Vif In Stata. VIF 1 1-R2 where R2 is the R-squared value for that xs regression on the other x variables So when you only have 2 x variables youre regression x1 on x2 and x2 on x1. It is used to explain how much amount multicollinearity correlation between predictors exists in a regression analysis.
As a rule of thumb vif values less than 10 indicates no multicollinearity between the variables. It is used to explain how much amount multicollinearity correlation between predictors exists in a regression analysis. A First Regression Analysis Simple Linear Regression Multiple Regression Transforming Variables Regression Diagnostics.
The results of the mixed effects regression between loneliness and in-home behavior are presented in Table 3.
Tolerance defined as 1VIF is used by many researchers to check on the degree of collinearity. Variance inflation factors VIF measure how much the variance of the estimated regression coefficients are inflated as compared to when the predictor variables are not linearly related. The variance inflation factor is a useful way to look for multicollinearity amongst the independent variables. After each regression we will manually compute the tolerance using the formula 1-R2 and the variance inflation factor VIF by 1tolerance.
