SUMMARY OUTPUT Regression Statistics Multiple R 0.873296211 0.762646272 R Square

Question

SUMMARY OUTPUT Regression Statistics Multiple R 0.873296211 0.762646272 R Square Adjusted R So 0.643969409 Standard Erre 4.80196987 Observation ANOVA icance F MS 3 444.546512 148.182171 6.42624222 0.026509524 Regression 6 138.353488 23.0589146 Residual 582.9 Total cients tandard Errol t Stat p. Lower 95% Upper 95% Lower 95.0% per 95.0% 136.1412621 62.9245496 2.16356355 0.07371354 -17 290.112088 17.829564 290.11 2088 Intercept 0.237356 Home Runs 0.019100828 0.08919617 0.21414402 0.83752683 -0.199154348 0.176076352 0.06039 2.91546666 0.02678896 0.028297844 o 3238s486 0.02829784 0.32385486 1.5898728 0.16296592 1708.785124 362.78763 1708.7851. 362.78763 Runs Batting Avg. 672.9987472 423 RESIDUAL OUTPUT Observation Predicted Wins Residuals ABS Erroor 1 94.53903098 7.46096902 7.46096902 2 79.76522031 -0.7652203 0.76522031 3 94.07305981 0.92694019 0.92694019 4 99.3454284 -0.3454284 0.3454284 5 100.3754485 -2.3754485 2.3 6 104.0312396 1.96876036 1.96876036 7 101.292263 0.70773703 0.70773703 8 96.41008921 8.4100892 8.41008921 9 91.46550506 0.53449494 0.53449494 10 89.70271516 0.29728484 0.29728484 MAD- 31 2.372

Explanation / Answer

The correlation coefficient R = 0.87 which is close to 1, which implies there is high positive(as R >0) correlation between win percentage and the linear model consisting of run, home runs and batting average.

R-Square = 0.76, which implies 76% total variability in win percentage can be explained by the linear model of batting averages, runs and home runs.

Model Adjusted R-square is 0.64 which implies the model is pretty significant in determining win %

P-value of F-Statistics is 0.026 < 0.05, which implies the model is significant at 5% level.

All the p-value of t-statistics for the coefficients are greater than 0.05 except for that of the variable 'Runs'. So we will conclude that the variables 'home runs', 'Batting Average' are not siginificant to the model. Only the variable 'runs' is significant to the model.

In the model, we can definitely say that, the variables 'Home runs', 'Batting Averages' should be correlated to the variable 'Runs', and so it will lead to Multicollinearity in the model, that's why we found that the coefficients of those variables are not significant

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