{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Normal distribution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### In this lab we'll investigate the probability distribution that is most central to statistics: the normal distribution. If we are confident that our data are nearly normal, that opens the door to many powerful statistical methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Here we'll be working with measurements of body dimensions. This data set contains measurements from 247 men and 260 women, most of whom were considered healthy young adults." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings(\"ignore\")\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import io\n", "import requests\n", "\n", "df_url = 'https://raw.githubusercontent.com/akmand/datasets/master/openintro/bdims.csv'\n", "url_content = requests.get(df_url, verify=False).content\n", "bdims = pd.read_csv(io.StringIO(url_content.decode('utf-8')))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(507, 25)\n" ] }, { "data": { "text/html": [ "
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