This book describes the exploratory use of data analysis. The importance of searching for patterns in data is stressed through many worked examples. The interpretation of the output from statistical software is also emphasized. This up-to-date text includes modern techniques such as multidimensional scaling, cluster analysis, generalized linear models and structural equation models. Part I explains data types and the statistical knowledge required to use the book. Part II explains which methods to use. The final parts cover the analysis of variables in more detail. This outstanding volume will prove useful as a reference for statisticians, and valuable to advanced undergraduates and graduate students in fields such as psychology, sociology, economics, and medicine, or in other areas where quantitative methods are central.

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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 second 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, this title provides clear explanations of each technique, supported by figures and examples, using minimal technical jargon. With extensive exercises following every chapter, the book is a valuable resource for students on applied statistics courses and for applied researchers in many disciplines.

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