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The Prediction Properties of Classical and Inverse Regression for the Simple Linear Calibration Problem

Summary: [This abstract is based on the authors' abstract.] The classical approach to the calibration of measurement systems is examined. This method treats the standards as the regressor and the observed values as the response when calibrating the instrument. However, the resulting regression model must then be inverted in order to use the instrument. Inverse regression which treats the standards as the response and the observed measurements as the regressor is attractive because it is easily implemented in most software, but it violates some of the basic regression assumptions. The performance of both classical and inverse regression applied to calibration problems is compared.

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  • Topics: Statistics, Standards
  • Keywords: Calibration, Measurement system, Uncertainty, Response surface methodology (RSM), Inverse regression
  • Author: Parker, Peter A.; Vining, G. Geoffrey; Wilson, Sara R.; Szarka III, John L.; Johnson, Nels G.
  • Journal: Journal of Quality Technology