What is a variance inflation factor?

A variance inflation factor (VIF) provides a measure of multicollinearity among the independent variables in a multiple regression model. Detecting multicollinearity is important because while multicollinearity does not reduce the explanatory power of the model, it does reduce the statistical significance of the independent variables.

Does multicollinearity cause variance inflation?

Recall that we learned previously that the standard errors — and hence the variances — of the estimated coefficients are inflated when multicollinearity exists. A variance inflation factor exists for each of the predictors in a multiple regression model.

What factors affect the variance of a regression?

This identity separates the influences of several distinct factors on the variance of the coefficient estimate: s2: greater scatter in the data around the regression surface leads to proportionately more variance in the coefficient estimates The remaining term, 1 / (1 − Rj2) is the VIF.

Why is variance inflated by a factor of 842?

As you can see, three of the variance inflation factors —8.42, 5.33, and 4.41 —are fairly large. The VIF for the predictor Weight, for example, tells us that the variance of the estimated coefficient of Weight is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model.