From 8a23f4e015ebb70858638eb81c580bca2e491382 Mon Sep 17 00:00:00 2001
From: AnaCarvalho84 <131803922+AnaCarvalho84@users.noreply.github.com>
Date: Fri, 11 Aug 2023 22:35:28 +0100
Subject: [PATCH] Lab done

---
 your-code/main.ipynb | 104 ++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 94 insertions(+), 10 deletions(-)

diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 332f496..d2fce6c 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -9,11 +9,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "import scipy.stats as st\n",
+    "import numpy as np\n"
    ]
   },
   {
@@ -32,11 +34,55 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(172.14308590115726, 174.79024743217607)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "# your code here"
+    "heights = np.array([167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195])\n",
+    "\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": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(170.9117270472475, 176.02160628608584)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#This is using t distribution, we dont have more than 30 observations\n",
+    "\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))\n"
    ]
   },
   {
@@ -51,11 +97,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Our CI for porpotion is [0.20248138545542083,0.3118043288302934]\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "losses= 27\n",
+    "p=losses/n\n",
+    "n=105\n",
+    "#80% confidence\n",
+    "z_value = st.norm.ppf(1-(1-0.80)/2)\n",
+    "se = np.sqrt((p * (1-p))/n)\n",
+    "margin_of_error = z_value * se\n",
+    "lower_bound = p- margin_of_error\n",
+    "upper_bound = p+ margin_of_error\n",
+    "print(f\"Our CI for porpotion is [{lower_bound},{upper_bound}]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.20248138545542083, 0.3118043288302934)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#with st.norm - we dont need here the z value \n",
+    "st.norm.interval(0.80, loc = p, scale = se)"
    ]
   },
   {
@@ -131,7 +215,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -145,7 +229,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.10.9"
   }
  },
  "nbformat": 4,