diff --git a/module3/exo3/IF.ipynb b/module3/exo3/IF.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..ae5acbe09ac9e4e7c47fc4e7642160a6f7c3a919 --- /dev/null +++ b/module3/exo3/IF.ipynb @@ -0,0 +1,2377 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Sujet 6 : Autour du Paradoxe de Simpson" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "data_url = \"https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "data_file = \"Subject6_smoking.csv\"\n", + "\n", + "import os\n", + "import urllib.request\n", + "if not os.path.exists(data_file):\n", + " urllib.request.urlretrieve(data_url, data_file)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SmokerStatusAge
0YesAlive21.0
1YesAlive19.3
2NoDead57.5
3NoAlive47.1
4YesAlive81.4
5NoAlive36.8
6NoAlive23.8
7YesDead57.5
8YesAlive24.8
9YesAlive49.5
10YesAlive30.0
11NoDead66.0
12YesAlive49.2
13NoAlive58.4
14NoDead60.6
15NoAlive25.1
16NoAlive43.5
17NoAlive27.1
18NoAlive58.3
19YesAlive65.7
20NoDead73.2
21YesAlive38.3
22NoAlive33.4
23YesDead62.3
24NoAlive18.0
25NoAlive56.2
26YesAlive59.2
27NoAlive25.8
28NoDead36.9
29NoAlive20.2
............
1284YesDead36.0
1285YesAlive48.3
1286NoAlive63.1
1287NoAlive60.8
1288YesDead39.3
1289NoAlive36.7
1290NoAlive63.8
1291NoDead71.3
1292NoAlive57.7
1293NoAlive63.2
1294NoAlive46.6
1295YesDead82.4
1296YesAlive38.3
1297YesAlive32.7
1298NoAlive39.7
1299YesDead60.0
1300NoDead71.0
1301NoAlive20.5
1302NoAlive44.4
1303YesAlive31.2
1304YesAlive47.8
1305YesAlive60.9
1306NoDead61.4
1307YesAlive43.0
1308NoAlive42.1
1309YesAlive35.9
1310NoAlive22.3
1311YesDead62.1
1312NoDead88.6
1313NoAlive39.1
\n", + "

1314 rows × 3 columns

\n", + "
" + ], + "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": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = pd.read_csv(data_url)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SmokerStatusAge
\n", + "
" + ], + "text/plain": [ + "Empty DataFrame\n", + "Columns: [Smoker, Status, Age]\n", + "Index: []" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data.isnull().any(axis=1)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 1. Représentez dans un tableau le nombre total de femmes vivantes et décédées sur la période en fonction de leur habitude de tabagisme. Calculez dans chaque groupe (fumeuses / non fumeuses) le taux de mortalité (le rapport entre le nombre de femmes décédées dans un groupe et le nombre total de femmes dans ce groupe). Vous pourrez proposer une représentation graphique de ces données et calculer des intervalles de confiance si vous le souhaitez. En quoi ce résultat est-il surprenant ?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
SmokerNoYes
Status
Alive502443
Dead230139
\n", + "
" + ], + "text/plain": [ + "Smoker No Yes\n", + "Status \n", + "Alive 502 443\n", + "Dead 230 139" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_crosstab=pd.crosstab(data['Status'],data['Smoker'])\n", + "data_crosstab" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mortality of smokers is (%): 23.883161512027492\n" + ] + } + ], + "source": [ + "mortality_smoker=data_crosstab['Yes']/data_crosstab['Yes'].sum()*100\n", + "print(\"Mortality of smokers is (%):\",mortality_smoker['Dead'])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mortality of no smokers is (%) : 31.420765027322407\n" + ] + } + ], + "source": [ + "mortality_nosmoker=data_crosstab['No']/data_crosstab['No'].sum()*100\n", + "print(\"Mortality of no smokers is (%) :\",mortality_nosmoker['Dead'])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "mortality=(mortality_smoker,mortality_nosmoker)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pie(data_crosstab['Yes'], labels=['Alive','Dead'],autopct='%1.2f%%')\n", + "plt.title('Smokers')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confidence