+EG10kyLATy9pb9V5lpV7P6hf5WXs7d8Kb423D3IuAHoy7XuWSwF+Ac8yyTCoTyaDKCbPMlA2MIHyc0l5YSkbBlBolm8Min5sbDIhTeoH/RUWXqGSeyxD/UPYWGbn5Rl+/uSCJopVgWw3BiNsOwF+7t+pC63LhmEq6I1ttVOqP8DJxmMzJuz0O7h2MKzV1hJ6tTOkWR472TBIhrHd6+C73/og/vW7/pJ5h/i5O575JHxdMm985Ea3yYYx1ONEN03M/TR9BwX4jdzUcXuDDJ00MUU4DoYZfukPHsXvfOwp5/iPXjvELac3AwGX+iu9J3bS5U6G6L1qMnYaaYORrYwLB6LrkAOs/bNdxORwqUtVLBkvMmbiDEbVcQYcJ90jW5nhYxuvjDnxXGHveWwuf2CNjOVRltdq6oCy94xrEAH9Ql/ZHyIb5zhXGEp+NgzA7fivHQxxy6mN4ntPhlF0/tQphfA1y3zWSscjY3l3o2sGAcA+RL2OK8MgQ3Czwlge58CnrlVnxLAZNcqePleznJT0s4Ms0xrQSM0yfzloabi+gp/+ejjKKuU2VXDPcpd5Q7hn+VNFjuWLJzbwwC0nceeFHdx76YSjvXPT/JU7au5Z5oS8TFv91Dw3kzov7iUhvu7fvB/f8Z//AIDN7BAiUQqPXz/Ed/znP8B//r1HzO/5UlV4O/pcMQgwDx3PZ82D+sqe5bLUKVzu2nvenCVR7oELD2IA8OFHruLOC7u4v2gbP61Q6jgKciJvxievHuDqwQhPFHmxSaJAqffI+PMLivD2d4plxcFo7PQxe342DKbvH+d2PzRYGmM5tXppbvRqr/WYfc4Gwvip41I2sOula9ezvBXQLG/3OkXqOO1Zvv/mk3jOLSfCS6meMeZrw0PpA33PMjeWO8yzTAF+Zc+y+wCQIfzEjYFZFaKm7tcE+FVJiOjbUDaMPHcH/zNbPYzGOa4djjDMcmz3O1AqbJz7+yJU8Z8eWh1oRM9pOVsOP3363vUs279zw5AoVfDzXiiaKJqUhF7beV5xB/ajqcbIJo3dtBwfwPOFa83yyBil9ZrlckYmgHmWmTFH1SDf8oJb8azzO+Ap37ie2p6f/hoq4w7YPk+BB/jZd3s40prlkgxj7Mow6N3LxnpcJSfYwVBnogl7/MvPMfUDFDfka83p9lJMTYwkgShX8CtsFJZ2kbDn1XGOS7m0fbhmuZtSgB+XTk1YneGOFybD4OkA+QpKzLma947tdxJrYyxn45x5lqu9a/42fpQ2pVUjr6LJhpEq89LSTSA936WT2oClNGH0sPDOv+rBNDKMkTuodFNtCPbSxBS0OLnZNbl/AduJ84FEBwXoY/meZR7Y90iNbpl7tzlkUAwzplkOyDAmLT3y8yZ95TgPL9H7pMyz3ORl58fsdpRzDO4dpxzLF0/0cfncNn7x61+Biyc2nOVrJxNCRVAYh97R0MC53esYYzmUyocTCvC7djA0ngIy/EKkiTKezcOAoVfVj/gdPV8au/fSCfO5eM1ydTaMUOo43n6C2hrSLP/hJ6/h3ku7uKcoef5M5mHusdRxthhBEeBXSD4+VuhOaVLAA/y099cNpNXn5E4AOizNGfcY7w0y5LnV4vGA1owZE/2ADMMWJSm8x4V3c5jZfOhcz0xLw1b3aZ89v5DD/jAzfYXpU1KtteWe5Tc+9xJ+5qs+PRwP4i3z0+Dp6zT1NXEHMUA7JXh/0y1WXHRbdD9YquDn3X/yjj+9N7DyuaKtdM2rBmsilDqs7Dml7A72GT1dGOdXbgxNYFcoFZ+P78nm2V6UsoN13sKz7J+LP7nMc8oEQ4YQGwuLyR5JkHR7PE+mss4HjuNZNpPTsTk+rSRyuJ6VNMujCOdJN6koSlJ4ln3NMu+D+WpIbQU/r63++84nIiZ4b8g0y74MI3P7UjKWAX3P+syzfDgal+7bwSgLau+NsVw86/5YQO2m6oixxiOdoz4H61kmKaY+b7c/AcqeZT4h8vdN72en0CxnY9tPTvIsO5m1mGc5M2Nb8V5FWrN8RadJNoz1MZZzW+WryogKFUtwl1PHeKwwlim4y8mG4XmWyTgmb29VgB9Qr6PmWmp/Nt1JFR4rvMAnN33Psh3YeFESaqc/I/sYM5Y/UaNbHjKDndNJEmRFvtduqjXVvreH8qJOgi8xJwktKZNuqfrRNAF+o6xW21x3zH6aOOfGnwHuWXaOy7TlA2+C5eMby+eLiVdo4NzspbhayGomeZZtdDEf1OwzXOdZThOFvaH7fAKTNVl+6jheVYlr5kJGBuFolgNLq009y0niDj6cK/sDnN/p49PuPIebTmzgxc88Y/7W6yRmQpI510zhttNbuLI3NNk9aCDbOxwZ3WaWMc3yiPcbudOu1JH42PtDBW1MgF9mvSnca03vNXnlKGUjFSuiazUuVmO4UU3XSimtaQ4FVPr54vcH5QA/Kng0YJ7lOpJigLGDqedZDhYlsStD3LFA14Qmd1REiE8ogPL9J4/8lf2hWRGke6INl/BKlH9t/N+Xsz3oicF4zDXLuv1P7g2KlbfEVESswwlQhs2IAbiywRxlQ4N3PUZGENBc6+/DeZYTVdajAsAe83r6gWKmvcUvysay/T6UDaOTJCX5Wsae7RMbXVxlMow6p0itZrmbusay58hRigdyoXSsKnmK0V6zSYQ1hJlnuViB9Vfn6Lxom01mLHcSG2tEckv/vh0Mx7XGMkmObDYMdzJ0YzAqgmubGMvutaBVrlAMBK2ckbzEarXDmuU0Ucbhs9lNTSKButzoHP78palV1mvnkW1/rAyDB/gdUc2y9SxXGqbe78fFMibfB3lxz3syDEeznJGxrG/whd0+0kTh+oGXZ3nTBqzUPZhc++tLEbppYl6aU1td3HRyg3kqmQyDBR7Rw+Mv1fzFU/vm4fnElX3keY4f/fWPGgOR4IVNONbDVeRZTsoyjMPRuJSrteqcaZ/kBavKwuG3AWjrWbbXtMqz/OjVAyRKZ8DgdLxOl6gK8OMTBprghK7Ldi/F1WLSNemFDGnmecYDvaxaZfTawDLXWCZDr+qYMPsGXBmGUsoEzISMDIJrls1yW6c8qPvvZ0g/CthBO+TVJ0PlodtO4Tf+1mvwjLNhz7K9Znowufui9kT/cpFuzhjLhQyjU2iW/eIDvB3G08baOvL6mL3DzHhehp6xTMaE8fIyeYVexh473uPhWAfR0bNMz3ei9H2jgjH+teymCf7d73wcX/9v3lec48gsmXJdfy9NcDDMjGe5jvJgOjY/azlI+Z3Rhpr+XdlY5qnj0mAgUVmzbI0M8mrRI+VX5+O43lhuTKE4ftlzOi7Oie756S3dXzx1Y1CsvKlgRcTysVmblMJnP3AzPvfBm/WP4CkbA55lLm3xnkF/32TQuenmyIjQPzu6U1bV0ezHu3x+0Kg5FvsgT1VIRmY3LaexpGp3gA3wGzAdaxXdNCmVF8/z3KRW5UH5vrFMVTCB8Aqbr30lrEPLvm9+lpzDYcY0y26f44+Z2pts32F6/68UweF+N3cwDE9ebfGTzJwfh8sw6lZvQ/h9rsnYlZbfy31PXsIn0CFHmFIKN9jzlig9IYk1lqs0yzymQKkGMgzw54LaOHm7tTGWR1lzGQbXUdHPJRkG1yyn9sUDrLF8YrNrghIAVsEvwrMMwMkh6i+bcrnCTr+Dbprg4q72erqeZbu0S+3keX3J0D2308duv4NHruzj/R9/Gn/3Jz+Ib/+ZDzntGbLlYQ5plmnGHPL2DEaTB1ag0E6mymiWtQwjbKQ7beCe5QbRvICePLz9xbfj0+8+72XDGOODn3ga/9sv/BE+efUA51jFPn7uAIwMhQgay4cZzu30zAtG0pnQddnsxWuWfUMP0IYoN/zqPMs0ALoyDP01VDABsF4JX6tLUoG7Lu6Y/RP+eXDN8tP7Q3QShW3mTaGPhypGmu8Dy6P+lR+P3Sp3gJthoRcoSkJ5luk8fuujTwKwS5kkwyBdvc2zXPaUGj2xdx589eXGYGSLkozsxFgb4vozfV+znFC5a2vwKeVWvdLHd6VNvL38nmR5jmsHI/z739GFVPcH47IMo/Ask8Hq5wj38Vc9nPz1eTggkhcL8I3lTpoYpwOvuMmf3SoZRp7bFUF6Vg5HYa+WbrsrUTG/r9DkpoXWdTy2BglppJ+8MSiqnybY6nYaZ8N4+4tvxzs/7Zn6ZwUnhZX/Xrma5XKfHXxnHG90XnizlZMBALCrY9u9jk0d57W9uiiJ/Z7+plPHURuTkgyD67+3eh3kuW1D3XgQKnc9yMbIc5Q8y4eFhpi3n5rBdfT++ZUzd7jvnRPgZ7JhFHmWO0l5Ium1t5MkZmzggflU5KbsWc5MgSDnWnSsIwmw9983dG8U+ZybEMqz7Nee4O0D7DtIf6qasPLnVMt+9Ar24ShDJ1ET45/8AmdgmmWewz/Wk54k7ooOcMSMZe1ZdrV7PqXl4ULzx2cij18/RJoonDJFSay32h9oyTje6XeMzgrQDwuvCQ/ULyX12AtvpAjF5ynH58nNrnlgby4CCslgcQP87KCwYcpqd835kJTjE08f4Hf+7CkAMJHb5roEdI7UpqwwFv0Kg8Qgi9MsAzaVlypm5lVGOscOys0CFAD9wv+jz3suHrrtlNPBj7IcX//j78f3/tJH8N4/ewo3ndwobUtBk8NsPNlYHmTY2ehgp/DW0f0KXRetWdbPkV/5zmeSZznP61PH0dIq9yxbrV7FMb0lPJ4NAwDuuqA9sjwdod8pHQzHeO33/Bf8woc+hStFdUwVGJT8++kO9ih9H6oYB7iD9y57tnnqOJNneayPf8upTWz3UpvWqQjOG4zG2Cp0m3xyPXQmHOSdL4wH773hBt7eIDODWUmGYTzLAc1yVq7gRx4kHiyrrw+v4Ff2LF9h6QyH2Rj7g1FJhtHr6AGc+rRJE2Deh/Kv9P2IybO4Zpla5RvL3KihtlB7CToEndoGW7nZNcay/pnSSoWozE1sPKdlYzDLtaeU9kma5af2BhhlY/RShWee28Yzzm4Fj2mO7Xh/3b85lcRQfq947mV7Lcrt17/XX/kEg8c4kAaeMMviXIbhNbAqwC+UDcOVYZRzmmtpn/vc7zMdaxUkUeJwj2SdDIOnfAvJMPhYw/HfKQWmWWZ6ZJtnWW/nf8a2w05GecrXK8ZYdo+/X+FZNprlol+g+2U95Hb7JpkwqI16H8X7neVIE9vn8Pdyb6CNcXqHfW90ad/s9SLZD5W7jnG8dbwJkLMioOwxYk9ZIeBZjlAtr42xPBqPTWdZHeTk/kxFSeiGZHmOx68NcHa7Zzqa3b7uxNPUzYbx3o8+iR/7zT/Tn9noYKffManjSFPkzGInLSWN3EHGZG5gpbGJm42nksswSMZhI057Rdqa8zt9MxhpY3kDf/LYdXzg41cAlPW5VdphXVrUeu+4AUkMRlmUZpnOmzzLeW5n5XUeYydQqeEL7+7HbjvMxrhYGMh/+tgNXNgtG8tJYdRToAgRNJaHGTZ7HWOomfsVOK+tfifes+wZegBpYu2Sd22An5FhhAL86ieYNnWcm6/2ZXeeA+B6cP3zuLI3xB8/qp83XkqeoEPHyjDgdf4ETbb8jA5EyLNMuk2lFO68sGM+ezjMTPaKLSp3PQ4X2aBrk1YY/dxYvnE4KlXwMzKMnIxl17NMWWd4Llon3VJAs0zvVMhY5uwNMiM1AdzVql4nYWkqJ2mWy54ngiQLvEAFYANvAD2h5/C+p5vaNJpOFpKxrb7ot9H3LJNmOdh29lqGdPJ+HmGdDcOtnrjb1xOqJ28MikDMBP/0rz4f/+jznhs8Jt+XfzyCG2FabuP+nT//3BANnovn+afv+bvH92eCtHiAX0Xb/f6enwbP4EP77xbZXzjcs0z3mlbC6pwiIc+yCQzzNcuZKxHURpE9PhA3Kfcz+nAP9ZB7lkekWXb7bd53UN9jHH2pDfCjSW3Isxwylu2qq5cNo/g795BPCib38d/vLNfPeDB13GBc5LGGsw2PuQjtG7CTM8qNPqnfAVzZG3/meQxPwpydk+Badmp8zKZrYyxn4xwbRYdfrU1zf5/lOYZjK9/IshxP3Dg0EgzAepa1Ztk+GN/+Mx/Cf/q9T6KXJrh0cgO7Gx0zG6eZnzuLrW57t8M0y54eim7+STaY+Mv6ToAfK5GpX8IU53f7Jo3dyc0uXnvfRfzpYzfwsx/8JACU8pdWaYcpXZUp48n0aERsNgzaf5pSaV6WZ7nmYvGXvElS9dKxizZu91IMs9wJVrt4oh/eJtER+RM1y4cjbPdS7BYyHGMsB5bOtrqpMWInvcy+Zh7QnbOrWa7eltpNBi8wuSgJGRJ+Ngy6x6+97yL+3Ze/BH/5RbfbbbxGkNH55I0Bru4PzbPof95PETVJhuGL+YZM1kDsMM9yv1PWD45zO1G4q9At03m6uk031WSoiiM9jv57c8g8+XuDzBYlGdn3jEpXA9az3GNLs2Soc++xNZbLnuhE6f6NBmZ+LX/j216Db3nDPQC09yrPrYTBBMF2tbH8yBW9YnB+x9Xw+6S+QeBNKCktFhCu4FeSYbBngYINAd+znJsAaX7dgLCxXPV+OZrHgDc2lEeYzolutVJK51reG2BQaJZ5AGMVvgyDo6UR+vvQqpFr+BZtq5hghmQYeW4NW1rdI7hn2coN3PaZ61PjWbarCWNn4lZaFWKaZdof5SGul2Eo+FlSDllObe7gGngyDH7OoWwYVeWuS55lZb38NIYNRuVsGFaqYXdoJnodsl2YZ3mfNMt8NSCvDPCj7FmHQ/t+uefBj9vMWDZdLqvg50hTeYBfYf/48p4Ru8cc/s5t9TrGs3zYxrOcKCY7CctqYs7Vz78dk6J2bYzl0Tg3nruqTtE/4WycI8tsJbzROMdj1wcmEwagb8RGkYKGbvSnrh7gAx9/Gl/9mrvw23/ntTi703dmuIfDDBvdxDEam2qWuVcJqPAss0GVOgyq/kdsFsay8SxvdfF5z7sFJzY6djnYN5ZNnsaybndY5FnupFZL5Msw6gL0/PPuJEkgwG+yZtn/vik0G93d6JrgD8L3tJvjFSm3YmQYW73UeJZpchPyuG/1mVRnwgtZ5VnO2Is9KasFEPYsT86GURjLgeIOL3jGGVez7N1/mgxc2RsGPcv0mPmXx/UsB9rke5ZpZYK1zakKl9rBk5e5pX3ffZF5lkdjE3RCnmWnfHMgfWCV0V/yLFNREuZZ5hXOXnj5DF5/30XcfkYv4VPeZu59SxNljPBu4h43SZRTZY72Qdx0csNMCCmH+6YXVNhLtfSB2siz8IRwlj7haZQLiVXJswxlnid/AsW9uZQNA/BKhOc6oJWeNx7QvOPJMLg8zYc/d07O1iqZQaK9sPx+ADrX8pM3BtqLHjmRr5Nh6Kj84jlFeVzzjSgATnGgkIyJG0w5XE0n9zzus2efrlupgp9x6PiTCfu9r1lWKmws8+Avk9XBaJar+0VdAMgdv8gZ4GiWi4AxX4YxqVJbotzrTG3Vx2bXjjzLRoaRmeNR80MyDLp0JsAvUTarC3mW2elRX1KVe7iTKtO/8OA2ugZEUwkj7eu9H30Kz/v2n8fj1w/dAD/WyINhhs1eYrbj3vvQcfk1pzSd2XiMg1F4UuDj6PQTO0Ed+5rlSGM58Sap9LuJ20XtfQWgl42kByF459Hr6AFqWKQzSZTexxPXD00mDGJ3o4tukuC2YvB698/9IQDgdfdeNN7DTpqYqH/Kg9j1ZjxVhDTLZomLZBhb1oD/tDvP4sXPPIPbTm+Zz/C8rrxD+JY33IN3vPSyWeY8udnFVq+Dtz18m/mMn/7NGK1em03J37H2XoeMt2ae5cQ83OOcBfhFaJYnfW4SZOjvbHQwzHJHx1vlWaYZrxPc5YcqQ3tStwoZRi9NcLaYfAWLkvC0QRPOx2jomEaZ8u/Sz5XPPvuDq1nWX+vkG/xzMVWV6jzLV/aGpSX3GM+y4/Exbfe9U/Tclo1l8kDSPuma8RLhz7v9NJTSRvPhyJVh0LNvNMs15a79+8g9y/tD7llmMozcajovn93GD37Jw2agCEmPEmUHTh4ISH9TSgeh8WA6Dj13ZCyHNMt8yZpyyVdB556P3WsCwHjNueFE7aRMMHWe5W5qHQ+8r8pz3afTeQeNZbY0XWUgVOZZrpAZ0EoYebaJ09tdK8OI7Jvc1HHuNgr2vQsF+DkyjOJ7HvcQen/8bXi8AP8bD/DjHlSODfDz/sA+yAPAxrkurEIBkgT1Y/T++5rlOslAN1VOth3AOgM2uOwqyzEYZV6eZdsOU+7a66K5rpmwKe1oBZdPEq0M49rBELv9Tmk1i8swrGfZyjBsNoxygB/13RsV/W+3yGCj21fct+Jv/DQaZ8ModvKnj1/HU3tD/PmTe44D0U0dZ4umKDCvO+sDONagLVb/EipKkkV5lnl/nyr7FmVspTVR8RMEhXJKwSOmWbbRmTEyjF6amE68kyqT2+/x64fGuCH+wZufg3d+2mXceWEHr7vvIv7gk9dwbqeH+28+YT7Dk6PTw9JJrNh8kmeZBnrrWXZn2XwwufPCLn78y15ilhq7aWIGzpHnWX7bC2/D828/7WiWAeCrXnMX/r9veQAXdvsYjMb4iyv7+Je/8WdmH0BZ5pAWmmUqsRsKOBtEpo6jc+wUS8Z5IYkB6l9kNzVS+8eTOnHy/t44HGF3o4Oz2z2nMp3b3sR4oakZVXmWtwoZxu5Gx0yoQgYm18lOlGEoO/Dwr37O4LptAdfTaZKuVxyamkSDyWFx7nWrB/6gThWjntobVGiWVXC7qjRYoSVlgBmfzCtJ7wgZPH6hAW4wvPDyGfzmt70Gz73lFA6HXIbRMVKIkSeXAvhAGz6PA3a9rx2MzPUfsMkhl2H4zwE3vEyVvsTKaujvRrusFNIErmfZu180ID9BnmVKHWeMZRtz0eskpVSKPr4u059MUGYAgAf4KaPX9ydQfFClNHYAShIoPQgmzjkB9r7TszUYhZeAgepMLlZm4PWDzGvPtz1RVJ4bMCncJBxDsGRzsgC/vOxwCRYlqVh29j3/9D2XYfB+nCaKm7208l2vLkpivydDRj/fNgCLOxn81Q8/X7AfMMvp1niWuWaZcpxz42uSDIN+9mue+O1VWoehj8MC/K4fjrC70S05HBzPcnE4Ght4ZowrJs+yPTZNIHjyAP96UP+S8JsL9/62zYZBbX96f1jIMMqry/tFzA61YbJnWX/d6nX0ShEZy5GZtcqeZXs8+osOeo49V7bySL+L2HaljeXn/q8/hx/4lT8G4Ob9i1mK7qa2dDPlUL56MMTBcGxKXROfcf9NplrZV7/6LgDAy+8+73ReHZYrWWuKEqMhAiYF+NltrcbQ9Rb5Bgan37Gp44YVHbVvLJ/c7OJtL7wN/a72Sv/k+/4Cf+c//j6uHgzNkko5wM8aDJ20nLMWQCk9Tx3kWTZprsyydPW1ShKbFzRW7hE8NgXmFIbs9cMR7rywg9/+O6/D/TeHjWWKvB5kYzNz9tMKAWQsd/CWF9yKv/GKO7DdS/HwM07jObecKH32zQ/dgu/9oofwvNtP4TkVxyWq0nPZDAQ1gXrslgSLklQ8n77ejTqwOg0XtZM6OvJSPX59gKsHARkGzf59Y7nKs+zpqAkeMEfQZMgv3EF9u6/zvnBiA/2uHnCsd80Oulbu5AaaAdZ48N8/7ll+qjBOAbfctfa8lc/Vvw48G4atSud5lpNy7nJ/kKLntyTD8IqSAMDNJzcmpl3yl/mdbBiFYUn9gg3wgynIE8qzTPQ6XLPsGnsJ827xpekdr4If5T4O4WZAKEvn/H6Ga5b5vdrpd3BjMNK5viONkarCO4CrhQ0G+DEjzqRlc87FbTNgPf96G7cgUDB1XL86Gwb9WKVZJskFoI1Vkjxx+QP9je+HnCX7noc0RCcpV3bknmXa1zivyoZRXIuKFRgqmMXxV2topQGw/cKVvQHGue5/6iRKpq9kAX5Jou2GsGe5XobRSxXr0/VXOqNpNMv0cbrWVw9GOsg/kDpun6W2U961qdMsb7KYM12UJDLAzzeWizMej3PTb/Fg4km4k9Sir4rYrjP5I8vjYJSZQIRREcDRrfUs2+97HV1FiLRsaaJMjuXTW9VelOfeehL/v7/8PDx02ynn91o7pS/s/jAzg3SvmOnV2Y9dJ0DPlUBYGUa1seyUux7nwReJBiN/P700wWE2Ni/h3mHG2uB5VArN8rBYZgx6lrO42SC1277I1R5tn06iz7fp7NjZB3mWi0H12sGo9hoDVls6LIzlvUFW8izneV7IMFK84u7zeMXd5wEA//bLXxrcp1IKb3roFrzpoVsmttmXENCkxlaEyiufM36tguWuq47J9F8AiqWxuMwIm71UB8oVlQMfv24rUYY+X/IsVxgSVZ7lQcBYpvRRvY7tiAHr2crz8gDZ7ySFDKNcmMFknamTYXjnwa/3E8xYpvb78oSSZzlxBwNAG8QmwI8MaNIsK/1cZWNuRLkPBhUhKckwmDeZ3uNJEgxqDxA2CMZj/bOvWU6UMhmEQhX8iF5tNgzFDA7uWabMSPpnneO1SrNcnozwcyp5ThN9Tjx1HKDjD24cZthgGutJhORF5mflaif958LRHwcmvZNSxyF3swXwroz0+hsdrll2Mdkr/NR67Nx4NoyM3S9e5LTsWdZfeQnkKrijiXA9y/Z349yV1HBD2MhYvGsckmH4qzUK1gNJz/3jRdXdnY0O66/IyWHba1ZFjGfZGs+hbBh0TfyCY+Z6BIo9GWN9Cs8y7YuceTq4lcvaXM0yrUTxTCFclx7a9xYzlsdjHci4uzHZBOV9G2WtAtxVw0RNjgki6FN5nh+dCn79TmoGDPIsd1hZah/eedCSOt3ANFFmuXijYomD+JwHbzb6ZbO/RBnjhad2MRHtE2UYruHjp9GpNZaZjIOq6/mc8DzLZttOakraAnr5LQtE0NM5ZoXOu8c0y/xl5sutkzCe5UR3SCZAa8KLXNVJN4GuK72M1w9HE4MJ0sQG+G1UeJYPR2OM8+plsmkoLXWzZx+ol2G4AX7xmmVf6xizNEYd4kZHpw+i94qo8iz7z5vr8SsbFblXloQmEW7Euy7mw5P+A7ZQSqhEOPUrVrNs02eRl3gUkmFUGP1m0E+UmTBw6HoNMvKkuX8/6wQcF4axsnmWfc+y1mYXWlDKruPdXwrAeeI6yTDc1HH91BrLk4L7APacFGOmnyGHt5Nrlq8Xzo5Tm17FTO5ZdjTL7rNLDhLAlWFQyk9qV60MY4LcJzSJC8kwtns6I9IgC6fICuGWfXf/psAC0FB+P7gNF/Qs8wlmoK/2jQluSO8PRtjspjpY1Hjn3PbpoKnyipBjoDDP8jiHqbDm5oqnMY80y4VnOSrAT5UcFtz7SvukiW91nmW72uGfi7946GeYof3wipmPF5PQ3Y0u0yzT9txo1V+NZ5lJikamX7fH3mdp8UI4pZ/JWCZPK9tPcxlG0faMt92mj3U8y4PM9Cd6wmcn0CHbhJ6fza6dMFC560mOGcCTYbAbmHmTweiiJMwZU/VcBLeL2vuSIA8QUGiWU4Vup/qi+AF+Wa7zEneKpTzquDcijT1Ol3mWD0fliliTZRjuMo6fDsofTDi9js0BS9X1fGim53vNKTiQotL3BpkNlCqljtOf1UuCidMREn56njpOb/VwaqtnZp/xnmVrFLSFzs0xlie8mJ0imGQwGhvvvd9R86X7WUOdAhnoZc3y5EA9IOxZrrqU1LfRMQajcTAFnrMNLV939HtFRidRpVmuM5b5nyqzYVQEiO70O6VJK5dhhDzLA5YNg8swDjNXbwxYA9Gf4BJ0vXc3OsY45dD7Qqnk/PZ85nMume+tZ5kFNJKxzKLzE6WM/AEoB0lZzbI23n0ZBpc+UFGdOuj++IFOgDVwQ0VJiKqiJJ1iMk3v62DkTlK4Qcb77XK565qiJFWe5eJbvz/i19apPtbv4LBwPMRKxLgnrGyouRX86jzLZtJbMcH0tfr0vWJ//8inruGbfuL9GGVaxkCNVwAAic5JREFUgmTSplaljktU0INuzgdWIpONx0Y240s+bH595RzPTjLrNcvZOHecFjwImY6/HzCWeeW9KhkGlxEQIc2yXqUuTxB3Nzql9Jvh1HHuO8xXh/m1ignwI+qyYTQNjieDm3vxO0liZJHciN4bWGchn2yMKt5Bagr3LGdGhjHZluDnzOPE9D3VP7z2vov4nAdujjtX1pfRWcV4lldahtHv2JyCRrOcJJVeXD//YzYeIysyOySJMpKOmHQlPh3m3dUBfu4sOdazXBXscLLGs8yXKJ+4cYh7Lu2WPvPyu8/ju9/6YEk+0i8kINaznLFynu6D2kmUKVLAtWzlAL84Y/k73qIT9n/5j/229ixHZMMA7MA/VTYMT7N87WBUqQMjuklSeJZzM5D4HSk9Q1u92b86foCfvxpRm2e5wrNsA/zq3xlewW/SbJ8/u2lii6EQrWQYAQ+cf+1DMgxAG8vU7aXehIPrNgnqoElfvN23GQF8uRRgByE6rD8gHI7GSJTez5M3ysayeX8zLzCnYKOb4p0vvYwf+bWP4rFieZd/pqooCZX/BsrXlp7Pp+qyYaQtPMsBzTJ5+mjfdO15hLm/3OqnEaNnzs0Rric69C5z77gJhCTPck1Ky6oVjNfcewFP7Q1LFU7JAPA9y3QNrx2MomUYgL43wywvZ8NghgZl/uCEAvyqNNAh6VLOnv1EKfzWR5/Cb330KXzt6+7GHvMO2qIk7vETFU7zyY1/s3yfkQzDTjYIX35Ez12MZpmnLusnur3cs0zXL+xZdgN9Q8fyU+oBtr/1jVE+QSROTJBh2NRx7rXmxjC/z2TvVK1c9tgzXqdZbhocT5dl6EhIaBxOyqnjunayyicJdTIMCsqlZAuHw/FEB5b+vDtO0DPNPctf8IJbo85Tt0d/zVEeY2rbEf3JJdDv6uVSnnqmWxi+IXwZxrAwfMizTC9UG2OZAgYBmzoOcAsLVG7LK/B5JZ9D2TB8aFb6yasH+NTVQzz7YtlY7qYJ3hJ4YHqdBPvDzLyENwYj2wbfeEntNTq11QsG+DUxlimQUikvz/KEF9kkr5/Cs3znhR3cenoTF3Z1G7JxHiXDoLKplIvbL11KHTzPnzwrSrpQE+Cn711If+tvC+jOLM9zZzCuupSh1HGTZBh80OsmSdlYLqWOo6/e88aXx7m2kzqzkmfZ9bQSOxsdY2T5E45Quj0auHjwGz1rvKw84XulSqnjRhk6SYLtXsd4cjlGhuFV3uJ802c8G1f3h/jcB28ufcZ45Fh1PzLoqqL8N0vZMFzNcr+TmElDGxkG97KZstxe+reqyZA+p8TZhian/FniGlh+Tjy3Np/kVKaO455ldu/uvLCLb/3MewLnqu85eUqJbV7FssFEPiVj2dtEwdXUlj3L9vs8cJ9DKzO+gW3fPbuv/cEIe4ORqX7JU6Q57Vaq1vjxNcvkHdfXz35+6E3oOp6xHJN5Z5TloMtPUql+JzXvqTGWPc8rXY6qFTYtuXF/Z6olmpUcFM6esmHlyjDKnmXqj6xn2cow7PHs/uiaVDl2HBmGfzKseY0D/IrP83M0NkrilhzfH3qp44o/VZe7dt/fJNETlFjPcsc5ZzdVXmxQH4cHtTfRLK+2sVzIMOjZ6yT65azy4voyjMNRVhhJ2gNGaYwmeRhDUClowM0zGJMNoxf0LPsyjGpj+ZnntgEAP/P+TwAA7g4Yy5XHLkra0lLx3mFWmW6Ka4lPb5U7AQBFSdHms1Yd4BfpWTYyjPYqoZfffR7/z7e8Gj9dXDMAE41AkstwzbKfZ5k65a15yDASv9Mte5YrjV727I9zynmpKo0pgvbHU8dFG8udBGlqV2wIX1JUZWRy6UDYS+Zee9Jw+zKgy2e3cf1Qr4gYWQkzQkIyDEAby1s90m3a1RtgUoCfJ8MYakPtzHYPf/ToNQC6jzGpsbz0S6G+Yrvfwfd84UPmZ34/6XiuDEM/D1VVMfk5AtbTHPQsn4yQYXhLzX6GHL5vs4KUKLznS1+ED37i6dL+fGOZ2sefJX3v+NJ1YSwzTzBdpkFdURLHwJzcp3DNMr+u/J1vEk+h2zUOZpuwVzGUdqsswwgFgwJh6RI3JvgztzfIHM8yjUF++3QmkoAGlXlcXc1ybrIScKM9K2mW9TZ+vuAQ9JxwY41SNWrPcu7sq8qzTP1B6RxV2bvor9ZobbnrMSZ2Nzo2SNrIEbiHtzCWu+658z62UYBfnQxjigA/+nSVZ5nOPc/zInWcNXzzQs/Nc2lzqClbbCWjSQW/rjdO8FvYxp/GnTGTUqty1sBYHjuC+34ncSJCOXwM7RaBAaNsjE5fB/BMI8OgqNw8L4TpjQL8lNErWglEMWuL8CyTtOInfvvjAIC7b2pgLBcyDHoJ9wYja7SWikTYn09tdU3gllnKKjTTsZplQqdiGld6tH38icQ08M5l0n3vUJ7lmgC/SZqyaaABzRbGsNcdmJBn2bumB8MM3TSZOHMuyTCGk2UYXLvbSZSjkQZCmuVwG9wApXKbqgJvfKOb5D58n+OaCQY3JLdNURP9t8ORuwIEBAL8vOMfjDJ0EoUHbzuFX//TJwBo7+fBUBuqfv7hmKhtf5UMYKnjFDfodF7sUhBWokxGF8AOvv0iKHOzm+Ild5zDmx66GZeLyXhtezwZBl+SPvQmMXxS8Io7z5mMMRw6F9qGBtJ95lmm1FB+6rhtJoGidg1rPMtVmuUqdKYRPfj7qeOIJjIMOqR/ZJ3CilZA6j3LJs9yhQzD1XHabULvHhnLXEMaal+i3GV//1hcIqN1xfrZ9o1lX7NcDvCr0yzrbXgMwQHzLNtYnFHxO1+zbFeYgPK7x1ffCDs5JkcYnJXRrZ59r3Y3uqUgQv5uWL29e635WMRtdetZbpANIxDg17zctZ30mLYrul+2MMxhEdPka5ZN/YjAcW3qOFtAivIsR1Xwc4o2JY5cKKZMtQ9tr41lmPOY2I7GR1ogvUKzzHW+f+uN91YGiPEBo9dJjaaPgkjoQaiatdVBUbl+Ocq4PMssG4aXtu3kZhfndvq1QW+nt3u4fHYLH31iDzv9TpQniCAPu/EsD6xn2TdGuRFwaquHg+GB02aTN7axZ7mo4DceFwL9WM/yLIxlu49JOR3TYrlpOLJ5lv0Av8Go3TWIwZe90HU3nuXaPMu+sTzG7gbzWlRcSn/14HCUOdUkg+1MbCfqD3S9NCmt3FRplqsi++m7smY5LMNwA0BcwzSkWaYO+skbAxOoSQPjIctnTpg8yxXyoMPhGGmq8LzbT5nf7fQ7eLwI9qNn0BQTiHh0+CH8HNJp8Q5RerOqgXGzl2J/mOkVALYa8E/e/ny84BmnceHEBr73i543uTEIGMuBYCdrLE+eFPue5W6qPd03uLHMNMtKAb20LMMwxnJWfR2qDMwq0mL5flzSLLeTYVDfHpZh6O+DFfwcw7dom+NZtp8NapbZ7/l2e4MRbhyOcGbbllsPtS9NVHBcovfJ9yxTqj1f2lCVAYpWXuquJR3fn5xRCllanSLjlU/0aUUTsAG/pTzLqryCVfIsF+MX9Qnb/Q72BhmUAra6KfYKA5eeGX7u1K+Us2FUeZYph3SEZpm+pYmSo1luNnbSx0cBzzJlidLtc1dWzQpXRaAxYJ8XMzlTCoNsHJ2G1s2z7D6nLWxlplm29ypmNyttLPc7qfaEspft4ctnKj/vyDCMZ5kq+HGjqZ0MIxvnJU9NbOo4mye58JIXD8CXvfwOfP7zJ4vTH7rtFD76xB7uurjTaDZlsmGwAD/q/8oV/Ox+T2/18OhVrb8kzxqdQ2yeZYKCAGLLxPrBj9PQcTzLk2UYB0PtATdFSXyDbZ7GMnmWi2NSUAUZsrXlrr3f0/22M+f6Y1rNcoQMQ9n7w5+Z3X4H/W4aWOoMT36qcsZWapYrZBjuPvXXcc01o/N7am9gssf4mmUqsqCUYgF+ZCx7MowibZljLG+UvZCm1H2Mwca9oZQ5x3i6lDNIVRmA9Az7kqHPfO6l0Mdr8b39IRkGVVYcBjTLPjQA8nu51U+dzCqk400L7zL1Hdt91yACChnGhEp0QJyRmyRFoRXPgOXHbbLqZT235feCnvEc5esV0ixXGf6+/Ehv71bwI/YGGW4MRmbSYYx5r92nt3omdoPDbTQnG8ZYH08p19D3pUImgwVLuVhFqNzywTAr5S0OyzCUk0ISKE9Uw3mWXblUMQ0xbdjpd/DYtUPs9DtOEa2MGY2ESR3XcSUv3HETLHfdi9csh5wLzYuS2Emn2QeLlaDz2i/JRMgRVj1BpvfPOCZSZVYVYlLHKaWMNzpR7lvUTrOsv45zm570SMgwntobM81T/Rm5QTFJ4VmmoiTxy/Eh6CG/fuBKOWyAX/W2vU45zzItV5zc6tZmwiCed/tp/Mf3fQJ3X4iXYNCxB9mYeZZHpShogtqklPZ421Rm+u9+PtVYyLOsy2jHaQb512ngg9rE1HFJglGmy9nSNfID/EJV5GaFMZZNTm3rWc7zvDZ1XMizDFQHgBH+8i2XGFVBx+qliWN8nD/RD87QqWn+/ayK7FeFLq2kWc5coyyEP+EIpo5jwW+3nd5yzokv9w6zHL2OKlX0CgX4pYnChd0N3Hp6Ex9/aj+4ZE/Gfsxz7WTDIPkFMxzpHLMajypNDtuspPnQ4x5Kj2U9y/o4wwqZF4euiVO6vNdxcnZnhbHXKVYwrLHMPMuOxGLyimPstR/nWlbAt3U8y400y8X98w5NTgSg7MUG3Oc/JMOYlDrOz4ZB7A0y3DhkqeOYB5Xzt954r1Mkxh6r+JqwohUswI8kQkQ5Toc8y5M1y37QKEB9VOKcV3WeZboW4X5QG9TuMelne9/syihgJ58nNtxc33ry6u7MD/Dzi5To7eznD4ZZsYoy2Vi2Hn6asHDPcnOHFuBLSKwcksdrATZgWF+i3IxZoffCl2F0EoUbAdlMHVRdOE2UM6trYyJYGQbzLMdI45ofanFQqWYa+CbNlvgJ9zp6NkTpTPiz10ZvSrPva0Ug0YYn2I/NszyKNPx9SLfcRK8M2AqDVrOcVQYF0ctxoqh3b9ICFS9QW69qUngaRuNq74/bDv2ZWWuWJ60odFPFksLrz1alL5urDMMsdfN8yYXhV3FY31tJ95s6g0laZ1Puejg5Gwb3/HPt6aWTGzi/2y99virPcqgCmdkG7iACxE1U/HSH43F1gN9gNDZZTWxREnvNR55XypdhkBF6MLTBZc+7/TQAX9/qGuIxHbM/8Sfe/QUP4AtecKsp2ToKGFkEGXezKKDjD8jusniR1YA8y5RPuuYxov7U8Sz3XM9yntsqYmQwA24auqrsEKXjefeuDpuWb+y8V849bfD+Gw9g4NA5+1qrWfZyffvfh4Ji+aoKP4/9ga6Ma/X6Rfu8tm33Ozi9XfYsW62svZ7jcY4sp+BTN3Wcv5paruBXfS2NrMrzLPup2ELZMPhkpFKGkYTKXbuTWpq40xhO142eQy6Boc/4RZJKAX41eZZ1safwc2pS1fKVuOLrNJrloGeZ3pkilgdAKaNYUsjBquJJAPvcW+mGXVGJdVzy8+a+5Wk8yzngVLmcxIp7lnXqOF9wXwW/T5TMfJTRbJdmou2MsFLy85Q8y/YBqILaQkEjQH0HEeKBW0/i77/pfnzOg3GJt4m+kWFYz/K2F9hB0IN+uvB0m46QZBgRy+AhyNMwjKx8NUvPMu80Jk2S0kSVZDb+spofzDRLqICBCfBjx84KLVxVJ0rXSi/PWwNmUoUio1lukQ2j20nMYNbrJPiHb36uV3MPzrH9+1lVWY3a5VfwM6njavoBX/cdyk3Nz2+75xoMjmd5lAM9O9DSvumct/sd7A8zHAwzE9T4pgdvxmPXDsKe5Qh5gn8eenv7PUm2bIq86sC2KhlGG3xNbEiz3Gc5cYG4wC0+6dzqdxzNMi27UtVW41l2AvzsPusMhKR4MaKufaKNvXHueZa5DKNB32TbFXjGmbXsjyGhPMuhrDH8ezcokBm2bNfXDnR2pJ3iOtLzGWt30Od4Wj/yLCeKZBj28/4SvZ86blIFP8CNIeCe5dQflys8y3bC6+4/JMPw22s8y2QsF++TNZb1djTGA3qCesiCTq1xP8mzPK6d3PY6tp8n/NVBIKwdroP2EcyGkVhnn5+tI1GeZrlGhkHndYZNwKI9yyxuY1rNMo2h+Tgc01LFanuWi+C0Oj0Mh98oXcFPewe6rET2RkBTGQN1KDe8l5KMpkmaZUA/iDYTRdOHWeGLX3J5YvCVD8kw6CG/cZhhONZpxfzrQNfvZHEM66WDaT/ts2nbdWczjhpkrBdo+sezaTYMKspCxUwyz1g2hsEcPMuAfo5sgJ+bvqxOs0yDOpU9Jw9p1fKj2c4bZAcRRUm4558Hjl0+t23SHIaOUVuUJLA8WulZjpFhGM1yyLNsz89fiuaeZTL6TJ5lo/XXn93pUyENW2r5tfddxL9+10vcoENTVKgYUCL6H/5uhiaYdOl0fuHws7hRDE5b3el9Ir4Moz513OTVMyPDSPnEJcWenzouIb1yYiZJ24EAP6Bej+xPdOpQCmxZmRnLrP9o4uww2leveVxqFAzwY0YcVZ/lBVTc96e8Da/gxzPWUODpVsmzHDcm8eV/ricdF5MbHSCp2/EDv/LH+Op/9bsA7LU3RUkGETIME+DHnjemWabj71ekjuPXl7fdfqYuG4a9b+M8N/2B9Sx3nc/puBz9GTImrbGs2xWq4Mfv2f4wq60wTO9LMBPKFJrlcAU/6udt6jijWe7Z6z9Js0zPP03aP+P+m8zfYuPHTI555T6lbWw5amJe/Iu9VGtgLMdrlp1sGGmCLLNBZaGULU2gh3y/WCa0nhFVOrYPT6lkPcstpkQt6BXpw/YOuQwj7I2iB508yzZTgn5R/EExFuq0tCQm3rM8TVESwjWWJ8gwEpuL+zwrZsJpq9uOhbxagLskNqJyslWe5eL3pKM7GPkyjPDxzCDraJYnreDYAYCe47rJg1kKLqUqrDaWocoSmBgZhs7zyrSgAekKPz+SKoQ1y0WAZYUMoy47Av+553nHYuaAfhpMH15EoOo9IeNuYw4yDF7Ry5dnxWizbeU+917s+anjlPaSdRKFjW6CL3v5s/D6+y+ydtl91q080jWK9eoPAmNOh2V6abI6afP1uijlyjD8MYQ//lf29CSex7e4hXwKg415dLlm+drB0PyeqkTueBPFSFvZvM98xYhrlrkM490/94d45GmdVckEjBkZxuT3eZJnGdDXoaooiZ/WrZw6rtzPlIxl6BvFA/wA61m2RS6s0egby2R3dLyfaTviYJjVvq9hGUbxbk6TDaO4bHzFiJ5HygQGMM0yq+CXT9AsU99OxvKzmZQ0VhLL5a7cQG5jItAmWtoYP0lcbWO5q/Mo1ulhOP7SJUWndhJbyKRu1lYHeTWuH1Z5lmu2NS98bqoPNUlqPw3UzmuFZ4Kyi4SOb43lIkOAqc6k/z5d6jhd7CMuG0ZxTWcwoeDHm1zBz54XRYH7xnJb73osaaJsgN/Y9SzXBfhR53hiU3fgB8azrP8eo1kej/OodD4kF3E0yzXbWM2y+/uqAgu6vYCv6RhUaO19aPAGKgL8uAzDMxh4QBNpb83gSd7J4kTcSnLVE4Gps2HUDECDiokvYL0/WzMI8CtlwwhV8PPOM2Z5vefdCzcbhja++p0E/U4CpRS+7Y334v6bT5rPuNep+nhm4I8YXROl7MTGu1fbnnQhBisBLHs1eZ7lOs/ylb0BTmx0nGchpPl3A/ys1+zqgb2uZCxP0ixXQcaFmQQXq2FZER+QJGVvLT+OedfMNa4+Fp0vN5Z5Ngzab2We5WIz+hq+B+4xfUmBkRpkFODnGsvmM2ObMYNrenm7rPFcr1muos6zzM+jvWa57FnWMgzXs8zPL8+Z9CowZtOrwp0Lr77ngrO/SdjMHNNnw6D3RhdTiZdyrLhm2ZVhxHqWEwWTV1lhjA6L2p/Ws0wvJQ0MseWuAf0g3hhk2C6qhi0C34ihAL/gQ12c0ynPs+wH+PUbelW55qtJNoxZTCj4PmIq+BFnt/tIVLVneW7GMvPK8Fk+lZOdpD0m7WysZpl7DG1qwMnvSKpUYSy7k8YQd1/cwevvu4jn3nLKbbNjLJfPJ+RZ7qXJxKU3CtACwpo0V4bhGgx03QAmwzBpp1zPGNewlnNIl2UYTTTLfBAIepaLX9UV49iYoWbZVimjiVxZs0z9XKjctQ89L9zo3Oqlrma5uHdf8co78Oi1chlxID7Ar0kcRMKMA/++bvVTPHGjmTFSlcdYwU4+9MTAfQl41/PU3hCnt3uOYRnKs+zKMNikik0CH7vuGstVeaCroGZyedUoy80qDhmOPtQXK6VM0PukvPv07I+cydkY29s8M4nCdRqXJ1bw884l0Mf72TtoBYBiSMgjv9NnXv5EOVINP+OUTR1XaJYrU8eNa1dAaZUq9Pi5eZabr/4C7mqmndzYAL+qPMu+N97dt3K2AYDvfMsD+P/89AfxsjvPRbXPTBK8/bcrSqIZ5yjSg8Ztt9qe5U6ig5WGkz0VgO3QKb0SL0oyrQyDbpafoqbqJoa2HYzG2BuMjFZsEZSN5VGRlWKyZ9nPlNDWUKSlrthsGFzMPy1c3zpZs2w/e3q7q3NrB7JhKDUbiUiINLWGnl9yOaS/NdsVt4SM5QNPs1xZzIR5Jeg9i9FjXzq1gUsnNxzNchW7G1