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New Classes of Second-Order Equivalent-Estimation Split-Plot Designs

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If you have a question about this talk, please contact Mustapha Amrani.

Design and Analysis of Experiments

In many industrial experiments, complete randomization of the runs is impossible as, often, they involve factors whose levels are hard or costly to change. In such cases, the split-plot design is a cost-efficient alternative that reduces the number of independent settings of the hard-to-change factors. In general, the use of generalized least squares is required for model estimation based on data from split-plot designs. However, the ordinary least squares estimator is equivalent to the generalized least squares estimator for some split-plot designs, including some second-order split-plot response surface designs. These designs are called equivalent-estimation designs. An important consequence of the equivalence is that basic experimental design software can be used to analyze the data. We introduce two new families of equivalent-estimation split-plot designs, one based on subset designs and another based on a class of rotatable response surface designs constructed using supp lementary difference sets. The resulting designs complement existing catalogs of equivalent-estimation designs and allow for a more flexible choice of the number of hard-to-change factors, the number of easy-to-change factors, the number and size of whole plots and the total sample size.

This talk is part of the Isaac Newton Institute Seminar Series series.

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