From bda5028f24e8beb450d644c6146dd7d1b94c2518 Mon Sep 17 00:00:00 2001 From: Alexander Groenert Date: Tue, 30 Jul 2024 20:43:49 +0200 Subject: [PATCH] AlexanderGroenert --- your-code/main.ipynb | 1077 +++++++++++++++++++++++++++++++----------- 1 file changed, 798 insertions(+), 279 deletions(-) diff --git a/your-code/main.ipynb b/your-code/main.ipynb index b2b6f8d..4ab5abc 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -1,279 +1,798 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Before your start:\n", - "- Read the README.md file\n", - "- Comment as much as you can and use the resources (README.md file)\n", - "- Happy learning!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import numpy and pandas\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Challenge 1 - Exploring the Data\n", - "\n", - "In this challenge, we will examine all salaries of employees of the City of Chicago. We will start by loading the dataset and examining its contents." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Examine the `salaries` dataset using the `head` function below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We see from looking at the `head` function that there is quite a bit of missing data. Let's examine how much missing data is in each column. Produce this output in the cell below" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's also look at the count of hourly vs. salaried employees. Write the code in the cell below" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What this information indicates is that the table contains information about two types of employees - salaried and hourly. Some columns apply only to one type of employee while other columns only apply to another kind. This is why there are so many missing values. Therefore, we will not do anything to handle the missing values." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There are different departments in the city. List all departments and the count of employees in each department." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Challenge 2 - Hypothesis Tests\n", - "\n", - "In this section of the lab, we will test whether the hourly wage of all hourly workers is significantly different from $30/hr. Import the correct one sample test function from scipy and perform the hypothesis test for a 95% two sided confidence interval." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are also curious about salaries in the police force. The chief of police in Chicago claimed in a press briefing that salaries this year are higher than last year's mean of $86000/year a year for all salaried employees. Test this one sided hypothesis using a 95% confidence interval.\n", - "\n", - "Hint: A one tailed test has a p-value that is half of the two tailed p-value. If our hypothesis is greater than, then to reject, the test statistic must also be positive." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using the `crosstab` function, find the department that has the most hourly workers. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The workers from the department with the most hourly workers have complained that their hourly wage is less than $35/hour. Using a one sample t-test, test this one-sided hypothesis at the 95% confidence level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Challenge 3: To practice - Constructing Confidence Intervals\n", - "\n", - "While testing our hypothesis is a great way to gather empirical evidence for accepting or rejecting the hypothesis, another way to gather evidence is by creating a confidence interval. A confidence interval gives us information about the true mean of the population. So for a 95% confidence interval, we are 95% sure that the mean of the population is within the confidence interval. \n", - ").\n", - "\n", - "To read more about confidence intervals, click [here](https://en.wikipedia.org/wiki/Confidence_interval).\n", - "\n", - "\n", - "In the cell below, we will construct a 95% confidence interval for the mean hourly wage of all hourly workers. \n", - "\n", - "The confidence interval is computed in SciPy using the `t.interval` function. You can read more about this function [here](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.t.html).\n", - "\n", - "To compute the confidence interval of the hourly wage, use the 0.95 for the confidence level, number of rows - 1 for degrees of freedom, the mean of the sample for the location parameter and the standard error for the scale. The standard error can be computed using [this](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.sem.html) function in SciPy." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now construct the 95% confidence interval for all salaried employeed in the police in the cell below." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Bonus Challenge - Hypothesis Tests of Proportions\n", - "\n", - "Another type of one sample test is a hypothesis test of proportions. In this test, we examine whether the proportion of a group in our sample is significantly different than a fraction. \n", - "\n", - "You can read more about one sample proportion tests [here](http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/SAS/SAS6-CategoricalData/SAS6-CategoricalData2.html).\n", - "\n", - "In the cell below, use the `proportions_ztest` function from `statsmodels` to perform a hypothesis test that will determine whether the number of hourly workers in the City of Chicago is significantly different from 25% at the 95% confidence level." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Your code here:\n", - "\n" - ] - } - ], - "metadata": { - "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": 2 -} +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Before your start:\n", + "- Read the README.md file\n", + "- Comment as much as you can and use the resources (README.md file)\n", + "- Happy learning!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# import numpy and pandas\n", + "\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 1 - Exploring the Data\n", + "\n", + "In this challenge, we will examine all salaries of employees of the City of Chicago. We will start by loading the dataset and examining its contents." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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NameJob TitlesDepartmentFull or Part-TimeSalary or HourlyTypical HoursAnnual SalaryHourly Rate
0AARON, JEFFERY MSERGEANTPOLICEFSalaryNaN101442.0NaN
1AARON, KARINAPOLICE OFFICER (ASSIGNED AS DETECTIVE)POLICEFSalaryNaN94122.0NaN
2AARON, KIMBERLEI RCHIEF CONTRACT EXPEDITERGENERAL SERVICESFSalaryNaN101592.0NaN
3ABAD JR, VICENTE MCIVIL ENGINEER IVWATER MGMNTFSalaryNaN110064.0NaN
4ABASCAL, REECE ETRAFFIC CONTROL AIDE-HOURLYOEMCPHourly20.0NaN19.86
5ABBASI, CHRISTOPHERSTAFF ASST TO THE ALDERMANCITY COUNCILFSalaryNaN50436.0NaN
6ABBATACOLA, ROBERT JELECTRICAL MECHANICAVIATIONFHourly40.0NaN46.10
7ABBATE, JOSEPH LPOOL MOTOR TRUCK DRIVERSTREETS & SANFHourly40.0NaN35.60
8ABBATEMARCO, JAMES JFIRE ENGINEER-EMTFIREFSalaryNaN103350.0NaN
9ABBATE, TERRY MPOLICE OFFICERPOLICEFSalaryNaN93354.0NaN
\n", + "
" + ], + "text/plain": [ + " Name Job Titles \\\n", + "0 AARON, JEFFERY M SERGEANT \n", + "1 AARON, KARINA POLICE OFFICER (ASSIGNED AS DETECTIVE) \n", + "2 AARON, KIMBERLEI R CHIEF CONTRACT EXPEDITER \n", + "3 ABAD JR, VICENTE M CIVIL ENGINEER IV \n", + "4 ABASCAL, REECE E TRAFFIC CONTROL AIDE-HOURLY \n", + "5 ABBASI, CHRISTOPHER STAFF ASST TO THE ALDERMAN \n", + "6 ABBATACOLA, ROBERT J ELECTRICAL MECHANIC \n", + "7 ABBATE, JOSEPH L POOL MOTOR TRUCK DRIVER \n", + "8 ABBATEMARCO, JAMES J FIRE ENGINEER-EMT \n", + "9 ABBATE, TERRY M POLICE OFFICER \n", + "\n", + " Department Full or Part-Time Salary or Hourly Typical Hours \\\n", + "0 POLICE F Salary NaN \n", + "1 POLICE F Salary NaN \n", + "2 GENERAL SERVICES F Salary NaN \n", + "3 WATER MGMNT F Salary NaN \n", + "4 OEMC P Hourly 20.0 \n", + "5 CITY COUNCIL F Salary NaN \n", + "6 AVIATION F Hourly 40.0 \n", + "7 STREETS & SAN F Hourly 40.0 \n", + "8 FIRE F Salary NaN \n", + "9 POLICE F Salary NaN \n", + "\n", + " Annual Salary Hourly Rate \n", + "0 101442.0 NaN \n", + "1 94122.0 NaN \n", + "2 101592.0 NaN \n", + "3 110064.0 NaN \n", + "4 NaN 19.86 \n", + "5 50436.0 NaN \n", + "6 NaN 46.10 \n", + "7 NaN 35.60 \n", + "8 103350.0 NaN \n", + "9 93354.0 NaN " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Your code here:\n", + "\n", + "df = pd.read_csv(\"Current_Employee_Names__Salaries__and_Position_Titles.csv\")\n", + "\n", + "df.head(10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Examine the `salaries` dataset using the `head` function below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n", + "# Did it above muscle memory XD" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see from looking at the `head` function that there is quite a bit of missing data. Let's examine how much missing data is in each column. Produce this output in the cell below" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Name 0\n", + "Job Titles 0\n", + "Department 0\n", + "Full or Part-Time 0\n", + "Salary or Hourly 0\n", + "Typical Hours 25161\n", + "Annual Salary 8022\n", + "Hourly Rate 25161\n", + "dtype: int64" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Your code here:\n", + "\n", + "count_nan = df.isna().sum()\n", + "\n", + "display(count_nan)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also look at the count of hourly vs. salaried employees. Write the code in the cell below" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The number of employees paid by the hour is: 25161 and the number of salaried employees is: 25161\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "hourly_count = df[\"Salary or Hourly\"].value_counts().get('Hourly', 0)\n", + "\n", + "salaried_count = hourly_count = df[\"Salary or Hourly\"].value_counts().get('Salary', 0)\n", + "\n", + "print(\"The number of employees paid by the hour is:\", hourly_count, \"and the number of salaried employees is:\", salaried_count)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What this information indicates is that the table contains information about two types of employees - salaried and hourly. Some columns apply only to one type of employee while other columns only apply to another kind. This is why there are so many missing values. Therefore, we will not do anything to handle the missing values." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There are different departments in the city. List all departments and the count of employees in each department." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Department\n", + "POLICE 13414\n", + "FIRE 4641\n", + "STREETS & SAN 2198\n", + "OEMC 2102\n", + "WATER MGMNT 1879\n", + "AVIATION 1629\n", + "TRANSPORTN 1140\n", + "PUBLIC LIBRARY 1015\n", + "GENERAL SERVICES 980\n", + "FAMILY & SUPPORT 615\n", + "FINANCE 560\n", + "HEALTH 488\n", + "CITY COUNCIL 411\n", + "LAW 407\n", + "BUILDINGS 269\n", + "COMMUNITY DEVELOPMENT 207\n", + "BUSINESS AFFAIRS 171\n", + "COPA 116\n", + "BOARD OF ELECTION 107\n", + "DoIT 99\n", + "PROCUREMENT 92\n", + "INSPECTOR GEN 87\n", + "MAYOR'S OFFICE 85\n", + "CITY CLERK 84\n", + "ANIMAL CONTRL 81\n", + "HUMAN RESOURCES 79\n", + "CULTURAL AFFAIRS 65\n", + "BUDGET & MGMT 46\n", + "ADMIN HEARNG 39\n", + "DISABILITIES 28\n", + "TREASURER 22\n", + "HUMAN RELATIONS 16\n", + "BOARD OF ETHICS 8\n", + "POLICE BOARD 2\n", + "LICENSE APPL COMM 1\n", + "Name: count, dtype: int64\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "dep_counts = df['Department'].value_counts()\n", + "\n", + "print(dep_counts)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 2 - Hypothesis Tests\n", + "\n", + "In this section of the lab, we will test whether the hourly wage of all hourly workers is significantly different from $30/hr. Import the correct one sample test function from scipy and perform the hypothesis test for a 95% two sided confidence interval." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "20.6198057854942 4.3230240486229894e-92\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "from scipy import stats\n", + "\n", + "#we need to filter per hour employees only\n", + "\n", + "hourly_workers = df[df['Salary or Hourly'] == 'Hourly']\n", + "\n", + "# we should drop NaN to filter out numbers only\n", + "\n", + "hourly_wages = hourly_workers['Hourly Rate'].dropna()\n", + "\n", + "# Our HO is that the mean hourly wage of all hourly workers is equal to $30/hr.\n", + "# Our HA is that the mean hourly wage of all hourly workers is NOT equal to 30/hr.\n", + "\n", + "# Run our test\n", + "\n", + "t_statistic, p_value = stats.ttest_1samp(hourly_wages, 30)\n", + "\n", + "print(t_statistic, p_value)\n", + "\n", + "# The P value has 91 zeros in front if it (i.e. basically 0)), so we can conclude that the wage of all hourly workers is significantly different from 30$ per hour\n", + "# and also 20+ standard deviations from the mean\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are also curious about salaries in the police force. The chief of police in Chicago claimed in a press briefing that salaries this year are higher than last year's mean of $86000/year a year for all salaried employees. Test this one sided hypothesis using a 95% confidence interval.\n", + "\n", + "Hint: A one tailed test has a p-value that is half of the two tailed p-value. If our hypothesis is greater than, then to reject, the test statistic must also be positive." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.081997005712994 0.0010301701775482577\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "#Filter police values:\n", + "\n", + "police_salaries = df[(df['Department'] == 'POLICE') & (df['Salary or Hourly'] == 'Salary')]['Annual Salary'].dropna()\n", + "\n", + "# lets run our one sample test:\n", + "\n", + "t_statistic, two_tailed_p_value = stats.ttest_1samp(police_salaries, 86000)\n", + "\n", + "# we need to divide our two tailed p value by 2 because it is one tailed:\n", + "\n", + "p_value = two_tailed_p_value / 2\n", + "\n", + "print(t_statistic, p_value)\n", + "\n", + "# We are 3 standard deviations + of the mean, so the values have indeed gone up, and our p_value doesn't matter\n", + "# our p_value would only matter if the t_statistic was negative (assuming our HO is that values have gone up). Therefore we cannot reject the HO\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the `crosstab` function, find the department that has the most hourly workers. " + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Salary or HourlyHourly
Department
STREETS & SAN1862
WATER MGMNT1513
OEMC1273
AVIATION1082
GENERAL SERVICES765
TRANSPORTN725
PUBLIC LIBRARY299
FAMILY & SUPPORT287
CITY COUNCIL64
FINANCE44
LAW40
ANIMAL CONTRL19
POLICE10
MAYOR'S OFFICE8
CULTURAL AFFAIRS7
BUSINESS AFFAIRS7
HUMAN RESOURCES4
COMMUNITY DEVELOPMENT4
HEALTH3
FIRE2
PROCUREMENT2
BUDGET & MGMT2
\n", + "
" + ], + "text/plain": [ + "Salary or Hourly Hourly\n", + "Department \n", + "STREETS & SAN 1862\n", + "WATER MGMNT 1513\n", + "OEMC 1273\n", + "AVIATION 1082\n", + "GENERAL SERVICES 765\n", + "TRANSPORTN 725\n", + "PUBLIC LIBRARY 299\n", + "FAMILY & SUPPORT 287\n", + "CITY COUNCIL 64\n", + "FINANCE 44\n", + "LAW 40\n", + "ANIMAL CONTRL 19\n", + "POLICE 10\n", + "MAYOR'S OFFICE 8\n", + "CULTURAL AFFAIRS 7\n", + "BUSINESS AFFAIRS 7\n", + "HUMAN RESOURCES 4\n", + "COMMUNITY DEVELOPMENT 4\n", + "HEALTH 3\n", + "FIRE 2\n", + "PROCUREMENT 2\n", + "BUDGET & MGMT 2" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Your code here:\n", + "# so lets use crosstab on our hourly_workers df:\n", + "\n", + "crosstab = pd.crosstab(hourly_workers['Department'], hourly_workers['Salary or Hourly'])\n", + "\n", + "# display(crosstab)\n", + "\n", + "# Lets sort by the hourly value\n", + "\n", + "sorted_crosstab = crosstab.sort_values(by='Hourly', ascending=False)\n", + "\n", + "display(sorted_crosstab)\n", + "\n", + "print(\"Answer is the Streets and SAN department\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The workers from the department with the most hourly workers have complained that their hourly wage is less than $35/hour. Using a one sample t-test, test this one-sided hypothesis at the 95% confidence level." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-9.567447887848152 1.6689265282353859e-21\n" + ] + } + ], + "source": [ + "# Your code here:\n", + "\n", + "# Let;s filter by streets and san and hourly:\n", + "\n", + "department_hourly_wages = df[(df['Department'] == 'STREETS & SAN') & (df['Salary or Hourly'] == 'Hourly')]['Hourly Rate'].dropna()\n", + "\n", + "t_statistic, two_tailed_p_value = stats.ttest_1samp(department_hourly_wages, 35)\n", + "\n", + "p_value = two_tailed_p_value / 2\n", + "\n", + "print(t_statistic, p_value)\n", + "\n", + "# As the t_statistic is less than 0, negative in fact, which indicates much lower wages than 35 an hour - coupled with a very small p value\n", + "# we can conclude that the workers are justified in their assumption. The -9 standard deviations indicate very much that they are paid less\n", + "# the p_value indicates we are very sure about this" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 3: To practice - Constructing Confidence Intervals\n", + "\n", + "While testing our hypothesis is a great way to gather empirical evidence for accepting or rejecting the hypothesis, another way to gather evidence is by creating a confidence interval. A confidence interval gives us information about the true mean of the population. So for a 95% confidence interval, we are 95% sure that the mean of the population is within the confidence interval. \n", + ").\n", + "\n", + "To read more about confidence intervals, click [here](https://en.wikipedia.org/wiki/Confidence_interval).\n", + "\n", + "\n", + "In the cell below, we will construct a 95% confidence interval for the mean hourly wage of all hourly workers. \n", + "\n", + "The confidence interval is computed in SciPy using the `t.interval` function. You can read more about this function [here](https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.stats.t.html).\n", + "\n", + "To compute the confidence interval of the hourly wage, use the 0.95 for the confidence level, number of rows - 1 for degrees of freedom, the mean of the sample for the location parameter and the standard error for the scale. The standard error can be computed using [this](https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.sem.html) function in SciPy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now construct the 95% confidence interval for all salaried employeed in the police in the cell below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bonus Challenge - Hypothesis Tests of Proportions\n", + "\n", + "Another type of one sample test is a hypothesis test of proportions. In this test, we examine whether the proportion of a group in our sample is significantly different than a fraction. \n", + "\n", + "You can read more about one sample proportion tests [here](http://sphweb.bumc.bu.edu/otlt/MPH-Modules/BS/SAS/SAS6-CategoricalData/SAS6-CategoricalData2.html).\n", + "\n", + "In the cell below, use the `proportions_ztest` function from `statsmodels` to perform a hypothesis test that will determine whether the number of hourly workers in the City of Chicago is significantly different from 25% at the 95% confidence level." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Your code here:\n", + "\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python (ironhack)", + "language": "python", + "name": "ironhack" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}