384Jc8XMqUwa9hqRQwytkwRpErE6ShBCryLLPByAT4KTKWAzIM37Nc7HC7H9aP+j/TAHc4Ghd5aOPOgY4V+jz9fZiNK59FGpxmIcMwnstgNgw36DVmUuBX8NPt7TiaZZ0NA7jzwi5eekd4UOWXptazbFYFYiYq1aXJjWd5FtkwlPKyYbh/djzL+0Oc2uxWZgKw5cjd7UM66ceNDMOm8gJaaJaLn9NU5zMeF9lL+ET3GWe3zHah1ZZJ98NflQH0GMzTIeqSy/p7P8CPfh/KU02fqQrwo4/SZ8iz7GfDAKx21wT4+Zrlrnu+brlre+wDVko6RMje4DIQovn45DrG+D46LJtXWYbhBj/GyDAALXP8/rc/P5htJUSXa5bZqbUZhU2AXxFCHuudXnHPsr64lJNv0myJz3hTpT3LOfTNNjKMFgVJADvDpc7cr0BVt7TKB5Ebh5mJpl0EvsfvxmFW5Dsut5dejlNeNgwjw2itWVYmrU4TzfIsUsfxl3fSveefPb3V08UJAp7lboR3sy303AKu945Hm4egztMay1azXNdvmoC4PDde1Zigi5//2leg10nwX/7oMb1NC0+74xWMWB4dZnnU8rcrwwgF+PGlf/KulT1wNACYPMvM0wLAZBPQv3OP0QkYBsNsHN0xTzLu6PkbZtUZHkw2jFnIMMzEWf/sBly5/UJMesyO138CRYDfMMPHntjDRx69ZrJh1LaL/b0+BVn8BDxNrHHgX356XpqselUZo4kCk2HkJUPD1SzrAjqKfSQNGkxMhoHyxOzcTt/IMErlriO7NPoY7btTvG/ZODcBd/R4cIOSP8uxQdxWs2zPa3/gGpT8XXM0ywmPXSh+F+hnygF+bqVZW8FPf5CcXSc8GUZeSA0BO9YYJx15lk0Fv7BneZ9l1gnRrbE3ZlHBz/UsW4cgGdH7Q9dzrhTFI1U7NOmab/bam5tONgw+YWxhznHZynicRxvcq+1Z7pI3Vxuok2ZLvFIY3WidyNpWGmstw0jCnmX6GuNZHma5Tt22JM+yUvplzMZ5UA9MDyRl3KBzogC/9ppl61lukg2j6VJSCKfc9cSiJPqzJzY6RmJQMpazcWMZShOSxHo6fM9yowA/VpSkzuDgy7eHJtPH5Hdks5eaFRygnSyFD55+506rEZxBNo42lp0AP+/0tZRDf18ud80C17LqPMu7Gx1cOmXLztd5lqnNh6NxlF4ZsPelyiijSxelWZ5h6jizrM1Tx2Vhz3Jdfx2UYfQ7yHPg+375I/jaf/2+Ylm/gbEcIcOIyYZRJ+2ga9kkQNucg+/lBZjns2zIcS3rlb0hTm91nV04qRfJW1ahWSZuPmmfWfIsUx8ZvRyt3M/TSk6ew6T2MqtjI9ue0ARyklFHn+Mez/1h5jzTXGLDJ0yKeY0zz1tsziUJ5Vn2PbfQAX5FG0iGQdkw9H5Jt+1qlqltt53ZxLd95j14zb1aq8v7WH742AA/Pgmi77j0pa1mmcM1y6YoCdUhILtH2XzvQDjOyGiWp5i0031NlPKKkjR3WvE0mE08y6ttLBc3hKo6TXqxzEujlNOZdRM1M83yDS/qNi51HM2OybO8OGOZv5SnNru4cTjCMBsHB2EyGCgbhs1Zq//eNs+yDfCLK3c9S89y09RxgM0DGdKzDUbjuemVAbdjcrNhxOVZPlHSLNd3BsYIGlvPcpPzo2em16LQjxvN7/5NF93wNMujsZE0TNpvxjzLJYmHUqZv2fKWojnWs+y2N0kUfv7rXo53vOSy+WwpGwbbHw3s+4Ms2hNCj23Vcr/J3lGjWd6cYTYMI8MIaJZNURKSm0V5lvXf+p5nGQD+9LHrOBhlRUGZ+nbxrqjueHTdm+rF/Qm7DfCL75t4vl7/OJRLnFZAOa5meYBTWz1PesG+DyzF62ffbcu5HSuFsnr9wgCLzQqg3GNSOeSMspeo8IQ/NIGcdB2Nscz6wr3ByAkWe+HlMwDKfXVIhuFPvkIyDPIs8/PVMgz9uXsvncBbX3ArXnrHWXtuvveZFeAAdJ/zZa+4A2eL689XOXm7D4bj2gC/nvGw2t8p790EmgfHh4YIOw7bCsRUtMpWhnQ1yyG74sKJDZza6mJno73dQ062TurnWW5uI5jJRa4dUNFZYBofaYGQoRfrWU7Ng+l2TFv9jvm5vQxDb7fvBRI0CfAbZGPcGIycwKB5wwej09s9HBYFSkJGK2UKObutX2i+RA9Mo1kuqvxk4yjPjsmG0Vh3FdgX9yxHVPADrLHcSQOe5TkbyzztUqM8y+b5TtFLE0ezXNefdFOFnX4Hj147NNs0kVR0A0ZPLPz+hrJheKujRTaVZp7lqmtGfQt5iUITiqHnWeaP7qWTm85ScDkbRnmStj/M4j3LzKsTwsowqjXLM82GUZJh1KSOi9Ash/IskwH0Z0/sYZjlyCasigBePuo647y4H3HZMOz3/uNGfXezbBjFuOT9Xntg9fehFaAx6weuHYxwaqvrpsrjhjMZTKy/ygP75PEyJc1yZHfLDSU6v2xs71eS2D6Mp2LkjpvYuBQ/c8Z4nBdBcHZff+uz7g1uS4YcwD3L5YlzKTZi7EqbFLS2nJ7r7V4H737rg7hwYoPtx82Y4aeK86G/9zqJF+CX1doo3YBzzqSOY2kDp/Es+5Ilnjru0Bv/FGymK74N5y3PvxX/9ZtfNVW6VXpeEuVnw2i+LyPDQHhFp4oVN5abeZYTdpP5w3L7mS2b37CFBwywD1CVZzkqz/JorMuMLtCzzB9sCty7djAMGq2vePZ5fO8XPYR7L+k8iMZYplnlFDIMm2c53rM8kzzL3GiJLLZhPcvK8aAButOep7GcJooFUdljx6aOozRbpFmetMyklMK9l3bxoU9cZTKM5oZAm2viGMtBLaHnWR7nUR69lHnnq2QoxrPcczXLgH1faYD2A/wI/nyWNMssap0GxhuHWXQWHDpW1QBjKvjFaJZn0N/wFQggXJTEegFjjOXyc0Np/J64MSj2m03u87kXuObZ4IbdJOryf1sDs4Vm2Tu0NsJoUlf+HD3/VCjp1Ga3UiISCvIK9RdbTB5gCmU07Gdpl1yzTJN5I8Mo2jHMxnjbw7fiV77xlbiJSUDoHZt0HX3P8r6XjQEAbjm1ie9524N450svO9u6Ffz013I/E86zHPQsU5aMirzn3MNqKtxVXFvScp/a7JZkGJs1k9ugDIOMvymyYfDLQquTXN5C199PLao1yyyDSFCGoRzJShucctcBCUoTTIBfnh+hbBi+Zjkyz3KaKOchvXx2yzw8/dYyDPIsF0uOXjaMuAp+OfYG2Uw0hLGEjOWr+6Pgtex3UrzpoVtYXtzCWC7ewWnKXWckw4jyLNOMdvrHM0l0wEmi4pf8jGeZGa4EBfjNCwqWAdwlTFuUJLydYyx3U2PAUGGHOu67dAIffuSqyQEeo1nm7QWapxPkbQZCUolyNoxh5LXn3vmqCQb1LX65a8CuPvkBfnW65Kq/JUzysTcYRa+WTArwo93EaJbroutj8WUYXCJUVcGvvty1uzKn2+ka9QfDcUSaQLbPujLonresdp8193WrhQyjSuZAhoYvEaD+lWyfp4qCJH7mgFCeZW4w8WwYF4pMNPQsbPdsJVt6xqLL/lZolikgk1fwG2Y5+p20VNnTL1BSBf2dVjIoZsgfQz//+bfi733u/c7vyNsLsBSa3iPCJSPEaOym8VOkyzVa/ICEkaSGnma5aiJy36UT+OF3vhAvu+ucI1nZG2a12bK6gZVs+i4r7rdSzVf6eB95qjCW6RnvstRxg9E4UPilvoLfLDDe7pJnufnxaIsjplmmbBjkWa5vLh+g+E27jXuWp8yGcaMyz/LkbbVmecEBfqxhZ7b1S/D0/jDqoTap48YzkGEUnU2sZ5B/nRZd/WtymXOTOm/bps7zU8dRrt95wQP8uE5vlFEC9SrjqTBauyk2ugn2BiN87Im9Wp0zcd/NJ3BjkOGPPnWt2EcDrxlVYmvxXtUZmzRAcYaRXv0OS79XlZua+hbyZvLJLg10XIahB6FyG3kQjNMG6ouKZw/QA/2sZBhNNMt1nqpYSkVJxva6+isSUcUmaCBm75KfJehgmNUWe+LtAiZolskBEHH9XRmG+3lqYzMZhv5a8iwbY9keVynbZ1vPsva0n/Q9ywH9Mu+vtBxT//5Xv/lV+PC3v8EYmduBgjqxvS19jgfU68m8bgcFuwHVE9ymAX409vipy+rgmmUjpQqtYI3d7cbjPKgJJi1+aAyzqePcbBFV56eUwqvuuYBuYmUYf/bEDeS5du5VEcqzbFci9Dv4A29/Pr7gBbdW7iPcHvv9qa0uvvMtz8XnPngLgEKGkVkbgMdRUIBkXeq4WUDjTOpplqcN8OMl4Sdu1/hIC4Q6X3pBJhl4fKbLDa6Nri0C0nbgMNkwDimYpfACsKjQKng0/N4gMwP0InA8y4URePVgGKf9pKVez9PZPMDPyjBijR1gNgF+ej9JVGCnkWGwPNO+ZtnXbM2aDjsmLylMM/tJeZb7HX2uP/m+T+Dl7/4VHIyyictMVBHtd//8itlHk/YCzZ8JAJUDv/5bOUo9OnUck89UadLoHP2iJEDZWM7GeaWRVTWxsxN3d4Ie672bJMMgo+dqhaQKAG49vYk0UbjtTPXgGwtdQ3o2R+PcGscBzbK/XOpzdruPL3jBrfi0O22QlC8XORhOTrXnpo6r/mwn1atLMTIYJ8uE71nul2U7k+DFLTgkw6CnPFHa0KTrSF3PUzcKz/JWzzUU2G23nn/7O67X3+im2OylQWOZgqeil6PJs0znlyqWrYcm/Ppvg2zs5LqH2SZOAmKKkox9GcZkhxOXWFTJMLj3mdC1GVzvKcCyvATeSeqvaFXFyDAmPb+JbdsfP3odAHDnhZ3Kz1O73OfYOrUUFN743Eu4yPTUMbia5QRf+MLbjWyGB/j5nmXSLNMkYVYOLh96DrRn2R6jha3syJzqHFA+65Fn+ZDyLNefFPcs0/e3nt4EAOZZbluURG+/Nxw5VcR6ETNk6vxuHOpS07PQEMbCDbszRrM8auTh9SPgm+dw1J1nrITBGiCzMUq7qYoyAH0ZRshYXkiAHxkkPD3XiDwj1dsB+n5TpwsAB4NsYod954UddBKF3/3YFQDNZBjTaJadoiTe5iHN8qAmmM3ZNqlPHQdoYzlR1mh2y6K7coKsJoVZJ1E4RI1nWSmnz4mdU5hsGBUbUIrAawejyrY96/wOfv/vfcZsZBhFM2gCMypWWA6G43K56xpvN5EmCt/11ged3/lOhMPhZI037yLqjpkqFe314oOnb1zdd+kEbj+zZfqIGGyAnzchTIA8YxIB6PfbGsv691f2rbFcNcEM5VkmTy+Hxp5QQZ1oD1txGa1nOTF54HVRkuL447xyJc5kN5iUZzlxdfAkyYyRMvI+hPpUvyvk/e3PffCT+NAnrhbpZtm1Lb7SpDDUB9FYMfKM5ckOPitZ+ZPHbgAA7jhfbSx3A845bvy1tVWrJmFAWRroV0nMYW2DWTm4fOwkwW1rG2PZeJYRzhhTuV3zQy0Oq1mOTB1XnHWS2M/ecmrT2batDINmk3uHmfNANMmzTJ3esoqScM1bjCFK18xqN91k7bHQDH8YmSeXXoyYnMwxdCI9y2Qw1RrLc5Zh8GPybBhVFcX4doA2/ij1H6DbO+kybnRT3HlhBx97cs/sI5Zp8izXBfgpoCTDGEXKMNzUcVUBfim2+52SPh+wAx1d/3GEZ9n3NtH7lRZSDdp9YxlGxeDjFHuoucGzMJQB225elITSBfrlrodZ9fWqo+RZHmUTn11+b+s0yxS7EEOdPOgld5zFr37zqxo5POoC/Mi7RX/XpaBJs1wYy3uFDMPPsxx4f0qaZa8tZGTu8FLtDTXLZD6alVyFQrOs90FtGRbSjDoZxuQKfuTVLTzLhS0Q05/zTBeVFfwSK135ud//JP7Vf/+YqfprP6O/H3iBrM5+jHa3yIZBqeMmTfaU61m+dHKjVqbJPazmPIuvWi7WbszkEzl/AtNJEyfAz63d4Fbwm5dnuRM4b6ClZplNLoD4CcZqG8vkkR3EeZZNNgylzINNnmVrLE+XDWNvkDkPC0VH12VaoAf86aLTW2S5637AswzEGaL0kYx5lmOj+Tk6dRwZy/Ee7Vm9eN0kzrNM99gYy6psLMfqZtuiddLFsdixjbE8wWjrd1L8x7/5afiylz/LbBfTofzPL3smtopCI7sN8mEazXKLLDOKeaFCmuVSgF+DCn7GWB6HjYB+N3Gy0vBOuF+SYVQ/i92K5WRuICnmXY59fyYVJTnBqnzNa4Di0DXMmKeO3qlSuetx3AqAj+9ZrqtOSPA/T/Ysx177uH3GUpc6Tnu3yOupPM+y/tyVvSHSROHERsfzADKDjiYzTH+bo/zs2wC/9pplOqzNhqGNKdL6Urv8LCmc2NRxFBcwNJ7lcIBfVTtzs8JEvysbW3T9B9kYg2xcnEfZczvMdLn6UJtJztHUs8yP/8ePXq+VYAD2HXO9q/bet7SVnWfe76O6qTKSwMNSgJ+VWALz0yxTP9hJEvcdaGUs08SyuGaRT/5ayDBIJzwxzzItfSYKf3FlHwBw6+ktZ9u2qeNMnuVh5hgTD9x6Ev/HlzyMFz/zTOW29HA9WRjLO0uq4Pfsm3Zx6eQGHnn6IGpbU7qU0kVleatBkJe7jvMsF4bCjDy43U6cZ/nsdh+dROFmthpRSh03WoRnWXdMQc9yxeU3AX6dBM84u43LRfT5MIvzNrz14dvw+vtvwiNP75sKjjFM41kG9PmOs3IQXqiyVuxki+sAq/Is7/Q7TllZtyx64TF10s9Vt59/9X9PA89GN9UBfrHeTTKWA3pPIN6zPCvoEDbLwbisWWZZHJoWRQB036yUu6Iw6dl18izXHDNNVHSbHCN0Bte2ynPrB7Eqpd9j6vfIiHpqb4CTm93S9qGiPk6568BzG5JhNM2zTH0Nn+iSZplSxwEw6StD76yp4BiTHYnl+d0LpI6raycPSOVtDn1mmI1xOBxjNHZlRGRMHY7CxbzMfsbxqeP87cbjHH/y2HW87eHbaj8fCox0PMu1W9e3gyhP/BOTHm4wGjt9D1VJpDGriZa/CSYVZ4LpNcvF1zzXEQNHyrN8PVKzzCOeadt7bnJzBreJ2ge8PKzeMsTr7rtY+1KQ8fHnT2oDflmp405sdvEP3vwcAMAH/uLpqO15sFSWT/b0hCAZRpNyxfzrtOhsGJOP++p7LuD//qZXmuCINAnoZuesWebebEeznIWjuc12nsyIfh5GyDCIk5td3HPTiWbtJWO55QTC3OuSDCOsWY4N8OOBPaFr9g2vfzbe/dYH7Dbs/d40qeNYgF/FRazMhuEtG1LBn+bZMOo1y/yz88SPX8hYsO5hIZfg17CNDCNJVKmAyqT91AXjOZ9LmniWpzuPqv35h1egXK/2czzAj35/ZX9o0nnx/fDzrargV8qzbAL8yjKMWN8y7ZK+amN2XKQuszKMw2F19qRYzzKgDWpyFlBRsBh5EY07gC3O5E84uPd5mOkxyn/f6VtdFKm6/x3nNih7sxf3vtPk8JGrB9gbZBM9y0FjmU2U2vYFTmVIr808fd9g5OZZTor223SR8+mL3DzL4XbHUtYsx+1jpT3L9JI98rQ2Mk9P8HhxI+tvvOIO3HF+B6+776Lzt/YBfvYBaWos9ToJzm738KeP68CrZaWO63cSvObei/jG19+Nuy/uRm3PDUY/WXssVNFJB3tM3r7KAGlLN02iZAJJosxKBFDjWZ6zDIN0b1yGQYZb1XtNnoxtryoXLR3Oi2lSxwF2MClpCVW4gl+Msax8zXJgEz/va13quLpJYmoGfV+z7HoT+w1lGPSaVHnSqVJjbNDjtNhsGPpnng2DPG6zkC9s9jo4HI3NezdpN3UeMQ7PkDSJNHKfsVRqlpUnw0AR4FfcczLi/D5HFRaWW/oazjZAOG3iZiAbRnvPsn13dVESFAF+5Il1y6BzYjXLgF4ZtMZyfDYMkv8B1ZNmLoMYFoby4ch9p2izwaj6XfNlGDTexMowPvq4Du571vnt2s/3Om6/whnn7Yp06B3ab6skZaNiMlHWLFuP+tw0y4l95vgRWnmW2eQibxDgt9LGMnXGT+0NsbvRmWhkci3VRjfF5zx4s/nb9MayvaJtvGgXT2zgDz55FcBiPcudNDFBBHQ9v/LVd0Vv7wScecnaY1HK5gmO8yxTRzobo/SvvPj2RtIC245AUZJIKUlbNroJHr2mA0HDMozwm/2G59yEH958IS6ddDX6w2xyyeBpmCZ1HFA2Kgm+PEoMR/EV/Gx+1TjPAe/k6T0hb8l4XH0NTRnlNDzA0G5pn7FjiQkcrHnfTmx28Pj1wYI0y/orL8HMPaA6Ndv0RubNpzZwfrePDz+i+8pJkwt+W+qulfYsxz2jVbrgttg+s/yMU2EEOm6SlFPH+ekPEwVkCHuWS3mWKzzLOz1uLDfTLFvPsnVqjExREnvNDobVmuUqrX+ITmLz/DaVYdDlyCqkVNqg1t9zXXTo2tatbJHRS5O8zYYBfjQJmCTRtEXQ+Dnor9ME+NVNdGkcHmV5oCgJ3KIkcxobeapBpcr3pgm0hc5xfkQq+HXSxNy4Sycn5w20M93AvshYbukV5B1tG8/ixRN90/ktstw1oNvb7yStXqREWb3XNJplk1omJptBQl9nYwR88UsuOxOnWLiXlxiMssbVkZpwfrePx64dAiCNN3XU9Z62jW6KV91zwfxMz2tsgF9bpkkdR9sH77MK5T+NreDnLr/GPEb8uU6TRAe1NJBhlDXL7nLpRmTeVcKvrhaCSsjOQiowCToP8lzqAL/U+TtvRhvNMgD86Je+CH/vc+4zP0/OU8vuW51mWc0mG0YbrGbZ/b2Cfk55wYyQZtkf0Mm3xl8Faqaf/cFv/la3JnVc5KnS8enj5FDJigBwOqb1LJd33K1YkQmh8/zabBhKxWXs4f1AVVYc/hk6xt4gc8Z7ehdrZRiF0U0ODjLmYz3LGXsG6qBnw0kdR3mWp0gdV1fcx2QkKWQYvA9WUEVRkuq0erPAFn/zK/g13xcP8MsR3yevtGcZ0C/F3iDDTYXHrI4q/aP+m77YbVMp8Re+jWeRJwlfpAwDaO/1A/SExfUsN386+f1o4lmeV87GWELZMPxlqFlzYXcDj18/1Dk7x2NsdFIMsxGGo/rUcT7UqQ9G85Vh0D1qO4FIkyT4via0Rs2IzdNdkmFEXICkMPbyXHf4nSJ3LEBeqYpBskIyxJcNgbKWfBIxWnAKtJlXUA0nJMPg70E3TWai9T211cNJlvpw0n745awbqJOknWZ5FhPN2mwYuRvQqGUYnmfZawd96/7OnczQtn6k/4UTfTx460k8eNsp8ztjzEf6lhPlfu0kSSHDyI3BDzDNcjAbRrxnuZtap8XeIMNmRDVWAE5xlKrVIS4z5Lrok5ssiK34qmUY4ffRyDC8AL9Jk4GkWAWjFcxJ/YMxlvnnim9189s9r85Et9SXMc+yn2c50bnC29ZgiOUtz78Ft5zaKO67fWHaOAC5J75JUOT6GMsn+hM/60egu3/TX9vKMCgzRDbOW8swiEVW8AOAXhFl3oZEKZYuanKxgfA+WFsaaJYXsbxc245UGe8IMe9sGOd39QrEkzcGOuNAN8W1w5EZLKKDEYprN5i7Z7nQLLfMMpMmYY8W9/gQwyyuAqSbZzne4EmVwqjQJ3dTm37STyXFqQpU8r11dH2a6mbrDGEK8lvEe2IDyGzwKZ/MdhIVHWw3CT4hmuR4jD3mRjeNnuTy8WOWmmX/OVRK6WGfBfht9lLsFJOg3PGK8u3011CeZe6N1vt027LRTfGTX/kyr32Js99JJObZts9+Ntbe0VSpqNRxpihJzHjAKsjtDbJoGaM2q2w/EHo+uNyL3vcbnmeZNhtk1TIwk2e58E7vbnTx7W+6H69mq33h7eB4lie9Nz3jWXbPE4hfRatqP1GWYRSe5WyM4cgvSuJqluclUXzG2W0846zWc/MxsM3p8nP1JU51rIGxnAIYRnmWeTYMHxrU26aOA2BKEcdICXzIWFZquja0od9JJg46VaSJll8AQJa3Gzzc9E7x2TDmqQ2OQU8U7M+jrDrJ/qy4sKsnhY9eO8Awy0vV5JpqXofZeK7L9NOmjuskSXCACGXDGEZX8HPTvsWePgV0amM5YQF+1YOY0ddXeJb9LCWxHbMtSjLZs7wIGYZd5qdVprG5dxT4G6sfnoSriaw/N9631L2XX/fau3D1YBR1/Dr9ZhtMeWhvVwrawLF5loH/40sexkYnxb//nb9wSzUHvN0hDzipxkxe4UhNcKh9VdDHrGdZFRX84MgwTOq4QN/QVLNsZRij