Fitting Straight Lines When Both Variables are Subject to Error

Article

Mandel, John   (1984, ASQC)   National Bureau of Standards

Journal of Quality Technology    Vol. 16    No. 1
QICID: 5485    January 1984    pp. 1-14
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Article Abstract

(This paper was presented at the Journal of Quality Technology Session of the 27th Annual Fall Technical Conference of the Chemical and Process Industries Division of the American Society for Quality Control and the Section on Physical and Engineering Sciences of the American Statistical Association in Midland, Michigan, October 13-14, 1983.)Least squares linear regression is one of the most widely used statistical techniques. Almost all textbooks or statistical methods provide the necessary formulas for the fitting process, based on the assumption that there is no error in the independent variable. How these formulas should be modified when both variables are subject to error is dealt with in detail using as an example an interlaboratory study.

Keywords

Statistics,Calibration,Interlaboratory comparisons,Inverse regression,Least squares,Linear regression


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