• 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 relative skill 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 has an average relative skill of 1550? Round your answer down to the nearest integer.
• What is the predicted number of wins in a regular season for a team that has an average relative skill of 1450? Round your answer down to the nearest integer.

This is the data:

 OLS Regression Results ============================================================================== Dep. Variable: total_wins R-squared: 0.228 Model: OLS Adj. R-squared: 0.227 Method: Least Squares F-statistic: 182.1 Date: Sun, 17 Apr 2022 Prob (F-statistic): 1.52e-36 Time: 23:52:54 Log-Likelihood: -2385.4 No. Observations: 618 AIC: 4775. Df Residuals: 616 BIC: 4784. Df Model: 1 Covariance Type: nonrobust ============================================================================== coef std err t P>|t| [0.025 0.975] ------------------------------------------------------------------------------ Intercept -85.5476 9.305 -9.194 0.000 -103.820 -67.275 avg_pts 1.2849 0.095 13.495 0.000 1.098 1.472 ============================================================================== Omnibus: 24.401 Durbin-Watson: 1.768 Prob(Omnibus): 0.000 Jarque-Bera (JB): 11.089 Skew: -0.033 Prob(JB): 0.00391 Kurtosis: 2.347 Cond. No. 1.97e+03 ============================================================================== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 1.97e+03. This might indicate that there are strong multicollinearity or other numerical problems.