In statistical multiple regression models, when the independent variables are related between them, the problem of multicollinearity arises. It results in regression coefficients which may not be statistically significant as the coefficients of interrelated independent variables reflect – to a lesser or greater extent – the influence which one variable exerts on another.

A condition in which a set of predictor variables are highly correlated among themselves.

a case of multiple regression in which the predictor variables are themselves highly correlated

A state of high intercorrelations between independent variables

A statistical phenomenon that occurs when two or more independent variables are so highly correlated that interpretation of the effects of their variation on the dependent variable is virtually impossible to determine.

A situation where there is correlation between the independent variables used in explaining the change in a dependent variable. When this condition exists, you cannot have confidence in the individual coefficients of the independent variables. To Top

Multicollinearity is any linear relationship amongst explanatory variables in a regression model. It can affect two or more of them. The original definition referred to an exact linear relationship, but later it was extended to mean a nearly perfect relationship.