A statistical procedure for estimating how explanatory variables relate to a dependent variable. It can be used to obtain estimates from calibration data by minimizing the errors in fitting the data (Y = *a* + *b*_{1}X_{1} + *b*_{2}X_{2} …). Typically, Ordinary Least Squares is used for estimation, but least absolute values can be used. Regression analysis is useful in that it shows relationships, and it shows the partial effect of each variable (statistically controlling for the other variables) in the model. As the errors in measurement increase, the regression model shrinks the magnitude of the relationship towards zero. See Allen and Fildes (2001).

- Allen, P. G. & R. Fildes (2001), “Econometric
forecasting,” in J. S. Armstrong (ed.),
*Principles of Forecasting.*
Norwell, MA: Kluwer Academic Press.