when ri2 is equal to 0 and therefore when vif or tolerance is equal to 1 the ith independent variable is not correlated to the remaining ones meaning that multicollinearity does not exist 1
in general terms the higher the vif the higher the possibility that multicollinearity exists and further research is required when vif is higher than 10 there is significant multicollinearity that needs to be corrected 1example of using viffor example suppose that an economist wants to test whether there is a statistically significant relationship between the unemployment rate independent variable and the inflation rate dependent variable including additional independent variables that are related to the unemployment rate such as new initial jobless claims would be likely to introduce multicollinearity into the model the overall model might show strong statistically sufficient explanatory power but be unable to identify if the effect is mostly due to the unemployment rate or to the new initial jobless claims this is what the vif would detect and it would suggest possibly dropping one of the variables out of the model or finding some way to consolidate them to capture their joint effect depending on what specific hypothesis the researcher is interested in testing 