The variance inflation factor can be used to reduce multicollinearity by _________.
A) Eliminating variables for a multiple regression model
B) Decreasing homoscedasticity
C) Evaluating the distribution of residuals
D) Testing the null hypothesis that all regression coefficients equal zero

Question

Quizplus · Accepted Answer

The variance inflation factor (VIF) is a measure of how much the variance of the estimated regression coefficient for a predictor variable is increased due to multicollinearity with the other predictor variables in the model. VIF values greater than 1 indicate that there is some degree of multicollinearity. One way to reduce multicollinearity is to eliminate variables from the model. This can be done by using stepwise regression, which allows the researcher to compare different models and select the one that has the best fit based on certain criteria (e.g., adjusted R-squared or Akaike information criterion). Other methods for reducing multicollinearity include principal component analysis (PCA), partial least squares (PLS), and ridge regression.

The Variance Inflation Factor Can Be Used to Reduce Multicollinearity