Synopsis
This book emphasises the exploratory use of data analysis. Worked examples help the reader to identify and describe patterns in data. Part one explains data types and the statistical knowledge required to use this book, part two explains which methods to use and the final section covers analysis of variables in detail. This book is designed to be of interest to social and behavioural undergraduates as well as Msc students, statisticians and librarians.
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From the Back Cover
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.
About the Author
Brian S. Everitt is Professor of Behavioural Statistics and Head of the Biostatistics and Computing Department at the Institute of Psychiatry, King’s College London, UK
Graham Dunn is Professor of Biomedical Statistics and Head of the Biostatistics Group within the School of Epidemiology and Health Sciences, University of Manchester, UK
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Applied Multivariate Data Analysis
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Hodder Education Publishers, 1992
ISBN 10: 0340545291
ISBN 13: 9780340545294
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