Variance Inflation Factor (VIF)

The Variance Inflation Factor (VIF) is a measurement used in statistics to assess how much the variance of a regression coefficient increases due to multicollinearity, which is when independent variables in a model are highly correlated. A high VIF value indicates that an independent variable is redundant, meaning it doesn’t provide unique information. Typically, a VIF above 10 suggests significant multicollinearity, raising concerns about the reliability of the model’s results. By identifying and addressing high VIF values, one can improve the accuracy and interpretability of regression analyses.