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
You should remove all three predictors because VIF values of 22, 18, and 31 all exceed the commonly used threshold of 10, and some analysts use 5. That level of multicollinearity inflates coefficient standard errors, makes individual coefficient estimates unstable, and harms interpretability.
Full Explanation
Removing all three predictors is correct because VIF values of 22, 18, and 31 exceed the common threshold of 10, and some analysts use 5. These values indicate severe multicollinearity. Multicollinearity inflates coefficient standard errors, makes individual coefficient estimates unstable, and harms interpretability. Remedies include removing redundant predictors, combining them, or applying ridge regression. The claim that no action is needed because VIF values under 40 are acceptable for large datasets is wrong; there is no acceptable VIF-under-40 rule. The claim that standardizing the three predictors using PROC STDIZE before re-running PROC HPREG solves the problem is wrong; standardization reduces scale differences but does not reduce multicollinearity. The claim that increasing the training dataset size is appropriate because VIF decreases with more observations is wrong; VIF does not improve with sample size alone.