From 300cb85110613b7cbdd48d67ecbfa22207c0b651 Mon Sep 17 00:00:00 2001
From: mariabourbon <mariabourbon27@hotmail.com>
Date: Thu, 16 Nov 2023 19:30:24 +0000
Subject: [PATCH 1/2] solved lab

---
 your-code/main.ipynb | 103 ++++++++++++++++++++++++++++++++++++++-----
 1 file changed, 91 insertions(+), 12 deletions(-)

diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index 332f496..e8b3338 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 pandas as pd\n",
+    "import numpy as np\n",
+    "import scipy.stats as st"
    ]
   },
   {
@@ -32,11 +35,28 @@
   },
   {
    "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 = [167, 167, 168, 168, 168, 169, 171, 172, 173, 175, 175, 175, 177, 182, 195]\n",
+    "std = 4 # of the population\n",
+    "mean = sum(heights)/len(heights)\n",
+    "mean\n",
+    "n = len(heights)\n",
+    "st.norm.interval(0.80,loc=mean,scale=std/np.sqrt(n))"
    ]
   },
   {
@@ -51,11 +71,63 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# your code here"
+    "import scipy.stats as stats\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# CONFIDENCE INTERVALS WITH PROPORTION"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(0.20248138545542083, 0.3118043288302934)\n",
+      "(0.18698561776452813, 0.3273000965211861)\n"
+     ]
+    }
+   ],
+   "source": [
+    "total = 105\n",
+    "losses = 27\n",
+    "\n",
+    "sample_proportion = losses / total\n",
+    "\n",
+    "#São os níveis que queremos usar para dizer o quanto estamos confiantes nas nossas estimativas.\n",
+    "confidence_80 = 0.80\n",
+    "confidence_90 = 0.90\n",
+    "\n",
+    "#Isso é uma estimativa de quão longe nossa estimativa (percentual de lojas com prejuízo) pode estar da verdade..\n",
+    "standard_error = np.sqrt((sample_proportion * (1 - sample_proportion)) / total)\n",
+    "\n",
+    "z_score_80 = stats.norm.ppf((1 + confidence_80) / 2)\n",
+    "z_score_90 = stats.norm.ppf((1 + confidence_90) / 2)\n",
+    "\n",
+    "#É uma quantidade que diz o quão para cima ou para baixo nossa estimativa pode variar.\n",
+    "margin_of_error_80 = z_score_80 * standard_error\n",
+    "margin_of_error_90 = z_score_90 * standard_error\n",
+    "\n",
+    "confidence_interval_80 = (sample_proportion - margin_of_error_80, sample_proportion + margin_of_error_80)\n",
+    "confidence_interval_90 = (sample_proportion - margin_of_error_90, sample_proportion + margin_of_error_90)\n",
+    "\n",
+    "\n",
+    "print(confidence_interval_80)\n",
+    "print(confidence_interval_90)"
    ]
   },
   {
@@ -76,7 +148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -94,7 +166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -121,17 +193,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "# your code here"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -145,7 +224,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.11.5"
   }
  },
  "nbformat": 4,

From d530b19ba25877b05792291b609dd85012ac9509 Mon Sep 17 00:00:00 2001
From: mariabourbon <mariabourbon27@hotmail.com>
Date: Thu, 16 Nov 2023 19:34:36 +0000
Subject: [PATCH 2/2]  solved lab

---
 your-code/main.ipynb | 5 +----
 1 file changed, 1 insertion(+), 4 deletions(-)

diff --git a/your-code/main.ipynb b/your-code/main.ipynb
index e8b3338..b86e3e0 100644
--- a/your-code/main.ipynb
+++ b/your-code/main.ipynb
@@ -90,7 +90,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -108,17 +108,14 @@
     "\n",
     "sample_proportion = losses / total\n",
     "\n",
-    "#São os níveis que queremos usar para dizer o quanto estamos confiantes nas nossas estimativas.\n",
     "confidence_80 = 0.80\n",
     "confidence_90 = 0.90\n",
     "\n",
-    "#Isso é uma estimativa de quão longe nossa estimativa (percentual de lojas com prejuízo) pode estar da verdade..\n",
     "standard_error = np.sqrt((sample_proportion * (1 - sample_proportion)) / total)\n",
     "\n",
     "z_score_80 = stats.norm.ppf((1 + confidence_80) / 2)\n",
     "z_score_90 = stats.norm.ppf((1 + confidence_90) / 2)\n",
     "\n",
-    "#É uma quantidade que diz o quão para cima ou para baixo nossa estimativa pode variar.\n",
     "margin_of_error_80 = z_score_80 * standard_error\n",
     "margin_of_error_90 = z_score_90 * standard_error\n",
     "\n",