interval for mortality of smokers is (%) : (20.419137788669218, 27.347185235385766)\n" + ] + } + ], + "source": [ + "z_score = 1.96\n", + "n_yes=443+139\n", + "se_smoker = np.sqrt(mortality_smoker['Dead'] * (100 - mortality_smoker['Dead'])/n_yes)\n", + "lsmoker = mortality_smoker['Dead'] - z_score* se_smoker #lower limit of the CI\n", + "usmoker = mortality_smoker['Dead'] + z_score* se_smoker #upper limit of the CI\n", + "CIsmoker = (lsmoker,usmoker)\n", + "print (\"Confidence interval for mortality of smokers is (%) :\",CIsmoker)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.pie(data_crosstab['No'],labels=['Alive','Dead'],autopct='%1.2f%%')\n", + "plt.title('No Smokers')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confidence interval for mortality of no smokers is (%) : (28.057932601982447, 27.24599393736745)\n" + ] + } + ], + "source": [ + "n_no=502+230\n", + "se_nosmoker = np.sqrt(mortality_nosmoker['Dead'] * (100 - mortality_nosmoker['Dead'])/n_no)\n", + "lnosmoker = mortality_nosmoker['Dead'] - z_score* se_nosmoker #lower limit of the CI\n", + "unosmoker = mortality_smoker['Dead'] + z_score* se_nosmoker #upper limit of the CI\n", + "CInosmoker = (lnosmoker,unosmoker)\n", + "print (\"Confidence interval for mortality of no smokers is (%) :\",CInosmoker)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__The mortality is higher in no smokers group than in smokers group, but this rate of no smoker mortality is not included in its confidence interval.__ " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2. Reprenez la question 1 (effectifs et taux de mortalité) en rajoutant une nouvelle catégorie liée à la classe d'âge. On considérera par exemple les classes suivantes : 18-34 ans, 34-54 ans, 55-64 ans, plus de 65 ans. En quoi ce résultat est-il surprenant ? Arrivez-vous à expliquer ce paradoxe ? De même, vous pourrez proposer une représentation graphique de ces données pour étayer vos explications." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SmokerStatusAgeAgeGroup
0YesAlive21.018-34
1YesAlive19.318-34
2NoDead57.555-64
3NoAlive47.135-54
4YesAlive81.4>65
5NoAlive36.835-54
6NoAlive23.818-34
7YesDead57.555-64
8YesAlive24.818-34
9YesAlive49.535-54
10YesAlive30.018-34
11NoDead66.0>65
12YesAlive49.235-54
13NoAlive58.455-64
14NoDead60.655-64
15NoAlive25.118-34
16NoAlive43.535-54
17NoAlive27.118-34
18NoAlive58.355-64
19YesAlive65.7>65
20NoDead73.2>65
21YesAlive38.335-54
22NoAlive33.418-34
23YesDead62.355-64
24NoAlive18.018-34
25NoAlive56.255-64
26YesAlive59.255-64
27NoAlive25.818-34
28NoDead36.935-54
29NoAlive20.218-34
...............
1284YesDead36.035-54
1285YesAlive48.335-54
1286NoAlive63.155-64
1287NoAlive60.855-64
1288YesDead39.335-54
1289NoAlive36.735-54
1290NoAlive63.855-64
1291NoDead71.3>65
1292NoAlive57.755-64
1293NoAlive63.255-64
1294NoAlive46.635-54
1295YesDead82.4>65
1296YesAlive38.335-54
1297YesAlive32.718-34
1298NoAlive39.735-54
1299YesDead60.055-64
1300NoDead71.0>65
1301NoAlive20.518-34
1302NoAlive44.435-54
1303YesAlive31.218-34
1304YesAlive47.835-54
1305YesAlive60.955-64
1306NoDead61.455-64
1307YesAlive43.035-54
1308NoAlive42.135-54
1309YesAlive35.935-54
1310NoAlive22.318-34
1311YesDead62.155-64
1312NoDead88.6>65
1313NoAlive39.135-54
\n", + "

1314 rows × 4 columns

