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Methodological Foundations of Statistical Lot Inspection

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Frontiers in Statistical Quality Control 6

Part of the book series: Frontiers in Statistical Quality Control ((FSQC,volume 6))

Abstract

Statistical lot inspection is an application of the theory of statistical hypothesis testing in the industrial problem of deciding on hypotheses on the quality of finite lots by means of random samples from the lot. Both the systematic industrial implementation and the theoretical investigation of statistical lot inspection trace back to the work of H. F. Dodge at Bell Telephone Laboratories in the 1920s. Investigations on statistical lot inspection have received enormous interest in academic literature. However, rigorous methodological foundations are still missing. In particular, there is no generally accepted model for statistical lot inspection under continuous quality characteristics

This paper presents an elementary model for statistical lot inspection, with special emphasis on the topic of continuous quality characteristics. Under this model, the paper develops most powerful tests for the most important lot quality indicators under continuous quality characteristics: lot mean, average square deviation from target, lot minimum

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Author information

Authors and Affiliations

  1. Institute for Applied Mathematics and Statistics, University of Würzburg, Germany

    Rainer Göb

Authors
  1. Rainer Göb
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Editor information

Editors and Affiliations

  1. Institut für Statistik und Ökonometrie, Freie Universität Berlin, Garystraße 21, Berlin, 14195, Germany

    Hans-Joachim Lenz  & Peter-Theodor Wilrich  & 

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© 2001 Springer-Verlag Berlin Heidelberg

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Göb, R. (2001). Methodological Foundations of Statistical Lot Inspection. In: Lenz, HJ., Wilrich, PT. (eds) Frontiers in Statistical Quality Control 6. Frontiers in Statistical Quality Control, vol 6. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57590-7_1

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  • DOI: https://doi.org/10.1007/978-3-642-57590-7_1

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1374-6

  • Online ISBN: 978-3-642-57590-7

  • eBook Packages: Springer Book Archive

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