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.
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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.
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This page is a summary of: Ridge Regression and multicollinearity: An in-depth review, Model Assisted Statistics and Applications, October 2018, IOS Press,
DOI: 10.3233/mas-180446.
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