diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 332f496..2c5eae9 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -9,11 +9,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import scipy.stats as st\n"
    ]
   },
   {
@@ -30,13 +32,96 @@
     "**Hint**: function `stats.norm.interval` from `scipy` can help you get through this exercise. "
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "173.46666666666667"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
+    "\n",
+    "heights_sum = sum(heights)\n",
+    "\n",
+    "heights_sum/15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(172.14308590115726, 174.79024743217607)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
+    "\n",
+    "alpha = 0.8\n",
+    "\n",
+    "mean = heights_sum/15\n",
+    "\n",
+    "std = 4\n",
+    "\n",
+    "n = 15\n",
+    "\n",
+    "st.norm.interval(0.8, loc=mean, scale = std/np.sqrt(n))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(170.9117270472475, 176.02160628608584)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "## same using t-distribution\n",
+    "\n",
+    "heights = np.array([167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195])\n",
+    "\n",
+    "s = heights.std(ddof=1)\n",
+    "mean = heights.mean()\n",
+    "n = len(heights)\n",
+    "\n",
+    "st.t.interval(0.8, n-1, loc = mean, scale = s/np.sqrt(n))\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "##  t-interval are fatter on the tail, allow more extreme values"
    ]
   },
   {
@@ -51,11 +136,62 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Our CI with 80% level of confidence for proportion is [0.20248138545542083, 0.3118043288302934]\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "sample_size = 105\n",
+    "losses = 27\n",
+    "\n",
+    "p = losses/sample_size\n",
+    "\n",
+    "n = 105\n",
+    "\n",
+    "# 80% confidence\n",
+    "\n",
+    "z_value = st.norm.ppf( 1 - (1 - 0.8)/2)\n",
+    "\n",
+    "margin_of_error = z_value * np.sqrt((p * (1- p))/n)\n",
+    "\n",
+    "lower_bound = p - margin_of_error\n",
+    "upper_bound = p + margin_of_error\n",
+    "\n",
+    "print(f\"Our CI with 80% level of confidence for proportion is [{lower_bound}, {upper_bound}]\")\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Our CI with 80% level of confidence for proportion is [0.18698561776452813, 0.3273000965211861]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 90% confidence\n",
+    "\n",
+    "z_value = st.norm.ppf( 1 - (1 - 0.9)/2)\n",
+    "\n",
+    "margin_of_error = z_value * np.sqrt((p * (1- p))/n)\n",
+    "\n",
+    "lower_bound = p - margin_of_error\n",
+    "upper_bound = p + margin_of_error\n",
+    "\n",
+    "print(f\"Our CI with 80% level of confidence for proportion is [{lower_bound}, {upper_bound}]\")"
    ]
   },
   {
@@ -131,7 +267,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -145,7 +281,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,