VIF stands for Variance Inflation Factor. During regression analysis, VIF assesses whether factors are correlated to each other (multicollinearity), which could affect p-values and the model isn’t going to be as reliable.
If a VIF is greater than 10, you have high multicollinearity and the variation will seem larger and the factor will appear to be more influential than it is. If VIF is closer to 1, then the model is much stronger, as the factors are not impacted by correlation with other factors.
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