Elementary Statistical Methods and Measurement Error.
The way in which sources of physical variation interact with a data collection plan decides what can be learned from the resulting dataset, and how measurement error is reflected in the dataset especially. Implications of this fact are not usually given sufficient attention in the majority of stati...
| Publicado en: | American Statistician Vol. 64; no. 1; pp. 46 - 52 |
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| Autores principales: | , , , , , , , |
| Formato: | Artículo |
| Publicado: |
American Statistical Association
February 2010
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=511475970&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 511475970 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00031305 STT jtl: American Statistician issn: 00031305 maglogo: N pubinfo: dt: February 2010 vid: 64 iid: 1 pid: 543 pub: American Statistical Association artinfo: ui: 511475970 ppf: 46 ppct: 6 formats: tig: atl: Elementary Statistical Methods and Measurement Error. aug: au: Vardeman, Stephen B. Wendelberger, Joanne R. Burr, Tom Hamada, Michael S. Moore, Leslie M. Jobe, J. Marcus Morris, Max D. Wu, Huaiqing su: Statistics education Error analysis in mathematics Measurement sug: subj: Statistics education Error analysis in mathematics Measurement ab: The way in which sources of physical variation interact with a data collection plan decides what can be learned from the resulting dataset, and how measurement error is reflected in the dataset especially. Implications of this fact are not usually given sufficient attention in the majority of statistics courses. Even the most basic statistical methods have their practical effectiveness curtailed by measurement variation; and comprehending how measurement variation interacts with data collection and the methods is useful for quantifying the nature of measurement error. The writers highlight how straightforward one- and two-sample statistical methods can be effectively used in introducing important concepts of metrology and the effects of those concepts when reaching conclusions on the basis of data. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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