Ridge Regression and multicollinearity: An in-depth review

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.