écartype2_empirique

module2/exo2/exercice.ipynb

@@ -20,7 +20,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -32,13 +32,13 @@
     }
    ],
    "source": [
-    "moyenne_A = np.mean(A)\n",
+    "moyenne_A = np.mean(A, 0)\n",
     "print(moyenne_A)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -50,13 +50,13 @@
     }
    ],
    "source": [
-    "minimum_A = np.min(A)\n",
+    "minimum_A = np.min(A, 0)\n",
     "print(minimum_A)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -68,7 +68,7 @@
     }
    ],
    "source": [
-    "maximum_A = np.max(A)\n",
+    "maximum_A = np.max(A, 0)\n",
     "print(maximum_A)"
    ]
   },
@@ -108,6 +108,24 @@
     "print(écartype_A)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.334094455301447\n"
+     ]
+    }
+   ],
+   "source": [
+    "écartype2_A = np.std(A,0,ddof = 1)\n",
+    "print(écartype2_A)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,