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: Fri, 21 Feb 2020 Prob (F-statistic): 5.03e-278 Time: 18:29:25 Log-Likelihood: -1819.8 No. Observations: 618 AIC: 3648. Df 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 ============================================================================== 

• In general, how is a multiple linear regression model used to predict the response variable using predictor variables?
• 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.