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" + ], + "text/plain": [ + " Smoker Status Age AgeGroup\n", + "0 Yes Alive 21.0 18-34\n", + "1 Yes Alive 19.3 18-34\n", + "2 No Dead 57.5 55-64\n", + "3 No Alive 47.1 35-54\n", + "4 Yes Alive 81.4 >65\n", + "5 No Alive 36.8 35-54\n", + "6 No Alive 23.8 18-34\n", + "7 Yes Dead 57.5 55-64\n", + "8 Yes Alive 24.8 18-34\n", + "9 Yes Alive 49.5 35-54\n", + "10 Yes Alive 30.0 18-34\n", + "11 No Dead 66.0 >65\n", + "12 Yes Alive 49.2 35-54\n", + "13 No Alive 58.4 55-64\n", + "14 No Dead 60.6 55-64\n", + "15 No Alive 25.1 18-34\n", + "16 No Alive 43.5 35-54\n", + "17 No Alive 27.1 18-34\n", + "18 No Alive 58.3 55-64\n", + "19 Yes Alive 65.7 >65\n", + "20 No Dead 73.2 >65\n", + "21 Yes Alive 38.3 35-54\n", + "22 No Alive 33.4 18-34\n", + "23 Yes Dead 62.3 55-64\n", + "24 No Alive 18.0 18-34\n", + "25 No Alive 56.2 55-64\n", + "26 Yes Alive 59.2 55-64\n", + "27 No Alive 25.8 18-34\n", + "28 No Dead 36.9 35-54\n", + "29 No Alive 20.2 18-34\n", + "... ... ... ... ...\n", + "1284 Yes Dead 36.0 35-54\n", + "1285 Yes Alive 48.3 35-54\n", + "1286 No Alive 63.1 55-64\n", + "1287 No Alive 60.8 55-64\n", + "1288 Yes Dead 39.3 35-54\n", + "1289 No Alive 36.7 35-54\n", + "1290 No Alive 63.8 55-64\n", + "1291 No Dead 71.3 >65\n", + "1292 No Alive 57.7 55-64\n", + "1293 No Alive 63.2 55-64\n", + "1294 No Alive 46.6 35-54\n", + "1295 Yes Dead 82.4 >65\n", + "1296 Yes Alive 38.3 35-54\n", + "1297 Yes Alive 32.7 18-34\n", + "1298 No Alive 39.7 35-54\n", + "1299 Yes Dead 60.0 55-64\n", + "1300 No Dead 71.0 >65\n", + "1301 No Alive 20.5 18-34\n", + "1302 No Alive 44.4 35-54\n", + "1303 Yes Alive 31.2 18-34\n", + "1304 Yes Alive 47.8 35-54\n", + "1305 Yes Alive 60.9 55-64\n", + "1306 No Dead 61.4 55-64\n", + "1307 Yes Alive 43.0 35-54\n", + "1308 No Alive 42.1 35-54\n", + "1309 Yes Alive 35.9 35-54\n", + "1310 No Alive 22.3 18-34\n", + "1311 Yes Dead 62.1 55-64\n", + "1312 No Dead 88.6 >65\n", + "1313 No Alive 39.1 35-54\n", + "\n", + "[1314 rows x 4 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bins= [17,34,54,64,100]\n", + "labels = ['18-34','35-54','55-64','>65']\n", + "data['AgeGroup']=pd.cut(data['Age'], bins=bins, labels=labels,)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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SmokerNoYes
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" + ], + "text/plain": [ + "Smoker No Yes \n", + "Status Alive Dead Alive Dead\n", + "AgeGroup \n", + "18-34 213 6 176 5\n", + "35-54 180 19 196 41\n", + "55-64 81 40 64 51\n", + ">65 28 165 7 42" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_cross_age=pd.crosstab(data['AgeGroup'], [data['Smoker'],data['Status']])\n", + "data_cross_age" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mortality of smokers by age range is (%): AgeGroup\n", + "18-34 3.597122\n", + "35-54 29.496403\n", + "55-64 36.690647\n", + ">65 30.215827\n", + "Name: Dead, dtype: float64\n" + ] + } + ], + "source": [ + "mortality_smoker_age=(data_cross_age['Yes']/data_cross_age['Yes'].sum()*100)\n", + "print(\"Mortality of smokers by age range is (%):\", mortality_smoker_age['Dead'])" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confidence intervals for mortality of smokers by age are (%) : (AgeGroup\n", + "18-34 0.501334\n", + "35-54 21.915187\n", + "55-64 28.678290\n", + ">65 22.581964\n", + "Name: Dead, dtype: float64, AgeGroup\n", + "18-34 6.692911\n", + "35-54 37.077618\n", + "55-64 44.703005\n", + ">65 37.849691\n", + "Name: Dead, dtype: float64)\n" + ] + } + ], + "source": [ + "n_yes_age=5+41+51+42\n", + "se_smoker_age = np.sqrt(mortality_smoker_age['Dead'] * (100 - mortality_smoker_age['Dead'])/n_yes_age)\n", + "lsmoker = mortality_smoker_age['Dead'] - z_score* se_smoker_age #lower limit of the CI\n", + "usmoker = mortality_smoker_age['Dead'] + z_score* se_smoker_age #upper limit of the CI\n", + "CIsmoker_age =(lsmoker,usmoker)\n", + "print (\"Confidence intervals for mortality of smokers by age are (%) :\", CIsmoker_age)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "hideOutput": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mortality of no smokers by age range is (%): AgeGroup\n", + "18-34 2.608696\n", + "35-54 8.260870\n", + "55-64 17.391304\n", + ">65 71.739130\n", + "Name: Dead, dtype: float64\n" + ] + } + ], + "source": [ + "mortality_nosmoker_age=(data_cross_age['No']/data_cross_age['No'].sum()*100)\n", + "print(\"Mortality of no smokers by age range is (%):\", mortality_nosmoker_age['Dead'])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Confidence intervals for mortality of no smokers by age are (%) : (AgeGroup\n", + "18-34 0.548711\n", + "35-54 4.703063\n", + "55-64 12.492714\n", + ">65 65.919935\n", + "Name: Dead, dtype: float64, AgeGroup\n", + "18-34 4.668680\n", + "35-54 11.818676\n", + "55-64 22.289895\n", + ">65 77.558326\n", + "Name: Dead, dtype: float64)\n" + ] + } + ], + "source": [ + "n_no_age=6+19+40+165\n", + "se_nosmoker_age = np.sqrt(mortality_nosmoker_age['Dead'] * (100 - mortality_nosmoker_age['Dead'])/n_no_age)\n", + "lnosmoker = mortality_nosmoker_age['Dead'] - z_score* se_nosmoker_age #lower limit of the CI\n", + "unosmoker = mortality_nosmoker_age['Dead'] + z_score* se_nosmoker_age #upper limit of the CI\n", + "CInosmoker_age =(lnosmoker,unosmoker)\n", + "print (\"Confidence intervals for mortality of no smokers by age are (%) :\", CInosmoker_age)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data_cross_age.plot(kind='bar')\n", + "plt.ylabel('Number of persons')\n", + "plt.xlabel('Age group (years)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The mortality rate of no smokers at age >65 years is higher than smokers (70% against 30%, IC=95). This paradoxe coule be explained by different size of samples between smokers and no smokers, and other factors like health conditions are not respected in analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3. Afin d'éviter un biais induit par des regroupements en tranches d'âges arbitraires et non régulières, il est envisageable d'essayer de réaliser une régression logistique. Si on introduit une variable Death valant 1 ou 0 pour indiquer si l'individu est décédé durant la période de 20 ans, on peut étudier le modèle Death ~ Age pour étudier la probabilité de décès en fonction de l'âge selon que l'on considère le groupe des fumeuses ou des non fumeuses. Ces régressions vous permettent-elles de conclure sur la nocivité du tabagisme ? Vous pourrez proposer une représentation graphique de ces régressions (en n'omettant pas les régions de confiance)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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SmokerStatusAgeAgeGroupDeath
0YesAlive21.018-340.0
1YesAlive19.318-340.0
2NoDead57.555-641.0
3NoAlive47.135-540.0
4YesAlive81.4>650.0
5NoAlive36.835-540.0
6NoAlive23.818-340.0
7YesDead57.555-641.0
8YesAlive24.818-340.0
9YesAlive49.535-540.0
10YesAlive30.018-340.0
11NoDead66.0>651.0
12YesAlive49.235-540.0
13NoAlive58.455-640.0
14NoDead60.655-641.0
15NoAlive25.118-340.0
16NoAlive43.535-540.0
17NoAlive27.118-340.0
18NoAlive58.355-640.0
19YesAlive65.7>650.0
20NoDead73.2>651.0
21YesAlive38.335-540.0
22NoAlive33.418-340.0
23YesDead62.355-641.0
24NoAlive18.018-340.0
25NoAlive56.255-640.0
26YesAlive59.255-640.0
27NoAlive25.818-340.0
28NoDead36.935-541.0
29NoAlive20.218-340.0
..................
1284YesDead36.035-541.0
1285YesAlive48.335-540.0
1286NoAlive63.155-640.0
1287NoAlive60.855-640.0
1288YesDead39.335-541.0
1289NoAlive36.735-540.0
1290NoAlive63.855-640.0
1291NoDead71.3>651.0
1292NoAlive57.755-640.0
1293NoAlive63.255-640.0
1294NoAlive46.635-540.0
1295YesDead82.4>651.0
1296YesAlive38.335-540.0
1297YesAlive32.718-340.0
1298NoAlive39.735-540.0
1299YesDead60.055-641.0
1300NoDead71.0>651.0
1301NoAlive20.518-340.0
1302NoAlive44.435-540.0
1303YesAlive31.218-340.0
1304YesAlive47.835-540.0
1305YesAlive60.955-640.0
1306NoDead61.455-641.0
1307YesAlive43.035-540.0
1308NoAlive42.135-540.0
1309YesAlive35.935-540.0
1310NoAlive22.318-340.0
1311YesDead62.155-641.0
1312NoDead88.6>651.0
1313NoAlive39.135-540.0
\n", + "

1314 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Smoker Status Age AgeGroup Death\n", + "0 Yes Alive 21.0 18-34 0.0\n", + "1 Yes Alive 19.3 18-34 0.0\n", + "2 No Dead 57.5 55-64 1.0\n", + "3 No Alive 47.1 35-54 0.0\n", + "4 Yes Alive 81.4 >65 0.0\n", + "5 No Alive 36.8 35-54 0.0\n", + "6 No Alive 23.8 18-34 0.0\n", + "7 Yes Dead 57.5 55-64 1.0\n", + "8 Yes Alive 24.8 18-34 0.0\n", + "9 Yes Alive 49.5 35-54 0.0\n", + "10 Yes Alive 30.0 18-34 0.0\n", + "11 No Dead 66.0 >65 1.0\n", + "12 Yes Alive 49.2 35-54 0.0\n", + "13 No Alive 58.4 55-64 0.0\n", + "14 No Dead 60.6 55-64 1.0\n", + "15 No Alive 25.1 18-34 0.0\n", + "16 No Alive 43.5 35-54 0.0\n", + "17 No Alive 27.1 18-34 0.0\n", + "18 No Alive 58.3 55-64 0.0\n", + "19 Yes Alive 65.7 >65 0.0\n", + "20 No Dead 73.2 >65 1.0\n", + "21 Yes Alive 38.3 35-54 0.0\n", + "22 No Alive 33.4 18-34 0.0\n", + "23 Yes Dead 62.3 55-64 1.0\n", + "24 No Alive 18.0 18-34 0.0\n", + "25 No Alive 56.2 55-64 0.0\n", + "26 Yes Alive 59.2 55-64 0.0\n", + "27 No Alive 25.8 18-34 0.0\n", + "28 No Dead 36.9 35-54 1.0\n", + "29 No Alive 20.2 18-34 0.0\n", + "... ... ... ... ... ...\n", + "1284 Yes Dead 36.0 35-54 1.0\n", + "1285 Yes Alive 48.3 35-54 0.0\n", + "1286 No Alive 63.1 55-64 0.0\n", + "1287 No Alive 60.8 55-64 0.0\n", + "1288 Yes Dead 39.3 35-54 1.0\n", + "1289 No Alive 36.7 35-54 0.0\n", + "1290 No Alive 63.8 55-64 0.0\n", + "1291 No Dead 71.3 >65 1.0\n", + "1292 No Alive 57.7 55-64 0.0\n", + "1293 No Alive 63.2 55-64 0.0\n", + "1294 No Alive 46.6 35-54 0.0\n", + "1295 Yes Dead 82.4 >65 1.0\n", + "1296 Yes Alive 38.3 35-54 0.0\n", + "1297 Yes Alive 32.7 18-34 0.0\n", + "1298 No Alive 39.7 35-54 0.0\n", + "1299 Yes Dead 60.0 55-64 1.0\n", + "1300 No Dead 71.0 >65 1.0\n", + "1301 No Alive 20.5 18-34 0.0\n", + "1302 No Alive 44.4 35-54 0.0\n", + "1303 Yes Alive 31.2 18-34 0.0\n", + "1304 Yes Alive 47.8 35-54 0.0\n", + "1305 Yes Alive 60.9 55-64 0.0\n", + "1306 No Dead 61.4 55-64 1.0\n", + "1307 Yes Alive 43.0 35-54 0.0\n", + "1308 No Alive 42.1 35-54 0.0\n", + "1309 Yes Alive 35.9 35-54 0.0\n", + "1310 No Alive 22.3 18-34 0.0\n", + "1311 Yes Dead 62.1 55-64 1.0\n", + "1312 No Dead 88.6 >65 1.0\n", + "1313 No Alive 39.1 35-54 0.0\n", + "\n", + "[1314 rows x 5 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.loc[data.Status == 'Alive', 'Death']=0\n", + "data.loc[data.Status == 'Dead', 'Death']=1\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.lmplot(x=\"Age\", y=\"Death\",hue=\"Smoker\", data=data, logistic=True, y_jitter=.1)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import statsmodels.api as sm\n", + "from statsmodels.formula.api import logit" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.364541\n", + " Iterations 8\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Logit Regression Results
