From f10d2df18e37bf608faa0261f4893745ae4e1c57 Mon Sep 17 00:00:00 2001
From: 123bbe3bd54604f74fd95f73d2f93478
 <123bbe3bd54604f74fd95f73d2f93478@app-learninglab.inria.fr>
Date: Tue, 2 Jun 2020 10:18:18 +0000
Subject: [PATCH] Premier essai

---
 module3/exo1/syndrome-grippal.csv | 1848 +++++++++++++++++++++++++++++
 module3/exo3/exercice.ipynb       |  465 +++++++-
 module3/exo3/simpson_paradox.csv  | 1315 ++++++++++++++++++++
 3 files changed, 3625 insertions(+), 3 deletions(-)
 create mode 100644 module3/exo1/syndrome-grippal.csv
 create mode 100644 module3/exo3/simpson_paradox.csv

diff --git a/module3/exo1/syndrome-grippal.csv b/module3/exo1/syndrome-grippal.csv
new file mode 100644
index 0000000..15d7901
--- /dev/null
+++ b/module3/exo1/syndrome-grippal.csv
@@ -0,0 +1,1848 @@
+# @source="réseau Sentinelles, INSERM, Sorbonne Université, http://www.sentiweb.fr", @meta={"period":[198444,202011],"geo":["PAY","1"],"geo_ref":"insee","indicator":"3","type":"all","conf_int":true,"compact":false}, @date=2020-06-02T10:31:09+02:00
+week,indicator,inc,inc_low,inc_up,inc100,inc100_low,inc100_up,geo_insee,geo_name
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+199635,3,1674,93,3255,3,0,6,FR,France
+199634,3,1273,122,2424,2,0,4,FR,France
+199633,3,540,0,1246,1,0,2,FR,France
+199632,3,1453,176,2730,3,1,5,FR,France
+199631,3,3010,752,5268,5,1,9,FR,France
+199630,3,2992,706,5278,5,1,9,FR,France
+199629,3,1559,144,2974,3,1,5,FR,France
+199628,3,3109,541,5677,5,1,9,FR,France
+199627,3,4456,1595,7317,8,3,13,FR,France
+199626,3,2871,1138,4604,5,2,8,FR,France
+199625,3,2308,856,3760,4,1,7,FR,France
+199624,3,2490,1067,3913,4,2,6,FR,France
+199623,3,2819,1262,4376,5,2,8,FR,France
+199622,3,4005,1984,6026,7,4,10,FR,France
+199621,3,7042,4291,9793,12,7,17,FR,France
+199620,3,10619,7365,13873,18,12,24,FR,France
+199619,3,11898,8065,15731,21,14,28,FR,France
+199618,3,18338,13352,23324,32,23,41,FR,France
+199617,3,24954,19100,30808,43,33,53,FR,France
+199616,3,29313,23868,34758,51,42,60,FR,France
+199615,3,51703,43644,59762,89,75,103,FR,France
+199614,3,44414,36774,52054,77,64,90,FR,France
+199613,3,30622,25062,36182,53,43,63,FR,France
+199612,3,31818,25852,37784,55,45,65,FR,France
+199611,3,24427,19499,29355,42,33,51,FR,France
+199610,3,20910,16198,25622,36,28,44,FR,France
+199609,3,19594,15228,23960,34,26,42,FR,France
+199608,3,19046,14850,23242,33,26,40,FR,France
+199607,3,23041,18338,27744,40,32,48,FR,France
+199606,3,21810,17153,26467,38,30,46,FR,France
+199605,3,25275,20410,30140,44,36,52,FR,France
+199604,3,43359,36948,49770,75,64,86,FR,France
+199603,3,54691,48016,61366,94,82,106,FR,France
+199602,3,105606,96106,115106,182,166,198,FR,France
+199601,3,223830,209274,238386,386,361,411,FR,France
+199552,3,397122,378252,415992,688,655,721,FR,France
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+199544,3,13168,9820,16516,23,17,29,FR,France
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+199541,3,14980,10851,19109,26,19,33,FR,France
+199540,3,16245,11610,20880,28,20,36,FR,France
+199539,3,18499,13844,23154,32,24,40,FR,France
+199538,3,13123,9410,16836,23,17,29,FR,France
+199537,3,4895,2666,7124,8,4,12,FR,France
+199536,3,3659,1574,5744,6,2,10,FR,France
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+199531,3,1946,92,3800,3,0,6,FR,France
+199530,3,1898,46,3750,3,0,6,FR,France
+199529,3,2148,687,3609,4,1,7,FR,France
+199528,3,3312,1430,5194,6,3,9,FR,France
+199527,3,4853,2683,7023,8,4,12,FR,France
+199526,3,4986,2911,7061,9,5,13,FR,France
+199525,3,4571,2520,6622,8,4,12,FR,France
+199524,3,8058,5054,11062,14,9,19,FR,France
+199523,3,13820,9801,17839,24,17,31,FR,France
+199522,3,14124,9999,18249,24,17,31,FR,France
+199521,3,11931,8655,15207,21,15,27,FR,France
+199520,3,18846,14311,23381,33,25,41,FR,France
+199519,3,26261,21488,31034,45,37,53,FR,France
+199518,3,39231,33258,45204,68,58,78,FR,France
+199517,3,88640,79343,97937,153,137,169,FR,France
+199516,3,140488,129439,151537,243,224,262,FR,France
+199515,3,181007,169110,192904,313,292,334,FR,France
+199514,3,248704,235144,262264,431,408,454,FR,France
