From bf071fd178eab4db039f3aab62ca32495c53c380 Mon Sep 17 00:00:00 2001
From: dathomasss <thomasgiret9@icloud.com>
Date: Wed, 8 Jan 2025 21:44:02 +0100
Subject: [PATCH] week5 lab3 done

---
 lab-intro-probability.ipynb | 186 ++++++++++++++++++++++++++++++------
 1 file changed, 159 insertions(+), 27 deletions(-)

diff --git a/lab-intro-probability.ipynb b/lab-intro-probability.ipynb
index 5893fc1..88cca79 100644
--- a/lab-intro-probability.ipynb
+++ b/lab-intro-probability.ipynb
@@ -38,11 +38,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.8844772466215431"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "from scipy.stats import binom, norm\n",
+    "n = 460\n",
+    "p = 0.97\n",
+    "binom_ = binom(n, p)\n",
+    "proba = binom_.cdf(450)\n",
+    "\n"
    ]
   },
   {
@@ -72,11 +88,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.49"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "from scipy.stats import geom\n",
+    "\n",
+    "p = 0.3\n",
+    "geom_p = geom(p)\n",
+    "proba = 1 - (geom_p.cdf(2))\n",
+    "proba\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -107,11 +141,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.01289822084039205"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "from scipy.stats import poisson\n",
+    "#p(x > 550)\n",
+    "\n",
+    "m = 500\n",
+    "poiss = poisson(m)\n",
+    "proba = 1 - poiss.cdf(550)\n",
+    "proba"
    ]
   },
   {
@@ -123,11 +174,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 38,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "from scipy.stats import expon\n",
+    "\n",
+    "lambda_ = 500 * 24\n",
+    "poiss = poisson(lambda_)\n",
+    "proba_result = 1 - poiss.cdf(550)\n",
+    "proba_result"
    ]
   },
   {
@@ -157,10 +224,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 42,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3934693402873666"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambdda = 0.1\n",
+    "la = expon(scale= 1/lambdda)\n",
+    "result = la.cdf(5)\n",
+    "result"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -173,10 +256,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 44,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.7768698398515702"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambdda = 0.1\n",
+    "la = expon(scale= 1/lambdda)\n",
+    "result = la.cdf(15)\n",
+    "result"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -196,11 +295,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6826894921370859"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "mean = 150\n",
+    "std = 10\n",
+    "\n",
+    "norm_dist = norm(loc=mean,scale=std)\n",
+    "proba = norm_dist.cdf(160) - norm_dist.cdf(140)\n",
+    "proba"
    ]
   },
   {
@@ -219,17 +334,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4511883639059735"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "mean_during = 50\n",
+    "panne = 30\n",
+    "\n",
+    "lambda2 = 1 / mean_during\n",
+    "lambda2_inv = expon(scale= 1/lambda2)\n",
+    "lambda2_inv.cdf(30)\n",
+    "\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -243,9 +375,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.12.7"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }