Contingency_test 2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "junior-lobby",
   "metadata": {},
   "source": [
    "# Thực hiện 9 loại kiểm định cho bảng chéo\n",
    "\n",
    "**BS. Lê Ngọc Khả Nhi**\n",
    "\n",
    "Trong bài này, Nhi tạo ra 1 công cụ phân tích bảng chéo, cho phép thực hiện đồng thời 6 loại kiểm định theo phương pháp phổ quát là Power divergence statistic và goodness of fit test, do 2 tác giả Noel Cressie và Timothy R. C. Read lập ra vào năm 1984.\n",
    "\n",
    "Phương pháp Power divergence của Cressie và Read có tham số $\\lambda$, gồm các giá trị có thể:\n",
    "\n",
    "+ $\\lambda$ = 1 tương đương với kiểm định Chi squared cổ điển cùa Pearson\n",
    "\n",
    "+ $\\lambda$ = 0 tương đương với G-test (log-likelihood ratio test)\n",
    "\n",
    "+ $\\lambda$ = -0.5 tương đương với trị số thống kê của Freeman và Tukey\n",
    "\n",
    "+ $\\lambda$ = -1 tương đương với kiểm định log-likelihood ratio hiệu chỉnh\n",
    "\n",
    "+ $\\lambda$ = -2 tương đương với trị số thống kê của Neyman\n",
    "\n",
    "+ $\\lambda$ = 2/3 là giá trị đề xuất bởi Cressie và Read năm 1984\n",
    "\n",
    "Ngoài ra, nếu là bảng chéo 2x2, ta có thể thực hiện 3 loại kiểm định chính xác (Exact tests) bao gồm :\n",
    "\n",
    "+ Kiểm định Fisher (Fisher exact test, năm 1922)\n",
    "\n",
    "+ Kiểm định Boschloo (1970) \n",
    "\n",
    "+ Kiểm định Barnard (1947)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "employed-sender",
   "metadata": {},
   "outputs": [],
   "source": [
    "warning_status = \"ignore\"\n",
    "import warnings\n",
    "warnings.filterwarnings(warning_status)\n",
    "with warnings.catch_warnings():\n",
    "    warnings.filterwarnings(warning_status, category=DeprecationWarning)\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scipy.stats.contingency import expected_freq\n",
    "from scipy.stats import power_divergence, boschloo_exact, fisher_exact, barnard_exact\n",
    "\n",
    "from dataclasses import dataclass, field\n",
    "\n",
    "def check_variable(data: pd.DataFrame, x: str, y:str) -> bool:\n",
    "\n",
    "    return all(i in data.columns for i in [x, y])\n",
    "\n",
    "@dataclass\n",
    "class Contingency_test:\n",
    "\n",
    "    data: pd.DataFrame = field(init=True)\n",
    "    x: str = field(init=True)\n",
    "    y: str = field(init=True)\n",
    "\n",
    "    def __post_init__(self):\n",
    "        if not check_variable(self.data, self.x, self.y):\n",
    "            raise ValueError(\"x or y not in data columns\")\n",
    "\n",
    "        self.observed = pd.crosstab(self.data[self.x], self.data[self.y])\n",
    "\n",
    "        self.expected = pd.DataFrame(\n",
    "            expected_freq(self.observed),\n",
    "            index=self.observed.index,\n",
    "            columns=self.observed.columns,\n",
    "        )\n",
    "\n",
    "        for xtab, name in zip([self.observed, self.expected], [\"Quan sát\", \"Giả định\"]):\n",
    "\n",
    "            if (xtab < 5).any(axis=None):\n",
    "                print(f\"Lưu ý: có giá trị nhỏ hơn 5 trong bảng {name}\")\n",
    "\n",
    "        print(\"Bảng phân bố tần suất\")\n",
    "        print(\"-\" * 20)\n",
    "        print(self.observed)\n",
    "\n",
    "    def power_divergence(self, correction: bool = False) -> pd.DataFrame:\n",
    "        \"\"\"\n",
    "        Cressie-Read power divergence statistic and goodness of fit test.\n",
    "        \"\"\"\n",
    "        dof = float(\n",
    "            self.expected.size - sum(self.expected.shape) + self.expected.ndim - 1\n",
    "        )\n",
    "\n",
    "        n = self.observed.values.sum()\n",
    "\n",
    "        if dof == 1 and correction:\n",
    "            self.observed = self.observed + 0.5 * np.sign(self.expected - self.observed)\n",
    "\n",
    "        ddof = self.observed.size - 1 - dof\n",
    "\n",
    "        methods = [\n",
    "            \"Pearson Chi2\",\n",
    "            \"Cressie-Read\",\n",
    "            \"G-test\",\n",
    "            \"Freeman-Tukey\",\n",
    "            \"mod-log-LR\",\n",
    "            \"Neyman\",\n",
    "        ]\n",
    "\n",
    "        stats = []\n",
    "\n",
    "        for met, lambda_ in zip(methods, [1.0, 2 / 3, 0.0, -1 / 2, -1.0, -2.0]):\n",
    "\n",
    "            if dof == 0:\n",
    "                chi2, p, cramer = 0.0, 1.0, \"NA\"\n",
    "            else:\n",
    "                chi2, p = power_divergence(\n",
    "                    self.observed, self.expected, ddof=ddof, axis=None, lambda_=lambda_\n",
    "                )\n",
    "\n",
    "                cramer = np.sqrt(chi2 / (n * (min(self.expected.shape) - 1)))\n",
    "\n",
    "            stats.append(\n",
    "                {\n",
    "                    \"Phương pháp\": met,\n",
    "                    \"df\": dof,\n",
    "                    \"\\u03C7\" + \"\\u00b2\": chi2,\n",