+199513,3,209839,197191,222487,363,341,385,FR,France
+199512,3,122434,112497,132371,212,195,229,FR,France
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+199510,3,49387,42703,56071,86,74,98,FR,France
+199509,3,39696,33259,46133,69,58,80,FR,France
+199508,3,38008,32225,43791,66,56,76,FR,France
+199507,3,30550,25479,35621,53,44,62,FR,France
+199506,3,37351,32007,42695,65,56,74,FR,France
+199505,3,27876,23218,32534,48,40,56,FR,France
+199504,3,27643,22818,32468,48,40,56,FR,France
+199503,3,24844,20391,29297,43,35,51,FR,France
+199502,3,23480,19249,27711,41,34,48,FR,France
+199501,3,26556,21916,31196,46,38,54,FR,France
+199452,3,22036,17495,26577,38,30,46,FR,France
+199451,3,26912,21750,32074,47,38,56,FR,France
+199450,3,28831,23470,34192,50,41,59,FR,France
+199449,3,21161,16559,25763,37,29,45,FR,France
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+199443,3,11254,8002,14506,20,14,26,FR,France
+199442,3,14406,10653,18159,25,18,32,FR,France
+199441,3,14073,10341,17805,24,18,30,FR,France
+199440,3,15962,11850,20074,28,21,35,FR,France
+199439,3,15878,11643,20113,28,21,35,FR,France
+199438,3,15366,11100,19632,27,20,34,FR,France
+199437,3,5949,3561,8337,10,6,14,FR,France
+199436,3,2806,1133,4479,5,2,8,FR,France
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+199433,3,4272,977,7567,7,1,13,FR,France
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+199427,3,713,0,1459,1,0,2,FR,France
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+199425,3,5431,3222,7640,9,5,13,FR,France
+199424,3,6288,3723,8853,11,7,15,FR,France
+199423,3,6440,1052,11828,11,2,20,FR,France
+199422,3,845,0,2449,1,0,4,FR,France
+199421,3,2197,395,3999,4,1,7,FR,France
+199420,3,2217,852,3582,4,2,6,FR,France
+199419,3,1483,406,2560,3,1,5,FR,France
+199418,3,3705,1940,5470,6,3,9,FR,France
+199417,3,2288,1064,3512,4,2,6,FR,France
+199416,3,5156,3176,7136,9,6,12,FR,France
+199415,3,6952,4678,9226,12,8,16,FR,France
+199414,3,4671,2784,6558,8,5,11,FR,France
+199413,3,3343,1641,5045,6,3,9,FR,France
+199412,3,4305,2412,6198,7,4,10,FR,France
+199411,3,5399,3327,7471,9,5,13,FR,France
+199410,3,4548,2529,6567,8,4,12,FR,France
+199409,3,5635,3251,8019,10,6,14,FR,France
+199408,3,9539,6652,12426,17,12,22,FR,France
+199407,3,14639,11188,18090,25,19,31,FR,France
+199406,3,17308,13395,21221,30,23,37,FR,France
+199405,3,18489,14290,22688,32,25,39,FR,France
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+199403,3,29882,24817,34947,52,43,61,FR,France
+199402,3,47402,41415,53389,82,72,92,FR,France
+199401,3,118119,108182,128056,205,188,222,FR,France
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+199346,3,76034,68003,84065,133,119,147,FR,France
+199345,3,32289,27022,37556,56,47,65,FR,France
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+199342,3,19187,14893,23481,33,26,40,FR,France
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+199340,3,13805,10348,17262,24,18,30,FR,France
+199339,3,14783,11173,18393,26,20,32,FR,France
+199338,3,10492,7329,13655,18,12,24,FR,France
+199337,3,7023,4503,9543,12,8,16,FR,France
+199336,3,4361,2445,6277,8,5,11,FR,France
+199335,3,5589,3066,8112,10,6,14,FR,France
+199334,3,2984,1209,4759,5,2,8,FR,France
+199333,3,3217,758,5676,6,2,10,FR,France
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+199329,3,3714,1668,5760,6,2,10,FR,France
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+199327,3,2574,918,4230,4,1,7,FR,France
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+199321,3,5942,3670,8214,10,6,14,FR,France
+199320,3,5137,3157,7117,9,6,12,FR,France
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+199318,3,5027,3075,6979,9,6,12,FR,France
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+199316,3,24932,19468,30396,43,33,53,FR,France
+199315,3,22181,17147,27215,39,30,48,FR,France
+199314,3,38266,30865,45667,67,54,80,FR,France
+199313,3,62987,54506,71468,110,95,125,FR,France
+199312,3,63493,55956,71030,111,98,124,FR,France
+199311,3,80548,72628,88468,140,126,154,FR,France
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+199301,3,75288,67244,83332,131,117,145,FR,France
