This website is using cookies to ensure you get the best experience possible on our website.
More info: Privacy & Cookies, Imprint
The variance inflation factor (VIF) is a statistical metric used in multivariate linear regression to measure multicollinearity among the independent variables. Multicollinearity occurs when there are high correlations between the independent variables, which can affect the stability and accuracy of the regression coefficients.
The VIF is used to determine how much the variance of the regression coefficients is inflated due to multicollinearity. It quantifies the extent to which the variance of the estimator for a regression coefficient is larger than it would be if the variable were not correlated with the other independent variables.
A VIF value is used to determine how much the variance of the regression coefficients is inflated due to multicollinearity.
A VIF value of 1 indicates that multicollinearity is not present, while values above 1 indicate that multicollinearity is present. The higher the VIF value, the stronger the multicollinearity. Generally, a VIF value above 5 or 10 is thought to indicate significant multicollinearity, which should be considered.
The VIF is often used to check the independent variables in multivariate linear regression and, if necessary, to remove or transform variables to reduce multicollinearity and improve the stability of the regression coefficients. A low VIF value indicates that the variable has little dependence on the other independent variables and has a small effect on the accuracy of the regression analysis.