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# Multivariate Analysis Procedures

• CALIS — Fits structural equation models
• CANCORR — Canonical correlation, partial canonical correlation, and canonical redundancy analysis
• CORRESP — Simple and multiple correspondence analysis
• FACTOR — Factor and component analyses and rotations
• MDS — Fits two- and three-way, metric and nonmetric multidimensional scaling models
• MULTTEST — Addresses the multiple testing problem by adjusting the p-values from a family of hypothesis tests
• PLS — Performs principal components regression
• PRINCOMP — Principal component analysis
• PRINQUAL — Principal component analysis of qualitative, quantitative, or mixed data
• TRANSREG — Fits linear models with optimal nonlinear transformations of variables
• TREE — Produces a tree diagram, also known as a dendrogram or phenogram, from a data set created by the CLUSTER or VARCLUS procedure that contains the results of hierarchical clustering as a tree structure
• TCALIS — Fits structural equation models

# Multivariate Analysis

The multivariate analysis procedures are used to investigate relationships among variables without designating some as independent and others as dependent.

Below are highlights of the capabilities of the SAS/STAT procedures that perform multivariate analysis:

• exploratory and confirmatory factor analysis
• principal components analysis
• canonical correlation and partial canonical correlation
• canonical redundancy analysis
• simple and multiple correspondence analysis
• analysis of covariance structures
• structural equation modeling and path analysis
• general COSAN model
• optimization methods include Levenberg-Marquart algorithm, ridge-stabilized Newton-Raphson, quasi-Newton, and conjugate gradient algorithms
• estimation methods include maximum likelihood, least squares, generalized least squares, weighted least squares, and diagonally weighted least squares
• equality and inequality constraints