Correcting for Multicollinearity

Multicollinearity occurs when two or more predictor variables are highly correlated with each other. This can lead to inconsistent estimation results when using linear regression models because the coefficients obtained can be difficult to interpret. To correct for this issue, we can use techniques such as stepwise regression, ridge and lasso regression, or principal components analysis. Stepwise regression removes one predictor at a time to determine the best combination of predictors that explain the response variable. Ridge and lasso regression add a penalty function that shrinks the coefficients of the correlated predictors, while principal components analysis uses principal components in place of the correlated predictors. These methods can help to improve the accuracy of the model and make it easier to interpret the results.