Model Analysis/Reduction
Step 3: Check Predicting Variable Goodness of Fit (Linear)
Step 4: Check for Multicollinearity
Methodology:
Methodology:
Determine R-squared ( 𝑅 2 ) Value: •
• For each predictor, compute the Variance Inflation Factor ( 𝑉𝐼𝐹 ) to quantify the inflation in variance caused by correlations with other predictors. • Establish a VIF threshold using the greater of two values: 10 , or ൗ 1 1−𝑅 2 , to discern significant multicollinearity. • Evaluate the VIF results to confirm that no predicting variables exhibit multicollinearity.
Calculate the proportion of variance in the dependent variable that's predictable from the predicting variable. Examine the residual plots for patterns that indicate deviations from the linearity assumption.
Analyze Residuals: •
Conduct F-test: •
Use the F-test to check the overall significance of the regression model.
Inspect p-values: •
For each predictor, assess the p-value to determine its statistical significance.
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