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Situation when there is strong linear relationship among predictor variables, so that their correlation matrix becomes (almost) singular. This "ill condition" makes it hard to determine the unique role each of the predictors is playing: estimation problems arise and standard errors are increased. Bivariately very high correlated predictors are one example of multicollinearity.
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VIF understanding - does only >4 variables are multi-collinear and others are not?
In my point of view, multicollinearity is a problem if a vif value is bigger 10. …