There are many theories behind the threshold of VIF. It actually reduces the statistical significance of the independent variables. Multicollinearity does not reduce the explanatory power of the model. We are actually looking for the best model which can explain our dependent variable with the help of the independent variables. While building a model, detecting multicollinearity is a very much important step. The variable with high VIF is highly collinear with the other variables in the model. The ratio is calculated for each independent variable. Mathematically, the VIF of a regression model is the ratio of the overall model to the variance of a model that includes only that single independent variable. Variance Inflation Factor or VIF is basically a measure of multicollinearity of the independent variables in a multiple regression model.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |