1. What is Variance Inflation Factor (VIF)?

VIF measures how much the variance of a regression coefficient is inflated due to multicollinearity in the model. It quantifies the severity of multicollinearity in an ordinary least squares regression analysis.

2. How Does the Calculator Work?

The calculator uses the VIF formula:

Where:

• \( R^2 \) — Coefficient of determination from regressing one predictor on others

Explanation: The VIF provides an index that measures how much the variance of an estimated regression coefficient increases because of collinearity.

3. Importance of VIF Calculation

Details: VIF is crucial for detecting multicollinearity in regression models. High VIF values (typically >5 or 10) indicate problematic multicollinearity that may need to be addressed.

4. Using the Calculator

Tips: Enter the R² value (must be between 0 and 1). The calculator will compute the VIF which indicates how much multicollinearity has inflated the variance of your coefficient estimates.

5. Frequently Asked Questions (FAQ)

Q1: What is a good VIF value?
A: VIF < 5 is generally acceptable, 5-10 indicates moderate multicollinearity, and >10 indicates severe multicollinearity.

Q2: How is R² obtained for VIF calculation?
A: For each predictor, regress it against all other predictors in the model and obtain the R² from that regression.

Q3: What causes high VIF values?
A: High VIF values occur when predictors are highly correlated with each other (multicollinearity).

Q4: How to reduce high VIF?
A: Options include removing variables, combining correlated variables, or using regularization techniques like ridge regression.

Q5: Is VIF applicable to all regression types?
A: VIF is primarily used for ordinary least squares regression. Other models may require different multicollinearity diagnostics.