    "                    \"Cramer's V\": cramer,\n",
    "                    \"Giá trị p\": p,\n",
    "                    \"Phủ định H0\": \"Có\" if p < 0.05 else \"Không\",\n",
    "                }\n",
    "            )\n",
    "\n",
    "        out = pd.DataFrame(stats)\n",
    "\n",
    "        return out.style.hide_index()\n",
    "\n",
    "    def exact_test(self) -> pd.DataFrame:\n",
    "        \"\"\"\n",
    "        Exact tests for 2x2 cross-table\n",
    "        \"\"\"\n",
    "        # verify that the table is 2x2\n",
    "        if self.observed.shape != (2, 2):\n",
    "            raise ValueError(\"Chỉ áp dụng cho bảng chéo 2x2\")\n",
    "\n",
    "        methods = [\"Boschloo\", \"Fisher\", \"Barnard\"]\n",
    "        stats = []\n",
    "\n",
    "        for i, met in enumerate(methods):\n",
    "\n",
    "            if i == 0:\n",
    "                res = boschloo_exact(self.observed)\n",
    "                stat, p = res.statistic, res.pvalue\n",
    "            elif i == 1:\n",
    "                stat, p = fisher_exact(self.observed)\n",
    "            elif i == 2:\n",
    "                res = barnard_exact(self.observed)\n",
    "                stat, p = res.statistic, res.pvalue\n",
    "\n",
    "            stats.append(\n",
    "                {\n",
    "                    \"Phương pháp\": met,\n",
    "                    \"Trị số tk\": stat,\n",
    "                    \"Giá trị p\": p,\n",
    "                    \"Phủ định H0\": \"Có\" if p < 0.05 else \"Không\",\n",
    "                }\n",
    "            )\n",
    "\n",
    "        out = pd.DataFrame(stats)\n",
    "\n",
    "        return out.style.hide_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dynamic-expansion",
   "metadata": {},
   "source": [
    "# Thí dụ minh họa:  Bộ dữ liệu Sinh non và Covid-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "metric-tobago",
   "metadata": {},
   "outputs": [
    {
     "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>ID</th>\n",
       "      <th>Tuổi</th>\n",
       "      <th>COVID</th>\n",
       "      <th>Tiền sử SN</th>\n",
       "      <th>Tiền sử PT</th>\n",
       "      <th>Tuổi thai</th>\n",
       "      <th>Sinh non</th>\n",
       "      <th>ĐTĐTK</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>30-35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>260</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>30-35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>274</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>30-35</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>268</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>30-35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>277</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>30-35</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>277</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>225</td>\n",
       "      <td>35-40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>244</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>226</td>\n",
       "      <td>35-40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>240</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>227</td>\n",
       "      <td>30-35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>253</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>228</td>\n",
       "      <td>30-35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>251</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>229</td>\n",
       "      <td>25-30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>212</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>229 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID   Tuổi  COVID  Tiền sử SN  Tiền sử PT  Tuổi thai  Sinh non  ĐTĐTK\n",
       "0      1  30-35      1           0           0        260         0      0\n",
       "1      2  30-35      1           0           0        274         0      0\n",
       "2      3  30-35      1           1           0        268         0      0\n",
       "3      4  30-35      1           0           0        277         0      1\n",
       "4      5  30-35      1           0           0        277         0      0\n",
       "..   ...    ...    ...         ...         ...        ...       ...    ...\n",
       "224  225  35-40      0           0           0        244         1      0\n",
       "225  226  35-40      0           0           0        240         1      0\n",
       "226  227  30-35      0           0           0        253         1      0\n",
       "227  228  30-35      0           0           0        251         1      0\n",
       "228  229  25-30      0           0           0        212         1      0\n",
       "\n",
       "[229 rows x 8 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_excel('Covid_pretermbirth.xlsx')\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "periodic-quick",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 229 entries, 0 to 228\n",
