So I have everything done except for Step 6. In terms of coding. Can you figure this out for me, please? I will provide everything in the screenshots. Let me know which code you need to copy paste it.

Step 5: Multiple Regression: Predicting the Total Number of Wins using Average Points Scored and Average Relative Skill Instead of presenting a simple linear regression model to the coach, you can suggest a multiple regression model with the total number of wins as the response variable and the average points scored and the average relative skill as predictor variables. This regression model will help your coach predict how many games your team might win in a regular season based on metrics like the average points scored and average relative skill. This model is more practical because you expect more than one performance metric to determine the total number of wins in a regular season. Create this multiple regression model. Make the following edits to the code block below: 1. Replace ??RESPONSE_VARIABLE?? with the variable name that is being predicted. See the table included in the Project Three instructions above. Do not enclose this variable in quotes. For example, if the variable name is var0 then replace ??RESPONSE_VARIABLE?? with vard 2. Replace ??PREDICTOR_VARIABLE_1?? with the variable name for average points scored. Hint: See the table included in the Project Three instructions above. Do not enclose this variable in quotes. For example, if the variable name is var1 then replace ??PREDICTOR_VARIABLE_1?? with var1. 2. Replace ??PREDICTOR_VARIABLE_2?? with the variable name for average relative skill. Hint: See the table included in the Project Three instructions above. Do not enclose this variable in quotes. For example, if the variable name is var2 then replace ??PREDICTOR_VARIABLE_2?? with var2. For example, if the variable names are var0 for the response variable and var1, var2 for the predictor variables, then the expression in the code block below should be: model = smf.ols(var0 ~ vari + var2, nba_wins_df).fit() After you are done with your edits, click the block of code below and hit the Run button above. In [10]: import statsmodels. formula. api as smf # Multiple Regression --- TODO: make your edits here --- model2 = smf . ols( 'total_wins ~ avg_pts + avg_elo_n', nba_wins_df). fit() print(model2 . summary () ) OLS Regression Results Dep. Variable: total_wins R- squared: 0.837 Mode 1: OLS Adj. R-squared: 0.837 Method : Least Squares F-statistic: 1580 Date Sat, 18 Jun 2022 Prob (F-statistic) : 4.41e-243 Time : 22 :51 :11 Log-Likelihood: -1904.6 No. Observations: 618 AIC: 3815. Of Residuals: 615 BIC: 829. Df Model: Covariance Type: nonrobust coef std err P>| t] [0.025 0.975] Intercept -152.5736 4.500 -33.903 0.000 -161.411 -143.736 avg_pts 0. 3497 0. 048 7.297 2.000 0.256 0.44 avg_elo_n 0. 1055 0.002 47.952 0.060 3.101 0. 110 Omnibus : 89 . 087 Durbin-Watson: 1. 203 Prob (Omnibus ) : 0.050 Jarque-Bera (JB) : 160.540 Skew: 0. 869 Prob (JB) : 1.38e-35 Kurtosis: 4.793 Cond. No. 3. 19e+04In: Step 6: Multiple Regression: Predicting the Total Number of |'t'tlins using Average Points gcfgred, 1:I9t\\.rlerage Relative Skill, Average Points Differential and Average Relative Skill I eren la The coach also wants you to consider the average points differential and average relative skill diiferential as predictor variables in the multiple regression model. Create a multiple regression model with the total number of wins as the response variable, and average points scored, average relative skill, average points diiferential and average relative skill differential as predictor van'ables. This regression model will help your coach predict how many games your team might win in a regular season based on metrics like the average score, average relative skill, average points differential and average relative skill diiferential between the team and their opponents. You are to write this code block yourself. Use Step 5 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 used in this project is called nba_wins_df. 2. The variable avg JitlS represents average points scored by each team in a regular season. 3. The variable avg_elo_n represents average relative skill of each team in a regular season. 4. The variable avg lets_differential represents average points differential between each team and theiropponents in a regular season. 5. The variable avg_elo_differential represents average relative skill differential between each team and their opponents in a regular season. 6. Print the model summary. 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. # Write your code in this code block