How to adjust for multicollinearity through ridge regression.
What is it about?
Multicollinearity is the phenomenon in which two or more identified predictor variables in a multiple regression model are highly correlated. The presence of this phenomenon can have a negative impact on the analysis as a whole and can severely limit the conclusions of the research study. This paper reviews and provides examples of the different ways in which multicollinearity can affect a research project, how to detect multicollinearity and how one can reduce it through Ridge Regression applications. This paper is intended for any level of SAS® user.
Why is it important?
Multicollinearity is an assumption violation that if left unchecked, can have a detrimental effect on a model. Ridge Regression is one way that multicollinearity can be addressed, while maintaining the structure of the model.