      "Data columns (total 8 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   ID          229 non-null    int64 \n",
      " 1   Tuổi        229 non-null    object\n",
      " 2   COVID       229 non-null    int64 \n",
      " 3   Tiền sử SN  229 non-null    int64 \n",
      " 4   Tiền sử PT  229 non-null    int64 \n",
      " 5   Tuổi thai   229 non-null    int64 \n",
      " 6   Sinh non    229 non-null    int64 \n",
      " 7   ĐTĐTK       229 non-null    int64 \n",
      "dtypes: int64(7), object(1)\n",
      "memory usage: 14.4+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "raising-assumption",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ID', 'Tuổi', 'COVID', 'Tiền sử SN', 'Tiền sử PT', 'Tuổi thai',\n",
       "       'Sinh non', 'ĐTĐTK'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "refined-excellence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bảng phân bố tần suất\n",
      "--------------------\n",
      "Sinh non   0   1\n",
      "COVID           \n",
      "0         95  81\n",
      "1         32  21\n"
     ]
    }
   ],
   "source": [
    "test = Contingency_test(df, 'COVID','Sinh non')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "relative-scholarship",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_c8d8a_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >df</th>\n",
       "      <th class=\"col_heading level0 col2\" >χ²</th>\n",
       "      <th class=\"col_heading level0 col3\" >Cramer's V</th>\n",
       "      <th class=\"col_heading level0 col4\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col5\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_c8d8a_row0_col0\" class=\"data row0 col0\" >Pearson Chi2</td>\n",
       "      <td id=\"T_c8d8a_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_c8d8a_row0_col2\" class=\"data row0 col2\" >0.441202</td>\n",
       "      <td id=\"T_c8d8a_row0_col3\" class=\"data row0 col3\" >0.043894</td>\n",
       "      <td id=\"T_c8d8a_row0_col4\" class=\"data row0 col4\" >0.506543</td>\n",
       "      <td id=\"T_c8d8a_row0_col5\" class=\"data row0 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_c8d8a_row1_col0\" class=\"data row1 col0\" >Cressie-Read</td>\n",
       "      <td id=\"T_c8d8a_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_c8d8a_row1_col2\" class=\"data row1 col2\" >0.441892</td>\n",
       "      <td id=\"T_c8d8a_row1_col3\" class=\"data row1 col3\" >0.043928</td>\n",
       "      <td id=\"T_c8d8a_row1_col4\" class=\"data row1 col4\" >0.506211</td>\n",
       "      <td id=\"T_c8d8a_row1_col5\" class=\"data row1 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_c8d8a_row2_col0\" class=\"data row2 col0\" >G-test</td>\n",
       "      <td id=\"T_c8d8a_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_c8d8a_row2_col2\" class=\"data row2 col2\" >0.443406</td>\n",
       "      <td id=\"T_c8d8a_row2_col3\" class=\"data row2 col3\" >0.044003</td>\n",
       "      <td id=\"T_c8d8a_row2_col4\" class=\"data row2 col4\" >0.505483</td>\n",
       "      <td id=\"T_c8d8a_row2_col5\" class=\"data row2 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_c8d8a_row3_col0\" class=\"data row3 col0\" >Freeman-Tukey</td>\n",
       "      <td id=\"T_c8d8a_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_c8d8a_row3_col2\" class=\"data row3 col2\" >0.444661</td>\n",
       "      <td id=\"T_c8d8a_row3_col3\" class=\"data row3 col3\" >0.044065</td>\n",
       "      <td id=\"T_c8d8a_row3_col4\" class=\"data row3 col4\" >0.504881</td>\n",
       "      <td id=\"T_c8d8a_row3_col5\" class=\"data row3 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_c8d8a_row4_col0\" class=\"data row4 col0\" >mod-log-LR</td>\n",
       "      <td id=\"T_c8d8a_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_c8d8a_row4_col2\" class=\"data row4 col2\" >0.446018</td>\n",
       "      <td id=\"T_c8d8a_row4_col3\" class=\"data row4 col3\" >0.044133</td>\n",
       "      <td id=\"T_c8d8a_row4_col4\" class=\"data row4 col4\" >0.504232</td>\n",
       "      <td id=\"T_c8d8a_row4_col5\" class=\"data row4 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_c8d8a_row5_col0\" class=\"data row5 col0\" >Neyman</td>\n",
       "      <td id=\"T_c8d8a_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "      <td id=\"T_c8d8a_row5_col2\" class=\"data row5 col2\" >0.449050</td>\n",
       "      <td id=\"T_c8d8a_row5_col3\" class=\"data row5 col3\" >0.044282</td>\n",
       "      <td id=\"T_c8d8a_row5_col4\" class=\"data row5 col4\" >0.502786</td>\n",
       "      <td id=\"T_c8d8a_row5_col5\" class=\"data row5 col5\" >Không</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c0e78eeee0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.power_divergence(correction = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "packed-botswana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "<table id=\"T_d40b6_\">\n",
