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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| Multicollinearity |
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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