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Standard Error

  • Dean P. Foster
  • Robert A. Stine
  • Richard P. Waterman

Abstract

Control charts are important tools for ensuring quality in manufacturing. This class shows how control charts are constructed and used to monitor various types of processes. The key idea in developing these charts combines the empirical rule from Class 2 with our observation that summary measures such as the mean vary less than the original data. How much less? Today we show how variation in the average, as measured by its standard error, is related to variation in the individual values. We then determine, with the help of the normal model and the empirical rule, how to set one type of control limits.

Keywords

False Alarm Control Chart Assembly Line Control Limit Empirical Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • Dean P. Foster
    • 1
  • Robert A. Stine
    • 1
  • Richard P. Waterman
    • 1
  1. 1.Department of Statistics, Wharton SchoolUniversity of PennsylvaniaPhiladelphiaUSA

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