The Variance Inflation Factor (VIF) measures for how much multicollinearity exists in a regression model. Essentially, it measures for how much regression coefficients are affected by other independent variables in the model. Higher values of Variance Inflation Factor (VIF) are associated with multicollinearity. The generally accepted cut-off for VIF is 2.5, with higher values denoting levels of multicollinearity that could negatively impact the regression model.
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