Journal article
Designing and running super-efficient experiments: Optimum blocking with one hard-to-change factor
Journal of quality technology, v 40(1), pp 31-45
01 Jan 2008
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper discusses how to run 2(k) experiments for process improvement when there is one hardto-change factor. The paper studies the different ways of running these experiments and gives practical recommendations. It shows how to block designs to get small prediction variance and low cost. It presents an algorithm to allow the selection of efficient blocking relations, in 2(k) designs, where there is one hard-to-change factor and tabulates the results for 2(3) to 2(7) designs, in various block sizes. It presents methods for calculating the prediction variance and G-efficiency when there are hard-to-change factors. The calculations are demonstrated by applying them to 2(k) designs, and results are tabulated for various block sizes. We show that optimally blocked split-plot designs dominate randomized designs. A blocked split-plot design is both less expensive to run, because it requires fewer resets of the hard-to-change factor, and more precise, as it gives a lower variance of prediction than a completely randomized design.
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Details
- Title
- Designing and running super-efficient experiments: Optimum blocking with one hard-to-change factor
- Creators
- Frank T. Anbari - George Washington UniversityJames M. Lucas - Lucas Research
- Publication Details
- Journal of quality technology, v 40(1), pp 31-45
- Publisher
- Taylor & Francis
- Number of pages
- 15
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Richard C. Goodwin College of Professional Studies
- Web of Science ID
- WOS:000252103200003
- Scopus ID
- 2-s2.0-39449128875
- Other Identifier
- 991021861864204721
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InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Industrial
- Operations Research & Management Science
- Statistics & Probability