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.The dataframe for your team is called your_team_df.The variable 'pts' represents the points scored by your team.Calculate and print the mean points scored by your team during the years you picked.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)Assuming that the population standard deviation is unknown, use Python methods to carry out the hypothesis test.Calculate and print the test statistic rounded to two decimal places.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 .

Step 4. nypolliesis iest 101 Wie PopulduoI 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 the Run button above. Reach out to your instructor if you need more help with this step. In [ ]: M # Write your code in this code block section #The dataframe for my team 'Celtics' your_team_of your_team_df = your_years_leagues_df [ (your_years_leagues_of [' fran_id your_team_df = your_team_df . reset_index(drop=True) 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 the Run button above. In [ ]: ) 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 . 0obs print("Proportion of games won by your team when scoring more than 80 # Hypothesis Test # TODO: make your edits hereMarkdown 2 20121 1030WAS 2013 Celtics 89 86 15/1.9491 1435.2537 3 201211070BOS 2013 Celtics 100 94 1574.5995 1432.6027 H 4 201211090BOS 2013 Celtics 100 106 1562.3982 1541.7600 H printed only the first five observations. . . Number of rows in the dataset = 245 Step 3: Hypothesis Test for the Population Mean (1) 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 is var2 then 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 the Run button above. In [6]: 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 =", # Hypothesis Test # - - -- TODO: make your edits here - --- test_statistic, p_value = st. ttest_1samp(your_team_df ['elo_n' ], 1342) 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 = 1456.78 Hypothesis Test for the Population Mean Test Statistic = 27.57 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 hunothesis You only have to identify this value and doalldlysis. UU IIUL Iliake ally Cilallyes w wit cue UIUCK WEIUW. 1. The Assigned Team is Chicago Bulls from the years 1996 - 1998 Click the block of code below and hit the Run button above. In [3]: N 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_c columns_to_keep = ['game_id' , 'year_id' , 'fran_id' , 'pts' , 'opp_pts' , 'eld nba_orig_df = nba_orig_df [columns_to_keep] # The dataframe for the assigned team is called assigned team_of. # The assigned team is the Bulls from 1996-1998. assigned_years_league_df = nba_orig_df[ (nba_orig_df [ 'year_id' ]. betwee assigned_team_df = assigned_years_league_df [ (assigned_years_league_df 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_of ) ) game_id year_id fran_id pts opp_pts elo_n opp_elo_n game_location game_re 19951 1030CHI 1996 Bulls 105 1598.2924 1531.7449 H 1 19951 1040CHI 1996 Bulls 107 85 1604.3940 1458.6415 19951 1070CHI 1996 Bulls 117 8 1605.7983 1310.9349 3 19951 1090CLE 1996 Bulls 106 88 1618.8701 1452.8268 4 199511110CHI 1996 Bulls 110 106 1621.1591 1490.2861 H 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 is 2013- 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 the Run button above. In [4]: M # Range of years: 2013-2015 (Note: The line below selects all teams your_years_leagues_df = nba_orig_df[ (nba_orig_df [ 'year_id' ] . between(2 # 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_of [' fran_id 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)) game_id year_id fran_id pts opp_pts elo_n opp_elo_n game_location game_r 201210300MIA 2013 Celtics 107 120 1586.1121 1666.3193Project 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. Variable What does it represent? pts Points scored by the team in a game elo_n A measure of relative skill level of the team in the league year_id Year when the team played the games fran_id Name of the NBA team The ELO rating, represented by the variable elo_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. Team What does it represent Your Team This is the team that has hired you as an analyst. This is the team that you will pick below. See Step 2. Assigned This is the team that the management has assigned to you to compare against your team. Team See Step 1. 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. 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. The Assigned Team is Chicago Bulls from the years 1996 - 1998 Click the block of code below and hit the Run button above. In [3]: N import numpy as np import pandas as pd import scipy . stats as st import matplotlib . pyplot as plt from IPython . display import display, HTML nha orig if = nd read rew/ 'nhaallalo rev! )