Article

Carroll, Raymond J.; Spiegelman, Clifford H. *(1986, ASQC)* *University of North Carolina; National Bureau of Standards*

*Journal of Quality Technology*Vol. 18 No. 3- QICID: 5550 July 1986 pp. 170-173
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This paper discusses the effect of measurement errors in both variables when using the simple linear regression model. It is often stated that if the measurement error in x is small, then we can ignore this error and fit the model to data using ordinary least squares. There is some ambiguity in the statistical literature concerning the exact meaning of a small error. For example, Draper and Smith (1981) state that if the measurement error variance in x is small relative to the variability of the true x's, then "errors in the x's can be effectively ignored." See Montgomery and Peck (1983) for a similar statement. Scheffe (1973) and Mandel (1984) argue for a second criterion, which may be informally summarized that the error in x should be small relative to (the standard deviation of the observed Y about the line)/(slope of the line). We argue that for calibration experiments, both criteria are useful and important; the former for estimation of x given Y, and the latter for the lengths of confidence intervals for x given Y.

Statistics,Calibration,Inverse regression,Least squares,Linear regression,Measurement error

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