SUMMARY OUTPUT Regression Statistics Multiple R 0.97 R Square 0.93 Adjusted R Sq

Question

SUMMARY OUTPUT Regression Statistics Multiple R 0.97 R Square 0.93 Adjusted R Square 0.93 Standard Error 7.41 Observations 60.00 ANOVA Significance MS SS 0.00 Regression 190.72 41863.81 10465.95 Residual 3018.12 54.87 55 Total 44881.93 Coefficients Standard Error t Stat P-value Lower 95% 95% 3.68 12.05 37.00 51.76 Intercept 44.38 0.00 Years 3.93 5.65 4.79 0.43 11.21 0.00 Years Squared -0.13 0.10 0.01 10.12 0.00 -0.08 4.69 3.21 Gender 0.68 0.50 6.19 12.60 1.07 Years x Gender 0.46 2.34 0.02 0.16 1.98

Explanation / Answer

Part 1

Answer:

The formula for VIF is given as below:

VIF = 1/(1 – R2)

For the given model we are given a coefficient of determination or the value of R square as 0.93.

VIF = 1/(1 – 0.932) = 1/(1 - 0.8649) = 7.401925

The value of VIF indicates the magnitude of the inflation in the standard error associated with the particular weight due to multicollinearity.

This value of VIF shows that there is a serious problem of multicollinearity in this model and there is highest correlation between predictors.

(For VIF > 5, there is highly correlation with other predictors.)

Part 2

Answer:

The coefficients of the given regression model are statistically significant. (All p-values are approximately equal to 0.00 except gender.)

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