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. The VIF is then obtained by dividing one by the result of one minus this R-squared value.
Understanding and Creating VIF Plots to Detect Multicollinearity
Before diving into the specifics of VIF, it is important to understand that multicollinearity refers to a situation where two or more predictor variables in a multiple regression model are highly correlated. While there is no universal cutoff, many statisticians use specific thresholds to guide their decisions.
VIF > 5: High correlation, warranting investigation. Practical Considerations and Limitations.
Detecting Multicollinearity Using VIF Plots: A Practical Guide
These thresholds help researchers determine whether corrective action is necessary. When predictors are highly correlated, the model struggles to estimate the coefficients accurately, leading to inflated standard errors.
More About What is vif in statistics
Looking at What is vif in statistics from another angle can help expand the discussion and give readers a second clear paragraph under the same section.
More perspective on What is vif in statistics can make the topic easier to follow by connecting earlier points with a few simple takeaways.
