Variance Inflation Factor (VIF)
The Variance Inflation Factor (VIF) measures for multicollinearity in regression models
The Variance Inflation Factor (VIF) measures for how much multicollinearity exists in a regression model. Essentially, it measures for how much regression coefficients are affected by other independent variables in the model. Higher values of Variance Inflation Factor (VIF) are associated with multicollinearity. The generally accepted cut-off for VIF is 2.5, with higher values denoting levels of multicollinearity that could negatively impact the regression model.
Click on a button below to continue.
Statistician For Hire
DO YOU NEED TO HIRE A STATISTICIAN?
Eric Heidel, Ph.D. will provide statistical consulting for your research study at $100/hour. Secure checkout is available with PayPal, Stripe, Venmo, and Zelle.
- Statistical Analysis
- Sample Size Calculations
- Diagnostic Testing and Epidemiological Calculations