{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Étude du Paradoxe de Simpson : Effet du Tabagisme sur la Survie des Femmes à Whickham"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "En 1972-1974, une enquête a été menée sur la santé des femmes à Whickham, en Angleterre. L'objectif était d'évaluer la relation entre le tabagisme et la survie à long terme. Par simplicité, nous nous restreindrons aux femmes et parmi celles-ci aux 1314 qui ont été catégorisées comme __fumant actuellement__ ou __n'ayant jamais fumé__. Nous allons analyser ces données pour explorer le Paradoxe de Simpson."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Étape 1 : Préparation des Données"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Smoker</th>\n",
       "      <th>Status</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>19.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>57.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>47.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>81.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>36.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>23.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Dead</td>\n",
       "      <td>57.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>24.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>49.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>49.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>58.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>60.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>25.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>43.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>27.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>65.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>73.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>33.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Dead</td>\n",
       "      <td>62.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>56.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>59.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>25.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>36.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>20.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1284</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Dead</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1285</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>48.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1286</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>63.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1287</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>60.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1288</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Dead</td>\n",
       "      <td>39.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1289</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>36.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1290</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>63.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1291</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>71.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>57.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>63.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1294</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>46.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1295</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Dead</td>\n",
       "      <td>82.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1296</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1297</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>32.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1298</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>39.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Dead</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1300</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1301</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1302</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1303</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>31.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1304</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>47.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1305</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>60.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1306</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>61.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1307</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1308</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>42.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Alive</td>\n",
       "      <td>35.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1310</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1311</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Dead</td>\n",
       "      <td>62.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1312</th>\n",
       "      <td>No</td>\n",
       "      <td>Dead</td>\n",
       "      <td>88.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>No</td>\n",
       "      <td>Alive</td>\n",
       "      <td>39.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1314 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Smoker Status   Age\n",
       "0       Yes  Alive  21.0\n",
       "1       Yes  Alive  19.3\n",
       "2        No   Dead  57.5\n",
       "3        No  Alive  47.1\n",
       "4       Yes  Alive  81.4\n",
       "5        No  Alive  36.8\n",
       "6        No  Alive  23.8\n",
       "7       Yes   Dead  57.5\n",
       "8       Yes  Alive  24.8\n",
       "9       Yes  Alive  49.5\n",
       "10      Yes  Alive  30.0\n",
       "11       No   Dead  66.0\n",
       "12      Yes  Alive  49.2\n",
       "13       No  Alive  58.4\n",
       "14       No   Dead  60.6\n",
       "15       No  Alive  25.1\n",
       "16       No  Alive  43.5\n",
       "17       No  Alive  27.1\n",
       "18       No  Alive  58.3\n",
