Vif Regression Minitab. Common approaches include examining residual plots using lack -of- fit tests and viewing the correlation between predictors using the Variance Inflation Factor VIF. 5 the regression coefficient for that term is not estimated well.
5 the regression coefficient for that term is not estimated well. If the correlation of a predictor with other predictors is nearly perfect Minitab displays a message that the term cannot be estimated. Use the VIF to describe how much multicollinearity which is correlation between predictors exists in a regression analysis.
Multicollinearity is problematic because it can increase the variance of the regression coefficients making it difficult to evaluate the individual impact that each of the correlated predictors has on the response.
Given the potential for correlation among the predictors well have Minitab display the variance inflation factors VIF which indicate the extent to which multicollinearity is present in a regression analysis. 5 the regression coefficient for that term is not estimated well. Any individual VIF larger than 10 should indiciate that multicollinearity is present. 4 222e-016 then the predictor fails the test and is removed from the model.