Dep. Variable: Death No. Observations: 1314
Model: Logit Df Residuals: 1309
Method: MLE Df Model: 4
Date: Tue, 27 Jul 2021 Pseudo R-squ.: 0.3860
Time: 20:48:08 Log-Likelihood: -479.01
converged: True LL-Null: -780.16
LLR p-value: 4.919e-129
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
Intercept -3.7947 0.321 -11.809 0.000 -4.425 -3.165
Smoker[T.Yes] 0.4528 0.176 2.577 0.010 0.108 0.797
AgeGroup[T.35-54] 1.6950 0.336 5.039 0.000 1.036 2.354
AgeGroup[T.55-64] 3.1024 0.334 9.279 0.000 2.447 3.758
AgeGroup[T.>65] 5.4917 0.364 15.104 0.000 4.779 6.204
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Logit Regression Results \n", + "==============================================================================\n", + "Dep. Variable: Death No. Observations: 1314\n", + "Model: Logit Df Residuals: 1309\n", + "Method: MLE Df Model: 4\n", + "Date: Tue, 27 Jul 2021 Pseudo R-squ.: 0.3860\n", + "Time: 20:48:08 Log-Likelihood: -479.01\n", + "converged: True LL-Null: -780.16\n", + " LLR p-value: 4.919e-129\n", + "=====================================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "-------------------------------------------------------------------------------------\n", + "Intercept -3.7947 0.321 -11.809 0.000 -4.425 -3.165\n", + "Smoker[T.Yes] 0.4528 0.176 2.577 0.010 0.108 0.797\n", + "AgeGroup[T.35-54] 1.6950 0.336 5.039 0.000 1.036 2.354\n", + "AgeGroup[T.55-64] 3.1024 0.334 9.279 0.000 2.447 3.758\n", + "AgeGroup[T.>65] 5.4917 0.364 15.104 0.000 4.779 6.204\n", + "=====================================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "formula=('Death~Smoker+AgeGroup')\n", + "model=logit(formula=formula, data=data).fit()\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The results show that smoking has an influence in women mortality (p-value < 0,05) but age seems to be a better explicatif factor of mortality in different age groups (p-value <<< 0,05). Only 39 % (Pseudo R-sqaured = 0,3860) of mortality can be explained by smoking and age group." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/module3/exo3/Subject6_smoking.csv b/module3/exo3/Subject6_smoking.csv new file mode 100644 index 0000000000000000000000000000000000000000..6c47065bc7aa53c0c92eef463cf16384eb028aaa --- /dev/null +++ b/module3/exo3/Subject6_smoking.csv @@ -0,0 +1,1315 @@ +"Smoker","Status","Age" +"Yes","Alive",21 +"Yes","Alive",19.3 +"No","Dead",57.5 +"No","Alive",47.1 +"Yes","Alive",81.4 +"No","Alive",36.8 +"No","Alive",23.8 +"Yes","Dead",57.5 +"Yes","Alive",24.8 +"Yes","Alive",49.5 +"Yes","Alive",30 +"No","Dead",66 +"Yes","Alive",49.2 +"No","Alive",58.4 +"No","Dead",60.6 +"No","Alive",25.1 +"No","Alive",43.5 +"No","Alive",27.1 +"No","Alive",58.3 +"Yes","Alive",65.7 +"No","Dead",73.2 +"Yes","Alive",38.3 +"No","Alive",33.4 +"Yes","Dead",62.3 +"No","Alive",18 +"No","Alive",56.2 +"Yes","Alive",59.2 +"No","Alive",25.8 +"No","Dead",36.9 +"No","Alive",20.2 +"Yes","Alive",34.6 +"Yes","Alive",51.9 +"Yes","Alive",49.9 +"No","Alive",19.4 +"No","Alive",56.9 +"Yes","Alive",46.7 +"Yes","Alive",44.4 +"Yes","Alive",29.5 +"Yes","Dead",33 +"Yes","Alive",35.6 +"Yes","Alive",39.1 +"No","Dead",69.7 +"Yes","Alive",35.7 +"No","Dead",75.8 +"No","Alive",25.3 +"No","Dead",83 +"Yes","Dead",44.3 +"No","Alive",18.5 +"Yes","Alive",37.5 +"Yes","Alive",22.1 +"No","Alive",82.8 +"No","Alive",45 +"No","Dead",73.3 +"Yes","Alive",39 +"No","Alive",28.4 +"No","Dead",73.7 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