       "19      Yes  Alive  65.7\n",
       "20       No   Dead  73.2\n",
       "21      Yes  Alive  38.3\n",
       "22       No  Alive  33.4\n",
       "23      Yes   Dead  62.3\n",
       "24       No  Alive  18.0\n",
       "25       No  Alive  56.2\n",
       "26      Yes  Alive  59.2\n",
       "27       No  Alive  25.8\n",
       "28       No   Dead  36.9\n",
       "29       No  Alive  20.2\n",
       "...     ...    ...   ...\n",
       "1284    Yes   Dead  36.0\n",
       "1285    Yes  Alive  48.3\n",
       "1286     No  Alive  63.1\n",
       "1287     No  Alive  60.8\n",
       "1288    Yes   Dead  39.3\n",
       "1289     No  Alive  36.7\n",
       "1290     No  Alive  63.8\n",
       "1291     No   Dead  71.3\n",
       "1292     No  Alive  57.7\n",
       "1293     No  Alive  63.2\n",
       "1294     No  Alive  46.6\n",
       "1295    Yes   Dead  82.4\n",
       "1296    Yes  Alive  38.3\n",
       "1297    Yes  Alive  32.7\n",
       "1298     No  Alive  39.7\n",
       "1299    Yes   Dead  60.0\n",
       "1300     No   Dead  71.0\n",
       "1301     No  Alive  20.5\n",
       "1302     No  Alive  44.4\n",
       "1303    Yes  Alive  31.2\n",
       "1304    Yes  Alive  47.8\n",
       "1305    Yes  Alive  60.9\n",
       "1306     No   Dead  61.4\n",
       "1307    Yes  Alive  43.0\n",
       "1308     No  Alive  42.1\n",
       "1309    Yes  Alive  35.9\n",
       "1310     No  Alive  22.3\n",
       "1311    Yes   Dead  62.1\n",
       "1312     No   Dead  88.6\n",
       "1313     No  Alive  39.1\n",
       "\n",
       "[1314 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chargement des données\n",
    "url = \"https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv\"\n",
    "data = pd.read_csv(url)\n",
    "\n",
    "# Exploration des données\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Étape 2 : Analyse du Statut de Tabagisme et de la Survie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table de survie par statut de tabagisme :\n",
      "Status  Alive  Dead  total  Taux de mortalité\n",
      "Smoker                                       \n",
      "No        502   230    732           0.314208\n",
      "Yes       443   139    582           0.238832\n"
     ]
    }
   ],
   "source": [
    "# Création de la table de survie en utilisant les colonnes disponibles\n",
    "table_smoking = data.groupby(['Smoker', 'Status']).size().unstack(fill_value=0)\n",
    "table_smoking['total'] = table_smoking.sum(axis=1)\n",
    "table_smoking['Taux de mortalité'] = table_smoking['Dead'] / table_smoking['total']\n",
    "\n",
    "print('Table de survie par statut de tabagisme :')\n",
    "print(table_smoking)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Visualisation des taux de mortalité selon le statut de tabagisme\n",
    "sns.barplot(x=table_smoking.index, y=table_smoking['Taux de mortalité'])\n",
    "plt.title('Taux de mortalité selon le statut de tabagisme')\n",
    "plt.ylabel('Taux de mortalité')\n",
    "plt.xlabel('Statut de tabagisme')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interprétation : \n",
    "La cigarette est souvent blâmée pour sa dangerosité. Instinctivement, on pourrait s'attendre à ce que le tabagisme entraîne un risque de mortalité plus élevé, mais ce n'est pas forcément le cas ici. Cependant, d'après les résultats, les fumeurs semblent vivre plus longtemps. Ce résultat est surprenant, c'est pour ça qu'il est nommé « paradoxe », le paradoxe de Simpson. Ce paradoxe vient du fait que l'on n'a pas le contrôle absolu des personnes observées. En effet, il est possible que les non-fumeurs dans l'ensemble des données soient plus âgés en moyenne que les fumeurs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Étape 3 : Analyse par Catégories d'Âge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Table de survie par âge et statut de tabagisme :\n",
      "Status            Alive  Dead  total  Taux de mortalité\n",
      "GroupeAge Smoker                                       \n",
      "18-34     No        212     6    218           0.027523\n",
      "          Yes       172     5    177           0.028249\n",
      "35-54     No        180    19    199           0.095477\n",
      "          Yes       196    41    237           0.172996\n",
      "55-64     No         81    40    121           0.330579\n",
      "          Yes        64    51    115           0.443478\n",
      "65+       No         28   165    193           0.854922\n",
      "          Yes         7    42     49           0.857143\n"
     ]
    }
   ],
   "source": [
    "# Définition des classes d'âge\n",
    "bins = [18, 34, 54, 64, np.inf]\n",
    "labels = ['18-34', '35-54', '55-64', '65+']\n",
    "data['GroupeAge'] = pd.cut(data['Age'], bins=bins, labels=labels)\n",
    "\n",
    "# Table de survie par âge et statut de tabagisme\n",
    "table_smoking = data.groupby(['GroupeAge','Smoker', 'Status']).size().unstack(fill_value=0)\n",
    "table_smoking['total'] = table_smoking.sum(axis=1)\n",
    "table_smoking['Taux de mortalité'] = table_smoking['Dead'] / table_smoking['total']\n",
    "\n",
    "print('Table de survie par âge et statut de tabagisme :')\n",
    "print(table_smoking)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualisation des taux de mortalité selon le statut de tabagisme et par l'age\n",
    "table_smoking_reset = table_smoking.reset_index()\n",
    "plt.figure(figsize=(12, 8))\n",
    "sns.barplot(data=table_smoking_reset, x='GroupeAge', y='Taux de mortalité', hue='Smoker')\n",
    "plt.title('Taux de mortalité selon le statut de tabagisme par groupe d\\'âge', fontsize=16)\n",
    "plt.ylabel('Taux de mortalité', fontsize=12)\n",
    "plt.xlabel('Groupe d\\'âge', fontsize=12)\n",
    "plt.legend(title='Statut de tabagisme')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylim(0, 1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interprétation : \n",
