Variance Inflation Factor

The Variance Inflation Factor (VIF) is a measure used in regression analysis to identify how much the variance of an estimated coefficient is increased due to the presence of correlations among predictor variables. In simpler terms, it tells us how much multicollinearity (overlap) exists between variables. A high VIF indicates that a predictor is highly correlated with others, which can make it difficult to determine its individual effect accurately. To keep models reliable, VIF helps identify and address these issues, ensuring that the coefficients we estimate are stable and meaningful.