VIF Meaning and
Definition
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VIF is an acronym that stands for "Variance Inflation Factor." It is a statistical measure used in regression analysis to quantify the severity of multicollinearity among predictor variables. Multicollinearity occurs when there is a high degree of intercorrelation between two or more independent variables in a regression model.
The VIF is calculated for each predictor variable, providing a numerical value that indicates the amount of inflation in the variance of the estimated regression coefficient due to multicollinearity. A VIF value of 1 implies no multicollinearity, while values greater than 1 indicate varying degrees of multicollinearity.
The VIF is computed by taking the ratio of the variance of the estimated regression coefficient to the variance of the coefficient if that predictor variable was uncorrelated with all other predictor variables. In simpler terms, it measures how much the standard error of the estimated regression coefficient increases due to multicollinearity.
A commonly used rule of thumb is that a VIF value greater than 5 (or sometimes 10) suggests the presence of high multicollinearity that may significantly affect the interpretation and reliability of the regression results. In such cases, it may be necessary to address multicollinearity by removing correlated variables, transforming variables, or combining variables to create composite measures.
Overall, VIF is a statistical tool that assists researchers and analysts in identifying and addressing the issue of multicollinearity in regression models, thereby enhancing the accuracy and reliability of statistical inference and model predictions.
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