Hierarchical Bayes Statistical Analyses for a Calibration Experiment

Date
2006-01-01
Authors
Landes, Reid
Loutzenhiser, Peter
Vardeman, Stephen
Vardeman, Stephen
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Mechanical Engineering
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Statistics
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Mechanical EngineeringStatisticsIndustrial and Manufacturing Systems Engineering
Abstract

n this paper, hierarchical Bayes analyses of an experiment conducted to enable calibration of a set of mass-produced resistance temperature devices (RTDs) are considered. These were placed in batches into a liquid bath with a precise National Institute of Standards and Technology (NIST)-approved thermometer, and resistances and temperatures were recorded approximately every 30 s. Under the assumptions that the thermometer is accurate and each RTD responds linearly to temperature change, hierarchical Bayes methods to estimate the parameters of the linear calibration equations are used. Predictions of the parameters for an untested RTD of the same type and interval estimates of temperature based on a realized resistance reading are also available for both the tested RTDs and an untested one.

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This is a manuscript of an article published as "Hierarchical Bayes statistical analyses for a calibration experiment." IEEE Transactions on Instrumentation and Measurement, 2006, Vol. 55, No. 6, pp. 2165-2171. With Reid Landes and Peter Loutzenhiser. Posted with permission.

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