A Graph-Aided Method for Planning Two-Level Experiments When Certain interactions Are Important
作者:
C.F. J. Wu,
Youyi Chen,
期刊:
Technometrics
(Taylor Available online 1992)
卷期:
Volume 34,
issue 2
页码: 162-175
ISSN:0040-1706
年代: 1992
DOI:10.1080/00401706.1992.10484905
出版商: Taylor & Francis Group
关键词: Clear interaction;Eligible interaction;Feasible graphs;Interaction graphs;Linear graphs;Minimum aberration designs
数据来源: Taylor
摘要:
In planning a fractional factorial experiment prior knowledge may suggest that some interactions are potentially important and should therefore be estimated free of the main effects. In this article, we propose a graph-aided method to solve this problem for two-level experiments. First, we choose the defining relations for a 2n–kdesign according to a goodness criterion such as the minimum aberration criterion. Then we construct all of the nonisomorphic graphs that represent the solutions to the problem of simultaneous estimation of main effects and two-factor interactions for the given defining relations. In each graph a vertex represents a factor and an edge represents the interaction between the two factors. For the experiment planner, the job is simple: Draw a graph representing the specified interactions and compare it with the list of graphs obtained previously. Our approach is a substantial improvement over Taguchi's linear graphs.
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