Divide and Conquer In Reliability Analyses

dc.contributor.author Doganaksoy, Necip
dc.contributor.author Hahn, Gerald
dc.contributor.author Meeker, William
dc.contributor.author Meeker, William
dc.contributor.department Statistics
dc.date 2021-05-06T20:42:43.000
dc.date.accessioned 2021-08-15T01:49:02Z
dc.date.available 2021-08-15T01:49:02Z
dc.date.copyright Thu Jan 01 00:00:00 UTC 2009
dc.date.issued 2009-11-01
dc.description.abstract <p>All product is not created equal. Some units are more likely to fail in service than others. Thus, in reliability evaluations, you need to identify subpopulations with different failure susceptibility. This is accomplished through segmentation—a divide-and-conquer strategy that breaks down the product population into meaningful subpopulations so you can conduct separate analyses on each and then act on the resulting information.</p>
dc.description.comments <p>This article is published as Doganaksoy, N., Hahn, G.J., and Meeker, W.Q. (2009), Divide and Conquer in Reliability Analysis. <em>Quality Progress </em>42, November, 46–48. Posted with permission.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/stat_las_pubs/333/
dc.identifier.articleid 1329
dc.identifier.contextkey 22671698
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath stat_las_pubs/333
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/JvNVQ3ev
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/333/0-Response_from_ASQ.pdf|||Fri Jan 14 23:39:04 UTC 2022
dc.source.bitstream archive/lib.dr.iastate.edu/stat_las_pubs/333/2009_Meeker_DivideConquer.pdf|||Fri Jan 14 23:39:06 UTC 2022
dc.subject.disciplines Categorical Data Analysis
dc.subject.disciplines Probability
dc.subject.disciplines Statistical Methodology
dc.title Divide and Conquer In Reliability Analyses
dc.type article
dc.type.genre article
dspace.entity.type Publication
relation.isAuthorOfPublication a1ae45d5-fca5-4709-bed9-3dd8efdba54e
relation.isOrgUnitOfPublication 264904d9-9e66-4169-8e11-034e537ddbca
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