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Multivariate methods
Use multivariate analyses to evaluate relationships among variables from many
different perspectives. Perform multivariate tests of means, or fit
multivariate regression and MANOVA models. Explore relationships between two
sets of variables, such as aptitude measurements and achievement measurements,
using canonical correlation. Examine the number and structure of latent
concepts underlying a set of variables using exploratory factor analysis. Or
use principal component analysis to find underlying structure or to reduce the
number of variables used in a subsequent analysis. Discover groupings of
observations in your data using cluster analysis. If you have known groups in
your data, describe differences between them using discriminant analysis. And
much more.
Factor analysis 
- Works on datasets or correlation matrices
- Principal-components factor
- Principal factor
- Iterated principal factor
- ML factors
- Rotations
- Orthogonal and oblique rotations
- Kaiser normalization
- Varimax, quartimax, oblimax, parsimax, equamax, and promax rotation
- Minimum entropy rotation
- Comrey's tandem
- Rotate toward a target matrix
- Anti-image correlation matrices
- Kaiser–Meyer–Olkin measure of sampling adequacy
- Loading plots , score plots , and scree plots
- Squared multiple correlations
- Bartlett scoring
- Regression scoring
Principal components
- Works with datasets or correlation or covariance matrices
- Standard errors of eigenvalues and vectors
- Anti-image correlation matrices
- Kaiser–Meyer–Olkin measure of sampling adequacy
- Loading plots , score plots , and scree plots
- Squared multiple correlations
- Rotations
- Orthogonal and oblique rotations
- Kaiser normalization
- Varimax, quartimax, oblimax, parsimax, equamax, and promax rotation
- Minimum entropy rotation
- Comrey’s tandem
- Rotate toward a target matrix
Discriminant analysis
- Linear
- Quadratic
- Logistic Updated
- kth nearest neighbor Updated
- Classification tables
- Error rates
Zellner’s seemingly unrelated regression
- Two-step or maximum likelihood estimates
- Linear constraints
- Breusch-Pagan test for independent equations
Multivariate linear regression
- Breusch–Pagan test for independent equations
- Bayesian multivariate regression
Procrustes analysis
- Orthogonal, oblique, and unrestricted transformations
- Overlaid graphs comparing target variables and fitted values of
source variables
Canonical correlations
- Correlation matrices
- Loading matrices
- Rotate raw coefficients, standard coefficients, or loading matrices
- Compare rotated and unrotated coefficients or loadings
- Plot canonical correlations
Tetrachoric correlations
- Maximum likelihood or noniterative Edwards and Edwards estimator
- Tetrachoric correlation coefficient and standard error
- Exact two-sided significance level
See New in Stata 18 to learn about what was added in Stata 18.
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