{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from datascience import *\n", "import numpy as np\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plots\n", "plots.style.use('fivethirtyeight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lecture 18 ##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Student's lament ##" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Section Midterm
1 22
2 12
2 23
2 14
1 20
3 25
4 19
1 24
5 8
6 14
\n", "

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" ], "text/plain": [ "Section | Midterm\n", "1 | 22\n", "2 | 12\n", "2 | 23\n", "2 | 14\n", "1 | 20\n", "3 | 25\n", "4 | 19\n", "1 | 24\n", "5 | 8\n", "6 | 14\n", "... (349 rows omitted)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores = Table.read_table('scores_by_section.csv')\n", "scores" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Section count
1 32
2 32
3 27
4 30
5 33
6 32
7 24
8 29
9 30
10 34
\n", "

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" ], "text/plain": [ "Section | count\n", "1 | 32\n", "2 | 32\n", "3 | 27\n", "4 | 30\n", "5 | 33\n", "6 | 32\n", "7 | 24\n", "8 | 29\n", "9 | 30\n", "10 | 34\n", "... (2 rows omitted)" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores.group('Section')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Section Midterm average
1 15.5938
2 15.125
3 13.6667
4 14.7667
5 17.4545
6 15.0312
7 16.625
8 16.3103
9 14.5667
10 15.2353
11 15.8077
12 15.7333
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "scores.group('Section', np.average).show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Section Midterm
9 18
1 18
4 19
4 21
4 13
5 15
10 20
5 16
5 11
9 19
\n", "

... (17 rows omitted)

" ], "text/plain": [ "Section | Midterm\n", "9 | 18\n", "1 | 18\n", "4 | 19\n", "4 | 21\n", "4 | 13\n", "5 | 15\n", "10 | 20\n", "5 | 16\n", "5 | 11\n", "9 | 19\n", "... (17 rows omitted)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "random_sample = scores.sample(27, with_replacement = False)\n", "random_sample" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16.185185185185187" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.average(random_sample.column('Midterm'))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13.37037037037037" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "random_sample = scores.sample(27, with_replacement = False)\n", "np.average(random_sample.column('Midterm'))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "averages = make_array()\n", "\n", "for i in np.arange(50000):\n", " random_sample = scores.sample(27, with_replacement = False)\n", " new_average = np.average(random_sample.column('Midterm'))\n", " averages = np.append(averages, new_average) " ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "observed_average = 13.6667" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Table().with_column('Random Sample Average', averages).hist(bins = 25)\n", "plots.scatter(observed_average, 0, color = 'red', s=40);" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "#################" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.0594" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.count_nonzero(averages <= observed_average) / 50000" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.05234" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.count_nonzero(averages <= 13.60) / 50000" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "Table().with_column('Random Sample Average', averages).hist(bins = 25)\n", "plots.scatter(observed_average, 0, color='red', s=30)\n", "plots.plot([13.6, 13.6], [0, 0.35], color='gold', lw=2);" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "anaconda-cloud": {}, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 1 }