Publication date: 05/24/2021

The Fit Orthogonal option fits linear models that account for variability in X as well as Y. This is also known as Deming regression.

Note: For an example of this option, see Example of the Fit Orthogonal Option.

Select one of the following options to specify a variance ratio.

Univariate Variances, Prin Comp

Uses the univariate variance estimates computed from the samples of X and Y. This turns out to be the standardized first principal component. This option is not a good choice in a measurement systems application since the error variances are not likely to be proportional to the population variances.

Equal Variances

Uses 1 as the variance ratio, which assumes that the error variances are the same. Using equal variances is equivalent to the non-standardized first principal component line. Suppose that the scatterplot is scaled the same in the X and Y directions. When you show a normal density ellipse, you see that this line is the longest axis of the ellipse.

Fit X to Y

Uses a variance ratio of zero, which indicates that Y effectively has no variance.

Specified Variance Ratio

Lets you enter any ratio that you want, giving you the ability to use known information about the measurement error in X and response error in Y.

For more information about the options in the Orthogonal Fit Ratio menu, see Fitting Menus. For statistical details about this fit, see Fit Orthogonal.

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