How does the Variance Inflation Factor (VIF) quantify the severity of Multicollinearity in a regression analysis?
- By calculating the square root of the variance of a predictor.
- By comparing the variance of a predictor to the variance of the outcome variable.
- By measuring how much the variance of an estimated regression coefficient is increased due to multicollinearity.
- By summing up the variances of all the predictors.
VIF provides a measure of multicollinearity by quantifying how much the variance of an estimated regression coefficient increases if predictors are correlated. If the predictors are uncorrelated, the VIF of each variable will be 1. The higher the value of VIF, the more severe the multicollinearity.
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