       "  <thead>\n",
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       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >Trị số tk</th>\n",
       "      <th class=\"col_heading level0 col2\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col3\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_d40b6_row0_col0\" class=\"data row0 col0\" >Boschloo</td>\n",
       "      <td id=\"T_d40b6_row0_col1\" class=\"data row0 col1\" >0.302446</td>\n",
       "      <td id=\"T_d40b6_row0_col2\" class=\"data row0 col2\" >0.516395</td>\n",
       "      <td id=\"T_d40b6_row0_col3\" class=\"data row0 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d40b6_row1_col0\" class=\"data row1 col0\" >Fisher</td>\n",
       "      <td id=\"T_d40b6_row1_col1\" class=\"data row1 col1\" >0.804435</td>\n",
       "      <td id=\"T_d40b6_row1_col2\" class=\"data row1 col2\" >0.528414</td>\n",
       "      <td id=\"T_d40b6_row1_col3\" class=\"data row1 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_d40b6_row2_col0\" class=\"data row2 col0\" >Barnard</td>\n",
       "      <td id=\"T_d40b6_row2_col1\" class=\"data row2 col1\" >-0.679029</td>\n",
       "      <td id=\"T_d40b6_row2_col2\" class=\"data row2 col2\" >0.609462</td>\n",
       "      <td id=\"T_d40b6_row2_col3\" class=\"data row2 col3\" >Không</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c0e8ac4430>"
      ]
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     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.exact_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "digital-invasion",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bảng phân bố tần suất\n",
      "--------------------\n",
      "Sinh non   0   1\n",
      "COVID           \n",
      "0         95  81\n",
      "1         32  21\n"
     ]
    },
    {
     "data": {
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       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_2c648_\">\n",
       "  <thead>\n",
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       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >df</th>\n",
       "      <th class=\"col_heading level0 col2\" >χ²</th>\n",
       "      <th class=\"col_heading level0 col3\" >Cramer's V</th>\n",
       "      <th class=\"col_heading level0 col4\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col5\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_2c648_row0_col0\" class=\"data row0 col0\" >Pearson Chi2</td>\n",
       "      <td id=\"T_2c648_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_2c648_row0_col2\" class=\"data row0 col2\" >0.675448</td>\n",
       "      <td id=\"T_2c648_row0_col3\" class=\"data row0 col3\" >0.054310</td>\n",
       "      <td id=\"T_2c648_row0_col4\" class=\"data row0 col4\" >0.411159</td>\n",
       "      <td id=\"T_2c648_row0_col5\" class=\"data row0 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2c648_row1_col0\" class=\"data row1 col0\" >Cressie-Read</td>\n",
       "      <td id=\"T_2c648_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_2c648_row1_col2\" class=\"data row1 col2\" >0.676795</td>\n",
       "      <td id=\"T_2c648_row1_col3\" class=\"data row1 col3\" >0.054364</td>\n",
       "      <td id=\"T_2c648_row1_col4\" class=\"data row1 col4\" >0.410693</td>\n",
       "      <td id=\"T_2c648_row1_col5\" class=\"data row1 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2c648_row2_col0\" class=\"data row2 col0\" >G-test</td>\n",
       "      <td id=\"T_2c648_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_2c648_row2_col2\" class=\"data row2 col2\" >0.679807</td>\n",
       "      <td id=\"T_2c648_row2_col3\" class=\"data row2 col3\" >0.054485</td>\n",
       "      <td id=\"T_2c648_row2_col4\" class=\"data row2 col4\" >0.409653</td>\n",
       "      <td id=\"T_2c648_row2_col5\" class=\"data row2 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2c648_row3_col0\" class=\"data row3 col0\" >Freeman-Tukey</td>\n",
       "      <td id=\"T_2c648_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_2c648_row3_col2\" class=\"data row3 col2\" >0.682349</td>\n",
       "      <td id=\"T_2c648_row3_col3\" class=\"data row3 col3\" >0.054587</td>\n",
       "      <td id=\"T_2c648_row3_col4\" class=\"data row3 col4\" >0.408779</td>\n",
       "      <td id=\"T_2c648_row3_col5\" class=\"data row3 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2c648_row4_col0\" class=\"data row4 col0\" >mod-log-LR</td>\n",
       "      <td id=\"T_2c648_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_2c648_row4_col2\" class=\"data row4 col2\" >0.685137</td>\n",
       "      <td id=\"T_2c648_row4_col3\" class=\"data row4 col3\" >0.054698</td>\n",
       "      <td id=\"T_2c648_row4_col4\" class=\"data row4 col4\" >0.407824</td>\n",
       "      <td id=\"T_2c648_row4_col5\" class=\"data row4 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_2c648_row5_col0\" class=\"data row5 col0\" >Neyman</td>\n",
