Variance Inflation Factor (VIF) is a measure used to detect multicollinearity in multiple regression models. It quantifies how much the variance of the estimated regression coefficients is increased due to multicollinearity among the predictor variables. A high VIF indicates a high degree of correlation among independent variables, which can distort the results and interpretations of the model, making it crucial for validating model assumptions and diagnostics.
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