Vif Value In Regression Analysis. From the above we know that a VIF of 1 represents no multicollinearity and higher values indicate more multicollinearity is present. 4 rows Regression Analysis.
Variance inflation factor VIF is a measure of the amount of multicollinearity in a set of multiple regression variables. Variance inflation factors VIF measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related. By the existence of correlation among the predictor variables in the model.
VIF measures the number of inflated variances caused by multicollinearity.
A general rule of thumb for interpreting VIFs is as follows. Again it is a measure of how much the variance of the estimated regression coefficient b k is inflated. A general rule of thumb for interpreting VIFs is as follows. As we see from the formula greater the value of R-squared greater is the VIF.
