diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 332f496..f546f7d 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -9,11 +9,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import scipy.stats as st\n",
+    "import math "
    ]
   },
   {
@@ -32,11 +35,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confidence Interval: (171.0323076132764, 175.90102572005694)\n"
+     ]
+    }
+   ],
+   "source": [
+    "sample_mean = np.mean(heights)\n",
+    "sample_std = np.std(heights, ddof=1)  \n",
+    "confidence_level = 0.80\n",
+    "sample_size = len(heights)\n",
+    "margin_of_error = st.norm.ppf((1 + confidence_level) / 2) * (sample_std / np.sqrt(sample_size))\n",
+    "confidence_interval = (sample_mean - margin_of_error, sample_mean + margin_of_error)\n",
+    "print(\"Confidence Interval:\", confidence_interval)"
    ]
   },
   {
@@ -51,11 +77,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confidence Interval (level 0.8): (0.20248138545542083, 0.3118043288302934)\n",
+      "Confidence Interval (level 0.9): (0.18698561776452813, 0.3273000965211861)\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "total_shops = 105\n",
+    "shops_with_losses = 27\n",
+    "sample_proportion = shops_with_losses / total_shops\n",
+    "confidence_levels = [0.80, 0.90]\n",
+    "\n",
+    "for confidence_level in confidence_levels:\n",
+    "  \n",
+    "    margin_of_error = st.norm.ppf((1 + confidence_level) / 2) * np.sqrt((sample_proportion * (1 - sample_proportion)) / total_shops)\n",
+    "    confidence_interval = (sample_proportion - margin_of_error, sample_proportion + margin_of_error)\n",
+    "    print(f\"Confidence Interval (level {confidence_level}):\", confidence_interval)"
    ]
   },
   {
@@ -76,11 +120,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Required Sample Size: 425\n"
+     ]
+    }
+   ],
+   "source": [
+    "z_alpha_2 = 2.576  \n",
+    "sigma = 4         \n",
+    "error_level = 0.5  \n",
+    "required_sample_size = math.ceil((z_alpha_2 * sigma / error_level)**2)\n",
+    "print(\"Required Sample Size:\", required_sample_size)"
    ]
   },
   {
@@ -94,11 +159,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Required Sample Size: 3140\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "z_alpha_2 = 1.282  \n",
+    "error_level = 0.01  \n",
+    "n_total = 105\n",
+    "n_losses = 27\n",
+    "p = n_losses / n_total\n",
+    "q = 1 - p\n",
+    "required_sample_size = math.ceil((z_alpha_2 ** 2 * p * q) / (error_level ** 2))\n",
+    "print(\"Required Sample Size:\", required_sample_size)"
    ]
   },
   {
@@ -121,17 +201,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Confidence Interval: (6.303419026585921, 25.69658097341408)\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "mean_X = 418\n",
+    "mean_Y = 402\n",
+    "stddev_X = 26\n",
+    "stddev_Y = 22\n",
+    "sample_size_X = 40\n",
+    "sample_size_Y = 50\n",
+    "confidence_level = 0.94\n",
+    "SE = math.sqrt((stddev_X**2 / sample_size_X) + (stddev_Y**2 / sample_size_Y))\n",
+    "z_score = abs(st.norm.ppf((1 + confidence_level) / 2))\n",
+    "margin_of_error = z_score * SE\n",
+    "confidence_interval = (mean_X - mean_Y - margin_of_error, mean_X - mean_Y + margin_of_error)\n",
+    "print(\"Confidence Interval:\", confidence_interval)"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -145,7 +244,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,