+199253,3,56622,49107,64137,99,86,112,FR,France
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+199251,3,41156,35024,47288,72,61,83,FR,France
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+199249,3,39146,33847,44445,69,60,78,FR,France
+199248,3,32392,27499,37285,57,48,66,FR,France
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+199245,3,22023,17804,26242,39,32,46,FR,France
+199244,3,27619,22945,32293,48,40,56,FR,France
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+199241,3,24091,19953,28229,42,35,49,FR,France
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+199239,3,13535,10301,16769,24,18,30,FR,France
+199238,3,9837,7106,12568,17,12,22,FR,France
+199237,3,7480,5130,9830,13,9,17,FR,France
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+199235,3,6383,3687,9079,11,6,16,FR,France
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+199232,3,4963,2215,7711,9,4,14,FR,France
+199231,3,4233,1733,6733,7,3,11,FR,France
+199230,3,3091,1145,5037,5,2,8,FR,France
+199229,3,3586,1583,5589,6,2,10,FR,France
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+199227,3,5568,3134,8002,10,6,14,FR,France
+199226,3,5886,3229,8543,10,5,15,FR,France
+199225,3,5304,2805,7803,9,5,13,FR,France
+199224,3,5928,3482,8374,10,6,14,FR,France
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+199221,3,5059,2889,7229,9,5,13,FR,France
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+199201,3,247688,222574,272802,434,390,478,FR,France
+199152,3,349815,325499,374131,615,572,658,FR,France
+199151,3,378413,355928,400898,666,626,706,FR,France
+199150,3,198721,182878,214564,350,322,378,FR,France
+199149,3,93903,82992,104814,165,146,184,FR,France
+199148,3,45173,37384,52962,79,65,93,FR,France
+199147,3,35691,28224,43158,63,50,76,FR,France
+199146,3,23685,17531,29839,42,31,53,FR,France
+199145,3,19565,13551,25579,34,23,45,FR,France
+199144,3,21571,15477,27665,38,27,49,FR,France
+199143,3,28842,21958,35726,51,39,63,FR,France
+199142,3,27483,20385,34581,48,36,60,FR,France
+199141,3,28185,20655,35715,50,37,63,FR,France
+199140,3,26740,16049,37431,47,28,66,FR,France
+199139,3,14373,8734,20012,25,15,35,FR,France
+199138,3,7460,3700,11220,13,6,20,FR,France
+199137,3,2429,434,4424,4,0,8,FR,France
+199136,3,2380,0,4882,4,0,8,FR,France
+199135,3,2372,0,5025,4,0,9,FR,France
+199134,3,2099,0,5104,4,0,9,FR,France
+199133,3,1654,0,4102,3,0,7,FR,France
+199132,3,625,0,2190,1,0,4,FR,France
+199131,3,2464,0,5830,4,0,10,FR,France
+199130,3,2382,0,6857,4,0,12,FR,France
+199129,3,3837,40,7634,7,0,14,FR,France
+199128,3,3297,152,6442,6,0,12,FR,France
+199127,3,3164,934,5394,6,2,10,FR,France
+199126,3,6376,3324,9428,11,6,16,FR,France
+199125,3,8218,4776,11660,14,8,20,FR,France
+199124,3,8223,4660,11786,14,8,20,FR,France
+199123,3,6801,3596,10006,12,6,18,FR,France
+199122,3,6050,2682,9418,11,5,17,FR,France
+199121,3,6391,3385,9397,11,6,16,FR,France
+199120,3,8633,4683,12583,15,8,22,FR,France
+199119,3,7785,3986,11584,14,7,21,FR,France
+199118,3,10856,6457,15255,19,11,27,FR,France
+199117,3,15603,10950,20256,27,19,35,FR,France
+199116,3,15665,11184,20146,28,20,36,FR,France
+199115,3,15062,10715,19409,26,18,34,FR,France
+199114,3,27862,21583,34141,49,38,60,FR,France
+199113,3,32179,25829,38529,57,46,68,FR,France
+199112,3,27873,22312,33434,49,39,59,FR,France
+199111,3,46186,38876,53496,81,68,94,FR,France
+199110,3,88750,77784,99716,156,137,175,FR,France
+199109,3,139093,124326,153860,245,219,271,FR,France
+199108,3,216369,198630,234108,381,350,412,FR,France
+199107,3,208836,191789,225883,367,337,397,FR,France
+199106,3,134737,121280,148194,237,213,261,FR,France
+199105,3,73564,62716,84412,129,110,148,FR,France
+199104,3,48253,39031,57475,85,69,101,FR,France
+199103,3,29382,22258,36506,52,39,65,FR,France
+199102,3,29759,22044,37474,52,38,66,FR,France
+199101,3,42927,33706,52148,76,60,92,FR,France
+199052,3,41262,31607,50917,73,56,90,FR,France
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+199050,3,44125,36137,52113,78,64,92,FR,France
+199049,3,38379,30603,46155,68,54,82,FR,France
+199048,3,29571,22315,36827,52,39,65,FR,France
