• In general, how is a simple linear regression model used to predict the response variable using the predictor variable?
• 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 1: Hypothesis Test for the 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, can average points scored predict the total number of wins in the regular season?
• What is the predicted total number of wins in a regular season for a team that is averaging 75 points per game? Round your answer down to the nearest integer.
• What is the predicted number of wins in a regular season for a team that is averaging 90 points per game? Round your answer down to the nearest integer.

 OLS Regression Results ============================================================================== Dep. Variable: avg_elo_n R-squared: 0.823 Model: OLS Adj. R-squared: 0.823 Method: Least Squares F-statistic: 2865. Date: Sun, 23 Feb 2020 Prob (F-statistic): 8.06e-234 Time: 21:54:25 Log-Likelihood: -3222.7 No. Observations: 618 AIC: 6449. Df Residuals: 616 BIC: 6458. Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept 1207.2925 5.756 209.760 0.000 1195.990 1218.595 total_wins 7.3438 0.137 53.523 0.000 7.074 7.613 ============================================================================== Omnibus: 138.038 Durbin-Watson: 1.301 Prob(Omnibus): 0.000 Jarque-Bera (JB): 343.065 Skew: 1.141 Prob(JB): 3.19e-75 Kurtosis: 5.849 Cond. No. 135. ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.