An individual predictor's P value may test non-significant even though it is important. Multicollinearity causes problems in using regression models to draw conclusions about the relationships between predictors and outcome. In such cases, the analysis of variance for the overall model may show a highly significantly good fit, when paradoxically the tests for individual predictors are non-significant. If collinearity is so high that some of the x variables almost totally predict other x variables then this is known as multicollinearity. The degree to which the x variables are correlated, and thus predict one another, is collinearity.
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