Computer File
Multivariate Data Analysis (Seventh Edition)
Multivariate analysis techniques are popular because they enable organizations to create knowledge
and thereby improve their decision making. Multivariate analysis refers to all statistical techniques
that simultaneously analyze multiple measurements on individuals or objects under investigation.
Thus, any simultaneous analysis of more than two variables can be loosely considered multivariate
analysis.
Many multivariate techniques are extensions of univariate analysis (analysis of single-variable
distributions) and bivariate analysis (cross-classification, correlation, analysis of variance, and sim-
ple regression used to analyze two variables). For example, simple regression (with one predictor
variable) is extended in the multivariate case to include several predictor variables. Likewise, the
single dependent variable found in analysis of variance is extended to include multiple dependent
variables in multivariate analysis of variance. Some multivariate techniques (e.g., multiple regression
and multivariate analysis of variance) provide a means of performing in a single analysis what
once took multiple univariate analyses to accomplish. Other multivariate techniques, however, are
uniquely designed to deal with multivariate issues, such as factor analysis, which identifies the struc-
ture underlying a set of variables, or discriminant analysis, which differentiates among groups based
on a set of variables.
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