From 94930a44d0cb73670ccdba78609989e72feedec2 Mon Sep 17 00:00:00 2001
From: PedroFariaBlanc <82642165+PedroFariaBlanc@users.noreply.github.com>
Date: Wed, 17 May 2023 11:36:46 +0100
Subject: [PATCH] [lab-confidence-intervals] Pedro Faria Blanc

---
 your-code/main.ipynb | 121 ++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 109 insertions(+), 12 deletions(-)

diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 332f496..72d47f1 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -9,14 +10,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Libraries"
+    "# Libraries\n",
+    "\n",
+    "import numpy as np\n",
+    "import scipy.stats as st"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -32,14 +37,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "172.59744399428203\n",
+      "174.3358893390513\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "# confidence level of 80%\n",
+    "confidence_level = 0.80\n",
+    "\n",
+    "z_score = st.norm.ppf(confidence_level)\n",
+    "\n",
+    "heights = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
+    "\n",
+    "mean = np.mean(heights)\n",
+    "\n",
+    "# Assuming that the standard deviation of the sizes in the population is 4\n",
+    "std = 4\n",
+    "n = len(heights)\n",
+    "\n",
+    "left = mean - z_score * (std / np.sqrt(n))\n",
+    "right = mean + z_score * (std / np.sqrt(n))\n",
+    "\n",
+    "print(left)\n",
+    "print(right)"
    ]
   },
   {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -51,14 +92,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.32793330928514125\n",
+      "0.18635240500057298\n"
+     ]
+    }
+   ],
    "source": [
-    "# your code here"
+    "# your code here\n",
+    "\n",
+    "# confidence level of 80%\n",
+    "confidence_level = 0.80\n",
+    "\n",
+    "# In a sample of 105 shops with 27 losses \n",
+    "shops = 105\n",
+    "shops_losses = 27\n",
+    "sample_proportion = shops_losses / shops\n",
+    "\n",
+    "z_score = st.norm.ppf(confidence_level)\n",
+    "\n",
+    "error_margin = z_score* np.sqrt((sample_proportion * (1 - sample_proportion)) / 27)\n",
+    "\n",
+    "positive = sample_proportion + error_margin\n",
+    "negative = sample_proportion - error_margin\n",
+    "\n",
+    "print(positive)\n",
+    "print(negative)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.07983770947987007, 0.4344480048058441)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# for the 90% confidence level\n",
+    "confidence_level = 0.90\n",
+    "\n",
+    "z_score = st.norm.ppf(confidence_level)\n",
+    "\n",
+    "error_margin1 = z_score * np.sqrt((sample_proportion * (1 - sample_proportion)) / 27)\n",
+    "\n",
+    "ci = st.norm.interval(0.90, loc = sample_proportion, scale = error_margin1)\n",
+    "\n",
+    "print(ci)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -76,7 +171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,6 +179,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -94,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,6 +198,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -121,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +242,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.3"
   }
  },
  "nbformat": 4,