Methods for Designing and Analyzing Mixture Experiments

by John Cornell and Greg Piepel
Sponsored by ASQ-STAT
Wednesday, October 8, 2008 | $250

Mixture experiments involve varying the component proportions of a product and observing the changes in the product's properties. The component proportions cannot be varied independently because they must sum to 1.0 for each run in the experiment. Mixture experiments are useful in many product development areas, including foods and drinks, drugs, plastics, alloys, ceramics and glass, gasoline, fertilizers, textile fibers, and others. The course will provide an overview of various approaches and methods used in designing mixture experiments and analyzing the resulting data. Designs for simplex-shaped and irregular-shaped regions (the latter resulting from placing constraints on the component proportions) will be covered. Various types of mixture models that can be fitted to mixture data will be discussed, as will graphical techniques for interpreting component effects. Including process variables and/or a total amount variable in mixture experiments will be covered. Graphical and analytic methods for developing mixtures with optimum properties will also be discussed. Many examples from the presenters’ experiences will be used to illustrate the topics. The course is designed for statisticians and non-statisticians wanting to know about statistical methods for designing mixture experiments and analyzing the resulting data. Prerequisites are an understanding of basic statistics concepts and some previous exposure to experimental design and regression.