Importance of VIF in Linear Regression

What is VIF

Variance Inflation Factor (VIF) determines the multicollinearity amongst the independent variables (predictors). Multicollinearity is when there is a high correlation between your predictor variables, usually 0.8 or higher. This can adversely affect your regression analysis.

How is it calculated?

VIF of a predictor variable is calculated by regressing it against all other predictor variables. This gives the R² value which can be plugged into this formula

This will give the VIF value of a predictor.

• VIF = 1, not correlated
• VIF < 5, slightly correlated
• VIF > 5, highly correlated

These values are just guidelines and how high acceptable VIF values are depends on the problem statement.

If you don’t want to use VIF and have very few predictor variables, one can plot a correlation matrix and remove the highly correlated variables.

You might also wonder why do we calculate the p-value of predictor variables in Linear regression. Find out why here.