• What is the equation for your model?
• What are the results of the overall F-test? Summarize all important steps of this hypothesis test. This includes:

1. Null Hypothesis (statistical notation and its description in words)
2. Alternative Hypothesis (statistical notation and its description in words)
3. Level of Significance

?

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 Model : OLS Adj. R-squared: 0. 837 Method : Least Squares F-statistic: 1580. Date: Mon, 21 Jun 2021 Prob (F-statistic ) : 4. 41e-243 Time : 13:22:02 Log-Likelihood : -1904.6 No. Observations : 618 AIC : 3815. Of Residuals: 615 BIC: 3829. Df Model : 2 Covariance Type: nonrobust coef std err t 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 0.000 0. 256 0. 444 avg_elo_n 0. 1055 0. 002 47.952 0.090 0. 101 0. 110 Omnibus : 89 . 087 Durbin-Watson: 1. 203 Prob (Omnibus ) : 0.000 Jarque -Bera (JB) : 160.540 Skew: - 0. 869 Prob ( JB) : 1. 38e-35 Kurtosis : 4. 793 Cond. No. 3. 19e+04 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 3. 19e+04. This might indicate that there are strong multicollinearity or other numerical problems