diff --git a/module3/exo3/Essai_CI_band.ipynb b/module3/exo3/Essai_CI_band.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7fec51502cbc3200b3d0ffc6bbba1fe85e197f3d
--- /dev/null
+++ b/module3/exo3/Essai_CI_band.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index cdc852fa8ac0e89013e90f2e7eb18a707f814f39..0f05f09b810771a87c87571fc10662515f3dc416 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -385,7 +385,7 @@
    "cell_type": "code",
    "execution_count": 5,
    "metadata": {
-    "hideCode": false,
+    "hideCode": true,
     "hidePrompt": true
    },
    "outputs": [],
@@ -492,11 +492,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: lmfit in /opt/conda/lib/python3.6/site-packages (1.0.0)\n",
-      "Requirement already satisfied: scipy>=1.2 in /opt/conda/lib/python3.6/site-packages (from lmfit) (1.4.1)\n",
-      "Requirement already satisfied: uncertainties>=3.0.1 in /opt/conda/lib/python3.6/site-packages (from lmfit) (3.1.2)\n",
-      "Requirement already satisfied: numpy>=1.16 in /opt/conda/lib/python3.6/site-packages (from lmfit) (1.18.2)\n",
-      "Requirement already satisfied: asteval>=0.9.16 in /opt/conda/lib/python3.6/site-packages (from lmfit) (0.9.18)\n"
+      "Requirement already satisfied: lmfit in /opt/conda/lib/python3.6/site-packages (1.0.0)\r\n",
+      "Requirement already satisfied: asteval>=0.9.16 in /opt/conda/lib/python3.6/site-packages (from lmfit) (0.9.18)\r\n",
+      "Requirement already satisfied: numpy>=1.16 in /opt/conda/lib/python3.6/site-packages (from lmfit) (1.18.2)\r\n",
+      "Requirement already satisfied: scipy>=1.2 in /opt/conda/lib/python3.6/site-packages (from lmfit) (1.4.1)\r\n",
+      "Requirement already satisfied: uncertainties>=3.0.1 in /opt/conda/lib/python3.6/site-packages (from lmfit) (3.1.2)\r\n"
      ]
     }
    ],
@@ -516,6 +516,7 @@
    "source": [
     "# Import des librairies\n",
     "from lmfit.models import QuadraticModel, Model\n",
+    "import lmfit\n",
     "from sklearn.metrics import r2_score"
    ]
   },
@@ -567,41 +568,58 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 37,
+   "metadata": {
+    "hideCode": false,
+    "hideOutput": true,
+    "hidePrompt": true
+   },
+   "outputs": [],
+   "source": [
+    "# Mise en dictionnaire des meilleures paramètres calculés\n",
+    "coeffs_long_trend = result_long_trend.params.valuesdict() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
-    "hideCode": true,
     "hidePrompt": true
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('a', 0.012977452801551667),\n",
-       "             ('b', 0.7677869942992783),\n",
-       "             ('c', 314.5704951470056)])"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[Variables]]\n",
+      "    a:  0.01297745 +/- 1.4176e-04 (1.09%) (init = 0.01297745)\n",
+      "    b:  0.76778699 +/- 0.00913468 (1.19%) (init = 0.767787)\n",
+      "    c:  314.570495 +/- 0.12419930 (0.04%) (init = 314.5705)\n",
+      "[[Correlations]] (unreported correlations are < 0.100)\n",
+      "    C(a, b) = -0.969\n",
+      "    C(b, c) = -0.873\n",
+      "    C(a, c) =  0.756\n"
+     ]
     }
    ],
    "source": [
-    "# Affichage des meilleures paramètres calculés\n",
-    "coeffs_long_trend = result_long_trend.params.valuesdict()\n",
-    "coeffs_long_trend "
+    "#Affichage des meilleures paramètres calculés avec les intervalles de confiances\n",
+    "print(lmfit.fit_report(result_long_trend.params))"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "hideCode": false,
+    "hidePrompt": true
+   },
    "source": [
     "Ci-dessus, les différents paramètres de la courbe d'ajustement à long terme (fonction quadratique)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "hideCode": false,
     "hidePrompt": true
@@ -618,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "hideCode": false,
     "hidePrompt": true
@@ -631,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "hideCode": false,
     "hidePrompt": true
@@ -643,7 +661,7 @@
        "0.9936252499490063"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -666,9 +684,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
-    "hideCode": true,
+    "hideCode": false,
     "hidePrompt": true
    },
    "outputs": [
@@ -714,6 +732,16 @@
     "fig.tight_layout()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "hidePrompt": true
+   },
+   "source": [
+    "L'intervalle de confiance de la courbe ajustée n'est pas tracée sur les graphes précédents car il est très etroit.  \n",
+    "Celui-ci dégraderait la lisibilité des graphiques."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -736,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "hideCode": false,
     "hidePrompt": true
@@ -749,7 +777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -758,10 +786,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bee2fec88>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3f4fdc5c0>"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -810,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "hidePrompt": true
