VIF MEASURES how MUCH the variance (the square of the estimate's standard deviation) of an estimated regression coefficient is increased because of collinearity. If the VIF of a predictor variable were 9 (√9 = 3) this MEANS that the standard error for the coefficient of that predictor variable is 3 times as large as it would be if that predictor variable were uncorrelated with the other predictor variables.Steps of calculating VIF

1. Run linear regression in which ONE of the independent variable is considered as target variable and all the other independent variables considered as independent variables
2. CALCULATE VIF of the variable. VIF = 1/(1-RSquared)

VIF measures how much the variance (the square of the estimate's standard deviation) of an estimated regression coefficient is increased because of collinearity. If the VIF of a predictor variable were 9 (√9 = 3) this means that the standard error for the coefficient of that predictor variable is 3 times as large as it would be if that predictor variable were uncorrelated with the other predictor variables.Steps of calculating VIF