• 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
4. Report the test statistic and the P-value in a formatted table as shown below:

Table 3: Hypothesis Test for Overall F-Test

Statistic

Value

Test Statistic

X.XX

*Round off to 2 decimal places.

P-value

X.XXXX

*Round off to 4 decimal places.

1. Conclusion of the hypothesis test and its interpretation based on the P-value

• Based on the results of the overall F-test, is at least one of the predictors statistically significant in predicting the number of wins in the season?
• What are the results of individual t-tests for the parameters of each predictor variable?

Is each of the predictor variables statistically significant based on its P-value? Use a 1% level of significance.

• Report and interpret the coefficient of determination.
• What is 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 and average point differential of -5?
• 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 of 1600 and average point differential of +5?

In [10]: import statsmodels. formula. api as smf #Multiple Regression model2 = smf . ols( 'total_wins ~ avg_pts + avg_elo_n + avg_pts_differential', nba_wins_of) . fit() print (model2 . summary ( ) ) OLS Regression Results Dep. Variable: total_wins R - squared: 0. 876 Model : OLS Adj. R-squared: 0. 876 Method : Least Squares F-statistic: 1449 . Date: Mon, 21 Jun 2021 Prob (F-statistic ) : 5. 03e-278 Time : 13:24:45 Log-Likelihood : -1819.8 No. Observations : 618 AIC : 3648. Of Residuals: 614 BIC: 3665. Df Model : 3 Covariance Type: nonrobust coef std err t P> | t/ [0. 025 0. 975] Intercept -35 . 8921 9. 252 - 3.879 0.000 -54 . 062 -17.723 avg_pts 0. 2406 0. 043 5. 657 0.000 0. 157 0. 324 avg_elo_n 0. 0348 0.005 6.421 0.000 0. 024 0. 045 avg_pts_differential 1. 7621 0. 127 13.928 0.000 1. 514 2. 011 Omnibus : 181. 805 Durbin-Watson: 0.975 Prob (Omnibus ) : 0.000 Jarque -Bera (JB) : 506.551 Skew: - 1. 452 Prob (JB) : 1. 01e-110 Kurtosis : 6. 352 Cond. No. 7. 51e+04 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 7. 51e+04. This might indicate that there are