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Optimum Confidence Interval Analysis in Two-Factor Mixed Model with a Concomitant Variable for Gauge Study

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Optimization and Control Methods in Industrial Engineering and Construction

Part of the book series: Intelligent Systems, Control and Automation: Science and Engineering ((ISCA,volume 72))

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Abstract

Measurements often include a variety sources of variability due to measurement systems. The measurement systems study is performed to determine if the measurement procedure is appropriate for monitoring a manufacturing process. The measurement systems study here focuses on determining the amount of variability in a two-factor mixed model with a concomitant variable and no interaction in situations where two variables are correlated. The Analysis of variance is performed for the model and variabilities in the model are represented in a linear combination of variance components. Optimum confidence intervals are constructed using Modified Large Sample approach and Generalized Inference approach in order to determine the variability such as repeatability, reproducibility, parts, gauge, and the ratio of variability of parts to variability of gauge. A numerical example is provided to compute the optimum confidence intervals to investigate the variability in the model.

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Authors and Affiliations

  1. Department of Statistics, Pukyong National University, Busan, South Korea

    Dong Joon Park & Min Yoon

Authors
  1. Dong Joon Park
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  2. Min Yoon
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Correspondence to Min Yoon .

Editor information

Editors and Affiliations

  1. Dept of Mathematics & Statistics, Curtin University, Perth, West Australia, Australia

    Honglei Xu

  2. School of Built Environment, Curtin University, Perth, West Australia, Australia

    Xiangyu Wang

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Park, D.J., Yoon, M. (2014). Optimum Confidence Interval Analysis in Two-Factor Mixed Model with a Concomitant Variable for Gauge Study. In: Xu, H., Wang, X. (eds) Optimization and Control Methods in Industrial Engineering and Construction. Intelligent Systems, Control and Automation: Science and Engineering, vol 72. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-8044-5_4

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  • DOI: https://doi.org/10.1007/978-94-017-8044-5_4

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  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-017-8043-8

  • Online ISBN: 978-94-017-8044-5

  • eBook Packages: EngineeringEngineering (R0)

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