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A fully Bayesian approach to sample size determination for verifying process improvement
Safaie, Nasser
Safaie, Nasser
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dissertation
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2010-12
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Dissertation
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Electronic dissertations
Electronic dissertations
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Abstract
There has been significant growth in the development and application of Bayesian
methods in industry. The Bayes’ theorem describes the process of learning from experience and
shows how knowledge about the state of nature is continually modified as new data become
available. This research is an effort to introduce the Bayesian approach as an effective tool for
evaluating process adjustments aimed at causing a change in a process parameter. This is usually
encountered in scenarios where the process is found to be stable but operating away from the
desired level. In these scenarios, a number of changes are proposed and tested as part of the
improvement efforts. Typically, it is desired to evaluate the effect of these changes as soon as
possible and take appropriate actions. Despite considerable research efforts to utilize the
Bayesian approach, there are few guidelines for loss computation and sample size determination.
This research proposed a fully Bayesian approach for determining the maximum economic
number of measurements required to evaluate and verify such efforts. Mathematical models were
derived and used to establish implementation boundaries from economic and technical
viewpoints. In addition, numerical examples were used to illustrate the steps involved and
highlight the economic advantages of the proposed procedures.
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Thesis (Ph.D.)--Wichita State University, College of Engineering, Dept. of Industrial and Manufacturing Engineering
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Wichita State University
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Copyright Nasser Safaie, 2010. All rights reserved