+199047,3,23358,15890,30826,41,28,54,FR,France
+199046,3,20246,7930,32562,36,14,58,FR,France
+199045,3,17571,11591,23551,31,20,42,FR,France
+199044,3,16182,10305,22059,29,19,39,FR,France
+199043,3,17579,11588,23570,31,20,42,FR,France
+199042,3,13011,7875,18147,23,14,32,FR,France
+199041,3,19723,13330,26116,35,24,46,FR,France
+199040,3,20433,14614,26252,36,26,46,FR,France
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+198445,3,135223,101414,169032,246,184,308,FR,France
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diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 0bbbe37..fb86790 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -1,5 +1,465 @@
 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Sujet 6 : Autour du Paradoxe de Simpson\n",
+    "---\n",
+    "**Prérequis :** calcul de moyennes et de ratios, techniques de présentations graphiques simples, éventuellement régression logistique\n",
+    "\n",
+    "---\n",
+    "\n",
+    "En 1972-1974, à Whickham, une ville du nord-est de l'Angleterre, située à environ 6,5 kilomètres au sud-ouest de Newcastle upon Tyne, un sondage d'un sixième des électeurs a été effectué afin d'éclairer des travaux sur les maladies thyroïdiennes et cardiaques (Tunbridge et al. 1977). Une suite de cette étude a été menée vingt ans plus tard (Vanderpump et al. 1995). Certains des résultats avaient trait au tabagisme et cherchaient à savoir si les individus étaient toujours en vie lors de la seconde étude. 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\". Il y avait relativement peu de femmes dans le sondage initial ayant fumé et ayant arrêté depuis (162) et très peu pour lesquelles l'information n'était pas disponible (18). La survie à 20 ans a été déterminée pour l'ensemble des femmes du premier sondage.\n",
+    "\n",
+    "---\n",
+    "\n",
+    "**Votre mission si vous l'acceptez :**\n",
+    "\n",
+    "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 ?\n",
+    "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.\n",
+    "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).\n",
+    "4. Déposez votre étude dans FUN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import os\n",
+    "import urllib.request"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "En suivant la technique apprise dans le precédent exercice, on vérifie si le jeu de données existe déjàs ous la forme d'un fichier local .csv et si non, on le télécharge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "datas_file = 'simpson_paradox.csv'\n",
+    "datas_url = 'https://gitlab.inria.fr/learninglab/mooc-rr/mooc-rr-ressources/-/raw/master/module3/Practical_session/Subject6_smoking.csv'\n",
+    "\n",
+    "if not os.path.exists(datas_file):\n",
+    "    urllib.request.urlretrieve(datas_url, datas_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "raw_datas = pd.read_csv(datas_file, header=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On vérifie l'absence de données nulles par ligne"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [Smoker, Status, Age]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_datas[raw_datas.isnull().any(axis=1)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Exercice 1\n",
+    "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": "markdown",
+   "metadata": {},
+   "source": [
+    "- D'après l'énoncé, il faudrait classer les données selon que les femmes aient été fummeuses, donc on copie la DataFrame `raw_datas` en modifiant son index par la colonne `Smoker`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sorted_datas1 = raw_datas.set_index('Smoker').sort_index()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- On peut ensuite utiliser la fonction `.loc` pour avoir uniquement les fumeuses (`Yes`) ou les non fumeuses (`No`) et créer deux nouveau objets de type DataFrame `smokers` & `non_smokers`\n",
+    "    - le total de lignes étant le nombre total de femmes appartenant à ce groupe et peut être obtenu via la fonction `len`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "hideOutput": true
+   },
+   "outputs": [],