6NVhnqu9qh8gjzAAJ4jQzTZROB5GWeX7qNNV6tVApfRz8yUvuTyxjWRs2jzQ9Z+nVJKh9jXp63zcd9fdCS85flhZlKQ+tem8mEazbCRQkbtYfWO5GGRuiqh1XudZ7niDVhu6aVJKyh3LxcIzvtVNF5LqidPrhA2SGDpJMgPPctyAZo7ZIK3QPNGTIx5kp6/DXGUYJ8hYPsQoG9ucv6P6AD8fo1ke5ej25pu9A2h/TZIkfE60RE1Q4vs4zbJiHrYm18yWru6yqlXjcbXHxhrF4WwYVobR0LNc7K6ueJD1LM9/UmmNMZYZJ9Xe5Aw5OqmOiaD7Nk0f53uu6ttlv6+7ts+qKSHskwaM0Gkwxqg3KhsZBv0M4J6bTpiSzlW5wunbqvRh/GsMaUMZBhlnJc9yUZTEpI4znuXAeGxSgcVplj/w8Sv4pp94P64djIzuehKJYt75ChkGDwK0muWRW8GPPMs12TAoXaVf/W8SZGzSe9VOs6zJagKRJ+FmVnH3kRoZRpE6ztEs2765TXXftth+pvm2NnPMEdQsA3EBfqbcdeDcX33PBTx+/RDndyfLOaroTKHPJM/yIqv3Eb00aa1rTBL7Io8iKmoF98E2aVLBLyZh/TxJEgWWkMIWX5izZhkAHrt26KTnmlSUxIeuofZ0zG8lY2rNslLBc/KzYTSZqPBtmwS9mAqgiUK30yzAryrPMvXDtJoUnWe5+Fzd5GChmuWEvFf6Z6qulyQAMldqkOXtAoEJP9q+tl181WpGk2tnnzO4tqn3LPDj5GABft4Ei0/4lLcd4GXDKL73beQYQ8Bct9j3xPs4BULfOBxhu9cxxyTPcsi5RHK8mPGgmyp86uohfuK3P45bTm1Gj+FuP1Alwyh7lv3P2gC/6sk697A2WVWhw8RqfkOSHvq2gZO0BN+u5Fkuft6n++nnWYadPC+aVpplFhCZN9Asr3Q2DMBq/W6KMJZDWi7itjNb+IbXP3uqmQ+9BG0Cz8hYXmT1PoKyYbTBKUrS8oXgM9UYr/ypzR7SRC1c2+3je5YPs3JnMWtoIHjs2mEhw3Bz/kZrlp0Av/l1YuR1mHU2DMW8QoA9/6gKfgosv2q8t4VLJ3ppgt//xNP4k8eu1wb4VWuWKcUTeZYb6kKNZrn6ulLJ68VolvVXP+e6r61OavrgWLpOANEEzzLX7c7KWHa8irPzLPtoCYA1cBX7PWB/r6uMlV3Lfjo5wKaOsxk2YtqXOMefhPKOnyYKh6MxDkdjbPVsSe76ctfxAa9//Oh18/1fXNmP1ixzQ7gqdVzC4huGzDMS8toPRtXZMKjPGTUcI+kaWhlD/bZK6b7JaV9x56goTBvqi5Loe0WlxkOa5WGWz63UdQhqYSsZBr1faLbyuAbGcgsZxpyMAxqo2xgGZ7e1AbjIHMvEme0ezmy386iniQ3wG43zVvpIvknMMvpn3H8RP/s1n46zO+1XAWZBkrjZMGxZ3/kZJxvdFLsbHTx69aCQYfia5bhju3mW59NWADi300MnUTjTIo81oNtZuTzKfh6a1ERxmneu94wdQHi6t6989Z34xJUDfPE//83aAL+qzC2+B6jfWIYx2fO2WM2y6+3URkHCvPE0GQhPHprAz7mJDGNWg/WsNct0bUoBfkXaLQpCo2vnS17GY7cP5R5d22b3/lgNbLxnOfY9MZrpxG5/dV/nht/up6ZdUUVJIq6vrzXfjAzSp3LieW4zdZTOJbEyDOrfgfC1HWbV5a6pzxk1TK9KHx1GepYB/X44shw2kZ2HZpmMf5IHcYcX5aluK9Fsi5ECTbEtxQvEXrPVl2F0tVf0FEsnVIVJHTenm9YxS0fNO+UkUbiw2194jmUAePcXPNB6xpkmygb4tUwd52iWIyYanTTBXZEVBucJBXQSi9AsAzrI79FChkHL901lGNSpzzvP8ivuPo//9q2vxoWIyWyINEmC3kPuFQJ0Vg8g7vnhqeOaRIhzScXnPe9W/I/HbuD7fvmPcfdNuxNlGNUV/PTPptx1ZGOMZ7nGANxdYDYMOga9DjrAT5WM+ll4lvlgPGk/s5ZM+MecxURkUgU/41n2PLZj5ll2CjHQMxdYiufyo9AxQ1DaxHjPMhkq9tmn4+70uQyDZGvlPdPzEhPw/X/+tRfj+uEIX/fj79Op46I9y/oYP/X+T+Cxa4OKSXk5zzLgGoz03WGNZ5kq2Y3G40aFOehaxnqWAd0HhjTL43E7DS9vB1B+52gsuXFInuWUbWdT5i0y1av1LDfflrbx0zZOYvWN5U6Km05uRBl7ddkwZkF3yiXney+dmFiyex60NWQA3RFmztJri4mC41le3As1LalSTkUs61me7+oAFSbRqeN8zXIzz+Ro3H5pLgallJMWsSk8IMjbcVizHKN5V24Fv6bXjIwRes8PhlnlPiblWaa/9xsWJTH7rU0d55Y2nyfUbF6gKGUyDH9Vb5o2KaWKojCTvT7z0Cw7hsMM+quk0lhWzoBNxqcZzFnaM76pMRRYV0zXgaRL1gCPa383SRpnw6DP82eUx+TMyrP8aXeeAwDcfmYLf/DJa41kGADwNf/6fQCAmwNSTh4M7MowykGmg6zaENZGd5FSsZFnmYxl0q1P3sbPaU43ZJzn6Kj2jhw6h6rUceRZdld+9Ne28Uxt8SeWjbY1muVm48PKG8vveOllXNkbRH3WenPm61lum2f3n/yV58/VyzcPUhbgNxPP8pLTwTUhTWwpVGAxAX6ADvJ7359fwSjjnuV4TxHgDmAL7MMakyZJhZbQ1SyPsmr9Y2nbxK/cFdsW19CjeIn9YXUUfLdSs+waj02zYdDH6ouSaM/yIrLr+MYYZSahY3c9qcG0A2cvTTDMsomOD/7nWQ3WTu7mWXqW/WwYcFPHUfNtKrDig152kdA1NnmW2YoK3+ck/MpoddhUeOV27PRTo22tK3fdJuPRbcZYjsyG4e079J5QYB5ldDDtC2iWD4ZZ5fuYJgqj4VhLFRtplvXXYTHQxDxvVZrlJgU2wm3RE4eSZ7k4H+tZLmuWRy0daW1RRRbtaYqS6HfkCMkwXnH3+ejPWi/HfNrSmdKz3LYgyjLhAX6j8Rj9yLQ9nNhcqKsGRXkTgwUE+AEkwzjATr9jPJKDlp7lJtssg1SFBzGe0gngAX6RMoxCp5g30Cz7kgrjWR5kOLkZloGZPMslzTIZj/pnir1onme5xrO8uTjPcuoZcKNCo+h7lun0pjVcu50EGFR79E272HFmNVg7ZaRnsEv/2ph9q0KGUfzM/84ni9pjWDbegnmW2YoKEC+taJL2K1HuV/78bfc6xkiu9Syb9yb+Aj/jzBaA+Cq85esd6mf0BINLMAC3T6LrcjgcVwbKtzUafc9yzHvT7ybBFdomXtLqtpSNZerjyLPMr4EqvNHZuFqiMhfMxLLFpmQs44h5lptAz+i8lgNIL7lOBt+08GCptnkc+Tu0VjKMxE0dV5c3dJZcONHHwXCMbDxEL9W60CaaNsAdwFbZs1xdlMTTLI/091GpB73l7aYGKh2CjOW94Qint8PGclW1SfIu0T6tZzmqKSzLRE02jAVqln0ZBuVV9eUis5BhABVlfQPMQ7PsyDBm6Fn2T4V0rmQUK8/4dVdHysab410sHpNSBb/I+9BJ4z3LvrHO27Hd7+CpPR3sV1fumoprNHlObj+rjeWtSKeT/96HV7Aom8PY+X0wz3JWbSyThzobxxVOstvpr6Z/j9j2H775uTi7Y+WcRq/u63WaEpj88J9D2TDoGR62XHVuCx2pzSFLqeOOime5CaHKRrOEtEjz9iyuEpRwHkDjhOtE09Rxq4I+93JRkrZp+GK55dSWOV4nTZAqhbYV/ID5yZJmQVVRkmk8yxS00yR9FsAM3+IYdJ/3B9WR3pM0y1aG0cyzbHONV3/+7E4PO/3OVJrxWGzBkRzjsfbYdxJbGZTuy6yCrENlfUPwv8/Kw84N5Fl4q0k24JeEV4CnWWZ/UzzAryIbRsCznPue5ejnLYk2tPxMBB3PWKYfD8i5EAjwo+vaVIYBxHuW/V2HDFHtDELJWA6ljgOqx37qc5pqd41nObIoCQC85I6zzs+0RZM0meG2FF9LMgwvdRyL2UmUAnIdw7CIFS5iGs2yaWbeLFvSkTKWF5UNY50MvmlJlJtnuc21XWcZRjh13HzlNLcXgwKgn7k0Uc3zLCfuUtmq0qnQLCvlepYbGcvFfWtS8hdg/YdyJ8X7g1Flpzy5gp/+mbTnsW2JKUqy1evg177t1dhZUIadtPCekb6SKvgB5fOd2lim7CETNcvMCzyjft8JnJtBd/Waey/gR7/0RcbYI0oBfp73uCr9oU3dVvZ+Zr5nucFEMb6Cn9te/uxv91NjzFI6uboAvyardJfPbgOwKRMnUfYsl49F/cyg1rPMnD0VxjJV8Gtai4AOY4qStNLgkqd0Osdy1apQKXWcV5SE5CeLqCRK0LPaTrNsr1eOBlKlxkdaYeaeDYNe8GPkWe6kyiyntS5KwjZZp2uXqgpjec7nQMuNgNb2dRLFyl3H7WNdNMtJUl2UZByQwMRW8MvzZumzgLoAv6w6dVxaMcB4nmXKahJfwc/dfxUkxVgEVJ2P3gldwY+Menfwmnbg7Hoylvp26YFvdnmWZ2uAd9MELw/E3hhPfcCw1Zrl4ge/3LUqt6sqz3KsAfyulz8Ld12MKwluV3D1z/zZ3+l3TODpkzcGpb8TNjA2/p4989w2fuDtz8crnh0Xx+QbUqErQX2Fr1nmQXp8P1WOMso33LQWAT3fJttRi0eYyzCm6etpS38fdM5X98vGskKRBWQ8XmzqOONZbr4tbZOD8m/HbXekjOW5Z8OgQXSNvKPTQgMkQJ7lNqnjuGd5dQ03nzT1UsdlFLAy33M4udnFyc0unt4fau9dqhqnjgul91lFOkl1UZIM1lq+dqC9VDHGYaL0s9pUs+yvTNGgMM6rjdyq5WS/dHHTbBg2f/Hq9DXGCzeyWtTUM45DBlQbeg0CItNEYTzD1FWzNparUKgL8FOsCqVr6CVKlZ5HE7TEcjPrz8a15Utf9szodtM+jcFixl1dMISq1D55Y4BemgTH4yap4zif9cClxu0krhZ9iP+ZcZ5jOPJkGNzLz35Pk96q/TRdfbV5lqfwLBdfpylKAlR7lqlS6BM3DgH45ehVq/OeFjpSm/M1k4tiVSc6sLX5oVYXuwQ4n/13jGd5ha2PGcMLc7TWLLNN1knC4nuWh0WQ2SI0688ovMvdtPAsF8ZyU2MLWG3N8omNTtAATpSbZ5m8GpQBog4KSm2tWSZjmQ8KbTXLxS6MDCO2Y67Y7zJJE+2Fo2wHG11brY201bOKG+lGapYB+3zPTLPM9jPP6ojk1bSSCeX8jRclKUkKvC6oVPWv4USxCTYftHvdt3sdKKVM/u8nbhxWOhbMiswcHQ/+uT91I2Qs6z6+PsCPe5bDEjzqc5rWIrAyjGb9O4eaN025a74fvw27fV3C/LFrhbHsBfjlRX7pxWqW2/czfgW/Y+lZVkqXgZxbNgyjWV6/FHBt4brdttkw+AvcJFXQsvEr+B1mi5FhADqY5QMff9p4Xq8VJV9jg1t4h71C9laJv/PZ95X0gkBZs0xeoRMVKdzcbVWRcD4+aAYoe5a5F6nas+xu4+/LD/CLffxvP7OFOy/s4O4VqGRJkGFBKcH6rJLYLIuSAHaiEtOXJ6r4P6MHnd/quXqWFQWiVv8NKJe7TgKFfPzUfmOrw5g5dgncvffbfd03kQzjYDiurLzb1rPcBH/XoX6Gyl37f0srYj6qA/woG0bLAD+TR7/N9bD3fpqr6Zeu57/f7Xfw+HUtq3E9y9robFuwrC10nu2KkmgoXiBWqrQ+lkskacWy7iygh2GdpATTQgMkYEvcttkHsU7XLklswnrAapb7C5gsUZBfp/AsU7BMbLn0ddEsn93p49LJzdLvyWNBXN0fQilEBbMlnhESnxHANfRiyi5XVdqzeZYLw7vTTIZxbqePX/z6V+Dyue2ozy8CMuBCnmWaBBvv1JTvORklsZVbZzlQk+Gp1HxXZZQiCUY4dZypxuf9TaGs87dLy16+4Dm03+yz+MI9y4CeGNLvqmRENnvK/EwQumZ3nK9+h4wMw9MsVxV1qjKW6d0YNcw3bD3L7WUMXLM8ze2mTUO37ORWF9cPA5plBRP0uy55lk3mGOSNpCtHzlhOVDhgaBaYbBhrFKQ2LZ2Ue5bbDYL8dnQXOPucFho06fwHNamQZg0l4O8WmuWrhWeZvDeTqFpGXBfIY0E8vT/Ebr8T5T2kyPTmVczc3L4bzLNcddyO8ZB53hjlfqV9rbIkZhJWhqE9yxvdpCQXmVWQtTGmInYz6z5/URIYmhBayQRrg7Ja5tyrzBZaPeWp/QA0liA1aze1w/csd0xbKGNFlezOGtPzu8Z0Le66UL06Y4qJ1KaOs99XZsNItMa8rWd5mI1bvzO01fSp48KeZQBOUSZ+DbppgmE2Xppmud1Kt/46HpfTMtaxPpZLJIman2eZDL110t1OixvgN51nuZOomS2VLgKaGIw8Y3kR9994lpMEnSQxBjsNSJNIEsVSPM2liXOlpFk+GOFkxZKuj1Je6rjIC9DxjCQut6oa06tkGEopp2gHVWKcpwZ23lgZRrHC0knNdbFFSfTPM8uzHCnDmKVXa975+gkFmCqT+md3gmsLjLhtURWTA/7O2FWV2bfblLsufqZrzyfyO4WxXGUMk/EVI6tqy5WiOErd6gxJtkoyDMeTb6mv4Nc8rofLMNr6kZzUcdN4lo2xXP4bjyvhxvJWT5c315rlBcowvGew0bYgzzKKbBiR40OLY600F0/0ceFEfy77Po6eZb8oyTQBCKsU2R+D71keZuNiYJ7/edx5cQe9ToILu33HOxRrLAPakBtm0wV9LAsFG9EPaBlGbJo08oA29a75nrJ+hGe5SoZBf6NrbzXL63cvCDLguGfZz9oxswp+TWQYSXz1uRhmZfBPgmQYocwVyjF8XUNKqbAhn6hQnuXZn4O/akKeSC4R2+13AexX9vm3ndnCf/rqT8c9N81Pk0+VBM+xanc+dA4DPxtGxcpcdQW/dtkw6L6OxtN7lnU72t9v5d1PjuNZZvd0s9fB/jDDMGvnSGuLWd2Ywh6hiWrsJTtyxvJPf9XLTJqmWUMv/nE1lrOGOST5PoD10isD9j4PRmNs94EbgxE25/Rs+VzY3cCvf+urcXqrh3f/3B+a38eWegVQFDOJj/ZdJShvKXH1IN5YtprlZkEzddkwJgX4hQYKHZwJs68qI2ddoOtqjeW0HOBH13BazbKRYcR4lme7mjjv4lYE6ZLpOXeC+JQrqeDTAVXRNr+QCf1u5nied6NZZhP5XeNZrh4r77v5xOzbxnhqTweknd6qM5Z120mH3+8kOBy5+lsnwK/ifCjVX1OHEq/g13bVlbdvmrtNhw/1Zbzv5eP4VhFwfv1wNPU73wQ6UpvH2+r7m0lXjpyxvDvHJP2TghaOIqmy+T5H47ylZnk9PfLUEewPM5yGzht6psZLMWvO7ugVEup8t3ppow61myQ4wHgtDTQyzIir+yNcPrdVswXfVrXLs2wKJZSf10kBfiFvTMpkGEopnNnqRVcfW0WSYgJjZRjMs2w0y/qz08pNeh13f/Xtmq2+2J8AzAsFqiKWs9/YNpAR7esqqyYH2sAutpmjZtns0/PAu8ayHoeXWYSK+u9LpzbwpoduNukbOdR2yvCy0+/gcDSo9CzXV/BrXouA51meRT89zf2ukx+RBK7XcfNmG2P5YGT6gEUwTeo4uw1fu5zM+vbcS4Be/HUz+qYhTdxy19PkWV63ScZmsay4N9Ad6ZM3BjizPR+JTx3k5dhqWNY4TeMNjlVDsYEf0AF+0Z7lljIM6yXWzylfcq2apNSlwOp4BRl+/MteMjeJ2CKgXLLcs+xPFmYmw2igWa7S8LZl3pVgCVV4lu2kjrcBjmbZLYUdNuS5Y6NpBb8m+EaVzYZhjdETJsBveZ3Pt73xXjx02ym85Fln8dI7zgU/Q5eVJoDb/Q6euDFwnl/+GPQDBjeg5RQ2z3ITz7L+OszG7bNh8FWHGQT4hT3L+n76BdloFf/a4WjB5a7dr422dTzLDWJaWhzr2EIzp+MU4EcDZG70WM3P3RQNWDMZBkke9pmxfPHExsLb4ecxjYU6vXXULCdKuZrlg6Gjm6vfVms3mwb42Wp0+udOmhRGS7XhVLdk76exvPNCXDnhVUUV15V7lhPv3Z5Vueuep4GuI1VqphIvavq8g5GNZjkgmeCaZb8kb1X2D7+Qif7dHNptjqe/hj3Lk2UY82an38FbH76t9jP0fHFjmf8eiMuGQferdZ7lKUpV+/KdaQm1n/pe//y32ARpGQWUpvEs58XLF7uL42P1zYDOMdYsUyc8TTaMdfMsU0ewN9Bp27RneXEyDIIMtdgcy2Y7b2l8neAD/zAbY2+QRUfOU+o48rLFdoYhScWk/MgvvHwGn/mcm3B+t+wx7iQqKvXZukAV58iz3Od5ln0ZxpSvOq3ixewnqfC0toXet7mnjkNRwS8Y4Mc1y2UPWqhpFGQGgOmg5+dZJo8mXfsdZizvrICxHANdx8Pimd4tzqFpnmXqc5rWIqCPaq1zg4ZXMM1KAnV7oXfpRIyxvEjNcnGoNq8obUK5+KOrqjY/1PGFHoZV7wBmCckwpinHyYOc1gmqlrc3zJDnOZ5YlrHc2rMc751bOdjAT9ULT0TqfW3+2mYZAej9TgOepKp9PPumXfzTv/qCYJ9wcrM719RYi4ZWmcgLt9HlnmX3Oi3Ss6zT9M2ub5lGD9nsOFRy1/5MOKnjvHLXlZ7lRJXyLM8xvq9kXG2x/slolle8zy97lmlyzApvMAO0OsCvXTYMq1meIhuGCn/flDqtPvVj/v3c7No+ebGe5farpqbcNfS7FbsHkWE04MxWDxvdxJlNHXVIB0cZMdoZy+s5ySBjeX+Q4cYgw2A0XoqxbPOYtvMsr6OtnNAaNWCqF8YannxpE0B0/tLQYNFr4OH0+aF3vrDxasAqo5ReZj4cZlBKGw5GsuJ5lqcudz1hksJJkhl7lheWDcPmegX8PMu8dDUc17JSVanjbE58/rtZYw0U8sDre7UTkGH0FlDAaRroXMizvFMY+W5RJ/v5WhnGOMcIzZ59XpSk7SqAq1lutYtiP5qgsbwx2bO8UM2y8Sy3MZb1V8pEE50tqfGRjjGf//xb8ZI7zs4tNd0qkia6AybDo80gSM/iuqWO2+raAL8nr+s0RMvxLJfzmMaw3ppl6x17mozl6DzL+itN8BoXJWHPKQX5tdGv3nyqXMZ7nTEyjNEY/SIq3s8zbbyyUwf4xXt3E6VmugScBiZN84BSvU3yLNPPzvcBu8QxsBeQDSPx+vWdUDaMFXeQ0GNDnuWdfll2xfvPSUVJ8qxZXI+RYWTTl7sGppRh1Dz3RrOcVhvLixzfzXStxSG5ZrlJBT8xlhvQ6yR4xtnqakBHEdIsZ9lx9iyP8MSNQwDA2WV4lmmZs+GKRqeBwbFqKNiB/+qBNpabVPADtLeG/zwJP6MDwDzLa3gNZw3pMg+GmXEY+BH0M/Msp/Ee/VSpmS4B061eROo4bdOW84FriYb+fuyVu1Yq/DxSdg29jf7dfIqSuP3KA7eewt/97PvwsrtsxolVCPCLgSZ11ljW7U69e0FUGctpkQ0jz5utQhnP8njcWkrkTLKmuNx1z31VgB93HC603LU3YWu0bfGVEhdEZ0tqfijhOGGM5by9Z/ko5FmmBPfL8CzT9WsuwyDjb+ZNmjs8G8bVfdIsx1fwA7hnOe6YNLngz/ikAL/jhJVhjI3RYAP8fM3ylJ7lBhX8lMJMNctGhjF3zbJysmH4GS/o+Q+Vuw61LFE2sM94pedwCtQUblx96cue6XyGAuVW3Vg2MozRGErZdKGpE+BXnjz70ErAeNxMjkC7zrIcbdWdbsGa6T3LwdRxmySrWY1sGGoqzbL+mudFWsbIa7baT7KwdEzNe+NZbv7IzMrbtGioWt/eIMMThQzj7DLyLE+ZOm4tPcvKFmUgzzJ12JPgEeb658jOsEazvI7XcNZQX3Awsp5lkznCW8WY2lhuWMFvHnmWF5I6jsswKoqSlMpdoybPMsvNTPuZfbsn9yskw1hmnuUYTDaMUYZumphJYFWe5Ymp4/KmeZbJszwjGcYUl7tOB9zvpNjoJiXPOs/931ngxGgazbKRYaD8btVu1/hIwrGCXvxBsUzVyrOcrKcMI0kUNroJ9gcZnrxReJYXWMGPSI9hgB8ZEgAL8Isud61PmCZ40Z7lQGBXP01KvzuupEW2hYNhZqqhJb5nuSb9VBOsVnzyZ2etWaZjzj91HHm3