Multivariate analysis plays an important role in the understanding
of complex data sets requiring simultaneous examination of all
variables. Breaking through the apparent disorder of the
information, it provides the means for both describing and
exploring data, aiming to extract the underlying patterns and
structure. This intermediate-level textbook introduces the reader
to the variety of methods by which multivariate statistical
analysis may be undertaken. Now in its 2nd edition, 'Applied
Multivariate Data Analysis' has been fully expanded and updated,
including major chapter revisions as well as new sections on neural
networks and random effects models for longitudinal data.
Maintaining the easy-going style of the first edition, the authors
provide clear explanations of each technique, as well as supporting
figures and examples, and minimal technical jargon. With extensive
exercises following every chapter, 'Applied Multivariate Data
Analysis' is a valuable resource for students on applied statistics
courses and applied researchers in many disciplines.
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