diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 332f496..a1925e3 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"
+    "# Libraries\n",
+    "import scipy.stats as st\n",
+    "import numpy as np\n",
+    "from math import nan\n"
    ]
   },
   {
@@ -32,11 +35,54 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(172.14308590115726, 174.79024743217607)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "heights = np.array([167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195])\n",
+    "alpha = 0.80\n",
+    "std = 4\n",
+    "mean= np.mean(heights)\n",
+    "n= len(heights)\n",
+    "\n",
+    "st.norm.interval(0.80, loc =mean, scale = std/np.sqrt(n))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(170.9117270472475, 176.02160628608584)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# USING T-DIST\n",
+    "s= heights.std(ddof=1)\n",
+    "mean= heights.mean()\n",
+    "n=len(heights)\n",
+    "\n",
+    "st.t.interval(0.80, n-1, loc=mean, scale= s/np.sqrt(n))"
    ]
   },
   {
@@ -51,11 +97,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Our CI for porpotion is [0.20248138545542083,0.3118043288302934]\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "sample_size= 105 \n",
+    "losses= 27\n",
+    "n=105\n",
+    "p=losses/sample_size\n",
+    "#80% confidence\n",
+    "z_value = st.norm.ppf(1-(1-0.80)/2)\n",
+    "se = np.sqrt((p*(1-p))/n)  #standard error\n",
+    "margin_of_error = z_value * se\n",
+    "lower_bound = p- margin_of_error\n",
+    "upper_bound = p+ margin_of_error\n",
+    "\n",
+    "print(f\"Our CI for porpotion is [{lower_bound},{upper_bound}]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.20248138545542083, 0.3118043288302934)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "st.norm.interval(0.80, loc=p, scale=np.sqrt ((p*(1-p))/n))"
    ]
   },
   {
@@ -76,11 +162,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "# your code here\n"
    ]
   },
   {
@@ -94,7 +180,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,