Project Two: Hypothesis Testing

This notebook contains the step-by-step directions for Project Two. It is very important to run through the steps in order. Some steps depend on the outputs of earlier steps. Once you have completed the steps in this notebook, be sure to write your summary report.

You are a data analyst for a basketball team and have access to a large set of historical data that you can use to analyze performance patterns. The coach of the team and your management have requested that you perform several hypothesis tests to statistically validate claims about your team's performance. This analysis will provide evidence for these claims and help make key decisions to improve the performance of the team. You will use the Python programming language to perform the statistical analyses and then prepare a report of your findings for the team’s management. Since the managers are not data analysts, you will need to interpret your findings and describe their practical implications.

There are four important variables in the data set that you will study in Project Two.

The ELO rating, represented by the variableelo_n, is used as a measure of the relative skill of a team. This measure is inferred based on the final score of a game, the game location, and the outcome of the game relative to the probability of that outcome. The higher the number, the higher the relative skill of a team.

In addition to studying data on your own team, your management has also assigned you a second team so that you can compare its performance with your own team's.

Reminder: It may be beneficial to review the summary report template for Project Two prior to starting this Python script. That will give you an idea of the questions you will need to answer with the outputs of this script.

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Step 1: Data Preparation & the Assigned Team

This step uploads the data set from a CSV file. It also selects the Assigned Team for this analysis. Do not make any changes to the code block below.

1. TheAssigned TeamisChicago Bullsfrom the years1996 - 1998
   

Click the block of code below and hit theRunbutton above.

import numpy as np

import pandas as pd

import scipy.stats as st

import matplotlib.pyplot as plt

from IPython.display import display, HTML

​

nba_orig_df = pd.read_csv('nbaallelo.csv')

nba_orig_df = nba_orig_df[(nba_orig_df['lg_id']=='NBA') & (nba_orig_df['is_playoffs']==0)]

columns_to_keep = ['game_id','year_id','fran_id','pts','opp_pts','elo_n','opp_elo_n', 'game_location', 'game_result']

nba_orig_df = nba_orig_df[columns_to_keep]

​

# The dataframe for the assigned team is called assigned_team_df.

# The assigned team is the Bulls from 1996-1998.

assigned_years_league_df = nba_orig_df[(nba_orig_df['year_id'].between(1996, 1998))]

assigned_team_df = assigned_years_league_df[(assigned_years_league_df['fran_id']=='Bulls')]

assigned_team_df = assigned_team_df.reset_index(drop=True)

​

display(HTML(assigned_team_df.head().to_html()))

print("printed only the first five observations...")

print("Number of rows in the dataset =", len(assigned_team_df))

printed only the first five observations... Number of rows in the dataset = 246

Step 2: Pick Your Team

In this step, you will pick your team. The range of years that you will study for your team is2013-2015. Make the following edits to the code block below:

1. Replace??TEAM??with your choice of team from one of the following team names.
   
   Bucks, Bulls, Cavaliers, Celtics, Clippers, Grizzlies, Hawks, Heat, Jazz, Kings, Knicks, Lakers, Magic, Mavericks, Nets, Nuggets, Pacers, Pelicans, Pistons, Raptors, Rockets, Sixers, Spurs, Suns, Thunder, Timberwolves, Trailblazers, Warriors, Wizards
   
   Remember to enter the team name within single quotes. For example, if you picked the Suns, then ??TEAM?? should be replaced with 'Suns'.

After you are done with your edits, click the block of code below and hit theRunbutton above.

# Range of years: 2013-2015 (Note: The line below selects all teams within the three-year period 2013-2015. This is not your team's dataframe.

your_years_leagues_df = nba_orig_df[(nba_orig_df['year_id'].between(2013, 2015))]

​

# The dataframe for your team is called your_team_df.

# ---- TODO: make your edits here ----

your_team_df = your_years_leagues_df[(your_years_leagues_df['fran_id']=='Hawks')]

your_team_df = your_team_df.reset_index(drop=True)

​

display(HTML(your_team_df.head().to_html()))

print("printed only the first five observations...")

print("Number of rows in the dataset =", len(your_team_df))

printed only the first five observations... Number of rows in the dataset = 246

Step 3: Hypothesis Test for the Population Mean (I)

A relative skill level of 1342 represents a critically low skill level in the league. The management of your team has hypothesized that the average relative skill level of your team in the years 2013-2015 is greater than 1342. Test this claim using a 5% level of significance. For this test, assume that the population standard deviation for relative skill level is unknown. Make the following edits to the code block below:

1. Replace??DATAFRAME_YOUR_TEAM??with the name of your team's dataframe.See Step 2 for the name of your team's dataframe.

2. Replace??RELATIVE_SKILL??with the name of the variable for relative skill.See the table included in the Project Two instructions above to pick the variable name. Enclose this variable in single quotes. For example, if the variable name isvar2then replace ??RELATIVE_SKILL?? with 'var2'.

3. Replace??NULL_HYPOTHESIS_VALUE??with the mean value of the relative skill under the null hypothesis.

After you are done with your edits, click the block of code below and hit theRunbutton above.

import scipy.stats as st

​

# Mean relative skill level of your team

mean_elo_your_team = your_team_df['elo_n'].mean()

print("Mean Relative Skill of your team in the years 2013 to 2015 =", round(mean_elo_your_team,2))

​

​

# Hypothesis Test

# ---- TODO: make your edits here ----

test_statistic, p_value = st.ttest_1samp(your_team_df['pts'], 115)

​

print("Hypothesis Test for the Population Mean")

print("Test Statistic =", round(test_statistic,2))

print("P-value =", round(p_value,4))

Mean Relative Skill of your team in the years 2013 to 2015 = 1539.22 Hypothesis Test for the Population Mean Test Statistic = -19.62 P-value = 0.0

Step 4: Hypothesis Test for the Population Mean (II)

A team averaging 110 points is likely to do very well during the regular season. The coach of your team has hypothesized that your team scored at an average of less than 110 points in the years 2013-2015. Test this claim at a 1% level of significance. For this test, assume that the population standard deviation for relative skill level is unknown.

You are to write this code block yourself.

Use Step 3 to help you write this code block. Here is some information that will help you write this code block. Reach out to your instructor if you need help.

1. The dataframe for your team is called your_team_df.


2. The variable 'pts' represents the points scored by your team.


3. Calculate and print the mean points scored by your team during the years you picked.


4. Identify the mean score under the null hypothesis. You only have to identify this value and do not have to print it.(Hint: this is given in the problem statement)


5. Assuming that the population standard deviation is unknown, use Python methods to carry out the hypothesis test.


6. Calculate and print the test statistic rounded to two decimal places.


7. Calculate and print the P-value rounded to four decimal places.

Write your code in the code block section below. After you are done, click this block of code and hit theRunbutton above. Reach out to your instructor if you need more help with this step.

import scipy.stats as st

your_team_df = ('Hawks')

​

#Average points of team

mean_pts_your_team = your_team_df['pts'].mean()

print("Average Points of your team in the years 2013 to 2015", round(mean_pts_your_team,2))

​

#Hypothesis Test

test_statistic, p_value = st.ttest_1samp(your_team_df['pts'])

print("Hypothesis test for population mean")

print("Test Statistic =", round(test_statistic,2))

print("P-value =", round(p_value,4))

​

--------------------------------------------------------------------------- TypeError                                 Traceback (most recent call last)  in        3        4 #Average points of team ----> 5 mean_pts_your_team = your_team_df['pts'].mean()       6 print("Average Points of your team in the years 2013 to 2015", round(mean_pts_your_team,2))       7   TypeError: string indices must be integers

Step 5: Hypothesis Test for the Population Proportion

Suppose the management claims that the proportion of games that your team wins when scoring 80 or more points is 0.50. Test this claim using a 5% level of significance. Make the following edits to the code block below:

1. Replace??COUNT_VAR??with the variable name that represents the number of games won when your team scores over 80 points.(Hint: this variable is in the code block below).

2. Replace??NOBS_VAR??with the variable name that represents the total number of games when your team scores over 80 points.(Hint: this variable is in the code block below).

3. Replace??NULL_HYPOTHESIS_VALUE??with the proportion under the null hypothesis.

After you are done with your edits, click the block of code below and hit theRunbutton above.

from statsmodels.stats.proportion import proportions_ztest

​

your_team_gt_80_df = your_team_df[(your_team_df['pts'] > 80)]

​

# Number of games won when your team scores over 80 points

counts = (your_team_gt_80_df['game_result'] == 'W').sum()

​

# Total number of games when your team scores over 80 points

nobs = len(your_team_gt_80_df['game_result'])

​

p = counts*1.0/nobs

print("Proportion of games won by your team when scoring more than 80 points in the years 2013 to 2015 =", round(p,4))

​

​

# Hypothesis Test

# ---- TODO: make your edits here ----

test_statistic, p_value = proportions_ztest('counts', 'nobs', counts*1.0/nobs)

​

print("Hypothesis Test for the Population Proportion")

print("Test Statistic =", round(test_statistic,2))

print("P-value =", round(p_value,4))

--------------------------------------------------------------------------- TypeError                                 Traceback (most recent call last)  in        1 from statsmodels.stats.proportion import proportions_ztest       2  ----> 3 your_team_gt_80_df = your_team_df[(your_team_df['pts'] > 80)]       4        5 # Number of games won when your team scores over 80 points  TypeError: string indices must be integers

Step 6: Hypothesis Test for the Difference Between Two Population Means

The management of your team wants to compare the team with the assigned team (the Bulls in 1996-1998). They claim that the skill level of your team in 2013-2015 is the same as the skill level of the Bulls in 1996 to 1998. In other words, the mean relative skill level of your team in 2013 to 2015 is the same as the mean relative skill level of the Bulls in 1996-1998. Test this claim using a 1% level of significance. Assume that the population standard deviation is unknown. Make the following edits to the code block below:

1. Replace??DATAFRAME_ASSIGNED_TEAM??with the name of assigned team's dataframe.See Step 1 for the name of assigned team's dataframe.

2. Replace??DATAFRAME_YOUR_TEAM??with the name of your team's dataframe.See Step 2 for the name of your team's dataframe.

3. Replace??RELATIVE_SKILL??with the name of the variable for relative skill.See the table included in Project Two instructions above to pick the variable name. Enclose this variable in single quotes. For example, if the variable name isvar2then replace ??RELATIVE_SKILL?? with 'var2'.

After you are done with your edits, click the block of code below and hit theRunbutton above.

import scipy.stats as st

​

mean_elo_n_project_team = assigned_team_df['elo_n'].mean()

print("Mean Relative Skill of the assigned team in the years 1996 to 1998 =", round(mean_elo_n_project_team,2))

​

mean_elo_n_your_team = your_team_df['elo_n'].mean()

print("Mean Relative Skill of your team in the years 2013 to 2015  =", round(mean_elo_n_your_team,2))

​

​

# Hypothesis Test

# ---- TODO: make your edits here ----

test_statistic, p_value = st.ttest_ind(??DATAFRAME_ASSIGNED_TEAM??[??RELATIVE_SKILL??], ??DATAFRAME_YOUR_TEAM??[??RELATIVE_SKILL??])

​

print("Hypothesis Test for the Difference Between Two Population Means")

print("Test Statistic =", round(test_statistic,2))

print("P-value =", round(p_value,4))

End of Project Two¶

Download the HTML output and submit it with your summary report for Project Two. The HTML output can be downloaded by clickingFile, thenDownload as, thenHTML. Do not include the Python code within your summary report.