Open    Access



Practical Aspects for Designing Statistically Optimal Experiments



Author(s)

Mark J. Anderson, Patrick J. Whitcomb

Full-Text PDF XML     890 Views

DOI:10.17265/2328-224X/2014.03.001

Stat-Ease, Inc., Minneapolis, MN USA

Due to operational or physical considerations, standard factorial and response surface method (RSM) design of experiments (DOE) often prove to be unsuitable. In such cases a computer-generated statistically-optimal design fills the breech. This article explores vital mathematical properties for evaluating alternative designs with a focus on what is really important for industrial experimenters. To assess “goodness of design” such evaluations must consider the model choice, specific optimality criteria (in particular D and I), precision of estimation based on the fraction of design space (FDS), the number of runs to achieve required precision, lack-of-fit testing, and so forth. With a focus on RSM, all these issues are considered at a practical level, keeping engineers and scientists in mind. This brings to the forefront such considerations as subject-matter knowledge from first principles and experience, factor choice and the feasibility of the experiment design.

design of experiments, optimal design, response surface methods, fraction of design space.