An Overview of Composite Designs Run as Split-Plots

Geoff Vining () and Scott Kowalski
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Geoff Vining: Virginia Tech, Department of Statistics
Scott Kowalski: Minitab, Inc.

A chapter in Frontiers in Statistical Quality Control 8, 2006, pp 342-351 from Springer

Abstract: Summary Many industrial experiments involve factors that are hard-to-change as well as factors that are easy-to-change, which naturally leads to split-plot experiments. Unfortunately, the literature for second-order response surface designs traditionally assumes a completely randomized design. Vining, Kowalski, and Montgomery (2004) outline the general conditions for response surface experiments such that the ordinary least squares estimates of the model are equivalent to the generalized least squares estimates. Vining and Kowalski (2004) use this result to derive conditions for exact tests of most of the model parameters and the Satterthwaite’s procedure for the other parameters. This paper summarizes the results of Vining, Kowalski, and Montgomery (2004) and Vining and Kowalski (2004). It illustrates how to modify standard central composite designs to accommodate the split-plot structure. It concludes with a fully analyzed example.

Keywords: Ordinary Little Square; Model Matrix; Central Composite Design; Generalize Little Square; Ordinary Little Square Estimate (search for similar items in EconPapers)
Date: 2006
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-1687-7_21

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DOI: 10.1007/3-7908-1687-6_21

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