Elementary Statistical Methods and Measurement Error

dc.contributor.author Vardeman, Stephen
dc.contributor.author Wendelberger, Joanne
dc.contributor.author Burr, Tom
dc.contributor.author Vardeman, Stephen
dc.contributor.author Hamada, Michael
dc.contributor.author Moore, Leslie
dc.contributor.author Jobe, Marcus
dc.contributor.author Morris, Max
dc.contributor.author Wu, Huaiqing
dc.contributor.department Statistics
dc.contributor.department Industrial and Manufacturing Systems Engineering
dc.date 2018-02-18T14:04:03.000
dc.date.accessioned 2020-06-30T04:47:54Z
dc.date.available 2020-06-30T04:47:54Z
dc.date.copyright Fri Jan 01 00:00:00 UTC 2010
dc.date.issued 2010-01-01
dc.description.abstract <p>How the sources of physical variation interact with a data collection plan determines what can be learned from the resulting dataset, and in particular, how measurement error is reflected in the dataset. The implications of this fact are rarely given much attention in most statistics courses. Even the most elementary statistical methods have their practical effectiveness limited by measurement variation; and understanding how measurement variation interacts with data collection and the methods is helpful in quantifying the nature of measurement error. We illustrate how simple one- and two-sample statistical methods can be effectively used in introducing important concepts of metrology and the implications of those concepts when drawing conclusions from data.</p>
dc.description.comments <p>This is an Accepted Manuscript of an article published by Taylor & Francis in The American Statistician on January 1, 2012, available online: <a href="http://www.tandfonline.com/10.1198/tast.2009.09079" target="_blank">http://www.tandfonline.com/10.1198/tast.2009.09079</a>.</p>
dc.format.mimetype application/pdf
dc.identifier archive/lib.dr.iastate.edu/imse_pubs/138/
dc.identifier.articleid 1140
dc.identifier.contextkey 10322777
dc.identifier.s3bucket isulib-bepress-aws-west
dc.identifier.submissionpath imse_pubs/138
dc.identifier.uri https://dr.lib.iastate.edu/handle/20.500.12876/44428
dc.language.iso en
dc.source.bitstream archive/lib.dr.iastate.edu/imse_pubs/138/2010Vardemanand_many.pdf|||Fri Jan 14 20:01:20 UTC 2022
dc.source.uri 10.1198/tast.2009.09079
dc.subject.disciplines Industrial Engineering
dc.subject.disciplines Systems Engineering
dc.subject.keywords Accuracy
dc.subject.keywords Bias
dc.subject.keywords Calibration
dc.subject.keywords Data collection
dc.subject.keywords Linearity
dc.subject.keywords Precision
dc.subject.keywords Repeatability
dc.subject.keywords Reproducibility
dc.subject.keywords Statistical education
dc.title Elementary Statistical Methods and Measurement Error
dc.type article
dc.type.genre article
dspace.entity.type Publication
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relation.isOrgUnitOfPublication 51d8b1a0-5b93-4ee8-990a-a0e04d3501b1
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