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Informationen zum 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 Klappentext 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. Zusammenfassung 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. Inhaltsverzeichnis 1 Multivariate data and multivariate statistics. 1.1 Introduction. 1.2 Types of data. 1.3 Basic multivariate statistics. 1.4 The aims of multivariate analysis. 2 Exploring multivariate data graphically. 2.1 Introduction. 2.2 The scatterplot. 2.3 The scatterplot matrix. 2.4 Enhancing the scatterplot. 2.5 Coplots and trellis graphics. 2.6 Checking distributional assumptions using probability plots. 2.7 Summary. Exercises. 3 Principal components analysis. 3.1 Introduction. 3.2 Algebraic basics of principal components. 3.3 Rescaling principal components. 3.4 Calculating principal component scores. 3.5 Choosing the number of components. 3.6 Two simple examples of principal components analysis. 3.7 More complex examples of the application of principal components analysis. 3.8 Using principal components analysis to select a subset of variables. 3.9 Using the last few principal components. 3.10 The biplot. 3.11 Geometrical interpretation of principal components analysis. 3.12 Projection pursuit. 3.13 Summary. Exercises. 4 Correspondence analysis. 4.1 Introduction. 4.2 A simple example of correspondence analysis. 4.3 Correspondence analysis for two-dimensional contingency tables. 4.4 Three applications of correspondence analysis. 4.5 Multiple correspondence analysis. 4.6 Summary Exercises. 5 Multidimensional scaling. 5.1 Introduction. 5.2 Proximity matrices and examples of multidimensional scaling. 5.4 Metric least-squares multidimensional scaling. 5.5 Non-metric multidimensional scaling. 5.6 Non-Euclidean metrics. 5.7 Three-way multidimensional scaling. 5.8 Inference in multidimensional scaling. 5.9 Summary. Exercises. 6 Cluster analysis. 6.1 Introduction. 6.2 Agglomerative hierarchical clustering techniques. 6.3 Optimization methods.
