PROC HPREG is run on a dataset with 50 predictors. VIF values for three predictors are 22, 18, and 31. Which action is most appropriate based on these VIF values?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Here's the deal — b is correct because VIF values of 22, 18, and 31 all exceed the commonly used threshold of 10 (some analysts use 5), indicating severe multicollinearity among these predictors. Multicollinearity inflates coefficient standard errors, makes individual coefficient estimates unstable, and harms interpretability.
Full explanation below image
Full Explanation
B is correct because VIF values of 22, 18, and 31 all exceed the commonly used threshold of 10 (some analysts use 5), indicating severe multicollinearity among these predictors. Multicollinearity inflates coefficient standard errors, makes individual coefficient estimates unstable, and harms interpretability. Remedies include removing redundant predictors, combining them, or applying ridge regression. A is wrong; there is no 'acceptable VIF under 40' rule. C is wrong; standardization reduces scale differences but does not reduce multicollinearity. D is wrong; VIF does not improve with sample size alone.