+   "source": [
+    "smokers = sorted_datas1.loc['Yes']\n",
+    "non_smokers = sorted_datas1.loc['No']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Total fumeuses = 582 / Total non-fumeuses = 732\n"
+     ]
+    }
+   ],
+   "source": [
+    "smokers_total = len(smokers)\n",
+    "non_smokers_total = len(non_smokers)\n",
+    "print('Total fumeuses = {} / Total non-fumeuses = {}'.format(smokers_total, non_smokers_total))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- On peut accéder au nombre total de femmes décédées par groupe  via `(non_)smokers.loc[smokers['Status'] == 'Dead']`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fumeuses mortes = 139 / Non-fumeuses mortes = 230\n"
+     ]
+    }
+   ],
+   "source": [
+    "smokers_dead = len(smokers.loc[smokers['Status'] == 'Dead'])\n",
+    "non_smokers_dead = len(non_smokers.loc[non_smokers['Status'] == 'Dead'])\n",
+    "print('Fumeuses mortes = {} / Non-fumeuses mortes = {}'.format(smokers_dead, non_smokers_dead))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- On calcule le taux de mortalité par groupe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mortalité chez les femmes fumeuses : 4.19\n",
+      "Mortalité chez les femmes non-fumeuses : 3.18\n"
+     ]
+    }
+   ],
+   "source": [
+    "smokers_mortality = smokers_total / smokers_dead\n",
+    "non_smokers_mortality = non_smokers_total / non_smokers_dead\n",
+    "print('Mortalité chez les femmes fumeuses : {:.2f}'.format(smokers_mortality))\n",
+    "print('Mortalité chez les femmes non-fumeuses : {:.2f}'.format(non_smokers_mortality))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "J'ai du me tromper quelque part car je ne trouve pas que le résultat soit surprenant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e2b95d630>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(nrows=1, ncols=2)\n",
+    "smokers['Status'].value_counts().plot.bar(title='Fumeuses', ax=axes[0])\n",
+    "non_smokers['Status'].value_counts().plot.bar(title='Non-fumeuses', ax=axes[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f4e2b887c50>],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sorted_datas1['Status'].value_counts().plot.bar(subplots=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Exercice 2\n",
+    "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": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e2b72c1d0>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "smokers.set_index('Age').sort_index().loc[18 : 34]['Status'].value_counts().plot.bar(title='Fumeuses 18-34 ans')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e2b6920b8>"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "smokers.set_index('Age').sort_index().loc[34 : 54]['Status'].value_counts().plot.bar(title='Fumeuses 34-54 ans')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e2b999908>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEZCAYAAACHCd7XAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAE2tJREFUeJzt3X+0ZWV93/H3Z2ZQCL8n3JlOIDA2GY2SFNDrjwRLUgaIRoVpDVYS22lLM7GaFhrbZLQ2QdvVRdr8MjVNMsE0E8UAaghEawBHjWnjQgfBXxkESxAmDDMXcBAJOoF8+8fetzkOdzjn3rl3Dve579daZ+2z93723t995sznPOc5Z5+bqkKStPgtG3cBkqT5YaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoepIkdyd5LMnXB27fMe66DqUkv5tk336PwfKB9ZXk0YF1VwzZ3/Ik/znJfUkeSXJrkuNmaPfRft8rFuK81DafNDqQV1XVR8ZdxJj916p661OsP62qvjzivt4G/ADw/cA9wKnANwYbJPlx/D+pg2APXSNL8kNJdu637O4k5/T3L0vyviTv6Xuhn0/y7CRvTrInyb1JzhvY9tgk70qyK8lf9j3Y5QP7es9A27WDPdck/yzJXf1x/qIPw+m2/yLJjiRfTXJDklP65UnyK30tDyf5XJLvXdhHDZIcD1wK/ERVfaU6X6iqbwy0ORb4eeBnRtjfO/rH8mtJbkny9wfWXZbkmiS/1z82X0wyObD+Z/vH+pEkX0qyfn7PVuNkoGu+vQp4N3A8cCtwA93z7ETg7cBvDbTdCjwOfDdwBnAe8C+HHSDJkcCvAS+vqqPper639es2AG8B/hEwAfwp8Pv9pucBZwHPBo4D/jHw4FMc6g1JHupD89UzrP9EkvuT/EGStU+xn+/rz/NH+/Z3JHnjfm3+C/AbwP1PsZ9pnwZOB1YC7wXel+TwgfXnA1fRneP1wDsBkjwH+Cnghf3j9sPA3SMcT4tFVXnz9i03uv/kXwf29rc/7Jf/ELBzhrbn9PcvA24aWPeqfj/L+/mjgaILmtXAN4EjBtpfBHxsYF/vGVi3tt92BXBkX9erB7fv230YuHhgfhnwV8ApwNnAHcBLgGVDHoPnA9/eH+9HgEeAMwfWnwU8oz+XdwJfAFYcYF8/1tf+LuAI4O8BU8C5/fpJuhekFYPnOYt/r6/SDf9MP24fGVj3POCx/v53A3uAc4DDxv088zb/N3voOpANVXVcf9swi+12D9x/DHigqp4YmAc4ii5gDwN2JdmbZC9d733VsANU1aN0vevX99t/KMn39KtPAd4xsM+HgAAnVtVH6cL314HdSbYkOeYAx/hMVT1YVY9X1f8CrqTr9U+v/0RV7auqvcAlwLOA5wLs90HqyQPn/faqeqyqPkfXg/6RJMuA/wFcUlWPDzv3fv9v6oeUHu7P8VjghIEmg738vwIOT7KiuvH+S+lCf0+Sq5bah92tM9A1G48C3zY90493T8xxX/fS9dBPGHjhOKaqTp3pWMDfGdy4qm6oqnOBNcDtwG8P7PcnB/Z5XFUdUVV/1m/3a1X1AroPJZ8N/PsR6y26F4ah66vqqIHbPcDnBtrs7xi6HvrVSe6nG04B2Dk4Nj6tX/azwGuA46vqOODhIbX9bZFV762ql9K98BXwC6Nsp8XBQNds3EHX23tFksOAtwLPnMuOqmoXcCPwS0mOSbIsyXcl+cG+yW3AWUlO7j8wfPP0tklWJzm/H0v/Jt2wzvS7gN8E3pzk1L7tsUku7O+/MMmL+9ofpfuWyfR23yLJjyY5qq/rPOB1dOPRJDk1yenpvop4FPBLwF8COw5wrv+Xbiz/PyR5ZpLn0r3D+CBdGH8H3Zj46XTDOwAvAG6eYXdH043HTwErkvwc3YvCUEmek+TsJM/sz/2xA52/FicDXSOrqoeBNwBX0AXYo8DOp9zoqf1TunHoP6cbB34/XY+bqroJuJqud3sLXfhNWwa8CbiPbkjlB/u6qKpr6XqdVyX5Gt3Y9sv77Y6h68l/FfgK3Qeiv3iA2i7pz3Ev8N/ovqHy8X7d6r62rwF30Y17v7Kq/vopzvUiul7xg8CHgP9YVduqc//0jS6oAXZX1b4Z9nMD3ecEd/Tn8A26dyWjeCZwOfAA3bDMKroPkNWIVPkHLiSpBfbQJakRBrokNcJAl6RGGOiS1AgDXZIacUh/2e2EE06otWvXHspDStKid8sttzxQVUMv4jukgb527Vq2b99+KA8pSYtekq+M0s4hF0lqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDumFRYvF2s0fGncJTbn78leMuwRpSbCHLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWrESIGe5Lgk709ye5IdSb4/ycokNyW5s58ev9DFSpIObNQe+juAP66q7wFOA3YAm4FtVbUO2NbPS5LGZGigJzkGOAt4F0BV7auqvcAFwNa+2VZgw0IVKUkabpQe+t8FpoD/meTWJFckORJYXVW7APrpqgWsU5I0xCiBvgJ4PvAbVXUG8CizGF5JsinJ9iTbp6am5limJGmYUQJ9J7Czqm7u599PF/C7k6wB6Kd7Ztq4qrZU1WRVTU5MTMxHzZKkGQwN9Kq6H7g3yXP6ReuBPweuBzb2yzYC1y1IhZKkkYz6e+j/GrgyyTOAu4B/TvdicE2Si4F7gAsXpkRJ0ihGCvSqug2YnGHV+vktR5I0V14pKkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJasSKURoluRt4BHgCeLyqJpOsBK4G1gJ3A6+pqq8uTJmSALjs2HFX0JbLHh53BfNqNj30f1BVp1fVZD+/GdhWVeuAbf28JGlMDmbI5QJga39/K7Dh4MuRJM3VqIFewI1JbkmyqV+2uqp2AfTTVQtRoCRpNCONoQNnVtV9SVYBNyW5fdQD9C8AmwBOPvnkOZQoSRrFSD30qrqvn+4BrgVeBOxOsgagn+45wLZbqmqyqiYnJibmp2pJ0pMMDfQkRyY5evo+cB7wBeB6YGPfbCNw3UIVKUkabpQhl9XAtUmm27+3qv44yaeBa5JcDNwDXLhwZUqShhka6FV1F3DaDMsfBNYvRFGSpNnzSlFJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGjFyoCdZnuTWJB/s51cmuSnJnf30+IUrU5I0zGx66JcAOwbmNwPbqmodsK2flySNyUiBnuQk4BXAFQOLLwC29ve3AhvmtzRJ0myM2kP/VeBngL8ZWLa6qnYB9NNV81ybJGkWhgZ6klcCe6rqlrkcIMmmJNuTbJ+amprLLiRJIxilh34mcH6Su4GrgLOTvAfYnWQNQD/dM9PGVbWlqiaranJiYmKeypYk7W9ooFfVm6vqpKpaC7wW+GhVvQ64HtjYN9sIXLdgVUqShjqY76FfDpyb5E7g3H5ekjQmK2bTuKo+Dny8v/8gsH7+S5IkzYVXikpSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhoxNNCTHJ7kU0k+m+SLSd7WL1+Z5KYkd/bT4xe+XEnSgYzSQ/8mcHZVnQacDrwsyUuAzcC2qloHbOvnJUljMjTQq/P1fvaw/lbABcDWfvlWYMOCVChJGslIY+hJlie5DdgD3FRVNwOrq2oXQD9ddYBtNyXZnmT71NTUfNUtSdrPSIFeVU9U1enAScCLknzvqAeoqi1VNVlVkxMTE3OtU5I0xKy+5VJVe4GPAy8DdidZA9BP98x7dZKkkY3yLZeJJMf1948AzgFuB64HNvbNNgLXLVSRkqThVozQZg2wNclyuheAa6rqg0k+CVyT5GLgHuDCBaxTkjTE0ECvqs8BZ8yw/EFg/UIUJUmaPa8UlaRGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRQwM9yXcm+ViSHUm+mOSSfvnKJDclubOfHr/w5UqSDmSUHvrjwJuq6rnAS4A3JnkesBnYVlXrgG39vCRpTIYGelXtqqrP9PcfAXYAJwIXAFv7ZluBDQtVpCRpuFmNoSdZC5wB3Aysrqpd0IU+sGq+i5MkjW7kQE9yFPAB4NKq+tosttuUZHuS7VNTU3OpUZI0gpECPclhdGF+ZVX9Qb94d5I1/fo1wJ6Ztq2qLVU1WVWTExMT81GzJGkGo3zLJcC7gB1V9csDq64HNvb3NwLXzX95kqRRrRihzZnAPwE+n+S2ftlbgMuBa5JcDNwDXLgwJUqSRjE00KvqfwM5wOr181uOJGmuvFJUkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYMDfQkv5NkT5IvDCxbmeSmJHf20+MXtkxJ0jCj9NB/F3jZfss2A9uqah2wrZ+XJI3R0ECvqk8AD+23+AJga39/K7BhnuuSJM3SXMfQV1fVLoB+umr+SpIkzcWCfyiaZFOS7Um2T01NLfThJGnJmmug706yBqCf7jlQw6raUlWTVTU5MTExx8NJkoaZa6BfD2zs728ErpufciRJczXK1xZ/H/gk8JwkO5NcDFwOnJvkTuDcfl6SNEYrhjWoqosOsGr9PNciSToIXikqSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqxEEFepKXJflSki8n2TxfRUmSZm/OgZ5kOfDrwMuB5wEXJXnefBUmSZqdg+mhvwj4clXdVVX7gKuAC+anLEnSbK04iG1PBO4dmN8JvHj/Rkk2AZv62a8n+dJBHFPf6gTggXEXMUx+YdwVaAwWxXOTt2XcFYzqlFEaHUygz/RI1JMWVG0BthzEcXQASbZX1eS465D253NzPA5myGUn8J0D8ycB9x1cOZKkuTqYQP80sC7Js5I8A3gtcP38lCVJmq05D7lU1eNJfgq4AVgO/E5VfXHeKtMoHMrS05XPzTFI1ZOGvSVJi5BXikpSIwx0SWqEgS5JjTDQF6EkR467BklPPwdzYZEOsSQ/AFwBHAWcnOQ04Cer6g3jrUxLWZKffqr1VfXLh6qWpc4e+uLyK8APAw8CVNVngbPGWpEER/e3SeBf0f0syInA6+l+uE+HiD30Raaq7k2+5VcXnhhXLRJAVb0NIMmNwPOr6pF+/jLgfWMsbckx0BeXe/thl+qvzv03wI4x1yRNOxnYNzC/D1g7nlKWJgN9cXk98A66t7M7gRuBN461IulvvRv4VJJr6X6o7x8CvzfekpYWrxRdRJJMVNXUuOuQDiTJC4CX9rOfqKpbx1nPUmOgLyJJ7gT+Arga+EBV7R1zSdKTJFkFHD49X1X3jLGcJcVvuSwiVbUOeCtwKvCZJB9M8roxlyUBkOT8gU7Hn/TTD4+3qqXFQF9kqupTVfXTdH8C8CFg65hLkqb9J+AlwB1V9SzgHOD/jLekpcVAX0SSHJNkY5IPA38G7KILdunp4K+r6kFgWZJlVfUx4PRxF7WU+C2XxeWzwB8Cb6+qT467GGk/e5McBfwpcGWSPcDjY65pSfFD0UUkScp/MD1N9b8x9BjdO/8fB44Frux77ToEDPRFIMmvVtWlSf6Imf8Q9/ljKEt6kiSnAOuq6iNJvg1YPn3lqBaeQy6Lw7v76S+OtQrpKST5CWATsBL4LroL4H4TWD/OupYSe+iS5kWS2+g+pL+5qs7ol32+qr5vvJUtHfbQF4Ekn2eGoRYgwN9U1WmHuCRpJt+sqn3TPx6XZAUzP2+1QAz0xeGVMywLcBLwlkNci3Qgf5LkLcARSc4F3gD80ZhrWlIccllkkpwO/BjwGror8T5QVe8cb1USJFkGXAycR9fhuAG4wm9mHToG+iKQ5NnAa4GL6P64xdXAv6uqU8ZamLSfJBMA/ojceHil6OJwO903BV5VVS+tqv+Of9hCTxPpXJbkAbrn6peSTCX5uXHXttQY6IvDq4H7gY8l+e0k6+ne0kpPB5cCZwIvrKpvr6qVwIuBM5P82/GWtrQ45LKI9FfibaAbejmb7oe5rq2qG8damJa0JLcC51bVA/stnwBunP4KoxaePfRFpKoeraorq+qVdN9wuQ3YPOaypMP2D3P4/+Poh42hniXLQF+kquqhqvqtqjp73LVoyds3x3WaZw65SDooSZ4AHp1pFXB4VdlLP0QMdElqhEMuktQIA12SGmGgS1IjDHRJaoSBLkmN+H/ubY2Q7S9FAQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "smokers.set_index('Age').sort_index().loc[55 : 64]['Status'].value_counts().plot.bar(title='Fumeuses 55-64 ans')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e2b5d5438>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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/luT+JGcDFwEnJ7kbOLmblySN0J4zrVBVZ02z6MQh1yJJmgevFJWkRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUiBkv/Zf0PLJu/1FX0JZ13x11BUNlD12SGmGgS1IjDHRJaoSBLkmNMNAlqRHzOsslyT3AY8AzwNNVNT6MoiRJszeM0xZ/rqoeHMJ+JEnz4JCLJDVivoFewHVJbkmydhgFSZLmZr5DLsdX1beSHAJsTHJXVd3Uv0IX9GsBjjjiiHkeTpI0nXn10KvqW93zduAqYPUU66yvqvGqGh8bG5vP4SRJz2HOgZ7khUn23TkNnALcMazCJEmzM58hl0OBq5Ls3M/Hq+ovh1KVJGnW5hzoVfVN4Ngh1iJJmgdPW5SkRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNWJegZ7k1CRfS/KNJOcPqyhJ0uzN556iy4A/BF4LvAw4K8nLhlWYJGl25tNDXw18o6q+WVVPAZ8ETh9OWZKk2ZrPTaIPA+7rm78feMWuKyVZC6ztZnck+do8jql/6mDgwVEXMZP8zqgr0Agsit9NLsyoKxjUkYOsNJ9An+qVqH/WULUeWD+P42gaSSaqanzUdUi78ndzNOYz5HI/8KK++cOBb82vHEnSXM0n0P8eODrJi5P8EHAmcPVwypIkzdach1yq6ukk7wCuBZYBl1fVnUOrTINwKEvPV/5ujkCq/tmwtyRpEfJKUUlqhIEuSY0w0CWpEQa6pKFJ8sJR17CUzefCIu1GSX7juZZX1ft3Vy3SrpL8DHAZsA9wRJJjgXOq6tzRVra02ENfPPbtHuPAr9L76oXDgLfT+3I0aZQuAf4N8BBAVd0GvHqkFS1B9tAXiaq6ECDJdcBPVtVj3fw64NMjLE0CoKruS/7JN4I8M6palioDffE5Aniqb/4pYOVoSpF+4L5u2KW6K8d/Hdgy4pqWHAN98fko8JUkV9H7MrQ3AX8y2pIk3g58gN4w4P3AdcCvjbSiJcgrRRehJD8FvKqbvamqbh1lPVKSsaqaHHUdS52BvkglOQTYa+d8Vd07wnK0xCW5G/gH4FPAZ6rqkRGXtCR5lssik+SNfX88f9M9f2G0VWmpq6qjgd8EjgG+muSaJG8dcVlLjoG++PxP4JXA16vqxcBJwP8ebUkSVNVXquo36N2e8mFgw4hLWnIM9MXn+1X1ELBHkj2q6gZg1aiL0tKWZL8ka5J8AfhbYCu9YNdu5Fkui88jSfYBvghckWQ78PSIa5JuAz4HvLeqvjzqYpYqPxRdZLrvyniC3n9XvwzsD1zR9dqlkUiSMkxGzkBfhJIcCRxdVX+V5AXAsp1Xjkq7U5LfrarzkvwZU98k/o0jKGvJcshlkUnyH4G1wEHAUfQu5PgQcOIo69KS9dHu+X0jrUKAPfRFJ8kmeh823VxVx3Vtt1fVvx5tZZJGzR764vNkVT2180uQkuzJFP/qSrtDktuZ+vcvwLNVdexuLmlJM9AXn79JcgGwd5KTgXOBPxtxTVq6TpuiLcDhwAW7uZYlzyGXRSbJHsDZwCn0/nCuBS7zDAONWpJVwFuAX6R3BfNnquoPRlvV0mKgL0JJxgD8MiSNWpKXAGcCZ9G7ucWngP9cVUeOtLAlyitFF4n0rEvyIHAX8LUkk0neM+ratKTdRe8MqzdU1auq6vfxxhYjY6AvHucBxwM/XVU/XFUHAa8Ajk/yn0ZbmpawXwC+DdyQ5NIkJ9IbCtQIOOSySCS5FTi5qh7cpX0MuG7nKYzSKHRXMJ9Bb+jlBHpfzHVVVV030sKWGHvoi8fyXcMcfjCOvnwE9Ug/UFWPV9UVVXUavTNcNgHnj7isJcdAXzyemuMyabeqqoer6sNVdcKoa1lqHHJZJJI8Azw+1SJgr6qyly4tcQa6JDXCIRdJaoSBLkmNMNAlqREGuiQ1wkCXpEb8fyZDlbbxS8S5AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "smokers.set_index('Age').sort_index().loc[65:]['Status'].value_counts().plot.bar(title='Fumeuses 65+ ans')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +476,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-
diff --git a/module3/exo3/simpson_paradox.csv b/module3/exo3/simpson_paradox.csv
new file mode 100644
index 0000000..6c47065
--- /dev/null
+++ b/module3/exo3/simpson_paradox.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
+"Yes","Alive",40.1
+"No","Alive",51.2
+"No","Alive",22.9
+"No","Alive",41.9
+"Yes","Alive",58.1
+"Yes","Alive",37.3
+"No","Alive",41.7
+"Yes","Dead",36.3
+"Yes","Dead",80.7
+"Yes","Alive",33
+"Yes","Alive",38.6
+"Yes","Alive",27.9
+"No","Alive",47.6
+"No","Dead",77.6