ypkrlGJp4HL6dNG+GhkGz81Mn501tM+6fmVtZBgmwG+MHjOW3Qp+9vNV2TDSBCYQvlWe5ax9pVVXojMDz3LFu3Rio1s6/41usjDZEoeONM3YNs712k2sZ1lkGEItNBgOssz5udE+SLO8ZjIMQM+cdQW/AXpp4lSpWhS2Qla7AL919IpSpgBAB/iliYrWbNtsGE01ywEZRleMZSJR2nOWjXJ7XSo8yzOr4Bctw1g/z3JSyDCqA/zoJ1dXqZQKTiKSBCXN8jwq+Jkl8Jp9G2N5xft8en61Z1kZzzGX9dhCO6rSe5oo1SoIngf4LbsoiTnPinbcfGqzJENUSmGzm2JvkC1WhmFWN5pva97rIr2oBPgJM4FegIPhFJ7lYpN1y7MMwHQE+8MMZ7Z7S8ksYYqStPQsr6Odx7MBPLU3wOmtbvS1p/MlzXLs6YdShjXJ93vUIc/lYBTyLM9WhtHkut98ahOXTs6usqbJhjHnW07eY9ImOzIMz/Dll+F5t50KGm1ObuYFZsMIsd3r4ORmF+d2Vru8O13Xw9HYkWHwy0unWTd+8b4pbfDgmGDkcfty13A0y+2hbava8YNf/IJgue+tXmEsL2F8b5U6rvg6LtJhSOo4YSbQi0OZBdq80PQwrlvqOEBnxNgfjnAwXE6OZYAVJWmZDWMe3qV5ow0J/f2jVw9xfjfeGPKf2dgl+pCxbD3L0Yc/slgZxth4DnlpcGB2mSS6DbTi//sXv2CmT/isDP5JkAwDRl/stoEbvvw6/C+ffV9wfzrPsv7eeqvn4Fku9lm36yRR+IWvf3l0ifplwWUY3TRhedWT0mfqAtS5N7aNZzkb5zNZyZhmUj9JQnXhRLgPpqxRi/Us09f2K91GsyyeZWEW0IM1lWa52GTV9WshaNb81N4QZ5egVwassbfVNhvG+l12U4UPAB69dogLgXLSddsCwGDULs+yeJbDJKrIhjEa41zHC/DzPMvTDpxUrTEmqHXW/cqsdNeToOc0ZNgq8JzJcR5DrnOep2c5NrjqQoMJ7rKge21kGGnZ8KPTrDOW+XVulGfZTOzz1isZswrwaztJpED4tUkdV2xjNctxiLEs1EKGGuWhPE55lgHdEegAv0NcPru1lDbQNd9pmA2jawL81s/QS5Qd8B+7doh7btptsG271ZA3PvcSEqVMJD/AK/it3zWcNeTtPBxm2Oi6xVr8PMvTVvN66LZT+PF3/SU8dNupqfbThoV5lsmrGAzwcz3LMe+wLhrjaZbn8O7T9VnHfsWHl7vupgkeuPUkXnT5DG5nfT19pirHMuDq25sYvaViMy1QNT812k9bY7lHwZwLNJaNbr7FtsVGeU6e5bi9rJ/1IiwU37M8jbG8bnmWAe1Z3h9mePzaYGn6Owo22WoY4LfOmmUFylua4/Hrh7hwIv7a05ysqbF825kt/PWXP8v5neRZtpjUccOY1HHTHUsphRc/6+xyYgQWFBhL+x8H8oHryaL+Ps/jPIY8zzLpoJeVDWNdoOfrYKhTx912Zgv/5m+8xMm8E+NZ5tcibfDwO/e8deo4bnC32oXeT/G1aV+3ZTzLS0gd10YWWpxpXhSRibW4xbMs1EIvzmAqzbL+um55lgFtoP7Rp65jf5gtzVimQXWnoQxjnSv4UTaMJ/cGGI1znG9w7WnwMLnBpzh/ybNsSRJgONQyjKqiJNY7tX4TY2JRqbBo77astetlNJ5lxD1/tM0X/4vfNJlz5qNZpuPNfNcLx2bDGOPkVkUO5eK+1AX4tdcsc4/09J7l6WQY7dqxtQzNMn1tpVnWX6kgULRMr/GRhGMFDRiHI506rk2lrLWWYfRSfOLpfQDAuWVrlhsG+IXKN68LuihJjseu6TLjVcElIaizHzQM8AsRyrt6XCFjzCl3TcaymZjpz67jxJjwddjzwsgwgp5layzHBiGR5/93/uwpkw9+Hq9+TFGSdYGu62A0riygQkNerQyDG71tjeXWnuXw/ppi4w2ajdMmwG+RMgzl9jdtth2Pdbnr2F2sn/UiLBRjeEwjwyiesnWVYZDj51yDILNZYrJhNPUsG83yzJs0d5IiwO9RMpYbXPvEk2FMM4AYzfI6XsQZQ9kWDkZjo1lOvQE2OQKG1KLOwQb45c7P+nse4BepWS7SzY2KkuTAfM7BaJZnvufFw8tdVzlzyLMcSptm92O/bzLJcgzdtsYy3OemLWZFpaHRu1TPcounkHuWxw08y+tnvQgLhWaLJhtGi9kjDahrmTquazvIJlKAWXLzyQ1cPNGv9WyEWJT2ch6oQobx6NUDAMD5JsZyywC/EH0vn/BxJlHAcDRGNs5L14X6hdT7eR2hc1pYgF+hM+ZHS+gFQBPNskJWGMsHw6KI1Dw8y/R1DfsVH7o+VO667jO1qeN4gF9Lz3Lbe+UHhrZlUlGSKiiWZqHSqymkQGRgj3Ot7Y89XZFhCLXwaGFg2jzL6zc322TShyYG2yx5+4ufgbc+fFvjjtB4lufRqDmTKO1Re+w6eZbjZRjWWC40y1NYDL1AkYLjSpoo7BdGWNmz7K5irLtsJVGL0Cy7nmXHcErs85vncR40pRSyouQy9dfzyLFus2HMfNcLh/cVlZ7liAB1fu+ayBj4R2exejXNHpKW7y5JspbhWW7lCCLPcp4XBX/i9iHGslBLKcBvCkH9OhrLXCe8zKIkadK8zLbRka6h4UKazUevHmK333EmLTHbArPxLEuAn0Uphb3BCADPEqL/1vHyUa+zZhmgd26+50C7HwdSx7kBfrGaZbsCaIzlOZwCteUovBPcWO11wudjsmHUjF9unuWWnuUV0Sw3zoaxRM1ym9Plpxe7agOIDEOYgAnwG84gddwaGsuUQ/L0VnftjH2boWDJDWmD0h3ZY9cOcb5B2jignDpuGsNNAvwsiQL2Bq5nuSrP8robUkqpuevU/QA/fzndKUoSKcMgpwb/3cyZYgl81eDXp1qGEeFZZhejTQU/YIpsGJ7WvS1ts5yQsbyMPrJVuWsWK5A3qOC3XqO/sHDoBW5aOphzZqeHC7t9POv89kzbtggoh+Sy0sZNwzprlinA77Frh4214so8s80q+IXoS4CfIU0U9o2xPKGC3xprlgF9XvNekeHaSf4zQHmWw+Wuq+CeZfO7OYzwR7EoCVBtLNMn5pENQ7XcztlHRTuakii9mtL0vtpy1wvMs0xfp/As5zmKCn4iwxBmgJFhTFHu+sRGF//9b792pu1aFDRrXkdj2ff2rROkWX78+iHuvflEw23dZ3a6AD/Xg3qc6SQJRoVlR9dlq5ciUUC/MJ6VZzyvK4laXOq4ccCzTGnggPhy14lSxqlhjiGa5Vpm5lmegQyj7fV0ViTa7aLYTzvp0XJkGPprK88ym6TGpmUExFgWJmDzLJPhcbwWI2jWvKy0cdOwzp5lquB3MMyMdz+WthX8QkjqOMtzbzmJn3r/JwBY4/jNz7sFd1zYMQVzjooMI0kW4FkurlEWCvBTdpmYf7aORKmyZ3keKowjco8B9/rs9MP9jNEs11bwm4EMYxY3axoZBtr1czt9Xe1wkTJLU+66xfnSNjnyQrMct5PjZfkIjZmFZ3md2TQyjOUE902DzVCwfveMKvgNshzdhinzShX8pjGWUyl3TXzanefM9xtFgN/uRhcvvcP+vm1hg1WjlyZzH/zpiQoF+AHas0w53qNkGElZhjGPd59ehaPwRvDr84bn3FTxGf21NnVcSznFbCr48UnWNDKMduP7y+8+h3/0ec/FfZearQBOw1SeZSbD0DnM47YTz7JQi1nSpgIPx8xooByS6yjDoFWAdbxjqtAsD0ZZY6PF19lPY+jubuj73yQbx1Hlnpt2zff9bsWSdfHrpoUNVo3veuuDeOa5+cZYmGwYwQp+WqtsCmFHXM5QgN985snru2Llw6/5828/XfEZ/aG6oiR8bthEjuBIb2aQDWOaO5Io1eq97XdSvP3Ft09x5Pa0eQZpm7wQLYtmWZgJ9OLbctfr30E2gYylJhXkVgVbgnj97hk1eZCNGxez8Sd400goHrj1JH74nS/Eiy6fab2Po0KSKPTSBINsbDzLPm0LG6war7rnwtyPYWQYY2MSm79R6jibgzluf5S1iO9n1hjP8nrfYgC2j3jD/TdVeuHp1/EBfg3yLM/EsxzeX+P9LCADzKyYJnWcXdFpplle77UyYe7MMlhqHXnG2S384y98CJ/9wM3Lbkpj1jvAz2rlm5ZJp/OdhWdZKYVX3XPh2K2oVPGlL3smABiNsk/bwgbHEZM6Lnd/BrSnkgZz/bcYzTJwWEodN5OmevtcX3mXz4ufeRbf9BnPxne97cHKz8SkPnWLkrSTYbRWLnHP8jSaZbU+7y21sk1zrWe5yIYhMgxhFtCLf3hMNctKKbz5ebcsuxmtMAF+a3jPeHqfpvmt6XxnUcFPcPmWNzwbX/CCW3D72a3g3/1UckI1Jt/ruBzgpwrPch4wpKsIB/jN/j60zce7ivQ6Cf7mq+6s/YyRYVRIj/hngKaa5fA+msBlBNNMYBZRtXJW0Gm2OV+ThaZYuYm97uJZFmrxA/zW5WUSyiWI1wneCTY2lmeoWRZclFK488Ju7d+B9ZygLRq7HJw7PwPFM8wC/GJ0lYu65MazvJjDLZ1zOz1822feg8+4PxwACLTPajGTPMsz8ixTnuV1YLoAv8KzjOL9Es+yMAtK+s81eZkEIPVKEK8TvMmNA/xmmDpOaMZRKXe9CKwMoyZ1HOI1y6H3fB6TFmOoHJN7rJTCl73ijtrP8GuxeM9yeH+N97NOnmVKHdd2ewWgWLkRz7IwE0yA3xTlroXlcBQ0ywAaB/j5FfzWJWjlKCCa5XjoGa8rSjJuKMMo/27qZpawOW7lHhNtNctpSyOb45S7nsLfv9XrYLu3Hv5TO2FruT30u5Ujj75i63FlhKXhe5bXPX/qcWK9i5JYejUpm0LQ+Y7Es7xwyMMmnuXJ0BXyYvL035RblCQ2z3L5GLO/D3QcucOWWcgw2r4ys/Isf/NnPBvXD0ftd7BA6DTbTtgSpZAjx7iBZ1mMZaEWX7MsY+D6sM4GyzSe5dRM8HRaIPGALQ661DJBmQwPNALcpfykyDNusspF7W8xnmV6N9dxEj4vXM9yk9Rx7PuZaJbb35MLJzYw/4SJM2LKZ1BPRnWu5dhdTOUmVEqdUUr9glLqI8XX0xWf+1ml1BWl1M9Mczxh8XSYsdxJlBgea0TnqGiWG1fw01+H2ViMtgWTKCUTlEhMNoxggF/x+yYV/AKfmcd9oD3Kq2Vpr1lm280gG8ZxYdpnULHJ6KLKXX8rgF/K8/wuAL9U/Bzi3QC+eMpjCUuAOoGBGB5rxzprlmeVDUOe2cXyostn1jIn+TKwMoxQgF/zoiShz8xFszxFQYijCr/ObfMst+6rZhAkuG6Y1HEtJwo6vq88Sa1jWmP5TQDeU3z/HgBvDn0oz/NfAnBtymMJS4Be/INhJobHmnEU8iwDzY1lOu/haCzBfQvmVfdcwPf95ectuxlrgfUs08/8bxSA5H62jkV5lm0FP3m3CMfobVvuegbX87jckmk9y4lSwUlq7TbtDmW4mOf5I8X3nwRwcZqdKaXepZR6r1LqvY899tiUTRNmwYmNLgBgbyDG8rph8yyv333jHVjrCn7jfC0nCsLxgB7NkAxDLxM3LXddfYxZYjzLs9/12tJWTuGvJrTBNbhb7WLtsKsb7a9Zlpez0NQxMcBPKfWLAELZuP82/yHP81wp1SAcoUye5z8I4AcB4OGHH55qX8Js2Oyl2Oym2B9max0wdhxJ11qGYb9vGuBnK/iN5ZkVVhaTZ9mkjuOGk1vuOmZEDxlp85gobxSV7Da6zbLUHGXaZsNwt2t3bH+SdRyYhWfZVs6M22aisZzn+Wur/qaU+pRS6lKe548opS4BeDTusMI6cWa7h7+4so9U0satFRSVvY46Nt7pNy1KYjTLozE2ezKgC6sJ6S3HAQ8XaZZtgN/k/YVlGFM3s8Slk5t4z5e+CC9+5pnZ73xN4StYbTXL7bNhKPZ9q12sHUazPEUhF+tZXowM46cAvKP4/h0AfnLK/QkryJntHoD1TkV2HFlnz/I0mmUjw8hykQ4JK4tJHVcR4MdTx0WVuw68JvOaKL/i7vPiWWa0z7PMtptBBb/jkhmDzrN1966aFfwBpjeWvwPA65RSHwHw2uJnKKUeVkr9c9Mupf4rgJ8A8Bql1MeVUp8x5XGFBXK6MJbF8FgvqPriOnagbp7ldp7lQSYBfsLqQh6tLKhZbl7uelF5loUy1OekDdOrKqWmzk1+HDXL9LJMU5TEVM6MHB+nKkqS5/kTAF4T+P17Afw19vOnT3McYbmcFWN5LbEBfktuSAt4kxsH+PHiDvLMCisKPZlUwc+XYTie5SgZRugY8vwvAm4st9k2y/MpJAXHT4ZBtNcsA6OGmmURoQoTOb0lMox1ZJ01y042jKap45xqWut37sLxgJ7xcWWAny133T513CxaKkyCrn2b/oY2aRjHbHBSDh6TyZEN8Gt3vmmiMCpmqYuSYQjHgLM74lleR2ye5SU3pAVONoxOw2wY3EMnz6ywovjlrkMBfnluf55E6DPrOFFeR6iPbTNGqim80qV2HJPbbQP82m2fKMU8y4sJ8BOOAeRZFmN5vSDN8joOmNNU8FPiWRbWACPDyMuDtlJKFyXJ3c/W7i/wIXn8F8N0Mozi6ww0y8dlKcEG+LU7306iMMqaZScWY1mYiMmG0XadSFgK53f62N3o4LYzW8tuSmNmUcFP70eeWWE1Ub4Mg/0tUXCLkkS8AovKsyyUmU6GURjaM9AsH5fJ0dSe5URhNNYyjNgxYqoAP+F4QMayZBZYL05v9/B7f289E8/wDqzfsoIfIKshwupiipIEgvgS8izTZ6NSx0k2jGWRTiHDoL5uFhX8jo1mmbzxU2iWh1mzCn7iWRYmckayYQgLxq3g1z4bhsgwhFWFnsxxQIZBAX4hPXPl/kLZMMTBsRCU8Sw3N6mM4ddWhsG+Py7dHU0K2j7eaQvPshjLwkRsURJ5XITFoIy3pfkkbRZVsQRh3vjZMByK1HFtsmFQeXixkxfHtKnj9LbTt+O43POpPctKPMvCHDi52YVqYbQIQlvoUWvqVebbAuJZFlYXI8MIVvCjv7k/10Gfocp6otdfHHTt2/Q3ttJqWxmGCn5/HGjbvbup48SzLMyINFE4vdWTAD9hYdAyW9Mcy0C5bLAgrCI2dZz7M2Cf26xBlTHaZrvXKX6eUUOFicwiG0brCn78+2Nyz8nAbTs50DKMcmBtHWIsC1Gc3uqK4SEsDONZbhjcp7e1z6mshgirismGESh3nRhDunm5641uAqWOn5dxmZDca5o8y7MI8DsuYzSd5XRFSZrlWZZsGEIU73r5s3Bio7vsZgjHBBpA2niW+YAlxrKwqtCTGZJh0PM/ClT3q8JIAdIE3TQ5JnkRVgN77dt7lmciw2i1h/XDpI5ruX2aKBwMM2dfkxBjWYjiC194+7KbIBwjqANrWr0PkNRxwnpQ8izXyTAiHmNK7dlJFPppYgxtYf7QtW+TXnUaCYfPMXEsT+9ZVtyzHLeNyDAEQVg5bGR/m1RMkjpOWH18qQV/bm2AX/xSMUkBOqlCt5OIZnmBTFOyehbZMKbNDrFuWM1yu+2TRGFYpI6LzU0txrIgCCsHjTltZBjA9BHmgjBvaJDOxnlp0C8H+EXsz2RkSNBLE9EsL5CEXfumzMLQPW532niWW84IeblrSR0nCMLaYmQYLY3laSPMBWHemGwY47KhVEorF/Ea8DzL3Y46Nkvyq0A6RYDftBX8gOmDBNeNWWiWbZ5l8SwLgrCmmAC/FtkwgNnqAAVhHtAYPRyPS88pPb+jBkvF3LvZTZNjYzitAqaC3xQBftP0VbTl8bnl000OEsUr+EVu0+pIgiAIc8SvRtZ2ezGWhVWFDOBhNi5p68t65sn7S5jB1ktFs7xIpjF4Z+NZdvd11LHn2257kWEIgnAkoP5LZBjCUYUG6VGWlz3Lxc+jBkvFykwwE/Q6olleJGai0irPsv46nWd5uoC3dcN60ls6UxLuWRYZhiAIawp1YG0D/EyRgOMyeghrBz3jg4Bn2U8rF2NHpczoEs/yYpmFZnmabBhkPR6XCdIsPcuxiLEsCMLKYSr4tfYsiwxDWG1cz7L7nNNTO2pS7jphAX6SDWOhTOMdpr5qFvfruNxx60mfxrPcrIKfGMuCIKwelDquZYDfNJ4eQVgEiTGWQ5plN3Vck3LXnaSQYcyuqcIE7OS8feq4aVbBTCq1YzZBatu982stmmVBENaWaYqS6O31VzGWhdWFZBgBzXIpwK9BNozCs3zcDKdlkk6hWZ7FKphJpXZMbrk933YnzO+TFCURBGFtMZrlFuWugekqagnCIjAyjPG4lHLMepbdz9ZhJphJgl5HiWZ5gUwlwyissOmKkhSyhNZ7WC+m1SzzYiaSOk4QhLVl2qIk5OmRAD9hVTG5lAOeZVuUJD5in3uWpYLfYlFKF4FZtmf5uKwmqCnzLDue5chddFodSRAEYY5MH+Cnv4pnWVhV6MkMZcNo41lWTArwOQ/ejHsunZhVU4UIEqVa9Te2+l77Y6vSN0ecKWUniWMsx+1EjGVBEFaOqSv4SYCfsOLYbBjjUmAY/Zg1qDJmi5IkeM29F/Gaey/OrK3CZFKlWnqWi69TeZan87SuG9MGNDoBfpHbiAxDEISVY/oAPzGWhdWGntFxXl6+t+WuKRfs5OeYXpU2JZeF6VGqXTaMWUjGlPf1qEOTg7aXLHU0yxLgJwjCmkLdV691uWv9VYxlYR0oa5abp47jAX7C4jm73cPZnV7j7WYysTfe6fa7WCem9iyLZlkQhKPA1J5lqeAnrDj80Sx7lvVXMpablLsWz/Jy+Kmvehl2+s1NKhOcN1W5a/p6PO69SR3Xcvs2nmUxlgVBWDmmzYZBHeA0A5AgzBM+SJfzLBee5Qblrk02DHnml8K5nX6r7UxfNY1jeUpZwroxS89yrMV9TJz2giCsE9QHtq7gN0WRAEFYBI5nuZRnWX/NsviSvDzAT1gfSDoxlWbZZIc4Hv3d1Jpl1dyzLG+VIAgrhylK0nLgn6ZIgCAsAr5kXg4Mcz3LMYhneT2ZxSrYsQvwg+7j204OHM1y5DZiLAuCsHIYzXLLCn6SDUNYdfijWaVZHo/jPctkOLSVLgnLwVQbnYFX+LikjoOa7lwlG4YgCEeC3Y0OlALObLfTAVJnKMaysKrwMXqSZrlJuWsJ8FsvZpln+fjYytOFMko2DEEQjgQ3n9rEL3/DK3H57Far7c0AdFxGD2ENYTIM7zklVcaogWaZHMqSOm69mEmAn9nX9O1ZB9QMPctiLAuCsNY889x262156V9BWEX4o5mWAvz0z6NxvGdZifRoLZlFTnj7fByPe0+a5ba4FfxEhiEIwjGFBh5JHSesKjw4yZ/U0fM7ytqUu5Znfp2YTanq6b3T68S0nuXE0SxHbtP6aIIgCCuKZAYQVh3+ZPpexdTzLMd4DGkXEuC3XszSs3xsUsdBTeVZ7jgyDPEsC4JwTJFsGMKqk9R4lhPjWW5e7lomiOuF6aumybNs9jWDBq0B4lkWBEGYAclMljYFYX642TDcodjIMBqVu9ZfxbO8Xswkz7Jyvx51dI7l9tt3WgT4yVslCMKRg2wP8bIJ60A5zzIZy801y7Kasl5oL+mU+wCljjsu915Nlw2DB/iJDEMQhOPKLLw1gjBP+LNZ0ix7nuUYw+C+m0/gcx+8GQ/cenKGrRTmTaLU1BMc41meQXvWgWknGPzdi92NpI4TBOHIIfpNYdXhT2YpGwZ5lotsGDGc2Oji+/7y82bRNGGBJFPqbwFW7vrYeJanO9eOo1mO248Yy4IgHDkkdZyw6jiaZT/PcrHmm5FnWZ7jI8tsPMvHK3XcK+8+j+1e2nr7pIVmWYxlQRCOHCYd0zHytAjrRV02DDKehkU2DHmKjy5KqZn1U9MVgV4fXn//TXj9/Te13p5f71jPsmiWBUE4ckgFP2HVcfMsu0Nxx2iWKcBPnuOjSprMLouFdHdxtPHki7EsCMKRI5UAP2HVYY9mVTYM41mWx/jIMssAv2PiWJ6atIVmWYxlQRCOHJI6Tlh1+CBdmQ2jCPATY/noomZoLMsKRBxt8iyLZlkQhCOHpI4TVp26bBj0/FKA33HRoh5H3vbwrXjotunS/Zk8y7No0DEgkWwYgiAIsykhKwjzREV4locNyl0L68nzbj+N591+eqp9GM+yPChRuEVJ4rYRGYYgCEcOkw1DBg9hReGPZlU2jKxBURLh+CKS5Wa4muW4bcRYFgThyEEeFjGWhVWFSyvS1B2KTYDfWDTLwmRoleI4FSWZBndckAA/QRCOKUaGIcaysKo08CyLESTUYSv4LbUZawOfm4pnWRCEY0sqxrKw4vBHs6RZNuWuJXWcEIFyvggT4HnNYyeiYiwLgnDkoL5QAvyEVYUP0qVsGMXzK0VJhBjo6ZDnJA63gl/cNmIsC4Jw5FDiWRZWHLeCX1WeZSl3LUzGapaX3JA1gb9vsWkZxVgWBOHIITIMYdVJHM9yOMBvkIlnWZiMeJabkbYoSiLGsiAIRw5JHSesOnyQrvIs2wC/hTVLWEPk+WgGD/ATY1kQhGOLpI4T1olSNgwJ8BNaIJ7lOHiAX+w1E2NZEIQjh1TwE1YdPkinqR/gp6CUBPgJcZhy1/KYRCEV/ARBEMBkGKmMHsJq4sgwAiN2qhQKFYYE+Am1mHLXYi1HkTh5lsWzLAjCMcXIMGTwEFYU/mT6MgzAPsOAGEFCHPKYxMEDamMvmRjLgiAcOaSCn7DqODKMwHPaZqlYOJ6Y1HFLbse6kDgBfuJZFgThmCKp44RVh4/RnYBcyE1vJc+xUI0tdy3PSQyOZ1k0y4IgHFeMZlkGD2FFUY5nuTwU0zMsj7AwCSXPSiPcCn5xF60zr8YIgiAsi0+/+zye3Bs4uk9BWDWUAvI8rFkmz7LolYVJSIBfMxwZRuQ2YiwLgnDkeOHlM3jh5TPLboYg1KIA5KjQLCeiQxXiMKnjltyOdaEjeZYFQRAEYT0gKUYwG4YSz7IQh3iWm+GonkSzLAiCIAirS11ZdvM7sX+ECdgAv6U2Y21wPctx24ixLAiCIAhLgJbPO8EAP/IsL7RJwjpCqePkWYmCv1OSOk4QBEEQVpmaSpMS4CfEIqnjmqGUMgazeJYFQRAEYYWhgTqkWe5IgJ/QEFmFiIdWc1TkGybGsiAIgiAsARqoQ5rlRDzLQiQmz7JMraIh5ZMUJREEQRCEFabOs5xaC0gQapEAv+akDXXeYiwLgiAIwhIgjal4loVpUBLg15imMQFiLAuCIAjCEqBhOpQNI224TCwcX4xnWZYhomla9EeMZUEQBEFYAmQIB/MsS1ESIRJblGS57Vgn0mKCKp5lQRAEQVhhaiv4STYMIRJT7lomVtE0XbkRY1kQBEEQloBS+n9S41kWA0iYiHiWG9P0/RJjWRAEQRCWQKJU0KsM8AC/RbZIWEdEs9ycNFWN4gHEWBYEQRCEJaAQ1isDzVNbCccXk2VQLLpoUqUaxQPIpRUEQRCEJaBUOBMGIOWuhXiMZnnJ7VgnkqSZH16MZUEQBEFYAkqpSs+yBPgJsdhsGPK0xNJJxLMsCIIgCCuPQjgTBgCktLQuBpAwASPDkEclmkSpRjNRMZYFQRAEYQkoVaNZLuQZYgAJsUiAXzydVDUKnu3MrymCIAiCIFRR592iPLCytC5MwuZZXnJD1ohUqUaTCzGWBUEQBGEJKIRzLAOsHK8YQMIERIbRnCRp5lkWGYYgCIIgLAFVl2dZyl0LDZFnJZ5OohrFA4ixLAiCIAhLoF6zLNkwhDjI6JNnJZ5ENStKovI8n19rpuDMM+7NX/e3fmjZzRAEQRCEufC+P7+CRCk8cOvJ0t/+5LHrePz6ABvdBA/eemrxjRPWhj/45DU8vT/Ei595ZtlNWRs+/MhV3Dgc4eHL9pr9m7/x0t/O8/zh0OfFsywIgiAIS2KSd0u8hYIwe5RCI5H3ynqWH3744fy9733vspshCIIgCHPhle/+FZzY7OKnvvJlpb99y7/9AH78vX+Ouy/u4Oe/7hVLaJ2wLvxPP/zf8St/+Bg++h2fteymrA3v/OH/jvf/+RX87t99vfmdUko8y4IgCIKwSsRU8JOgLWESqqH+VpAKfoIgCIKwFtRW8JPRWYhEQeQ6TWka4CevoyAIgiAsgdpsGJI6TohEKXlOmpJK6jhBEARBWH10nuXwMJxIURIhGpFhNCVNmhUHF2NZEARBEJaAgniWhelRCo28pIJ+70SzLAiCIAgrTlJTwS9NxbMsxCOPSTPShprlzvyaIgiCIAhCFVv9FNv98DBMnmXxGAqTUJAViKbceXEHj98YRH9ejGVBEARBWAL/29sewkY3Df4tNanjFtkiYR3RMoxlt2K9+IpX3omveGX858VYFgRBEIQlcPncduXfyFMoNpAwCYVm+luhOaJZFgRBEIQVI5WiJEIkSsmkat6IsSwIgiAIK0YqqeOESESGMX/EWBYEQRCEFSORAD8hEoVmBTaE5oixLAiCIAgrBpW7FhNImIh4lueOGMuCIAiCsGIkUpREiERSx82fqYxlpdQZpdQvKKU+Unw9HfjMQ0qpX1dKfVAp9QGl1BdOc0xBEARBOOqIZlmIRalmpZuF5kzrWf5WAL+U5/ldAH6p+NlnD8CX5Hl+P4A3APjHSqlTUx5XEARBEI4skg1DiEVBtO3zZlpj+U0A3lN8/x4Ab/Y/kOf5H+V5/pHi+08AeBTA+SmPKwiCIAhHFhvgt+SGCCuPZMOYP9MayxfzPH+k+P6TAC7WfVgp9SIAPQB/UvH3dyml3quUeu9jjz02ZdMEQRAEYT2xMgyxgoR6tGZ52a042kys4KeU+kUANwX+9Lf5D3me50qpvGY/lwD8SwDvyPN8HPpMnuc/COAHAeDhhx+u3JcgCIIgHGWk3LXQBFEtz5eJxnKe56+t+ptS6lNKqUt5nj9SGMOPVnzuBID/P4C/nef5b7RurSAIgiAcA1Ipdy1EopSSSdWcmVaG8VMA3lF8/w4AP+l/QCnVA/AfAPxonuf/dsrjCYIgCMKRRwL8hFgkwG/+TGssfweA1ymlPgLgtcXPUEo9rJT658Vn3gbg5QDeqZR6X/H/oSmPKwiCIAhHlkRSxwmR3HFhB3dd3Fl2M440E2UYdeR5/gSA1wR+/14Af634/scA/Ng0xxEEQRCE40Qq5a6FSP7mq+5cdhOOPFLBTxAEQRBWDCl3LQirgxjLgiAIgrBiSLlrQVgdxFgWBEEQhBVDyl0LwuogxrIgCIIgrBiJZMMQhJVBjGVBEARBWDFSKXctCCuDGMuCIAiCsGJIuWtBWB3EWBYEQRCEFcMG+C25IYIgiLEsCIIgCKuG8SwvuR2CIIixLAiCIAgrh5S7FoTVQYxlQRAEQVgxyFgW17IgLB8xlgVBEARhxUilKIkgrAxiLAuCIAjCipFIuWtBWBnEWBYEQRCEFUM0y4KwOoixLAiCIAgrhpFhyCgtCEtHXkNBEARBWDESk2BZPMuCsGzEWBYEQRCEFSOVoiSCsDKIsSwIgiAIK4Ytd73khgiCIMayIAiCIKwaiQT4CcLKIMayIAiCIKwYJMMQU1kQlo8Yy4IgCIKwYpg8y+JZFoSlI8ayIAiCIKwYxrMstrIgLB0xlgVBEARhxegUrmXRLAvC8hFjWRAEQRBWDJJhSOo4QVg+YiwLgiAIwophU8eJtSwIy0aMZUEQBEFYMRLRLAvCyiDGsiAIgiCsGMazLMnjBGHpiLEsCIIgCCuGlLsWhNVBjGVBEARBWDESKXctCCuDGMuCIAiCsIK88bk34QXPOL3sZgjCsaez7AYIgiAIglDmn/yVFyy7CYIgQDzLgiAIgiAIglCJGMuCIAiCIAiCUIEYy4IgCIIgCIJQgRjLgiAIgiAIglCBGMuCIAiCIAiCUIEYy4IgCIIgCIJQgRjLgiAIgiAIglCBGMuCIAiCIAiCUIEYy4IgCIIgCIJQgRjLgiAIgiAIglCBGMuCIAiCIAiCUIEYy4IgCIIgCIJQgRjLgiAIgiAIglCBGMuCIAiCIAiCUIEYy4IgCIIgCIJQgRjLgiAIgiAIglCBGMuCIAiCIAiCUIEYy4IgCIIgCIJQgRjLgiAIgiAIglCBGMuCIAiCIAiCUIEYy4IgCIIgCIJQgRjLgiAIgiAIglCBGMuCIAiCIAiCUIEYy4IgCIIgCIJQgRjLgiAIgiAIglCBGMuCIAiCIAiCUIEYy4IgCIIgCIJQgRjLgiAIgiAIglCByvN82W0IopR6GsBHptjFSQBPL2n7aY99DsDjU2y/zLYvc/tlXrdpt1/2dZ/m2i277fKurt/28q62R97Vdsi72o7j9K4+I8/z88G/5Hm+kv8B/OC6bj+DY793jdu+zOu+tOt2BK5762u3Am2Xd3XNtpd3dart5V1d8HVbgbbLu7qEY/P/qyzD+Ok13n7aY0/LMtu+7O2Xeex1vu7LPPY6X/dpWedn5rg+c3LdlrO9vKvL236Zx16JZ2ZlZRjHGaXUe/M8f3jZ7Vg35Lq1R65dO+S6tUOuW3vk2rVDrls75LppVtmzfJz5wWU3YE2R69YeuXbtkOvWDrlu7ZFr1w65bu2Q6wbxLAuCIAiCIAhCJeJZFgRBEARBEIQKxFgWBEEQBEEQhArEWF4QSqkfUko9qpT6ffa7B5VSv66U+j2l1E8rpU4Uv+8qpd5T/P7DSqlv8/aVKqV+Vyn1M4s+j0Uzq+umlPoapdTvK6U+qJT62iWcykJpeN16SqkfLn7/fqXUKwP7+ym+r6PKrK6bUuoLlVIfKJ6371z8mSwepdRtSqlfUUp9qDjvryl+f0Yp9QtKqY8UX08Xv1dKqe9TSv1xca2e7+3vhFLq40qp71/G+SyKWV43pdR3Fv3c7yulvnBZ57QIWly3e4r3+FAp9Y2B/R2LcXWW1+04jatiLC+OHwHwBu93/xzAt+Z5/lwA/wHANxW/fyuAfvH7FwD4MqXUZbbd1wD48Fxbuzr8CKa8bkqp5wD46wBeBOBBAJ+tlLpzEY1fIj+C+Ov21wGg+P3rAHy3Usr0DUqpzwdwfd4NXhF+BFNeN6XUWQDvBvCaPM/vB3CTUuo1i2j8khkB+IY8z+8D8JcA/E2l1H0AvhXAL+V5fheAXyp+BoDPBHBX8f9dAP6pt7+/D+BXF9HwJTOT66aU+iwAzwfwEIAXA/hGmtgdUZpetycBfDWA76rY33EZV2dy3Y7buCrG8oLI8/xXoR86zt2wg8EvAHgLfRzAtlKqA2ATwADAVQBQSt0K4LOgB/Ajz4yu270AfjPP8708z0cA/guAz59325dJw+t2H4BfLrZ7FMAVAA8DgFJqB8DXA/gH823xajCj6/YsAB/J8/yx4nO/yLY5suR5/kie579TfH8N2vC4BcCbALyn+Nh7ALy5+P5NAH401/wGgFNKqUsAoJR6AYCLAH5+cWewHGZ43e4D8Kt5no/yPL8B4AMoT/yODE2vW57nj+Z5/lsAhv6+jtO4OsPrdqzGVTGWl8sHoR9QQHtFbyu+/7cAbgB4BMDHAHxXnuc0gP9jAN8MYLy4Zq4cTa/b7wP4dKXUWaXUFoA3sm2OE1XX7f0APlcp1VFKPRPaK09/+/sAvhvA3iIbumI0vW5/DODZxapGB3rQOVbPW7ES9jwAvwngYp7njxR/+iS0EQzoAfrP2WYfB3BLsarx3QBKS+VHnWmuG/Tz+Aal1JZS6hyAV+GYPHeR162Of4xjOK5Oed2O1bgqxvJy+VIAX6GU+m0Au9CeUEAva2QAbgbwTADfoJR6llLqswE8muf5by+ltatDo+uW5/mHAXwntJfqZwG8r/jccaPquv0Q9ID7XuhB49cAZEqphwDckef5f1h8U1eKRtctz/OnAHw5gB8H8F8BfBTH6HkrViP+HYCvzfP8Kv9brnOVTspX+hUA/lOe5x+fUxNXkmmvW57nPw/gP0E/h/8KwK/jGDx301634zquzuB5O1bjamfZDTjO5Hn+BwBeDwBKqbuhl4EA4O0AfjbP8yGAR5VS/w16efd50J6sNwLYAHBCKfVjeZ7/1cW3fnm0uG5/muf5vwDwL4pt/hG0kXOsqLpuxRLa19HnlFK/BuCPALwCwMNKqY9C9xUXlFL/d57nr1xsy5dLi+uGPM9/GkWpVaXUu3CEBxGOUqoLPQD/n3me//vi159SSl3K8/yRQi7waPH7v4Dribq1+N1LoD1WXwFgB0BPKXU9z/NvxRFlRtcNeZ7/QwD/sNjn/4XieTyqNLxuVXwajtm4OqPrhuM0ropneYkopS4UXxMA/wuAf1b86WMAXl38bRtahP8HeZ5/W57nt+Z5fhnAFwH45aP8QlfR9Lp529wOrav6vxbb6uVTdd2KZdvt4vvXARjlef6hPM//aZ7nNxfP28sA/NFxM5SB5tfN2+Y0tKf0yGshlVIKeuD8cJ7n38P+9FMA3lF8/w4AP8l+/yVK85cAPF3oKf9Knue3F8/dN0Lrc4+yoTyT66Z0NoezxT4fAPAAjrDmu8V1C3LcxtVZXbdiX8dnXM3zXP4v4D/0stgj0CL5jwP4n6Gjb/+o+P8dsBUVdwD8BLRW8kMAvimwv1cC+Jlln9e6XDfo5fAPQev6XrPs81qx63YZwB9CB3r8IoBnBPZ3GcDvL/u81uW6Ffv5UPH/i5Z9Xgu6di+DXrr9APSS7PugdYxnoaPrP1JcpzPF5xWAHwDwJwB+D8DDgX2+E8D3L/vc1uG6QXtF6Zn7DQAPLfvcVuy63VS801ehg3E/DuCEt89X4oiPq7O8bjhG46qUuxYEQRAEQRCECkSGIQiCIAiCIAgViLEsCIIgCIIgCBWIsSwIgiAIgiAIFYixLAiCIAiCIAgViLEsCIIgCIIgCBWIsSwIgrDiKKUypdT7lFIfVEq9Xyn1DUXe57ptLiul3r6oNgqCIBxVxFgWBEFYffbzPH8oz/P7AbwOwGcC+F8nbHMZuqqlIAiCMAWSZ1kQBGHFKco977CfnwXgtwCcA/AMAP8SwHbx56/M8/zXlFK/AeBeAP8DwHsAfB90UZVXAugD+IE8z//3hZ2EIAjCmiLGsiAIworjG8vF764AeDaAawDGeZ4fKKXuAvCv8jx/WCn1SgDfmOf5ZxeffxeAC3me/wOlVB/AfwPw1jzP/8cCT0UQBGHt6Cy7AYIgCMJUdAF8v1LqIQAZgLsrPvd6AA8opb6g+PkkgLugPc+CIAhCBWIsC4IgrBmFDCMD8Ci0dvlTAB6EjkM5qNoMwFflef5zC2mkIAjCEUEC/ARBENYIpdR5AP8MwPfnWkd3EsAjeZ6PAXwxgLT46DUAu2zTnwPw5UqpbrGfu5VS2xAEQRBqEc+yIAjC6rOplHoftORiBB3Q9z3F3/4JgH+nlPoSAD8L4Ebx+w8AyJRS7wfwIwC+FzpDxu8opRSAxwC8eTHNFwRBWF8kwE8QBEEQBEEQKhAZhiAIgiAIgiBUIMayIAiCIAiCIFQgxrIgCIIgCIIgVCDGsiAIgiAIgiBUIMayIAiCIAiCIFQgxrIgCIIgCIIgVCDGsiAIgiAIgiBU8P8CZJAUM8vgk/UAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 864x864 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# copy from previous module\n",
+    "wheat = quandl.get(\"ODA/PWHEAMT_USD\")\n",
+    "wheat = wheat[\"Value\"]\n",
+    "\n",
+    "# compute change (copy from example above)\n",
+    "changes = wheat.pct_change()\n",
+    "ucl = changes.mean() + 3 * changes.std()\n",
+    "lcl = changes.mean() - 3 * changes.std()\n",
+    "\n",
+    "# copy plotting code from above\n",
+    "fig, ax, = plt.subplots(figsize=(12, 12))\n",
+    "changes.plot(ax=ax)\n",
+    "ax.hlines([ucl, lcl], xmin=changes.index.min(), xmax=changes.index.max())\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -641,7 +4827,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -655,7 +4841,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/Module 2.6.1 - Overfitting.ipynb b/notebooks/Module 2.6.1 - Overfitting.ipynb
index f0296a7..ad8e4c1 100644
--- a/notebooks/Module 2.6.1 - Overfitting.ipynb	
+++ b/notebooks/Module 2.6.1 - Overfitting.ipynb	
@@ -41,7 +41,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -59,7 +59,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1624f945d48>]"
+       "[<matplotlib.lines.Line2D at 0x168aaff2530>]"
       ]
      },
      "execution_count": 3,
@@ -68,7 +68,7 @@
     },
     {
      "data": {
-      "image/png": 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xkZlrTdcxS77mbvA1t19dr9chFEkqlAEuSYUqKcD7TRfQAF9zN/ia26+W11vMGLgk6VIl9cAlSTsY4JJUqCICPCJujYjvRsQTEXF30/XUKSKui4iHI+JURDwWEXc1XdOsRMQVEfGNiHig6VpmISKuioh7I+Kfh/++f73pmuoWEX88/O/6OxHx6Yh4ftM1TVtEfDIizkbEd3a0XR0RD0bE48PjS6bxXHMf4BFxBfBx4I3ADcA7IuKGZquq1bPAezPzlcBNwDtb/np3uovq7k5d8RfA32TmrwCvpuWvPSIOA38ErGXmq4ArqG7F2DafAm7d1XY38FBmHgUeGp5PbO4DHLgReCIzn8zMnwKfAW5ruKbaZOZTmfn14dc/ofqf+nCzVdUvIo4AbwI+0XQtsxARLwZ+C7gHIDN/mpnPNFvVTBwCXhARh4BF9rgReuky86vAj3Y13wacGH59Arh9Gs9VQoAfBr634/wMHQg0gIhYBV4LPNpsJTPxMeB9wPmmC5mRXwTOAX81HDb6RES8sOmi6pSZ/wb8ObAFPAX8Z2Z+udmqZublmfkUVJ004GXT+KUlBHjs0db6tY8R8SLg88C7M/PHTddTp4h4M3A2M082XcsMHQJ+FfjLzHwt8N9M6WP1vBqO+94GXA/8AvDCiPi9ZqsqWwkBfga4bsf5EVr4sWuniHgeVXgPMvMLTdczA68D3hIRp6mGyG6OiL9utqTanQHOZOaFT1f3UgV6m70e+NfMPJeZ/wt8AfiNhmualR9ExLUAw+PZafzSEgL8a8DRiLg+Iq6kmvS4v+GaahMRQTUueiozP9J0PbOQmR/IzCOZuUr17/crmdnqnllm/jvwvYh4xbDpFuCfGixpFraAmyJicfjf+S20fOJ2h/uBO4Zf3wHcN41fOvc3Nc7MZyPiXcCXqGatP5mZjzVcVp1eB/w+8O2I+Oaw7YOZ+cUGa1I9/hAYDDsmTwJ/0HA9tcrMRyPiXuDrVKutvkELt9RHxKeB3wauiYgzwJ8AHwI+FxF3Uv0he9tUnsut9JJUphKGUCRJezDAJalQBrgkFcoAl6RCGeCSVCgDXJIKZYBLUqH+H0NSmtRuK0BcAAAAAElFTkSuQmCC\n",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -92,22 +92,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1624fa06fc8>]"
+       "[<matplotlib.lines.Line2D at 0x168aeed7400>]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -137,7 +137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -147,22 +147,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1624fa3fa88>]"
