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.

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.

Is there a connection between variance inflation factor (Vif) and diagnostic plots?

INTRODUCTION This article focuses on the connection between the vari- ance inflation factor (VIF) and two diagnostic plots for least squares regression, partial regression plots, and par- tial residual plots (added-variable plots and component- plus-residual plots).

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.