+"No","Dead",58.1
+"Yes","Alive",26.2
+"No","Alive",45.4
+"No","Alive",62.4
+"No","Alive",62.5
+"No","Alive",39.5
+"No","Alive",27.6
+"Yes","Alive",31.4
+"No","Dead",85
+"No","Alive",18.9
+"No","Alive",35.3
+"Yes","Alive",25.4
+"No","Dead",72.8
+"Yes","Alive",58.3
+"No","Alive",27.3
+"No","Dead",55.9
+"No","Alive",32.8
+"Yes","Dead",53.6
+"No","Alive",55.9
+"Yes","Alive",48
+"Yes","Alive",56.1
+"No","Alive",18.3
+"Yes","Alive",20.2
+"No","Alive",62.8
+"Yes","Alive",18.6
+"No","Alive",46.3
+"No","Alive",36
+"Yes","Dead",55.5
+"Yes","Alive",18.6
+"No","Dead",65.7
+"No","Dead",76.5
+"Yes","Dead",61
+"No","Alive",26.8
+"Yes","Alive",47.6
+"No","Dead",70.5
+"No","Dead",81.8
+"Yes","Alive",32.5
+"No","Alive",23
+"No","Alive",83.7
+"Yes","Dead",62.8
+"Yes","Alive",45.9
+"No","Alive",59.9
+"Yes","Dead",66.5
+"No","Alive",47.5
+"No","Dead",89.3
+"No","Alive",57.2
+"Yes","Alive",21.3
+"Yes","Alive",34
+"No","Alive",59.5
+"Yes","Alive",50.1
+"No","Alive",56.1
+"Yes","Alive",30.6
+"Yes","Dead",63.8
+"Yes","Alive",27.4
+"Yes","Alive",32.5
+"No","Alive",22.5
+"Yes","Alive",24.2
+"No","Alive",56.8
+"Yes","Alive",28.9
+"Yes","Dead",87.8
+"Yes","Alive",19.4
+"No","Dead",88.4
+"Yes","Dead",35.7
+"No","Alive",33.7
+"No","Dead",62.3
+"Yes","Alive",63.6
+"Yes","Dead",71.7
+"No","Alive",48.5
+"No","Alive",82
+"Yes","Dead",40.8
+"Yes","Alive",31.3
+"No","Alive",25.1
+"No","Alive",24.4
+"No","Alive",32.2
+"No","Alive",53.1
+"No","Alive",47.5
+"No","Dead",26.3
+"No","Dead",66
+"Yes","Alive",41
+"No","Dead",58.3
+"No","Dead",86.8
+"Yes","Alive",49.7
+"No","Alive",21
+"No","Dead",75.8
+"No","Alive",50.5
+"No","Dead",63.5
+"Yes","Alive",33.1
+"No","Alive",30.7
+"Yes","Dead",59.4
+"No","Alive",67.2
+"Yes","Alive",20.7
+"No","Alive",41.6
+"No","Alive",37.7
+"No","Dead",89.7
+"No","Alive",45.2
+"Yes","Dead",59.9
+"No","Alive",55.4
+"Yes","Alive",44.8
+"No","Alive",18.3
+"No","Dead",76.7
+"Yes","Dead",62.3
+"Yes","Dead",48.4
+"No","Alive",82.7
+"No","Alive",27
+"No","Alive",30.9
+"No","Dead",82.9
+"No","Alive",40.5
+"Yes","Alive",56.8
+"No","Alive",26.5
+"Yes","Alive",41.9
+"Yes","Alive",29.9
+"No","Dead",75
+"No","Dead",66.3
+"No","Dead",87
+"No","Dead",79.5
+"Yes","Alive",51.7
+"No","Alive",30
+"No","Alive",78.2
+"No","Alive",80
+"Yes","Alive",62.9
+"Yes","Dead",78.3
+"Yes","Alive",49.8
+"Yes","Alive",36.5
+"No","Dead",60.1
+"Yes","Alive",62
+"No","Alive",19
+"No","Dead",87.6
+"No","Alive",24.3
+"No","Alive",22.2
+"Yes","Dead",68.4
+"No","Alive",32.5
+"Yes","Dead",89.2
+"Yes","Alive",44.5
+"Yes","Alive",43.3
+"Yes","Dead",57.4
+"Yes","Alive",45.6
+"Yes","Alive",18.1
+"Yes","Dead",63.4
+"No","Alive",67
+"Yes","Alive",55.6
+"Yes","Alive",51.9
+"No","Alive",23.3
+"Yes","Dead",57.6
+"No","Alive",38.4
+"No","Dead",35.2
+"Yes","Alive",26.2
+"No","Alive",60.3
+"Yes","Alive",48.7
+"Yes","Alive",23.7
+"Yes","Alive",46.9
+"Yes","Alive",18
+"No","Dead",65.8
+"Yes","Alive",33
+"Yes","Dead",83.1
+"No","Dead",58.4
+"Yes","Alive",23.2
+"No","Alive",66.7
+"No","Alive",58.8
+"Yes","Alive",56.7
+"Yes","Alive",21.5
+"No","Dead",78.3
+"No","Alive",51.5
+"Yes","Alive",63.5
+"Yes","Alive",57.8
+"Yes","Alive",59.5
+"Yes","Dead",47.8
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+"No","Dead",50.2
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+"No","Alive",23
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+"Yes","Alive",55.3
+"No","Alive",40.8
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+"Yes","Alive",41
+"Yes","Alive",50.2
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+"No","Dead",79
+"No","Dead",65.1
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+"No","Alive",46
+"No","Alive",40.2
+"No","Dead",89.2
+"No","Alive",26
+"No","Alive",43.4
+"No","Alive",48.8
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+"No","Dead",42.1
+"No","Alive",41.2
+"Yes","Alive",20.9
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+"Yes","Alive",24.9
+"No","Alive",33
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+"No","Alive",42.1
+"Yes","Alive",35.9
+"No","Alive",22.3
+"Yes","Dead",62.1
+"No","Dead",88.6
+"No","Alive",39.1
-- 
2.18.1