Methods of Fitting Straight Lines where Both Variables are Subject to Measurement Error

In this paper errors in variables methods for fitting straight lines to data are reviewed. In these methods the x and y variables are both assumed to be subject to measurement error and not, as in simple least squares linear regression, just one of them. The methods are described in a unified context using the statistical principle of the method of moments. Guidance is given on the choice of an appropriate method of estimating the slope and intercept of the fitted line. Formulas for the approximate standard errors of the estimators are provided in a technical appendix. A numerical example from biochemical studies is included to illustrate the methodology.