Publication date: 08/13/2020

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

The Fit Orthogonal option fits linear models that account for variability in X as well as Y.

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.

Want more information? Have questions? Get answers in the JMP User Community (community.jmp.com).

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