diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 332f496..419e0ba 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -9,11 +9,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import scipy.stats as stats\n",
+    "import statistics\n",
+    "import math"
    ]
   },
   {
@@ -32,11 +36,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "left end:  172.53715066357688\n",
+      "right end:  174.39618266975646\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'confidence interval for 80%: (172.14308590115726, 174.79024743217607)'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# data given\n",
+    "heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
+    "sigma = 4\n",
+    "n = len(heights)\n",
+    "mean = np.mean(heights)\n",
+    "z = (1-((1-0.80)/2))\n",
+    "\n",
+    "#calculating the interval\n",
+    "print(\"left end: \", mean - z* (sigma/np.sqrt(n)))\n",
+    "print(\"right end: \", mean + z* (sigma/np.sqrt(n)))\n",
+    "\n",
+    "### python way\n",
+    "\n",
+    "#converting heights into an array\n",
+    "a = np.array(heights)\n",
+    "\n",
+    "#calcularing the interval\n",
+    "f'confidence interval for 80%: {st.norm.interval(0.80, loc=mean, scale=sigma/np.sqrt(n))}'"
    ]
   },
   {
@@ -51,11 +91,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "confidence interval 80% = (0.20248138545542083, 0.3118043288302934)\n",
+      "confidence interval 90% = (0.18698561776452813, 0.3273000965211861)\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "shops = 105\n",
+    "shops_losses = 27\n",
+    "\n",
+    "#mean\n",
+    "mean = shops_with_losses / total_shops\n",
+    "\n",
+    "#calculting standard error\n",
+    "std_error = math.sqrt((sample_proportion * (1 - sample_proportion)) / total_shops)\n",
+    "\n",
+    "# prob for 80% and 90%\n",
+    "z1 = stats.norm.ppf((1 + 0.8) / 2)\n",
+    "z2 = stats.norm.ppf((1 + 0.9) / 2)\n",
+    "\n",
+    "# margin of error for confidence levels\n",
+    "margin80 = zscore_80 * std_error\n",
+    "margin90 = zscore_90 * std_error\n",
+    "\n",
+    "# confidence intervals\n",
+    "print(f'confidence interval 80% = {(mean - margin80, mean + margin80)}')\n",
+    "print(f'confidence interval 90% = {(mean - margin90, mean + margin90)}')\n",
+    "\n"
    ]
   },
   {
@@ -131,7 +200,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -145,7 +214,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,