A phenomenon in which two or more predictor variables in a multiple regression model are highly correlated, so that the coefficient estimates may change erratically in response to small changes in the model or data
A condition in which the predictor variables in a regression model are themselves highly correlated Multicollinearity often leads to models in which the coefficients are poorly estimated and, while a fitted model may be good for predictive purposes, it can be difficult to interpret the relative effects of the various predictor variables One of the important properties of a designed experiment is that it avoids such a condition
A statistical problem in multiple regression analysis in which the reliability of the regression coefficients is reduced, owing to a high level of correlation among the independent variables