What is variance inflation factor?
Variance inflation factor measures how much the behavior (variance) of an independent variable is influenced, or inflated, by its interaction/correlation with the other independent variables. Variance inflation factors allow a quick measure of how much a variable is contributing to the standard error in the regression.
How multicollinearity is detected?
Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable. One popular detection method is based on the bivariate correlation between two predictor variables.
How is the variance of a predictor inflated?
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.
How to interpret variance inflation factors for a regression model?
How do we interpret the variance inflation factors for a regression model? A VIF of 1 means that there is no correlation among the jth predictor and the remaining predictor variables, and hence the variance of bj is not inflated at all.
How is the variance of the weight coefficient inflated?
Therefore, the variance inflation factor for the estimated coefficient Weight is by definition: Again, this variance inflation factor tells us that the variance of the weight coefficient is inflated by a factor of 8.42 because Weight is highly correlated with at least one of the other predictors in the model.
How are variance inflation factors used to detect multicollinearity?
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.