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A Bayesian Approach to the Analysis of Industrial Experiments: An Illustration with Binomial Count Data

Summary: ´╗┐[This abstract is based on the authors' abstract.] Bayesian methods are used to analyze binomial count data collected from a designed experiment in an industrial application. In addition to fitting an appropriate model to the data, it is shown how the Bayesian approach provides an integrated framework to address model goodness-of-fit, model selection, response surface estimation, optimization, and prediction.

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  • Topics: Statistical Process Control (SPC)
  • Keywords: Bayesian methods, Goodness of fit, ARL contour plots, Factorial designs, Regression, Markov chains, Monte Carlo methods, Prediction, Response surfaces, Optimal design
  • Author: Weaver, Brian P.; Hamada, Michael S.
  • Journal: Quality Engineering