Fitting Straight Lines When Both Variables are Subject to Error
John Mandel · Journal of Quality Technology · 1984
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.