{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 6 : Autour du Paradoxe de Simpson" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "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": 3, "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": 4, "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": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(data_url)\n", "data" ] }, { "cell_type": "code", "execution_count": 5, "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": 5, "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": 6, "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": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_crosstab=pd.crosstab(data['Status'],data['Smoker'])\n", "data_crosstab" ] }, { "cell_type": "code", "execution_count": 7, "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": 8, "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": 9, "metadata": {}, "outputs": [], "source": [ "mortality=(mortality_smoker,mortality_nosmoker)" ] }, { "cell_type": "code", "execution_count": 10, "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": 11, "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": 12, "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": 13, "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": 14, "metadata": { "scrolled": true }, "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

\n", "
" ], "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": 14, "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": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SmokerNoYes
StatusAliveDeadAliveDead
AgeGroup
18-3421361765
35-541801919641
55-6481406451
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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": 15, "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": 16, "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": 17, "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": 18, "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": 19, "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": 20, "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": 21, "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": 21, "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": 22, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" ] }, { "data": { "image/png": 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\n", "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": 24, "metadata": {}, "outputs": [], "source": [ "import statsmodels.api as sm\n", "from statsmodels.formula.api import logit" ] }, { "cell_type": "code", "execution_count": 25, "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: 20:58:25 Log-Likelihood: -479.01
converged: True LL-Null: -780.16
LLR p-value: 4.919e-129
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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: 20:58:25 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": 25, "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." ] } ], "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 }