Fitting Linear Models • Multivariate Response Models

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Multivariate Response Models
Fit Relationships Using MANOVA
Multivariate models fit several responses (Y variables) to a set of effects. The functions across the Y variables can be tested with appropriate response designs.
In addition to creating standard MANOVA (Multivariate Analysis of Variance) models, you can use the following techniques:
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Repeated measures analysis when repeated measurements are taken on each subject and you want to analyze effects both between subjects and within subjects across the measurements. This multivariate approach is especially important when the correlation structure across the measurements is arbitrary.
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Canonical correlation to find the linear combination of the X and Y variables that has the highest correlation.
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Discriminant analysis to find distance formulas between points and the multivariate means of various groups so that points can be classified into the groups that they are most likely to be in. A more complete implementation of discriminant analysis is in the Discriminant platform.
The multivariate fit begins with a rudimentary preliminary analysis that shows parameter estimates and least squares means. You can then specify a response design across the Y variables and multivariate tests are performed.
Contents 
Example of a Multiple Response Model
The Manova Report
The Manova Fit Options
Response Specification
Choose Response Options
Custom Test Option
Multivariate Tests
The Extended Multivariate Report
Comparison of Multivariate Tests
Univariate Tests and the Test for Sphericity
Multivariate Model with Repeated Measures
Repeated Measures Example
Example of a Compound Multivariate Model
Discriminant Analysis
Example of the Save Discrim Option
Statistical Details for the Manova Personality
Multivariate Tests
Approximate F-Tests
Canonical Details

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Help created on 3/19/2020