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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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Polynomial regression multicollinearity assumption? [duplicate]
The difference between Linear regression and Polynomial regression is that in the last we manipulate our original explanatory variables in a way to create polynomial dependency between Y and X. For th …