+       "[<matplotlib.lines.Line2D at 0x168aef20dc0>]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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bf9ccdBkg3cxK5Azaunq4//UK7n5pNw1t3VzsaeCW+o2cM2oMZl4R5M8Y+sk1B10cogCXEa2ju5cH3qjgDy/t5lBLF0snJvClimcoSLEwwQuN/azYHMgbo5EwB123EYhKmrEvI1JnTy/3v76XC3+xmv/6v63MmjCaxz99Pvc2vOIP74HsoTjQDXOHuVnwsEX5xr4jmUbgMqJ09/r46/pq7nxxJ/ubOliUN5Y7rp7H4qnp/i8YzGh5oK2RcM9Bd1MLR68UBkUjcBkxKg+38aHfvco3n3iTTG8CDyw/j0c+ufh4eMPgRsuVlf5A7utUYR/urd8GWme46ZXCoGkELiPCv3Yd4rN/2Yi1cPd183nv7MxTLx0fzGh5MMvzw7krjFtuI+CmVwoRQiNwiWrWWla8Ws7196xl/OhRPPW5JbzvnAmnv+/HYEbLbtkw1y11uuWVQgTRCDxY1LuLOB3dvXzriTIe31jNe2dn8quPFpIyagA/8gMdLR8N+77/35cvj7z/707VGeyfebe8UoggWsgTDFqoEXFqmjr45AMbKKlq5JaLZ/CFi2ZodWIwOfEzr39Hp3W6hTxqoQRD395df1PPxHEbKhr40J2vsuvgEe6+bj63XDxT4R1sTvzMh/vNXhdSCyUYImGhhgDwyLpKvvO3LUwIzDKZNWF0uEuKTk79zIfzzV4XUoAHg3p3Ydfd6+O/nn6L+16v4F0zxvG7a+YN/p4lMnD6mY8IaqEEg1ve5Y9STW3dXLeimPter+AT75zCnz++0N3hXVoKt90GN93kf4zEedD6mY8ICvBgUO8ubJrau7l2RTEbKxr51VUFfPuDs4l1856OblnMop/5iKAWSrCodxdyzR3dXL+imG01zfzh2vm85+wo2JbVTYtZ9DMfdi4eqshIdqSjmxvuWcuW/c3c9bFzoyO8QYtZZFAU4OI6LZ09fPzP63izuok7P3Yu7z1nQrhLCp5w37lQXEUtlEim1Z1v09rZw41/XsvmqkbuvGYey+ZEUXhD+O9cKK6iEXikcsubWSHkD+91bKxs5LdXz+PSuRPDXVLw6c1BGQSNwCOVm97MCoG2rh5uuncd6yvquePqeXwgPwrD+yi9OSgDpBF4pNKbWce0d/Wy/N71rNtbz68/WsiHCiaFuySRiKAAj1R6Mwvw31HwP+5fzxvlh/nlVQVcXpgV7pJEIoYCPFJppdux8P7X7kP84soCPjwvu/9vEhlB1AOPVJFwr+kwzoLp7OnlUw9s4NVdh/jZR/K5cr7CW+RkCvBIFs43s/rem7nvLJgQzIjo6vHx2Qc3sWZ7HT8pmstVC3IcvZ6IW6mFIqcWpnuc9/T6+OLDm/jn1oP84PJzuGbRyOr5iwyGAlxOLQyzYHp9lq/8tYR/lNXw7Q+czfXn5zl2LZFooACXUwvxLBifz/L1x0t5cvN+vvq+WXziXVMduY5INFEPXE7NiSXdp3lT1FrLt58s47EN1XzxPTP47LunB+c5iEQ5jcDDwFrLzoNHaO/qDXcppxfsJd2nuTWALSnh+39/i78UV/LppdO45eIZwX0eIlFMI/AQq2/t4muPlfLPrQeJj/WweGo6S2dmsHRWBlPGJWNMBG2+G8xZMKe4NYAFfvroOu7tnchNS6bwtffNiqznLxLhFOAh9NquQ3zp0c00tHZzy8UzaG7vYc2OWn7w9Fv84GnIHZvEu2dlsHTWeBZPTScxPibcJQfPKTbB/XXybO7unch1iyfznQ+erfAWGSQFeAh09/r41fM7+MNLu5kyLpkVNyxkTpZ/hsd3mU3l4TbW7KhlzfY6HllfxX2vVzAq1sN5gdH5++dOZII3IczPYphO2gT3zp6J/NaXzUdj6vj+Ze9XeIsMgbHWhuxiCxYssOvXrw/Z9SJBxeFWvvDwZkqqGrl6YQ7f/dBskuJP/3uzo7uX4vJ61myv5aXtdew51EpSfAzf/sBsrlmU496g67Mw6I8ps/hx72SK2vbyi5veSUxhQbirE4loxpgN1toFbzuuAHfO3zbt49t/K8Nj4Kcfyef9Q7h/9e66Fr77ZBn/2nWYC2Zm8POP5Lt2NO4rKeGXj67lrt5JfMBTzx1X5ROr8BbplwI8hFo6e/ju38pYuWkfC/PS+M3V88hKTRzy+Xw+ywPFFfzkmW3ExRi+f/k5XFGY5arReEtnD7c8vIl/bq3l6oU5/PCKOcS5efd4kRA6XYCrBx5km6sa+eLDm6iqb+OWi2fwuXdPJ3aYQeXxGK4/P493zcjg1r+W8KVHSlhVVsOPPjyXcSmjglS5cyoPt/GJ+9exu66V7192DtefP9lVv3xEIpWGQEH06Loqrvz9a/T0Wh755PnccvHMYYd3X1PGJfPoJ8/nPy89i9Xb6njvr19mVdmBoJ3fCa/tPsRld73KweZO7r9pETe8I0/hLRIkCvAgaWrv5gdPv8WCvDSe+cK7WJg31pHrxHgMn7xwGk9/4Z1MSk3gUw9s5JaHN9HU1u3I9YbKWsv/vr6X61asJSNlFE99bglLpo8Ld1kiUaXfADfG3GOMqTXGlPU5NtYY87wxZmfgMc3ZMiPfA29U0NLZw7c/MBtvUpzj15uZOZonPrOEWy6ewdOlB3jvb15i9fZax687EF09Pr71tzK+8+QWls7MYOVn3sHk9ORwlyUSdQYyAr8XWHbSsW8AL1hrZwAvBD4fsTq6e/nzv8q5cGbGsfndoRAX4+GWi2fyxGeWMCYhjhv/vI5P/e8G9tS1hKyGkx1u6eTaFcXHlsb/8foFjE5w/heayEjUb4Bba18G6k86fDlwX+Dj+4ArgluWuzy6vopDLV18Zum0sFx/braXv3/+nXzlkpm8srOOS379Mt/5WxmHWjpDWsfWA81cdue/KKlq5I6rC/n6srOI8ajfLeKUofbAM621R989qwEyT/eFxpibjTHrjTHr6+rqhni5yNXd6+Pul/Ywf3Iai6Y40/ceiIS4GD7/nhms+eq7+diiXP6ytpILf76a372w0/GbZnV09/Lw2ko+8vvX6PH5ePST52vzYZEQGNA8cGNMHvC0tXZO4PNGa21qn79vsNb22wePxnngT2yq5kuPlPCn6xdw8ezT/h4Lud11Lfx81Tae3XKQzDGj+PIlM7lyfk5QR8R7D7Xy0NpKHl1fRUNbN/NyU7n72vmMH+POhUYikSrY88APGmMmWmsPGGMmApHx7lmI+XyW36/ZzazM0Vx01vhwl3OCaRkp3H3dAtbtrefHz2zl64+/yYpXy/nPS89m6ayMIU/l6+n18c+ttTxYXMErOw8R4zFccnYm1y6ezDumpeNRy0QkZIYa4E8BNwA/DTw+GbSKXOSFbbXsONjCbz5aGLHBtTBvLCs//Q7+UVbDz1dt48Z713H+1HRueMdkslKTmOBNID05vt/6DzS18/DaKh5eV8nB5k4mehP48iUz+ejCHDI14hYJi35bKMaYh4ClwDjgIPA94G/Ao0AuUAFcZa09+Y3Ot4mmFoq1lqLfv8ahlk5Wf2VpUBfsOKWrx8dDayu544Wd1Ld2HTseF2MYPzqBid4EJniPPiYy0ZuAxxhWbqzmhW21+KzlghkZXLt4Mu+elXH8OZ9mpx0RCY4ht1Cstdec5q/eM+yqXKy4vJ5NlY388Io5rghvgPhYDzekNPNvPa+zq76RmsxcagoWciAlnZqmDg40tVO2r4nn3zpIZ4/v2PelJ8dz8wVTuWZhLrnpSSeetM9dBvvutDOs3XtEZEB0L5Qh+u81uxmXEs+/zc/u/4sjRSBsk9LSyJ/gJb+pHJ7a6A/bZYXHvsxaS1N7NweaOmhu76YwN5VRsafZXOIUO+0cO64AF3GUAnwIyvY18fKOOr62bBYJcS7aNWeAYWuMITUpntSk+P7PeYqddvB6/cdFxFEK8CH4/ZrdjB4Vy7WLJ4e7lOMG0od2ImxP2mkH8O9gn5s79HOKyIC4o3kbQfbUtfBM2QGuO38yYyJlifhpdnyntPTEr8vN9YdrX8MN26Ii//UaGsDnO/5xUdHQzykiA6IAH6Q/vryH+BgPNy6ZEu5SjuvbGvF4jn+8cuWJX+dE2Obn+3voaWlQXe1/1BuYIiGhFkp/+rQmanKm8Xh3IVcvmkzG6AjaSGGgrZGjYdu31bJ8+fDDNj9fgS0SBgrwMzlpityfWlLxxfq4eXxX/98bSoPpQytsRaKGWihn0qc10WDi+EtcDpf11pDz3FPDO29pKdx2G9x0k//x5F71YKkPLTIiKcDPpLLS34oA7u/NpI0YPpVwaHizNgb6huNgqA8tMiKphXImgdZEW2o69/aM52JPA7OO1Axv1oZTC1/UGhEZcTQCP5NAa+KhI8k0EMen23cMvzXRZ1R/jBa+iMgQKMDPJD+fri99hT/FT+W8rjrmez3Db004MRdbREYktVD6sTo+kwNU86ObL4WzgrBhQ1GRv+cN/pF3U5N/VL98+fDPLSIjigK8H6vKakhNiuNdMzKCc0Kn5mJHG92iVqRfrgjwn/xjK83tPfykaG5Ir9vV4+OfWw+y7JwJxAXzlrF6w/HMdItakQGJ/B54aSn1rxbz3Bs7sN+7bfhzpgfhtd2HONLRw6VzJ4TsmsLAbw0gMsJFdoAHRmIF7XUc9iRQ3dQ+/DnTg7CqrIaUUbEsmT4uJNeTAM3UERmQyA7wwEisMMW/7VvJ6EkhG4n19Pp47q2DXHTW+NNvZiDO0EwdkQGJ7AAPjMRmmXbi8VHiSwnZSGzd3gbqW7u4dI7aJyGnWwOIDEhkB3hgJBZnLHNMGyW+5JCNxFaVHSAhzsOFs4I0+0QGTrcGEBmQyJ6F0mfOdEHyER7uHU9PQyOxDs+Z9vksz245yIUzM0iKj+z/RFFLM3VE+hXZI/A+I7HChiraTQw7l3/e8X/Ym6sbqWnuYJnaJyISwSJ/eBkYiRUcaoXb11CSOJ6zHb7kqrIa4mIMFwVj5aWIiEMiewTex+T0JLyJcZRUNzp6HWstq8pqWDJ9HN7ECNnzUkTkFFwT4MYYCnJS2VzV1P8XD8NbB5qprG9j2Tlqn4hIZHNNgAMUZnvZcfAIbV09jl1jVVkNHgOXzFb7REQim6sCvCAnlV6fZcv+ZseusaqshvOmpJOeEkGbFouInIKrAjw/OxWAkqpGR86/q7aFnbUtmn0iIq7gqgDPGD2KrNRENjsU4KvKDgDwPvW/RcQFXBXgAIU5qY7NRFm1pYZ5ualM8CY4cn4RkWByZYBX1bdzuKUzqOetqm+jbF+z7n0iIq7hugAvyEkFCPoo/NktNQAsO2diUM8rIuIU1wX4nKwxeAxBnw/+j7IaZk8cQ256UlDPKyLiFNcFeFJ8LDMzRwd1JsrB5g42VDSofSIiruK6AIfjb2Raa4NyvucC7RNtnSYibuLKAC/ISaWxrZvK+ragnO8fZTVMy0hm+vjRQTmfiEgouDPAAwt6gjEfvL61i+Lyei6dozcvRcRdXBngMzNTSIjzUBKENzL/+dZBen1Wqy9FxHVcGeCxMR7mZnmDMpXwH2UHyE5L5JxJY4ZfmIhICA0rwI0xy4wx240xu4wx3whWUQNRkJ1K2b4munt9Qz5Hc0c3/9p1mEvnTMAYE8TqREScN+QAN8bEAHcBlwKzgWuMMbODVVh/CnJS6ezxsb3myJDPsXpbLV29PrVPRMSVhjMCXwTsstbusdZ2AQ8DlwenrP4VBmFF5j/erCFzzCjm5aQFpygRkRAaToBnAVV9Pq8OHDuBMeZmY8x6Y8z6urq6YVzuRNlpiYxNjh/ygp72rl7W7KjlfedMwONR+0RE3MfxNzGttX+01i6w1i7IyMgI2nmNMRRke4c8E+WlHbV0dPu0dZqIuNZwAnwfkNPn8+zAsZApyEllR+0RWjoHv8Xa30sOkJYUx6IpYx2oTETEecMJ8HXADGPMFGNMPHA18FRwyhqYgpxUrIWyfYMbhW+vOcIzZQe4akEOsTGunEkpIkLsUL/RWttjjPkc8CwQA9xjrd0StMoGoKDPFmuLp6ZDaSmsXAmVlZCbC0VFkJ//tu+7/bntpMTH8qkLp4WyXBGRoBrW8NNa+4y1dqa1dpq19kfBKmqgxibHkzs2yT8TpbQUbr8dGhogO9v/ePvt/uN9bKxs4Pm3DnLzBVNJS44PdckiIkEz5BF4pCjISWVjRQPsfBnS0vx/4PjjypXHRuHWWn6+ahvjUuK56Z1TwlSxiEhwuL4BXJDtZV9jO7XVB8HrPfEvvV5/OyXglZ2HeGNPPZ9793SSR7n+d5eIjHCuD/BjC3omzoKmk97MbGry98IBn8/yi2e3k5WayDXn5Ya4ShGR4HN9gJ8zyUuMx1Ayc76/793QAD7f8Y+LigD/Pb/f3NfEly+ZyajYmDBXLSIyfK4P8MT4GGZljqakKx5uvdXf+66u9j/eeivk59PT6+OXz21nZmYKV8x722JRERFXiopGcEFOKv9Xuh/fjYvwnGLa4GMbqtlzqJW7r5tPjJbNi0iUcP0IHKAwx0tzRw97D7e+7e86unu544WdFOak8t7ZmWGoTkTEGVES4P4pg6e6M+EDb1RwoKmDry2bpXt+i0hUiYoAnz4+haT4mLfd2OpIRzd3rd7Fu2aM4x3TxoWpOhERZ0RFgMd4DHOzvG/b5Ph/Ximnoa2br75vVngKExFxUFQEOPjng7+1v5muHv8Wa4daOlnxyh7eP3cC+YF7poiIRJOoCfCCnFS6en1sq2kG4K7Vu2jv7uXLl2j0LSLRKSqmEYI/wMF/Z8KxyfE8+EYlV87PZvr4lPAWJiLikKgJ8EneBMaljGJzVROl1U1g4IsXzwx3WSIijomaADfGUJjjZc32WhraurhxyRSyUhPDXZaIiGOipgcO/g0eDrd2kRgXw2eWarMGEYluURXghbmpAPzHBVNJTxkV3mJERBwWNS0UgCXTxnHH1YW8TzvNi8gIEFUB7vEYLi/U3QZFZGSIqgAfsAFufiwiEsmiqgc+IAPc/FhEJNKNvABfufL45scez/GPV64Md2UiIoMy8gK8srLfzY9FRNxg5AV4bu4ZNz8WEXGLkRfgRUVn3PxYRMQtRl6A5+efdvNjERE3GZnTCPPzFdgi4nojbwQuIhIlFOAiIi6lABcRcSkFuIiISynARURcylhrQ3cxY+qAiiF++zjgUBDLcQM955FBzzn6Dff5TrbWZpx8MKQBPhzGmPXW2gXhriOU9JxHBj3n6OfU81ULRUTEpRTgIiIu5aYA/2O4CwgDPeeRQc85+jnyfF3TAxcRkRO5aQQuIiJ9KMBFRFzKFQFujFlmjNlujNlljPlGuOtxkjEmxxiz2hjzljFmizHmi+GuKVSMMTHGmE3GmKfDXUsoGGNSjTGPGWO2GWO2GmPOD3dNTjPGfCnwc11mjHnIGJMQ7pqCzRhzjzGm1hhT1ufYWGPM88aYnYHHtGBcK+ID3BgTA9wFXArMBq4xxswOb1WO6gG+Yq2dDSwGPhvlz7evLwJbw11ECN0BrLLWngUUEOXP3RiTBXwBWGCtnQPEAFeHtypH3AssO+nYN4AXrLUzgBcCnw9bxAc4sAjYZa3dY63tAh4GLg9zTY6x1h6w1m4MfHwE/z/qrPBW5TxjTDbwAeBP4a4lFIwxXuACYAWAtbbLWtsY1qJCIxZINMbEAknA/jDXE3TW2peB+pMOXw7cF/j4PuCKYFzLDQGeBVT1+byaERBoAMaYPGAeUBzmUkLhN8DXAF+Y6wiVKUAd8OdA2+hPxpjkcBflJGvtPuB2oBI4ADRZa58Lb1Uhk2mtPRD4uAbIDMZJ3RDgI5IxJgV4HLjFWtsc7nqcZIz5IFBrrd0Q7lpCKBY4F/i9tXYe0EqQXlZHqkDf93L8v7wmAcnGmGvDW1XoWf/c7aDM33ZDgO8Dcvp8nh04FrWMMXH4w/tBa+3KcNcTAkuAy4wxe/G3yC4yxjwQ3pIcVw1UW2uPvrp6DH+gR7OLgXJrbZ21thtYCbwjzDWFykFjzESAwGNtME7qhgBfB8wwxkwxxsTjf9PjqTDX5BhjjMHfF91qrf1VuOsJBWvtf1prs621efj//75orY3qkZm1tgaoMsbMChx6D/BWGEsKhUpgsTEmKfBz/h6i/I3bPp4Cbgh8fAPwZDBOGvGbGltre4wxnwOexf+u9T3W2i1hLstJS4DrgDeNMZsDx75prX0mfCWJQz4PPBgYmOwBbgxzPY6y1hYbYx4DNuKfbbWJKFxSb4x5CFgKjDPGVAPfA34KPGqMWY7/ltpXBeVaWkovIuJObmihiIjIKSjARURcSgEuIuJSCnAREZdSgIuIuJQCXETEpRTgIiIu9f+O5070h/74TwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -203,53 +203,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "619.4021889555413 358.2567596452094\n",
-      "1364.592854079204 3.214115253791102e+31\n"
+      "training error (linear) 730.2470867970751\n",
+      "training error (overfit) 473.83382409909007\n",
+      "training error (linear) 22967.97019073926\n",
+      "training error (overfit) 1.1863996553974773e+29\n"
      ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
     }
    ],
    "source": [
-    "# From the solutions, overfitting will obviously perform poorly on the test set compared to a simpler model.\n",
-    "# 50 points randomly from 0 to 100\n",
-    "x_test = np.random.random(50) * 100\n",
-    "error_test = np.random.randn(len(x_test)) * 5\n",
-    "y_test = x_test * 3 + 1 + error_test\n",
-    "\n",
-    "# Training errors\n",
-    "SSE_linear = np.sum((y_pred_linear - y) ** 2)\n",
-    "SSE_overfit = np.sum((y_pred_overfit - y) ** 2)\n",
+    "# qs 1\n",
+    "x2 = np.random.random(50) * 100\n",
     "\n",
-    "print(SSE_linear, SSE_overfit)  # Overfit is lower for training error\n",
+    "# qs 2'\n",
+    "# copy from code above: y = x*m + c + e\n",
+    "y2 = x2 * 3 + 1 + np.random.randn(len(x2)) * 5\n",
     "\n",
-    "plt.plot(x_test, y_test, 'go', alpha=0.5)\n",
+    "# qs 3\n",
+    "print(\"training error (linear) \" + str(np.sum((y_pred_linear - y) ** 2)))\n",
+    "print(\"training error (overfit) \" + str(np.sum((y_pred_overfit - y) ** 2))) # overfit has lower error\n",
     "\n",
-    "y_est_linear = model(x_test)\n",
-    "y_est_poly = model_30(x_test)\n",
+    "# qs 4 re-use models from above\n",
+    "y2_pred_linear = model(x2)\n",
+    "y2_pred_overfit = model_30(x2)\n",
     "\n",
-    "# Training errors\n",
-    "SSE_est_linear = np.sum((y_est_linear - y_test) ** 2)\n",
-    "SSE_est_overfit = np.sum((y_est_poly - y_test) ** 2)\n",
-    "\n",
-    "print(SSE_est_linear, SSE_est_overfit)  # SSE is **very** high for the overfit model."
