diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index deadaf0a7105a4b1ea3c6dcec2d1e37c720e1838..520aa14f3ea93d2e57327fe9a6b6ea54e897e55e 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -764,25 +764,59 @@
     "On remarque que le taux de mortalité est - nettement - plus élevé dans le groupe des non fumeuses, ce qui constitue le paradoxe de Simpson"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Taux de mortalité par classes d'age"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Nous allons regarder si les résultats persistent en prenant en compte les différentes classes d'age"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "443"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "        Smokers  Non-Smokers\n",
+      "18-34  0.037037     0.026432\n",
+      "35-54  0.170306     0.099476\n",
+      "55-64  0.443478     0.330579\n",
+      "65+    0.857143     0.854922\n"
+     ]
     }
    ],
    "source": [
-    "len(data[(data['Smoker'] == \"Yes\") & (data['Status'] == \"Alive\")].index)"
+    "classes_breaks = [0,35,55,65,150] \n",
+    "tab_alive = [ [len(data[(data['Smoker'] == \"Yes\") & (data['Status'] == \"Alive\") & (classes_breaks[i] <= data['Age']) & (data['Age'] < classes_breaks[i+1])]), len(data[(data['Smoker'] == \"No\") & (data['Status'] == \"Alive\") & (classes_breaks[i] <= data['Age']) & (data['Age'] < classes_breaks[i+1])])] for i in [0,1,2,3]]\n",
+    "tab_dead = [ [len(data[(data['Smoker'] == \"Yes\") & (data['Status'] == \"Dead\") & (classes_breaks[i] <= data['Age']) & (data['Age'] < classes_breaks[i+1])]), len(data[(data['Smoker'] == \"No\") & (data['Status'] == \"Dead\") & (classes_breaks[i] <= data['Age']) & (data['Age'] < classes_breaks[i+1])])] for i in [0,1,2,3]]\n",
+    "tab_deathrate = [ [tab_dead[i][0]/(tab_dead[i][0]+tab_alive[i][0]) , tab_dead[i][1]/(tab_dead[i][1]+tab_alive[i][1])] for i in [0,1,2,3]]\n",
+    "\n",
+    "print(pd.DataFrame(tab_deathrate, [\"18-34\",\"35-54\",\"55-64\",\"65+\"], [\"Smokers\", \"Non-Smokers\"]))"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "On remarque cette fois que, pour chaque classe d'âge, le résultat est attendu où le taux de mortalité est nettement supérieur pour le groupe des fumeuses, sauf pour les plus de 65 ans où les résultats sont sensiblement égaux."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {