Ridge Regression and multicollinearity: An in-depth review

Issue title: Special Issue – SAS Global Forum 2018

Guest editors: Jennifer Waller and Tyler Smith

Article type: Research Article

Authors: Schreiber-Gregory, Deanna N.

Affiliations: Henry M Jackson Foundation for the Advancement of Military Medicine, 6720A Rockledge Dr, Bethesda, MD 20817, USA | Tel.: +1 701 799 6905; E-mail: d.n.schreibergregory@gmail.com

Correspondence: [*] Corresponding author: Henry M Jackson Foundation for the Advancement of Military Medicine, 6720A Rockledge Dr, Bethesda, MD 20817, USA. Tel.: +1 701 799 6905; E-mail: d.n.schreibergregory@gmail.com.

Abstract: Multicollinearity is a phenomenon in which two or more identified predictor variables in a multiple regression model are co-dependent or 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 its impact through Ridge Regression.

Keywords: Ridge Regression, multicollinearity, regulation, SAS

DOI: 10.3233/MAS-180446

Journal: Model Assisted Statistics and Applications, vol. 13, no. 4, pp. 359-365, 2018