       "      <td id=\"T_2c648_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "      <td id=\"T_2c648_row5_col2\" class=\"data row5 col2\" >0.691471</td>\n",
       "      <td id=\"T_2c648_row5_col3\" class=\"data row5 col3\" >0.054950</td>\n",
       "      <td id=\"T_2c648_row5_col4\" class=\"data row5 col4\" >0.405665</td>\n",
       "      <td id=\"T_2c648_row5_col5\" class=\"data row5 col5\" >Không</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c0e78e5220>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
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       "<table id=\"T_add5f_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >Trị số tk</th>\n",
       "      <th class=\"col_heading level0 col2\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col3\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_add5f_row0_col0\" class=\"data row0 col0\" >Boschloo</td>\n",
       "      <td id=\"T_add5f_row0_col1\" class=\"data row0 col1\" >0.253958</td>\n",
       "      <td id=\"T_add5f_row0_col2\" class=\"data row0 col2\" >0.434415</td>\n",
       "      <td id=\"T_add5f_row0_col3\" class=\"data row0 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_add5f_row1_col0\" class=\"data row1 col0\" >Fisher</td>\n",
       "      <td id=\"T_add5f_row1_col1\" class=\"data row1 col1\" >0.769676</td>\n",
       "      <td id=\"T_add5f_row1_col2\" class=\"data row1 col2\" >0.434737</td>\n",
       "      <td id=\"T_add5f_row1_col3\" class=\"data row1 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_add5f_row2_col0\" class=\"data row2 col0\" >Barnard</td>\n",
       "      <td id=\"T_add5f_row2_col1\" class=\"data row2 col1\" >-0.821856</td>\n",
       "      <td id=\"T_add5f_row2_col2\" class=\"data row2 col2\" >0.420205</td>\n",
       "      <td id=\"T_add5f_row2_col3\" class=\"data row2 col3\" >Không</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c0e8a94340>"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Lưu ý: có giá trị nhỏ hơn 5 trong bảng Quan sát\n",
      "Lưu ý: có giá trị nhỏ hơn 5 trong bảng Giả định\n",
      "Bảng phân bố tần suất\n",
      "--------------------\n",
      "Sinh non      0   1\n",
      "Tiền sử SN         \n",
      "0           125  95\n",
      "1             2   7\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_a4f0b_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >df</th>\n",
       "      <th class=\"col_heading level0 col2\" >χ²</th>\n",
       "      <th class=\"col_heading level0 col3\" >Cramer's V</th>\n",
       "      <th class=\"col_heading level0 col4\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col5\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_a4f0b_row0_col0\" class=\"data row0 col0\" >Pearson Chi2</td>\n",
       "      <td id=\"T_a4f0b_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_a4f0b_row0_col2\" class=\"data row0 col2\" >4.189359</td>\n",
       "      <td id=\"T_a4f0b_row0_col3\" class=\"data row0 col3\" >0.135256</td>\n",
       "      <td id=\"T_a4f0b_row0_col4\" class=\"data row0 col4\" >0.040678</td>\n",
       "      <td id=\"T_a4f0b_row0_col5\" class=\"data row0 col5\" >Có</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_a4f0b_row1_col0\" class=\"data row1 col0\" >Cressie-Read</td>\n",
       "      <td id=\"T_a4f0b_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_a4f0b_row1_col2\" class=\"data row1 col2\" >4.192511</td>\n",
       "      <td id=\"T_a4f0b_row1_col3\" class=\"data row1 col3\" >0.135307</td>\n",
       "      <td id=\"T_a4f0b_row1_col4\" class=\"data row1 col4\" >0.040603</td>\n",
       "      <td id=\"T_a4f0b_row1_col5\" class=\"data row1 col5\" >Có</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_a4f0b_row2_col0\" class=\"data row2 col0\" >G-test</td>\n",
       "      <td id=\"T_a4f0b_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_a4f0b_row2_col2\" class=\"data row2 col2\" >4.310916</td>\n",
       "      <td id=\"T_a4f0b_row2_col3\" class=\"data row2 col3\" >0.137204</td>\n",
       "      <td id=\"T_a4f0b_row2_col4\" class=\"data row2 col4\" >0.037869</td>\n",
       "      <td id=\"T_a4f0b_row2_col5\" class=\"data row2 col5\" >Có</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_a4f0b_row3_col0\" class=\"data row3 col0\" >Freeman-Tukey</td>\n",
       "      <td id=\"T_a4f0b_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_a4f0b_row3_col2\" class=\"data row3 col2\" >4.510856</td>\n",
       "      <td id=\"T_a4f0b_row3_col3\" class=\"data row3 col3\" >0.140350</td>\n",
       "      <td id=\"T_a4f0b_row3_col4\" class=\"data row3 col4\" >0.033680</td>\n",
       "      <td id=\"T_a4f0b_row3_col5\" class=\"data row3 col5\" >Có</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_a4f0b_row4_col0\" class=\"data row4 col0\" >mod-log-LR</td>\n",
       "      <td id=\"T_a4f0b_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_a4f0b_row4_col2\" class=\"data row4 col2\" >4.825607</td>\n",
