Abstract
Multicollinearity can be a serious statistical problem in data analysis in which the contribution of each individual risk factor is being evaluated. Symptoms, effects and techniques that are useful in detecting the presence of multicollinearity in a data base are discussed. The mathematical basis of the principal-components analysis technique for detecting, quantifying, and adjusting the regression coefficients for the effects of multicollinearity in a data base was demonstrated.
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