A common rule of thumb is as follows: VIF = 1: No correlation. One approach is to remove one of the highly correlated predictors from the model, though this decision should be guided by theoretical understanding and the research objective.
Calculating VIF From R Squared Formula
VIF > 5: High correlation, warranting investigation. Practical Considerations and Limitations.
</ Addressing Multicollinearity Once a high VIF is detected, several strategies can be employed to mitigate the issue. The VIF is then obtained by dividing one by the result of one minus this R-squared value.
Calculating VIF From R Squared Formula
Interpreting VIF Values Interpreting the magnitude of VIF is essential for diagnosing data issues. In some cases, collecting more data can help stabilize the coefficient estimates, although this is not always feasible.
More About What is vif in statistics
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