       "      <td id=\"T_a4f0b_row4_col3\" class=\"data row4 col3\" >0.145164</td>\n",
       "      <td id=\"T_a4f0b_row4_col4\" class=\"data row4 col4\" >0.028040</td>\n",
       "      <td id=\"T_a4f0b_row4_col5\" class=\"data row4 col5\" >Có</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_a4f0b_row5_col0\" class=\"data row5 col0\" >Neyman</td>\n",
       "      <td id=\"T_a4f0b_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "      <td id=\"T_a4f0b_row5_col2\" class=\"data row5 col2\" >5.917844</td>\n",
       "      <td id=\"T_a4f0b_row5_col3\" class=\"data row5 col3\" >0.160755</td>\n",
       "      <td id=\"T_a4f0b_row5_col4\" class=\"data row5 col4\" >0.014988</td>\n",
       "      <td id=\"T_a4f0b_row5_col5\" class=\"data row5 col5\" >Có</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c0e78dbfd0>"
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    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_934e7_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >Trị số tk</th>\n",
       "      <th class=\"col_heading level0 col2\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col3\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_934e7_row0_col0\" class=\"data row0 col0\" >Boschloo</td>\n",
       "      <td id=\"T_934e7_row0_col1\" class=\"data row0 col1\" >0.043706</td>\n",
       "      <td id=\"T_934e7_row0_col2\" class=\"data row0 col2\" >0.066852</td>\n",
       "      <td id=\"T_934e7_row0_col3\" class=\"data row0 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_934e7_row1_col0\" class=\"data row1 col0\" >Fisher</td>\n",
       "      <td id=\"T_934e7_row1_col1\" class=\"data row1 col1\" >4.605263</td>\n",
       "      <td id=\"T_934e7_row1_col2\" class=\"data row1 col2\" >0.081649</td>\n",
       "      <td id=\"T_934e7_row1_col3\" class=\"data row1 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_934e7_row2_col0\" class=\"data row2 col0\" >Barnard</td>\n",
       "      <td id=\"T_934e7_row2_col1\" class=\"data row2 col1\" >2.046792</td>\n",
       "      <td id=\"T_934e7_row2_col2\" class=\"data row2 col2\" >0.041546</td>\n",
       "      <td id=\"T_934e7_row2_col3\" class=\"data row2 col3\" >Có</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1c0e8ad3bb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n",
      "Bảng phân bố tần suất\n",
      "--------------------\n",
      "Sinh non     0   1\n",
      "Tiền sử PT        \n",
      "0           94  83\n",
      "1           33  19\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_8e9a3_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >df</th>\n",
       "      <th class=\"col_heading level0 col2\" >χ²</th>\n",
       "      <th class=\"col_heading level0 col3\" >Cramer's V</th>\n",
       "      <th class=\"col_heading level0 col4\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col5\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_8e9a3_row0_col0\" class=\"data row0 col0\" >Pearson Chi2</td>\n",
       "      <td id=\"T_8e9a3_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_8e9a3_row0_col2\" class=\"data row0 col2\" >1.744379</td>\n",
       "      <td id=\"T_8e9a3_row0_col3\" class=\"data row0 col3\" >0.087278</td>\n",
       "      <td id=\"T_8e9a3_row0_col4\" class=\"data row0 col4\" >0.186585</td>\n",
       "      <td id=\"T_8e9a3_row0_col5\" class=\"data row0 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_8e9a3_row1_col0\" class=\"data row1 col0\" >Cressie-Read</td>\n",
       "      <td id=\"T_8e9a3_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_8e9a3_row1_col2\" class=\"data row1 col2\" >1.750666</td>\n",
       "      <td id=\"T_8e9a3_row1_col3\" class=\"data row1 col3\" >0.087435</td>\n",
       "      <td id=\"T_8e9a3_row1_col4\" class=\"data row1 col4\" >0.185793</td>\n",
       "      <td id=\"T_8e9a3_row1_col5\" class=\"data row1 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_8e9a3_row2_col0\" class=\"data row2 col0\" >G-test</td>\n",
       "      <td id=\"T_8e9a3_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_8e9a3_row2_col2\" class=\"data row2 col2\" >1.765492</td>\n",
       "      <td id=\"T_8e9a3_row2_col3\" class=\"data row2 col3\" >0.087804</td>\n",
       "      <td id=\"T_8e9a3_row2_col4\" class=\"data row2 col4\" >0.183941</td>\n",
       "      <td id=\"T_8e9a3_row2_col5\" class=\"data row2 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_8e9a3_row3_col0\" class=\"data row3 col0\" >Freeman-Tukey</td>\n",
       "      <td id=\"T_8e9a3_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_8e9a3_row3_col2\" class=\"data row3 col2\" >1.778644</td>\n",
       "      <td id=\"T_8e9a3_row3_col3\" class=\"data row3 col3\" >0.088131</td>\n",
       "      <td id=\"T_8e9a3_row3_col4\" class=\"data row3 col4\" >0.182316</td>\n",
       "      <td id=\"T_8e9a3_row3_col5\" class=\"data row3 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_8e9a3_row4_col0\" class=\"data row4 col0\" >mod-log-LR</td>\n",
       "      <td id=\"T_8e9a3_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_8e9a3_row4_col2\" class=\"data row4 col2\" >1.793604</td>\n",
       "      <td id=\"T_8e9a3_row4_col3\" class=\"data row4 col3\" >0.088500</td>\n",
       "      <td id=\"T_8e9a3_row4_col4\" class=\"data row4 col4\" >0.180488</td>\n",
       "      <td id=\"T_8e9a3_row4_col5\" class=\"data row4 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_8e9a3_row5_col0\" class=\"data row5 col0\" >Neyman</td>\n",
       "      <td id=\"T_8e9a3_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "      <td id=\"T_8e9a3_row5_col2\" class=\"data row5 col2\" >1.829218</td>\n",
       "      <td id=\"T_8e9a3_row5_col3\" class=\"data row5 col3\" >0.089375</td>\n",
       "      <td id=\"T_8e9a3_row5_col4\" class=\"data row5 col4\" >0.176220</td>\n",
       "      <td id=\"T_8e9a3_row5_col5\" class=\"data row5 col5\" >Không</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >Trị số tk</th>\n",
       "      <th class=\"col_heading level0 col2\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col3\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_e7752_row0_col0\" class=\"data row0 col0\" >Boschloo</td>\n",
       "      <td id=\"T_e7752_row0_col1\" class=\"data row0 col1\" >0.122303</td>\n",
       "      <td id=\"T_e7752_row0_col2\" class=\"data row0 col2\" >0.199034</td>\n",
       "      <td id=\"T_e7752_row0_col3\" class=\"data row0 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e7752_row1_col0\" class=\"data row1 col0\" >Fisher</td>\n",
       "      <td id=\"T_e7752_row1_col1\" class=\"data row1 col1\" >0.652063</td>\n",
       "      <td id=\"T_e7752_row1_col2\" class=\"data row1 col2\" >0.206827</td>\n",
       "      <td id=\"T_e7752_row1_col3\" class=\"data row1 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_e7752_row2_col0\" class=\"data row2 col0\" >Barnard</td>\n",
       "      <td id=\"T_e7752_row2_col1\" class=\"data row2 col1\" >-1.320749</td>\n",
       "      <td id=\"T_e7752_row2_col2\" class=\"data row2 col2\" >0.202961</td>\n",
       "      <td id=\"T_e7752_row2_col3\" class=\"data row2 col3\" >Không</td>\n",
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     "text": [
      "--------------------------------------------------\n",
      "Bảng phân bố tần suất\n",
      "--------------------\n",
      "Sinh non    0   1\n",
      "ĐTĐTK            \n",
      "0         115  85\n",
      "1          12  17\n"
     ]
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       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >df</th>\n",
       "      <th class=\"col_heading level0 col2\" >χ²</th>\n",
       "      <th class=\"col_heading level0 col3\" >Cramer's V</th>\n",
       "      <th class=\"col_heading level0 col4\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col5\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_9812e_row0_col0\" class=\"data row0 col0\" >Pearson Chi2</td>\n",
       "      <td id=\"T_9812e_row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "      <td id=\"T_9812e_row0_col2\" class=\"data row0 col2\" >2.664568</td>\n",
       "      <td id=\"T_9812e_row0_col3\" class=\"data row0 col3\" >0.107869</td>\n",
       "      <td id=\"T_9812e_row0_col4\" class=\"data row0 col4\" >0.102606</td>\n",
       "      <td id=\"T_9812e_row0_col5\" class=\"data row0 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_9812e_row1_col0\" class=\"data row1 col0\" >Cressie-Read</td>\n",
       "      <td id=\"T_9812e_row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "      <td id=\"T_9812e_row1_col2\" class=\"data row1 col2\" >2.655831</td>\n",
       "      <td id=\"T_9812e_row1_col3\" class=\"data row1 col3\" >0.107692</td>\n",
       "      <td id=\"T_9812e_row1_col4\" class=\"data row1 col4\" >0.103171</td>\n",
       "      <td id=\"T_9812e_row1_col5\" class=\"data row1 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_9812e_row2_col0\" class=\"data row2 col0\" >G-test</td>\n",
       "      <td id=\"T_9812e_row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "      <td id=\"T_9812e_row2_col2\" class=\"data row2 col2\" >2.648715</td>\n",
       "      <td id=\"T_9812e_row2_col3\" class=\"data row2 col3\" >0.107547</td>\n",
       "      <td id=\"T_9812e_row2_col4\" class=\"data row2 col4\" >0.103634</td>\n",
       "      <td id=\"T_9812e_row2_col5\" class=\"data row2 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_9812e_row3_col0\" class=\"data row3 col0\" >Freeman-Tukey</td>\n",
       "      <td id=\"T_9812e_row3_col1\" class=\"data row3 col1\" >1.000000</td>\n",
       "      <td id=\"T_9812e_row3_col2\" class=\"data row3 col2\" >2.652425</td>\n",
       "      <td id=\"T_9812e_row3_col3\" class=\"data row3 col3\" >0.107623</td>\n",
       "      <td id=\"T_9812e_row3_col4\" class=\"data row3 col4\" >0.103392</td>\n",
