diff --git a/lab-intro-probability.ipynb b/lab-intro-probability.ipynb
index 5893fc1..98dcbc1 100644
--- a/lab-intro-probability.ipynb
+++ b/lab-intro-probability.ipynb
@@ -16,6 +16,21 @@
     "Welcome to this Intro to Probability lab, where we explore decision-making scenarios through the lens of probability and strategic analysis. In the business world, making informed decisions is crucial, especially when faced with uncertainties. This lab focuses on scenarios where probabilistic outcomes play a significant role in shaping strategies and outcomes. Students will engage in exercises that require assessing and choosing optimal paths based on data-driven insights. The goal is to enhance your skills by applying probability concepts to solve real-world problems."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt    \n",
+    "import numpy as np\n",
+    "import scipy.stats as stats \n",
+    "from scipy.stats import expon\n",
+    "from scipy.stats import poisson\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -38,11 +53,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(88.44772466215431)"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "n = 460\n",
+    "p = 0.97\n",
+    "\n",
+    "prob = stats.binom.cdf(450, n, p) * 100 \n",
+    "prob\n"
    ]
   },
   {
@@ -72,11 +102,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "48.99999999999999"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "p = 0.3\n",
+    "\n",
+    "prob = ((1-p)**2) * 100\n",
+    "prob "
    ]
   },
   {
@@ -107,11 +151,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1.289822084039205)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "lam = 500\n",
+    "\n",
+    "prob = (1 - poisson.cdf(550, lam)) * 100\n",
+    "\n",
+    "prob "
    ]
   },
   {
@@ -123,11 +182,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(26.77043869515715)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "p_hour = 1 - poisson.cdf(550, 500)\n",
+    "\n",
+    "p_day = 1 - (1 - p_hour)**24\n",
+    "\n",
+    "p_day * 100\n"
    ]
   },
   {
@@ -157,10 +231,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(39.346934028736655)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lam = 1/10\n",
+    "\n",
+    "prob = (expon.cdf(5, scale=1/lam)) * 100\n",
+    "prob\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -173,10 +263,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(22.31301601484298)"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "prob = (1 - expon.cdf(15, scale=1/lam)) * 100\n",
+    "prob\n"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -196,11 +300,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(68.26894921370858)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "from scipy.stats import norm\n",
+    "med_weight = 150\n",
+    "\n",
+    "std = 10\n",
+    "\n",
+    "\n",
+    "prob = (norm.cdf(160, med_weight, std) - norm.cdf(140, med_weight, std)) * 100\n",
+    "prob"
    ]
   },
   {
@@ -219,17 +341,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(45.11883639059736)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "#code here"
+    "lam = 1/50\n",
+    "\n",
+    "prob = (expon.cdf(30, scale=1/lam)) * 100\n",
+    "prob\n"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "base",
    "language": "python",
    "name": "python3"
   },
@@ -243,7 +379,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.9"
+   "version": "3.13.9"
   }
  },
  "nbformat": 4,