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Prediction Intervals, Tolerance Intervals and Standards in Quality Control - Part 1

Author

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  • von Collani Elart

    (University of Würzburg, Sanderring 2, D-97070 Würzburg. collani@mathematik.uni-wuerzburg.de)

  • Baur Karl

    (E.ON Kernkraft GmbH, Treskowstr. 5, D-30457 Hannover. karl.baur@eon-energie.com)

Abstract

Industrial quality control is necessary at least for two reasons:Monitoring product or process quality in order to perform interventions for assuring a desired quality level.Monitoring product or process quality in order to meet certain requirements (imposed by law or customers) for documentation.Especially in the second case legal requirements or consumers demand that “professional” methods are used. Therefore, industry relies on the methods offered by “professional” bodies like the International Organization for Standardization (ISO) or its national counterparts for example the German DIN Deutsches Institut für Normung, because they are widely acknowledged as professional.One of the standards which are in use in German industries is DIN 55303 (Teil 5): “Statistische Auswertung von Daten - Bestimmung eines statistischen Anteilsbereichs” of February 1987 which corresponds to ISO 3207-1975 “Statistical interpretation of data: determination of a statistical tolerance interval.” This paper introduces in detail statistical tolerance intervals and subsequently examines the standard critical. This first part is devoted to the case that the normal approximation is used and results in the recommendation not to use the methods offered in the standard. A second part will investigate the case that the normal approximation is not made.

Suggested Citation

  • von Collani Elart & Baur Karl, 2002. "Prediction Intervals, Tolerance Intervals and Standards in Quality Control - Part 1," Stochastics and Quality Control, De Gruyter, vol. 17(1), pages 81-98, January.
    • Handle: RePEc:bpj:ecqcon:v:17:y:2002:i:1:p:81-98:n:8
    DOI: 10.1515/EQC.2002.81
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    References listed on IDEAS

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    1. E. Collani, 1999. "Control of production processes subjectto random shocks," Annals of Operations Research, Springer, vol. 91(0), pages 289-304, January.
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