    "Les taux de mortalité augmentent généralement avec l'âge, mais il est intéressant de noter que les fumeuses ont un taux de mortalité plus élevé à chaque classe d'âge, ce qui est un indicateur de l'impact du tabagisme sur la santé. Ce phénomène peut être expliqué par les effets à long terme du tabagisme sur des maladies telles que le cancer, les maladies cardiovasculaires, et les maladies pulmonaires. Ce qui est étrange ici c'est que le taux de mortalité est similaire pour les femmes de +65 ans.\n",
    "\n",
    "Ce paradoxe peut être expliqué simplement : Les fumeuses qui atteignent 85 ans sont donc une population sélectionnée, ayant survécu aux effets du tabac, tandis que les non-fumeuses à cet âge ont généralement une meilleure espérance de vie, malgré un nombre absolu de décès plus élevé. Ainsi, le taux de mortalité reste similaire en raison de la taille relative des groupes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Étape 4 : Régression Logistique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.412727\n",
      "         Iterations 7\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                  Death   No. Observations:                  582\n",
      "Model:                          Logit   Df Residuals:                      580\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Mon, 11 Nov 2024   Pseudo R-squ.:                  0.2492\n",
      "Time:                        18:00:48   Log-Likelihood:                -240.21\n",
      "converged:                       True   LL-Null:                       -319.94\n",
      "                                        LLR p-value:                 1.477e-36\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -5.5081      0.466    -11.814      0.000      -6.422      -4.594\n",
      "Age            0.0890      0.009     10.203      0.000       0.072       0.106\n",
      "==============================================================================\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.354560\n",
      "         Iterations 7\n",
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                  Death   No. Observations:                  732\n",
      "Model:                          Logit   Df Residuals:                      730\n",
      "Method:                           MLE   Df Model:                            1\n",
      "Date:                Mon, 11 Nov 2024   Pseudo R-squ.:                  0.4304\n",
      "Time:                        18:00:48   Log-Likelihood:                -259.54\n",
      "converged:                       True   LL-Null:                       -455.62\n",
      "                                        LLR p-value:                 2.808e-87\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -6.7955      0.479    -14.174      0.000      -7.735      -5.856\n",
      "Age            0.1073      0.008     13.742      0.000       0.092       0.123\n",
      "==============================================================================\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": [
    "# Création de la variable 'Death' pour indiquer si l'individu est décédé durant la période de 20 ans\n",
    "data['Death'] = data['Status'].apply(lambda x: 0 if x == 'Alive' else 1)\n",
    "\n",
    "# Modèles:\n",
    "#fumeuses\n",
    "model_smokers = smf.logit('Death ~ Age', data=data[data['Smoker'] == 'Yes']).fit()\n",
    "print(model_smokers.summary())\n",
    "\n",
    "#non-fumeuses\n",
    "model_non_smokers = smf.logit('Death ~ Age', data=data[data['Smoker'] == 'No']).fit()\n",
    "print(model_non_smokers.summary())\n",
    "\n",
    "# Tracer les courbes de probabilité de décès en fonction de l'âge pour chaque groupe\n",
    "age_range = np.linspace(data['Age'].min(), data['Age'].max(), 100)\n",
    "death_prob_smokers = model_smokers.predict(pd.DataFrame({'Age': age_range}))\n",
    "death_prob_non_smokers = model_non_smokers.predict(pd.DataFrame({'Age': age_range}))\n",
    "\n",
    "plt.plot(age_range, death_prob_smokers, label='Fumeuses', color='red')\n",
    "plt.plot(age_range, death_prob_non_smokers, label='Non-Fumeuses', color='blue')\n",
    "\n",
    "# Calcul des intervalles de confiance à 95%\n",
    "ci_smokers = 1.96 * np.sqrt(death_prob_smokers * (1 - death_prob_smokers) / len(data[data['Smoker'] == 'Yes']))\n",
    "ci_non_smokers = 1.96 * np.sqrt(death_prob_non_smokers * (1 - death_prob_non_smokers) / len(data[data['Smoker'] == 'No']))\n",
    "\n",
    "# Tracer les intervalles de confiance\n",
    "plt.fill_between(age_range,\n",
    "                 death_prob_smokers - ci_smokers,\n",
    "                 death_prob_smokers + ci_smokers,\n",
    "                 color='red', alpha=0.3)\n",
    "plt.fill_between(age_range,\n",
    "                 death_prob_non_smokers - ci_non_smokers,\n",
    "                 death_prob_non_smokers + ci_non_smokers,\n",
    "                 color='blue', alpha=0.3)\n",
    "\n",
    "plt.xlabel('Âge')\n",
    "plt.ylabel('Probabilité de décès')\n",
    "plt.legend()\n",
    "plt.title('Probabilité de décès en fonction de l\\'âge et du statut de tabagisme')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interprétation : \n",
    "Les régressions montrent une différence de probabilité de décès entre les fumeuses et les non-fumeuses en fonction de l'âge. Bien que ces modèles indiquent une tendance générale, ils ne peuvent pas prouver de manière définitive que le tabagisme est la seule cause de ces décès. En effet, d'autres facteurs (comme l'alimentation, l'activité physique, etc.) peuvent influencer la santé. Cependant, si on observe que les fumeuses ont une probabilité plus élevée de décéder à des âges plus jeunes, cela suggère clairement que le tabagisme a un impact négatif sur la santé. C'est un indice fort de la nocivité du tabac, même si ce n'est pas une preuve absolue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "Le Paradoxe de Simpson apparaît ici car les taux de mortalité semblent diverger en fonction du tabagisme dans les groupes d'âge, suggérant une conclusion différente lorsque l'on analyse toutes les femmes en tant que groupe unique comparé à une analyse par tranche d'âge."
   ]
  }
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