CFA Level 2 Exam 2025 – 400 Free Practice Questions to Pass the Test

A Variance Inflation Factor (VIF) greater than 5 is generally interpreted as an indication of high correlation among independent variables in a regression analysis. The VIF quantifies how much the variance of an estimated regression coefficient increases when your independent variables are correlated. A VIF value greater than 5 suggests that the multicollinearity between the independent variables is strong enough to potentially distort the estimates of the coefficients, making it difficult to determine the individual effect of each variable.

This level of multicollinearity can lead to inflated standard errors, which in turn can affect hypothesis testing and the overall reliability of the regression analysis. High multicollinearity can also make it challenging to identify the true relationship each independent variable has with the dependent variable, thereby compromising the interpretability of the model.

In contrast, a VIF value below 5 generally indicates acceptable levels of multicollinearity, suggesting that the independent variables maintain a more independent relationship, allowing for clearer insights into how each variable influences the dependent variable. Therefore, recognizing a VIF greater than 5 as a sign of high correlation among independent variables is essential for accurate model interpretation and ensuring robust regression results.