Presentation from the Feb 19, 2021 event on Orthogonal Regression. We have successfully used this approach for gage to gage correlations.
- Why would we want to do Orthogonal Regression?
- What are the computational differences between ordinary least squares (OLS) and Orthogonal Regression
- Perform an Orthogonal Regression study using JMP
- A method originally attributed to W. Edwards Deming takes into account the error in both variables.
- The result is completely invertible! It does not matter which is the “x” variable and which is the “y” variable.
- The method is referred to as “orthogonal regression” (regression when both input and output have variability).
- Creates a simple linear relationship only (no higher order terms): x2 = bx1 + a
- Requires only a knowledge (or estimate) of the ratio of the error in y to the error in x.
Please feel free to contact me with any comments or questions.
Many thanks to those who contributed to this presentation over the years: Lyle Dockendorf, Joe Liu, Jim Gillard, Hugh Quinn, and Brenda Scott.
- Orthogonal Regression uses an approach that minimizes the sum of the squared perpendicular differences
- Requires that you specify the ratio of the variance of the error in X and Y