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Managing Multicollinearity in Regression Analysis

Learn how to handle multicollinearity in regression analysis by removing variables with high p-values or insignificant coefficient estimates. Understand when to keep both correlated variables or remove the less significant one.

jermaine-rush
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Managing Multicollinearity in Regression Analysis

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  1. Dealing with Multicollinearity Remove the one with highest p-value (or smallest absolute t-test--it’s the same thing). neither is significant Two independent variables are highly correlated (positively or negatively). one is significant Remove the one with highest p-value. Does the model make sense? If yes, leave both in. If no, there could be a computational problem. Remove the one with the highest p-value. both are significant

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