Orthogonal regression (Deming regression) investigates and models the relationship between a response (Y) and a single predictor (X). Both the response and predictors are continuous variables. The difference between simple linear regression and orthogonal regression is that the predictor in orthogonal regression contains measurement error.

Orthogonal regression is often used in clinical chemistry and laboratory settings when you want to know if two instruments or two methods are measuring the same thing.

A laboratory wants to determine if two methods of testing glucose levels are equivalent. The laboratory would like to use the new technique if it provides the same measurements as the existing one because the new method is much less expensive. Blood was drawn from participants who fasted overnight, and the lab used the two methods to test the glucose level for each person.

Data: Glucose.MTW (available in the Sample Data folder).