# How to interpret an intercept VIF

I ran a multiple regression with six independent variables (A-F) and an Intercept. None of the independent variables has a VIF to worry about but the Intercept VIF is way too large:

     **VIF**   **Variable**
0   27.689838  Intercept
1    1.094412   A
2    1.203059   B
3    1.271636   C
4    1.182821   D
5    1.000848   E
6    1.240961   F


How do I interpret such a result? How can the intercept be correlated with another variable and which one should it be, considering that all the other VIFs are perfectly fine? Is such an outcome a showstopper or is there a way to work around it; dropping the intercept certainly isn't...

You did not give enough context to say much more, but your variable names indicate maybe that your variables are dummys for a factor. Maybe the reference level chosen (that is, the one dummy left out of the model) has very few observations? Or some other particularity of the data. You didn't tell us how you calculated the vif. Notable, the vif() function in R's car package will not calculate vif for the intercept.