From 3df2065c00508eb8a4ed6669826874f8142987a5 Mon Sep 17 00:00:00 2001
From: Marisan13 <96127669+Marisan13@users.noreply.github.com>
Date: Mon, 20 Nov 2023 09:14:34 +0000
Subject: [PATCH] Lab done

---
 your-code/main.ipynb | 80 +++++++++++++++++++++++++++++++++++++-------
 1 file changed, 67 insertions(+), 13 deletions(-)

diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 332f496..2f9e0e4 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"
    ]
   },
   {
@@ -32,11 +34,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(172.14308590115726, 174.79024743217607)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
+    "\n",
+    "n = len(heights)\n",
+    "mean = np.mean(heights)\n",
+    "std = 4\n",
+    "\n",
+    "st.norm.interval(0.8, loc=mean, scale = std /np.sqrt(n))"
    ]
   },
   {
@@ -51,11 +70,46 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.20248138545542083, 0.3118043288302934)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "from statsmodels.stats.proportion import proportion_confint\n",
+    "\n",
+    "# 80% \n",
+    "proportion_confint(count=27, nobs=105, alpha=0.2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.1869856177645281, 0.32730009652118613)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# 90% \n",
+    "proportion_confint(count=27, nobs=105, alpha=0.1)"
    ]
   },
   {
@@ -76,7 +130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,7 +175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +185,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -145,7 +199,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,