Response Surface Designs within a Split-Plot Structure
Abstract
In many industrial experiments, time and/or cost constraints often force certain factors in a designed experiment to be much harder to change than others. An appropriate approach to such an experiment restricts the randomization, which leads to a split-plot structure. This paper first establishes how one can modify the common central-composite design to efficiently accommodate a split-plot structure. The proposed designs allow pure-error estimates of the two variance components. We next discuss the conditions on the design that make ordinary least squares and weighted least squares estimates equivalent. These conditions are easy to obtain in practice. An important consequence is that people can use standard experimental design software to analyze second-order response surfaces. We give examples of central composite and Box-Behnken designs that meet the conditions. We conclude with an example.
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Those split-plot designs replicate a few treatments many times, mainly center and/or axial points, with much replication inside whole plots.
“These designs involve many replicates of a limited number of treatments (namely, the treatments corresponding to center points and/or axial points).Much of the replication in these designs is within the whole plots.”
From Optimal Blocked and Split-Plot Designs Ensuring Precise Pure-Error Estimation of the Variance Components (2020) · cites this paper as Vining et al. (2005)
The new-criterion designs differ fundamentally from the split-plot designs of Vining et al. (2005), Parker et al. (2006), Kowalski et al. (2002), and Kowalski et al. (2006).
“Consequently, the designs produced by the new criterion are fundamentally dierent from the split-plot designs proposed by Vining et al. (2005), Parker et al. (2006), Kowalski et al. (2002) and Kowalski et al. (2006).”
From Optimal Blocked and Split-Plot Designs Ensuring Precise Pure-Error Estimation of the Variance Components (2020) · cites this paper as Vining et al. (2005)
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Mylona, K., Gilmour, S. G., & Goos, P. (2020) · Technometrics · doi:10.1080/00401706.2019.1595153 · Cited by 13 · see citing works
- Authors
- Vining, G. G., Kowalski, S. M., & Montgomery, D. C.
- Affiliations
- Virginia Tech (US)
- Minitab Inc., State College, PA 16801
- Arizona State University (US)
- Venue
- Journal of Quality Technology
- Publisher
- Informa UK Limited
- Published
- 2005-04
- Volume
- 37
- Issue
- 2
- Pages
- 115-129
- ISSN
- 0022-4065 (ISSN-L)
- 2575-6230
- OpenAlex
- W1541037932
- Cited by
- 122
- References
- 0
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 4 · 2021: 5 · 2019: 2 · 2018: 8 · 2017: 8 · 2016: 5 · 2015: 7 · 2014: 3 · 2013: 1 · 2012: 11
- FWCI
- 15.36
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Quality Technology (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Social Sciences › Decision Sciences › Management Science and Operations Research › Simulation Techniques and Applications
- Keywords
- split-plot design, response surface methodology, central composite design, Box-Behnken design, variance components, weighted least squares, second-order response surface, industrial experiments
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