Exclusive Content & Downloads from ASQ

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.

Please sign-in or register to download this information. Registration is FREE and gives you access to ASQ's articles, case studies and general information.

Other Ways to Access content:

Join ASQ

Join ASQ as a Full member. Enjoy all the ASQ member benefits including access to many online articles.

Subscribe to Journal of Quality Technology

Access this and ALL OTHER Journal of Quality Technology online articles. You'll also receive the print version by mail.

  • 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