{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "tags": [ "remove-input" ] }, "outputs": [], "source": [ "from datascience import *\n", "path_data = '../../../assets/data/'\n", "import matplotlib\n", "matplotlib.use('Agg')\n", "%matplotlib inline\n", "import matplotlib.pyplot as plots\n", "plots.style.use('fivethirtyeight')\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Sampling from a Population\n", "\n", "The law of averages also holds when the random sample is drawn from individuals in a large population.\n", "\n", "As an example, we will study a population of flight delay times. The table `united` contains data for United Airlines domestic flights departing from San Francisco in the summer of 2015. The data are made publicly available by the [Bureau of Transportation Statistics](http://www.transtats.bts.gov/Fields.asp?Table_ID=293) in the United States Department of Transportation.\n", "\n", "There are 13,825 rows, each corresponding to a flight. The columns are the date of the flight, the flight number, the destination airport code, and the departure delay time in minutes. Some delay times are negative: those flights left early." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Date | Flight Number | Destination | Delay | \n", "
---|---|---|---|
6/1/15 | 73 | HNL | 257 | \n", "
6/1/15 | 217 | EWR | 28 | \n", "
6/1/15 | 237 | STL | -3 | \n", "
6/1/15 | 250 | SAN | 0 | \n", "
6/1/15 | 267 | PHL | 64 | \n", "
6/1/15 | 273 | SEA | -6 | \n", "
6/1/15 | 278 | SEA | -8 | \n", "
6/1/15 | 292 | EWR | 12 | \n", "
6/1/15 | 300 | HNL | 20 | \n", "
6/1/15 | 317 | IND | -10 | \n", "
... (13815 rows omitted)
" ], "text/plain": [ "Date | Flight Number | Destination | Delay\n", "6/1/15 | 73 | HNL | 257\n", "6/1/15 | 217 | EWR | 28\n", "6/1/15 | 237 | STL | -3\n", "6/1/15 | 250 | SAN | 0\n", "6/1/15 | 267 | PHL | 64\n", "6/1/15 | 273 | SEA | -6\n", "6/1/15 | 278 | SEA | -8\n", "6/1/15 | 292 | EWR | 12\n", "6/1/15 | 300 | HNL | 20\n", "6/1/15 | 317 | IND | -10\n", "... (13815 rows omitted)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "united = Table.read_table(path_data + 'united_summer2015.csv')\n", "united" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One flight departed 16 minutes early, and one was 580 minutes late. The other delay times were almost all between -10 minutes and 200 minutes, as the histogram below shows." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-16" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "united.column('Delay').min()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "580" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "united.column('Delay').max()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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