VIFværdier

VIFværdier, often referred to as Variance Inflation Factor or VIF, is a statistical measure used in regression analysis to detect multicollinearity. Multicollinearity occurs when independent variables in a regression model are highly correlated with each other. This high correlation can inflate the variance of the regression coefficients, making them unstable and difficult to interpret. VIF quantifies how much the variance of a regression coefficient is increased due to its linear dependence with other predictor variables.

A VIF value of 1 indicates no correlation between a given independent variable and any other independent variables in the model. As the VIF value increases, it suggests a higher degree of multicollinearity. Common rules of thumb suggest that VIF values above 5 or 10 may indicate problematic levels of multicollinearity. A VIF of 5 means that the variance of the coefficient is 5 times larger than it would be if that predictor variable were uncorrelated with the other predictors. Similarly, a VIF of 10 implies the variance is 10 times larger.

When high VIF values are detected, it suggests that the model may need to be revised. This could involve removing one or more of the highly correlated variables, combining them into a single variable, or using techniques like principal component analysis or ridge regression. Identifying and addressing multicollinearity is crucial for building robust and reliable regression models, ensuring that the influence of each independent variable on the dependent variable can be accurately assessed.