VIFværdi

VIFværdi is a term used in statistics to quantify multicollinearity, which is the presence of a linear relationship among predictor variables in a regression model. VIFværdi stands for Variance Inflation Factor. A high VIFværdi indicates that the variance of a regression coefficient is inflated due to its correlation with other predictor variables. This inflation can make it difficult to interpret the individual coefficients and can lead to unstable and unreliable regression results.

The VIFværdi for a specific predictor variable is calculated by regressing that variable against all other predictor variables in the model. The R-squared value from this regression is then used to compute the VIFværdi, with the formula being 1 / (1 - R-squared). A VIFværdi of 1 means there is no correlation between the predictor variable and the others. Generally, VIF values above 5 or 10 are considered problematic, suggesting that multicollinearity is likely present and warrants further investigation.

When high VIFværdier are detected, common solutions include removing one or more of the highly correlated predictor variables, combining them into a single variable, or employing dimensionality reduction techniques like Principal Component Analysis (PCA). Addressing multicollinearity is crucial for building robust and interpretable regression models.