From cae455428ecb297b8de81f5e92afc64a7f0b2330 Mon Sep 17 00:00:00 2001
From: ghv29 <vgoldie29@gmail.com>
Date: Sat, 14 Feb 2026 15:55:27 +0100
Subject: [PATCH] Lab Solved

---
 lab-intro-probability.ipynb | 176 ++++++++++++++++++++++++++++++------
 1 file changed, 150 insertions(+), 26 deletions(-)

diff --git a/lab-intro-probability.ipynb b/lab-intro-probability.ipynb
index 5893fc1..4e958c4 100644
--- a/lab-intro-probability.ipynb
+++ b/lab-intro-probability.ipynb
@@ -38,11 +38,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.8844772466215431)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "from scipy.stats import binom\n",
+    "n = 460 \n",
+    "p = 0.97\n",
+    "\n",
+    "binom_dist = binom(n,p)\n",
+    "\n",
+    "binom_dist.cdf(450)"
    ]
   },
   {
@@ -72,11 +89,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.49)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "from scipy.stats import geom\n",
+    "p = 0.3\n",
+    "geom_dist = geom(p)\n",
+    "\n",
+    "1 - geom_dist.cdf(2)\n",
+    "\n"
    ]
   },
   {
@@ -107,11 +140,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.01289822084039205\n"
+     ]
+    }
+   ],
    "source": [
-    "#code here"
+    "from scipy.stats import poisson\n",
+    "\n",
+    "mu = 500\n",
+    "poisson_dist = poisson(mu)\n",
+    "probability = 1 - poisson_dist.cdf(550)\n",
+    "print(probability)"
    ]
   },
   {
@@ -123,11 +169,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.2677043869515713\n"
+     ]
+    }
+   ],
    "source": [
-    "#code here"
+    "binom_dist = binom(24, probability)\n",
+    "probability_day = 1 - binom_dist.pmf(0)\n",
+    "print(probability_day)"
    ]
   },
   {
@@ -157,10 +213,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.3934693402873666)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from scipy.stats import expon\n",
+    "\n",
+    "mu = 10 \n",
+    "expon_dist = expon(scale = mu)\n",
+    "\n",
+    "expon_dist.cdf(5)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -173,10 +247,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.2231301601484298)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prob_break = 1 - expon_dist.cdf(15)\n",
+    "prob_break"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -196,11 +284,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.6826894921370859)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "from scipy.stats import norm \n",
+    "loc = 150\n",
+    "scale = 10\n",
+    "\n",
+    "norm_dist = norm(loc=loc, scale = scale)\n",
+    "norm_dist.cdf(160) - norm_dist.cdf(140)  \n"
    ]
   },
   {
@@ -219,17 +323,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.4511883639059735)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "scale = 50\n",
+    "expon_dist = expon(scale=scale)\n",
+    "expon_dist.cdf(30)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -243,7 +367,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,