multicollinearity

When a linear model has two or more highly correlated predictor variables, it is often said to suffer from multicollinearity. The danger of multicollinearity is that estimated regression coefficients can be highly uncertain and possibly nonsensical (e.g., getting a negative coefficient that common sense dictates should be positive). Multicollinearity is usually detected using variance inflation factors (VIF).