Dealing with multicollinearity using VIFs

Besides normality of residuals and homogeneity of variance, one of the biggest assumptions of linear modeling is independence of predictors. If one or more of the predictors in a model are correlated, then the model may produce unstable parameter estimates with highly inflated standard errors, resulting in an overall significant model with no significant predictors. In other words, bad news if your goal is to try and determine the contribution of each predictor in explaining the response. But there is hope!

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