Multicollinearity
arises when at least two highly correlated predictors are assessed
simultaneously in a regression model. The adverse impact of multicollinearity in regression analysis is very well recognized and much attention to its effect
is documented in the literature.
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| Epidemiologic Studies |
The statistical literature emphasizes that the
main problem associated with multicollinearity includes unstable and biased
standard errors leading to very unstable p-values for assessing the statistical
significance of predictors, which could result in unrealistic and untenable
interpretations. Multicollinearity does not affect the overall fit or the
predictions of the model. Read More>>>>>
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