game_id year_id fran_id pts opp_pts elo_n opp_elo_n game_location game_result 0 201210300MIA 2013 Heat 120 107 1666.3193 1586.1121 H W 1 201211020NYK 2013 Heat 84 104 1647.6675 1548.2699 A L 2 201211030MIA 2013 Heat 119 116 1650.0934 1554.4674 H W 3 201211050MIA 2013 Heat 124 99 1656.5652 1504.0280 H W 4 201211070MIA 2013 Heat 103 73 1659.7239 1361.5804 H W printed only the first five observations. .. Number of rows in the dataset = 246 ## Step 3: Hypothesis Test for the Population Mean (1) A relative skill level of 1340 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 1340. 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 ??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 [ ]: 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[elo_], ??NULL_HYPOTHESIS_VALUE??) print ("Hypothesis Test for the Population Mean") print ("Test Statistic =", round(test_statistic, 2) ) print ("P-value =", round(p_value,4) )