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Analyst Application Multivariate Statistics
Multivariate Analysis
Principal Components
You can use the Principal Components task to compute principal components from a single set of variables. The purpose of principal components analysis is to derive a small number of linear combinations (principal components) that retain as much of the information in the original variables as possible. Sometimes you can use a small number of principal components in place of the original variables for regression, plotting, clustering, and so on. In addition, you can also think of principal component analysis as a way to uncover approximate linear dependencies among a set of variables. Using the Principal Components task, you can specify what statistics are produced, produce component plots and scree plots, and save results to a data set.
Canonical Correlations
You can use the Canonical Correlations task to investigate the relationship between two sets of quantitative variables. The purpose is to describe the relationship by finding a small number of linear combinations from each set of variables that have the highest possible between-set correlations. Looking at plots of the canonical variables can help you to determine multivariate dependencies. You can specify the number of canonical variables, produce both canonical coefficients and canonical redundancy statistics, and predict one set of variables from another using regression. In addition, you can produce canonical variable plots and save canonical scores and statistics to data sets.
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