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Abstract
When an observation in a regression analysis has very large values on two or more predictor variables, artificial collinearities can be induced. The effects of such collinearities on a regression analysis are not well documented, although they can be shown to be similar in many respects to those resulting from approximate linear dependencies among the columns of predictor-variable values. The purpose of this article is to explore the effects of outlier-induced collinearities on the estimation of regression coefficients.
