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Sinopsis
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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Acerca del autor
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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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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Applied Multivariate Data Analysis
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