+    "print(\"training error (linear) \" + str(np.sum((y2_pred_linear - y2) ** 2)))\n",
+    "print(\"training error (overfit) \" + str(np.sum((y2_pred_overfit - y2) ** 2))) # overfit hsa large error\n"
    ]
   },
   {
@@ -284,7 +269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -296,7 +281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
@@ -317,7 +302,7 @@
        "['MMM', 'ABT', 'ABBV', 'ABMD', 'ACN']"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -328,69 +313,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "metadata": {
     "scrolled": true
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading batch 0\n",
-      "Downloading batch 10\n",
-      "Downloading batch 20\n",
-      "Downloading batch 30\n",
-      "Downloading batch 40\n",
-      "Downloading batch 50\n",
-      "Downloading batch 60\n",
-      "Downloading batch 70\n",
-      "Downloading batch 80\n",
-      "Downloading batch 90\n",
-      "Downloading batch 100\n",
-      "Downloading batch 110\n",
-      "Downloading batch 120\n",
-      "Downloading batch 130\n",
-      "Downloading batch 140\n",
-      "Downloading batch 150\n",
-      "Downloading batch 160\n",
-      "Downloading batch 170\n",
-      "Downloading batch 180\n",
-      "Downloading batch 190\n",
-      "Downloading batch 200\n",
-      "Downloading batch 210\n",
-      "Downloading batch 220\n",
-      "Downloading batch 230\n",
-      "Downloading batch 240\n",
-      "Downloading batch 250\n",
-      "Downloading batch 260\n",
-      "Downloading batch 270\n",
-      "Downloading batch 280\n",
-      "Downloading batch 290\n",
-      "Downloading batch 300\n",
-      "Downloading batch 310\n",
-      "Downloading batch 320\n",
-      "Downloading batch 330\n",
-      "Downloading batch 340\n",
-      "Downloading batch 350\n",
-      "Downloading batch 360\n",
-      "Downloading batch 370\n",
-      "Downloading batch 380\n",
-      "Downloading batch 390\n",
-      "Downloading batch 400\n",
-      "Downloading batch 410\n",
-      "Downloading batch 420\n",
-      "Downloading batch 430\n",
-      "Downloading batch 440\n",
-      "Downloading batch 450\n",
-      "Downloading batch 460\n",
-      "Downloading batch 470\n",
-      "Downloading batch 480\n",
-      "Downloading batch 490\n",
-      "Downloading batch 500\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import time\n",
     "\n",
@@ -410,7 +337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 34,
    "metadata": {
     "scrolled": true
    },
@@ -449,34 +376,34 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>ZTS</td>\n",
+       "      <th>0</th>\n",
+       "      <td>MXIM</td>\n",
        "      <td>2018-03-27</td>\n",
-       "      <td>81.86</td>\n",
+       "      <td>59.41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>1</td>\n",
-       "      <td>ZTS</td>\n",
+       "      <th>1</th>\n",
+       "      <td>MXIM</td>\n",
        "      <td>2018-03-26</td>\n",
-       "      <td>83.73</td>\n",
+       "      <td>61.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>2</td>\n",
-       "      <td>ZTS</td>\n",
+       "      <th>2</th>\n",
+       "      <td>MXIM</td>\n",
        "      <td>2018-03-23</td>\n",
-       "      <td>80.60</td>\n",
+       "      <td>58.82</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>3</td>\n",
-       "      <td>ZTS</td>\n",
+       "      <th>3</th>\n",
+       "      <td>MXIM</td>\n",
        "      <td>2018-03-22</td>\n",
-       "      <td>82.06</td>\n",
+       "      <td>60.04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <td>4</td>\n",
-       "      <td>ZTS</td>\n",
+       "      <th>4</th>\n",
+       "      <td>MXIM</td>\n",
        "      <td>2018-03-21</td>\n",
-       "      <td>84.10</td>\n",
+       "      <td>61.42</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -485,14 +412,14 @@
       "text/plain": [
        "     ticker       date  adj_close\n",
        "None                             \n",
-       "0       ZTS 2018-03-27      81.86\n",
-       "1       ZTS 2018-03-26      83.73\n",
-       "2       ZTS 2018-03-23      80.60\n",
-       "3       ZTS 2018-03-22      82.06\n",
-       "4       ZTS 2018-03-21      84.10"
+       "0      MXIM 2018-03-27      59.41\n",
+       "1      MXIM 2018-03-26      61.00\n",
+       "2      MXIM 2018-03-23      58.82\n",
+       "3      MXIM 2018-03-22      60.04\n",
+       "4      MXIM 2018-03-21      61.42"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -503,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -512,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -523,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -532,7 +459,7 @@
        "(Timestamp('2017-01-03 00:00:00'), Timestamp('2018-03-27 00:00:00'))"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -543,32 +470,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 52,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "date\n",
-       "2017-01-03    114.715378\n",
-       "2017-01-04    114.586983\n",
-       "2017-01-05    115.169696\n",
-       "2017-01-06    116.453639\n",
-       "2017-01-09    117.520300\n",
-       "                 ...    \n",
-       "2018-03-21    171.270000\n",
-       "2018-03-22    168.845000\n",
-       "2018-03-23    164.940000\n",
-       "2018-03-26    172.770000\n",
-       "2018-03-27    168.340000\n",
-       "Name: AAPL, Length: 310, dtype: float64"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "stock_to_predict = \"AAPL\"\n",
     "X = stocks[[column for column in stocks.columns if column != stock_to_predict]]\n",
@@ -577,7 +481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -595,25 +499,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "Input contains NaN, infinity or a value too large for dtype('float64').",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-22-0a93a051ca11>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\base.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    461\u001b[0m         \u001b[0mn_jobs_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    462\u001b[0m         X, y = check_X_y(X, y, accept_sparse=['csr', 'csc', 'coo'],\n\u001b[1;32m--> 463\u001b[1;33m                          y_numeric=True, multi_output=True)\n\u001b[0m\u001b[0;32m    464\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    465\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0matleast_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    717\u001b[0m                     \u001b[0mensure_min_features\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mensure_min_features\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    718\u001b[0m                     \u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mwarn_on_dtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 719\u001b[1;33m                     estimator=estimator)\n\u001b[0m\u001b[0;32m    720\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    721\u001b[0m         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)\u001b[0m\n\u001b[0;32m    540\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mforce_all_finite\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    541\u001b[0m             _assert_all_finite(array,\n\u001b[1;32m--> 542\u001b[1;33m                                allow_nan=force_all_finite == 'allow-nan')\n\u001b[0m\u001b[0;32m    543\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    544\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mensure_min_samples\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36m_assert_all_finite\u001b[1;34m(X, allow_nan)\u001b[0m\n\u001b[0;32m     54\u001b[0m                 not allow_nan and not np.isfinite(X).all()):\n\u001b[0;32m     55\u001b[0m             \u001b[0mtype_err\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'infinity'\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mallow_nan\u001b[0m \u001b[1;32melse\u001b[0m \u001b[1;34m'NaN, infinity'\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 56\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmsg_err\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtype_err\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     57\u001b[0m     \u001b[1;31m# for object dtype data, we only check for NaNs (GH-13254)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     58\u001b[0m     \u001b[1;32melif\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m \u001b[1;33m==\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'object'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mallow_nan\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: Input contains NaN, infinity or a value too large for dtype('float64')."
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
@@ -631,7 +519,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -647,12 +535,10 @@
        "HLT     True\n",
        "INFO    True\n",
        "IQV     True\n",
-       "TPR     True\n",
-       "WELL    True\n",
        "dtype: bool"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -666,7 +552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -675,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -687,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -698,16 +584,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
+       "LinearRegression()"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -722,7 +608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -731,22 +617,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 92,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x16252b7ed48>]"
+       "[<matplotlib.lines.Line2D at 0x168b6459570>]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -759,7 +645,7 @@
    ],
    "source": [
     "plt.plot(y_train_pred)\n",
-    "plt.plot(y_train.values)"
+    "plt.plot(y_train.values + 1)"
    ]
   },
   {
@@ -773,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -782,22 +668,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x162531ffc48>]"
+       "[<matplotlib.lines.Line2D at 0x168b20c5930>]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -822,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -844,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -857,16 +743,16 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1625458dc88>"
+       "<matplotlib.legend.Legend at 0x168b20a2f50>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -895,40 +781,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:442: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  fold_sizes = np.full(n_splits, n_samples // n_splits, dtype=np.int)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:102: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:102: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:102: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:102: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:102: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:102: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n",
-      "C:\\Users\\marin\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:102: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n",
-      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
-      "  test_mask = np.zeros(_num_samples(X), dtype=np.bool)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "from sklearn.model_selection import validation_curve\n",
     "\n",
@@ -939,18 +794,18 @@
     "x = x.reshape(-1, 1)  # Make 2D matrix, as required by sklearn\n",
     "degree = np.arange(0, 20)\n",
     "val_train, val_test = validation_curve(PolynomialRegression(), x, y,\n",
-    "                                       'polynomialfeatures__degree', degree, cv=7,\n",
+    "                                       param_name = 'polynomialfeatures__degree', param_range = degree, cv=7,\n",
     "                                       scoring=rms_error)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -997,6 +852,72 @@
     "2. A similar experiment can be performed with \"number of data points\" on the x-axis and train/test error. Create this plot, varying the size of the dataset. This plot tells us whether collecting more data will assist or not with improving the error, which can be critical in determining if funds need to be spent in this area."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# qs 1\n",
+    "degree = np.arange(1, 200)\n",
+    "err_train, err_test, n_data_point = [], [], []\n",
+    "\n",
+    "for d in degree:\n",
+    "    \n",
+    "    model = LinearRegression()\n",
+    "    model.fit(X_train.iloc[:, :d], y_train)\n",
+    "\n",
+    "    y_train_pred = model.predict(X_train.iloc[:, :d])\n",
+    "    y_test_pred = model.predict(X_test.iloc[:, :d])\n",
+    "    \n",
+    "    err_train.append(np.sqrt((y_train_pred - y_train.values)**2))\n",
+    "    err_test.append(np.sqrt((y_test_pred - y_test.values)**2))\n",
+    "\n",
+    "    n_data_point.append(np.prod(X_train.iloc[:, :d].shape))\n",
+    "\n",
+    "err_train, err_test, n_data_point = np.array(err_train), np.array(err_test), np.array(n_data_point)\n",
+    "\n",
+    "plot_with_err(degree, err_train, label='training scores')\n",
+    "plot_with_err(degree, err_test, label='testing scores')\n",
+    "plt.xlabel('number of stocks')\n",
+    "plt.ylabel('rms error')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
+    "\n",
+    "# qs 2\n",
+    "plot_with_err(n_data_point, err_train, label='training scores')\n",
+    "plot_with_err(n_data_point, err_test, label='testing scores')\n",
+    "plt.xlabel('nubmer of data points')\n",
+    "plt.ylabel('rms error')\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1026,6 +947,87 @@
     "2. Investigate Grid Search in scikit-learn, and use it to fit the best value of $\\lambda$."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'alpha': 29}\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from sklearn.linear_model import Ridge\n",
+    "from sklearn.model_selection import GridSearchCV\n",
+    "\n",
+    "# qs 1\n",
+    "# generate data from copying code above\n",
+    "n = 25\n",
+    "x = np.random.random(n) * 10\n",
+    "y = 3 * x + 1 + np.random.rand(n) * 25\n",
+    "\n",
+    "# fit a model using same code as above, make X column vec\n",
+    "liear_model = LinearRegression()\n",
+    "liear_model.fit(x[:, None],y)\n",
+    "y_pred1 = liear_model.predict(x[:, None])\n",
+    "\n",
+    "ridge_model = Ridge(alpha = 50)\n",
+    "ridge_model.fit(x[:, None], y)\n",
+    "y_pred2 = ridge_model.predict(x[:, None])\n",
+    "\n",
+    "# plot data\n",
+    "plt.plot(x, y, 'bo')\n",
+    "plt.plot(y_pred1, \"r-\")\n",
+    "plt.plot(y_pred2, \"b-\")\n",
+    "plt.title(\"Guess alpha\")\n",
+    "plt.show()\n",
+    "\n",
+    "# qs 2\n",
+    "param_search = np.array([i for i in range(250)])\n",
+    "search_space = {\"alpha\": param_search}\n",
+    "model = GridSearchCV(Ridge(), search_space)\n",
+    "model.fit(x[:, None], y)\n",
+    "print(model.best_params_)\n",
+    "\n",
+    "# replot\n",
+    "ridge_model = Ridge(alpha = model.best_params_[\"alpha\"])\n",
+    "ridge_model.fit(x[:, None], y)\n",
+    "y_pred2 = ridge_model.predict(x[:, None])\n",
+    "plt.plot(x, y, 'bo')\n",
+    "plt.plot(y_pred1, \"r-\")\n",
+    "plt.plot(y_pred2, \"b-\")\n",
+    "plt.title(\"Grid Search Alpha\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1036,7 +1038,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "env",
    "language": "python",
    "name": "python3"
   },
@@ -1050,7 +1052,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.10.3"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "e4dc6b0224d27235dec988ead42f740f3c8067d08028a6940d609f8d318d6bb4"
+   }
   }
  },
  "nbformat": 4,
diff --git a/notebooks/altair-temp-data/-04c14130d0300c970cba06bcbb67bbe6.json b/notebooks/altair-temp-data/-04c14130d0300c970cba06bcbb67bbe6.json
new file mode 100644
index 0000000..f6e8a5c
--- /dev/null
+++ b/notebooks/altair-temp-data/-04c14130d0300c970cba06bcbb67bbe6.json
@@ -0,0 +1 @@
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diff --git a/notebooks/altair-temp-data/-0987fe1346159f45f3357fc583c8855d.json b/notebooks/altair-temp-data/-0987fe1346159f45f3357fc583c8855d.json
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diff --git a/notebooks/altair-temp-data/-09a99fcf4a508a89c44ef7e04ef9cc31.json b/notebooks/altair-temp-data/-09a99fcf4a508a89c44ef7e04ef9cc31.json
new file mode 100644
index 0000000..fd0d5e0
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new file mode 100644
index 0000000..d7f0bd2
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new file mode 100644
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diff --git a/notebooks/altair-temp-data/-39c057fd57634838b29c4db3d28a0cff.json b/notebooks/altair-temp-data/-39c057fd57634838b29c4db3d28a0cff.json
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new file mode 100644
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diff --git a/notebooks/altair-temp-data/-4e992b2ede88bdc11fed97ef9d2fbbfe.json b/notebooks/altair-temp-data/-4e992b2ede88bdc11fed97ef9d2fbbfe.json
new file mode 100644
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diff --git a/notebooks/altair-temp-data/-4f127378876f7ad2b40744995648a508.json b/notebooks/altair-temp-data/-4f127378876f7ad2b40744995648a508.json
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diff --git a/notebooks/altair-temp-data/-61d853e42914629e3ccb99d082941587.json b/notebooks/altair-temp-data/-61d853e42914629e3ccb99d082941587.json
new file mode 100644
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new file mode 100644
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diff --git a/notebooks/altair-temp-data/-6a7ce80c67dae16530376525b6f4f819.json b/notebooks/altair-temp-data/-6a7ce80c67dae16530376525b6f4f819.json
new file mode 100644
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diff --git a/notebooks/altair-temp-data/-706bf09512dfcf2d1bcb3504d95498c4.json b/notebooks/altair-temp-data/-706bf09512dfcf2d1bcb3504d95498c4.json
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diff --git a/notebooks/altair-temp-data/-84162f28f877feddde62e1158a7020b9.json b/notebooks/altair-temp-data/-84162f28f877feddde62e1158a7020b9.json
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new file mode 100644
index 0000000..c2a9eaf
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new file mode 100644
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new file mode 100644
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