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ABSTRACT This article presents a new augmentation method to eliminate multicollinearity in observational datasets that contain several correlated variables. The purpose is to eliminate the correlations to facilitate the application… Click to show full abstract
ABSTRACT This article presents a new augmentation method to eliminate multicollinearity in observational datasets that contain several correlated variables. The purpose is to eliminate the correlations to facilitate the application of the least squares regression method. The procedure is based on the addition of new observations to the point in which an appropriate linear regression model can be constructed. Original data can be observational but the new information is obtained through designed experiments. The proposed method uses the R3 algorithm to perform the augmentations and the VIF statistic to determine the point in which the correlations have been significantly reduced.
Journal Title: Quality Engineering
Year Published: 2017
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