       "      <td id=\"T_9812e_row3_col5\" class=\"data row3 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_9812e_row4_col0\" class=\"data row4 col0\" >mod-log-LR</td>\n",
       "      <td id=\"T_9812e_row4_col1\" class=\"data row4 col1\" >1.000000</td>\n",
       "      <td id=\"T_9812e_row4_col2\" class=\"data row4 col2\" >2.663943</td>\n",
       "      <td id=\"T_9812e_row4_col3\" class=\"data row4 col3\" >0.107856</td>\n",
       "      <td id=\"T_9812e_row4_col4\" class=\"data row4 col4\" >0.102646</td>\n",
       "      <td id=\"T_9812e_row4_col5\" class=\"data row4 col5\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_9812e_row5_col0\" class=\"data row5 col0\" >Neyman</td>\n",
       "      <td id=\"T_9812e_row5_col1\" class=\"data row5 col1\" >1.000000</td>\n",
       "      <td id=\"T_9812e_row5_col2\" class=\"data row5 col2\" >2.710933</td>\n",
       "      <td id=\"T_9812e_row5_col3\" class=\"data row5 col3\" >0.108803</td>\n",
       "      <td id=\"T_9812e_row5_col4\" class=\"data row5 col4\" >0.099663</td>\n",
       "      <td id=\"T_9812e_row5_col5\" class=\"data row5 col5\" >Không</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
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       "      <th class=\"col_heading level0 col0\" >Phương pháp</th>\n",
       "      <th class=\"col_heading level0 col1\" >Trị số tk</th>\n",
       "      <th class=\"col_heading level0 col2\" >Giá trị p</th>\n",
       "      <th class=\"col_heading level0 col3\" >Phủ định H0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td id=\"T_b9bed_row0_col0\" class=\"data row0 col0\" >Boschloo</td>\n",
       "      <td id=\"T_b9bed_row0_col1\" class=\"data row0 col1\" >0.076360</td>\n",
       "      <td id=\"T_b9bed_row0_col2\" class=\"data row0 col2\" >0.122885</td>\n",
       "      <td id=\"T_b9bed_row0_col3\" class=\"data row0 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b9bed_row1_col0\" class=\"data row1 col0\" >Fisher</td>\n",
       "      <td id=\"T_b9bed_row1_col1\" class=\"data row1 col1\" >1.916667</td>\n",
       "      <td id=\"T_b9bed_row1_col2\" class=\"data row1 col2\" >0.113344</td>\n",
       "      <td id=\"T_b9bed_row1_col3\" class=\"data row1 col3\" >Không</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td id=\"T_b9bed_row2_col0\" class=\"data row2 col0\" >Barnard</td>\n",
       "      <td id=\"T_b9bed_row2_col1\" class=\"data row2 col1\" >1.632350</td>\n",
       "      <td id=\"T_b9bed_row2_col2\" class=\"data row2 col2\" >0.109453</td>\n",
       "      <td id=\"T_b9bed_row2_col3\" class=\"data row2 col3\" >Không</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import display\n",
    "\n",
    "for k in ['COVID', 'Tiền sử SN', 'Tiền sử PT', 'ĐTĐTK']:\n",
    "    \n",
    "    test = Contingency_test(df, k,'Sinh non')\n",
    "    out1 = test.power_divergence()\n",
    "    display(out1)\n",
    "    out2 = test.exact_test()\n",
    "    display(out2)\n",
    "    print('-'*50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "logical-converter",
   "metadata": {},
   "source": [
    "# Tài liệu tham khảo\n",
    "\n",
    "Noel Cressie and Timothy R. C. Read. Multinomial goodness‐of‐fit tests. Journal of the Royal Statistical Society: Series B (Methodological), 1984; 46(3), 440-464.\n",
    "\n",
    "Freeman, M.F. and J.W. Tukey (1950), Transformations related to the angular and the square root, Ann. Math. Statist. 21, 607-611.\n",
    "\n",
    "Campbell B. Read. Freeman--Tukey chi-squared goodness-of-fit statistics. Statistics & Probability Letters, 1993, vol. 18, issue 4, 271-278.\n",
    "\n",
    "J. Neyman and E. S. Pearson, On the use and interpretation of certain test criteria for purposes of statistical inference: Part i, Biometrika 20A (1928) 175–240.\n",
    "\n",
    "Xiangpan Ji, Wenqiang Gu, Xin Qian, Hanyu Wei, Chao Zhang. Combined Neyman-Pearson Chi-square: An Improved Approximation to the Poisson-likelihood Chi-square. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 2020; 961:163677\n",
    "\n",
    "Frank Yates. Contingency Tables Involving Small Numbers and the  𝜒2𝑡𝑒𝑠𝑡 . Supplement to the Journal of the Royal Statistical Society, 1934; 1: 217-235.\n",
    "\n",
    "\n",
    "Fisher, R. A. (1922). \"On the interpretation of χ2 from contingency tables, and the calculation of P\". Journal of the Royal Statistical Society. 85 (1): 87–94. doi:10.2307/2340521. JSTOR 2340521.\n",
    "\n",
    "R.D. Boschloo. “Raised conditional level of significance for the 2 x 2-table when testing the equality of two probabilities”, Statistica Neerlandica, 24(1), 1970\n",
    "\n",
    "https://en.wikipedia.org/wiki/Fisher%27s_exact_test\n",
    "https://en.wikipedia.org/wiki/Barnard%27s_test\n",
    "https://en.wikipedia.org/wiki/Boschloo%27s_test\n",
    "\n",
    "Barnard, G. A. “Significance Tests for 2x2 Tables”. Biometrika. 34.1/2 (1947): 123-138. DOI:dpgkg3"
   ]
  }
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