Thus, a VIF of 1.8 tells us that the variance (the square of the standard error) of a particular coefficient is 80% larger than it would be if that predictor was completely uncorrelated with all the other predictors. It’s called the variance inflation factor because it estimates how much the variance of a coefficient is “inflated” because of linear dependence with other predictors. LEARN MORE IN A SEMINAR WITH PAUL ALLISON The VIF may be calculated for each predictor by doing a linear regression of that predictor on all the other predictors, and then obtaining the R 2 from that regression. But many do not realize that there are several situations in which multicollinearity can be safely ignored.īefore examining those situations, let’s first consider the most widely-used diagnostic for multicollinearity, the variance inflation factor (VIF). Most data analysts know that multicollinearity is not a good thing. It occurs when there are high correlations among predictor variables, leading to unreliable and unstable estimates of regression coefficients. Multicollinearity is a common problem when estimating linear or generalized linear models, including logistic regression and Cox regression.
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