- Visualize and explore complex categorical and numeric data as well as high-dimensional data
- Understand information in large two-way and multiway tables
- Use biplots, triplots and perceptual maps to uncover relationships in your data

**Correspondence analysis (CORRESPONDENCE):** Describe the relationships between two nominal variables in a low-dimensional space while simultaneously describing the relationships between categories for each variable.

**Categorical regression (CATREG):** Predict the values of a categorical dependent variable from a combination of categorical independent variables. Optimal scaling techniques quantify the variables in such a way that the Multiple R is maximized. Regularization methods improve prediction accuracy by stabilizing the parameter estimates.

**Multiple correspondence analysis (MULTIPLE CORRESPONDENCE):**Analyze a categorical multivariate data matrix for two or more nominal variables.

**CATPCA:** Use alternating least squares to generalize principal components analysis to accommodate variables of mixed measurement levels. Specify a transformation type of nominal, ordinal or numeric on a variable-by-variable basis.

**Nonlinear canonical correlation (OVERALS):**Use alternating least squares to generalize canonical correlation analysis. It allows more than one set of variables to be compared to one another on the same graph.

**Proximity scaling (PROXSCAL):** Takes a matrix of similarity and dissimilarity distances between observations in a highdimensional space and assigns them to a position in a lowdimensional space so you can gain spatial understanding of how objects relate.

**Preference scaling (PREFSCAL):** Set up the preference scaling procedure (PREFSCAL) in syntax to perform multidimensional unfolding on two sets of objects to find a map that represents the relationships between these two sets of objects as distances between two sets of points.

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