    },
@@ -823,7 +851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "hidePrompt": true
    },
@@ -838,28 +866,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 38,
+   "metadata": {
+    "hideOutput": true,
+    "hidePrompt": true
+   },
+   "outputs": [],
+   "source": [
+    "# Affichage des meilleures paramètres calculés\n",
+    "coeffs_seasonal_oscillation = result_seasonal_oscillation.params.valuesdict()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
    "metadata": {
     "hidePrompt": true
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('amp', 2.5022539354204705),\n",
-       "             ('freq', 1.0044805631567757),\n",
-       "             ('shift', 316.19947719002874)])"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[Variables]]\n",
+      "    amp:    2.50225394 +/- 0.03484620 (1.39%) (init = 7)\n",
+      "    freq:   1.00448056 +/- 6.1469e-05 (0.01%) (init = 1)\n",
+      "    shift:  316.199477 +/- 0.02463756 (0.01%) (init = 0)\n"
+     ]
     }
    ],
    "source": [
-    "# Affichage des meilleures paramètres calculés\n",
-    "coeffs_seasonal_oscillation = result_seasonal_oscillation.params.valuesdict()\n",
-    "coeffs_seasonal_oscillation"
+    "#Affichage des meilleures paramètres calculés avec les intervalles de confiances\n",
+    "print(lmfit.fit_report(result_seasonal_oscillation.params))"
    ]
   },
   {
@@ -871,12 +909,14 @@
     "Avec ces résulats nous pouvons caractériser l'oscillation saisonnière comme suit :\n",
     "- +/- 2.5 *ppm* sur une année.\n",
     "- Une féquence de 1 année pour chaque oscillation. Ce qui conforme aux observations.\n",
-    "- Un décalage de 316.20 *ppm* qui correspond à la première mesure de la base de données."
+    "- Un décalage de 316.20 *ppm* qui correspond à la première mesure de la base de données.  \n",
+    "\n",
+    "Les intervalles de confiance sont là aussi très restreints. Nous faisons le choix de ne pas les représenter sur les graphiques à venir."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {
     "hidePrompt": true
    },
@@ -891,7 +931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {
     "hidePrompt": true
    },
@@ -903,7 +943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -912,10 +952,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4bea999f98>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3db000278>"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -977,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {
     "hidePrompt": true
    },
@@ -991,7 +1031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -1000,10 +1040,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c13b6dac8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa4041c7550>"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1039,7 +1079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {
     "hidePrompt": true
    },
@@ -1050,7 +1090,7 @@
        "0.997596462036615"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1090,7 +1130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {
     "hideOutput": true,
     "hidePrompt": true
@@ -1102,7 +1142,7 @@
        "(52,)"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1116,7 +1156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {
     "hidePrompt": true
    },
@@ -1128,7 +1168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "metadata": {
     "hidePrompt": true
    },
@@ -1146,7 +1186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "metadata": {
     "hidePrompt": true
    },
@@ -1157,7 +1197,7 @@
        "425.0"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1170,7 +1210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "metadata": {
     "hidePrompt": true
    },
@@ -1181,7 +1221,7 @@
        "425.0"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1203,7 +1243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -1212,10 +1252,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c13bd0710>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa40643fdd8>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1266,7 +1306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 35,
    "metadata": {
     "hidePrompt": true
    },
@@ -1281,7 +1321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 36,
    "metadata": {
     "hideCode": true,
     "hidePrompt": true
@@ -1290,10 +1330,10 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4c1373e470>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa4043843c8>"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1335,7 +1375,8 @@
     "hidePrompt": true
    },
    "source": [
-    "Ci-dessus la prédiction pour l'année 2025 complète (courbe verte) et sa tendance à long terme (courbe orange)."
+    "Ci-dessus la prédiction pour l'année 2025 complète (courbe verte) et sa tendance à long terme (courbe orange).  \n",
+    "Pour assurer une bonne lisibilité du graphique, les intervalles de confiance (très étroits) ne sont pas représentés."
    ]
   },
   {