See Step 6 in the Python script to answer the following questions: ln general, how is a multiple linear regression model used to predict the response variable using predictor variables? Whatis the equation for your model? What are the results of the overall Ftest? Summarize all important steps of this hypothesis test. This includes: a. Null Hypothesis (statistical notation and its description in words) b. Alternative Hypothesis (statistical notation and its description in words) c. Level ofSignificance d. Report the test statistic and the Pvalue in a formatted table as shown below: Table 3: Hypothesis Test for Overall FTest Statistic Value Test Statistic XXX *Round off to 2 decimal places. Pvalue X.XXXX *Round off to 4 decimal places. e. Conclusion of the hypothesis test and its interpretation based on the Pvalue Based on the results of the overall Ftest, is at least one of the predictors statistically significant in predicting the number of wins in the season? What are the results of individual ttests for the parameters of each predictor variable? is each of the predictor variables statistically significant based on its Pvalue? Use a 1% level of significance. Report and interpret the coefficient of determination. Whatis the predicted total number of wins in a regular season for a team that is averaging 75 points per game with a relative skill level of 1350, average point differential of 5 and average relative skill differential of 30? What is the predicted total number of wins in a regular season for a team that is averaging 100 points per game with a relative skill level of1600, average point differential of +5 and average relative skill differential of +95? In [10]: # Write your code in this code block section import statsmodels . formula . api as smf model3 = smf . ols( 'total_wins ~ avg_pts + avg_elo_n + avg_pts_differential + avg_elo_differential', nba. print (model3 . summary ( ) ) OLS Regression Results Dep. Variable: total_wins R - squared : 0. 878 Model: OLS Adj . R-squared: 9. 877 Method : Least Squares F-statistic: 1102. Date: Sun, 14 Aug 2022 Prob (F-statistic) : 3. 07e-278 Time : 17:32: 06 Log-Likelihood : 1815.5 No. Observations : 618 AIC : 3641 . Of Residuals: 613 BIC : 3663 Df Model : 4 Covariance Type: nonrobust coef std err P> t [0 . 025 0.975] Intercept 34.5753 25.867 1.337 0. 182 -16.223 85. 373 avg_pts 0. 2597 0 . 043 6. 070 0.000 0. 176 0 . 344 avg_elo_n 0. 0134 0. 017 -0. 769 0. 442 -0. 048 0. 021 avg_pts_differential 1. 6206 0. 135 12. 024 0.000 1. 356 1.885 avg_elo_differential 0. 0525 9. 018 2.915 0. 004 0. 017 9 . 088 Omnibus : 193. 608 Durbin-Watson: 9.979 Prob (Omnibus ) : 0.000 Jarque -Bera (JB) : 598 . 416 Skew: -1. 503 Prob ( JB) : 1. 14e-130 Kurtosis : 6. 769 Cond. No. 2. 11e+05 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 2. 1le+05. This might indicate that there are strong multicollinearity or other numerical problems