The variance inflation factor (VIF) is a measure used to detect multicollinearity in multiple linear regression models. It quantifies how much the variance of an estimated regression coefficient increases when your predictors are correlated. In simpler terms, a high VIF indicates that the predictor variable is highly correlated with other predictor variables, which can lead to unreliable estimates and less reliable statistical inferences.
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