Multicollinearity refers to the linear dependence among two or more variables. Although collinearity technically refers to the linear dependence among two variables, multicollinearity and collinearity are often used interchangeably. When there is a perfect linear dependence among predictors, statistical analyses such as multiple linear regression cannot be conducted with all included variables as the regression equation becomes unsolvable.
Consider a scenario where a researcher wants to know whether variability in test scores is a function of hair color (i.e., brown, black, red, and blond). Although an analysis of variance would likely be the statistical analysis of choice, the general linear model indicates that multiple linear regression could also be used. Of course, the [Page 1100]variable hair color could not be used in its original form given ...
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