diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 332f496..6936159 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -9,11 +9,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "import scipy.stats as st\n"
    ]
   },
   {
@@ -32,11 +35,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "left end:  169.66758574188816\n",
+      "right end:  177.26574759144518\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
+    "\n",
+    "s_mean = np.mean(heights)\n",
+    "s_std = np.std(heights, ddof=1)\n",
+    "n = len(heights)\n",
+    "confidence_level = 0.8\n",
+    "\n",
+    "Z_score = st.norm.ppf((1+confidence_level)/2)\n",
+    "\n",
+    "print(\"left end: \", s_mean-2*(s_std/np.sqrt(n)))\n",
+    "print(\"right end: \", s_mean+2*(s_std/np.sqrt(n)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(171.0323076132764, 175.90102572005694)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.norm.interval(0.8, loc=s_mean, scale=s_std/np.sqrt(n))"
    ]
   },
   {
@@ -51,11 +93,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "left end:  0.178506830600482\n",
+      "right end:  0.3497950561919708\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "l_confidence_level = 0.8\n",
+    "n_confidence_level = 0.9\n",
+    "\n",
+    "x = list(np.arange(106))\n",
+    "x = [True if i <=27 else False for i in x]\n",
+    "mean = np.mean(x)\n",
+    "n = len(x)\n",
+    "std = np.std(x)\n",
+    "\n",
+    "print(\"left end: \", mean-2*(std/np.sqrt(n)))\n",
+    "print(\"right end: \", mean+2*(std/np.sqrt(n)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.20927226997969411, 0.3190296168127587)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.norm.interval(0.8, loc=mean, scale=std/np.sqrt(n))"
    ]
   },
   {
@@ -131,7 +212,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -145,7 +226,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,