Confidence intervals for misclassification rates in a gauge R&R study

Richard K. Burdick; You Jin Park; Douglas Montgomery; Connie M. Borror

doi:10.1080/00224065.2005.11980332

Confidence intervals for misclassification rates in a gauge R&R study

Richard K. Burdick, You Jin Park, Douglas Montgomery, Connie M. Borror

Industrial, Systems and Operations Engineering

Research output: Contribution to journal › Article › peer-review

28 Scopus citations

Abstract

The objective of this paper is to develop a method for constructing confidence intervals for misclassification rates in a gauge repeatability and reproducibility (R&R) study. Confidence intervals are computed using generalized inference methods. Simulation results suggest confidence coefficients are generally greater than the stated value when a sufficient number of parts and operators are selected for the R&R study.

Original language	English (US)
Pages (from-to)	294-303
Number of pages	10
Journal	Journal of Quality Technology
Volume	37
Issue number	4
DOIs	https://doi.org/10.1080/00224065.2005.11980332
State	Published - Oct 2005

Keywords

Analysis of Variance
Gauge Studies
Measurement Systems Capability Analysis
Variance Components

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Strategy and Management
Management Science and Operations Research
Industrial and Manufacturing Engineering

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10.1080/00224065.2005.11980332

Cite this

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abstract = "The objective of this paper is to develop a method for constructing confidence intervals for misclassification rates in a gauge repeatability and reproducibility (R&R) study. Confidence intervals are computed using generalized inference methods. Simulation results suggest confidence coefficients are generally greater than the stated value when a sufficient number of parts and operators are selected for the R&R study.",

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AU - Borror, Connie M.

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AB - The objective of this paper is to develop a method for constructing confidence intervals for misclassification rates in a gauge repeatability and reproducibility (R&R) study. Confidence intervals are computed using generalized inference methods. Simulation results suggest confidence coefficients are generally greater than the stated value when a sufficient number of parts and operators are selected for the R&R study.

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KW - Measurement Systems Capability Analysis

KW - Variance Components

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Confidence intervals for misclassification rates in a gauge R&R study

Abstract

Keywords

ASJC Scopus subject areas

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