Exclusive Content & Downloads from ASQ

Experiments with Mixtures, Ill-Conditioning, and Ridge Regression

Summary: Experiments with mixtures require a special form of polynomial model called the canonical polynomial model. Moreover, many mixtures problems are also subject to additional constraints that often cause ill-conditioning, or collinearity. Using the eigenvalues and Variance Inflation Factors (VIF's) as measures of conditioning, we have looked at a variety of mixtures data sets. We have considered the effect on conditioning of such remedial measures as standardizing the variables, transforming to pseudocomponents, and ridge regression. In particular, ridge regression is used as a method for displaying the effects of collinearity on the regression coefficients. We conclude that since ill-conditioning is a problem in so many mixtures experiments, the mixtures practitioner should always use VIF's or eigenvalues to look for it.

Anyone with a subscription, including Site and Enterprise members, can access this article.

Other Ways to Access content:

Join ASQ

Join ASQ as a Full member. Enjoy all the ASQ member benefits including access to many online articles.

Subscribe to Journal of Quality Technology

Access this and ALL OTHER Journal of Quality Technology online articles. You'll also receive the print version by mail.

  • Topics: Statistics, Data Quality, Design of Experiments
  • Keywords: Statistics,Collinearity,Data analysis,Mixture experiments
  • Author: St. John, Ralph C.
  • Journal: Journal of Quality Technology