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Multicollinearity (or collinearity) is a statistical phenomenon in multiple linear regression analysis where two (or more) independent or predictor variables are highly correlated with each other, or intercorrelated. Presence of multicollinearity violates one of the core assumptions of multiple linear regression analysis and as such it is problematic; the predicted regression coefficients are not reliable anymore.

This entry discusses the issue of multicollinearity and why it might be problematic. It also outlines symptoms and diagnostics to determine whether or not multicollinearity is present. Finally, this entry discusses several ways of dealing with multicollinearity.

Context

In multiple linear regression analysis, several independent or predictor variables (usually denoted by X and sometimes referred to as regressors) are modeled to predict or estimate one dependent variable (usually denoted by Y). When ...

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