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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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Multicollinearity and categorical predictor with three levels

Scatter plots are for continuous variables and multicollinearity makes sense for continuous, but not for dummy variables. …