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タイトルImproved Test Planning and Analysis Through the Use of Advanced Statistical Methods
本文(外部サイト)http://hdl.handle.net/2060/20160007675
著者(英)Cook, Mylan; Glass, David E.; Maxwell, Katherine A.; Green, Lawrence L.; Vaughn, Wallace L.; Barger, Weston
発行日2016-01-04
言語eng
内容記述The goal of this work is, through computational simulations, to provide statistically-based evidence to convince the testing community that a distributed testing approach is superior to a clustered testing approach for most situations. For clustered testing, numerous, repeated test points are acquired at a limited number of test conditions. For distributed testing, only one or a few test points are requested at many different conditions. The statistical techniques of Analysis of Variance (ANOVA), Design of Experiments (DOE) and Response Surface Methods (RSM) are applied to enable distributed test planning, data analysis and test augmentation. The D-Optimal class of DOE is used to plan an optimally efficient single- and multi-factor test. The resulting simulated test data are analyzed via ANOVA and a parametric model is constructed using RSM. Finally, ANOVA can be used to plan a second round of testing to augment the existing data set with new data points. The use of these techniques is demonstrated through several illustrative examples. To date, many thousands of comparisons have been performed and the results strongly support the conclusion that the distributed testing approach outperforms the clustered testing approach.
NASA分類Numerical Analysis
レポートNONF1676L-21573
権利Copyright, Distribution as joint owner in the copyright


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