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Record Nr. |
UNINA9910877543803321 |
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Autore |
Everitt Brian |
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Titolo |
Applied multivariate data analysis / / Brian S. Everitt and Graham Dunn |
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Pubbl/distr/stampa |
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Chichester, : Wiley, 2001 |
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ISBN |
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1-118-88748-4 |
1-61344-638-1 |
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Edizione |
[2nd ed.] |
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Descrizione fisica |
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1 online resource (x, 342 p.) : ill |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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First published by Arnold. |
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Nota di contenuto |
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1. Multivariate Data and Multivariate Statistics -- 2. Exploring Multivariate Data Graphically -- 3. Principal Components Analysis -- 4. Correspondence Analysis -- 5. Multidimensional Scaling -- 6. Cluster Analysis -- 7. The Generalized Linear Model -- 8. Regression and the Analysis of Variance -- 9. Log‐Linear and Logistic Models for Categorical Multivariate Data -- 10. Models for Multivariate Response Variables -- 11. Discrimination, Classification and Pattern Recognition -- 12. Exploratory Factor Analysis -- 13. Confirmatory Factor Analysis and Covariance Structure Models -- Appendix A: Software Packages -- Appendix B: Missing Values -- Appendix C: Answers to Selected Exercises -- References -- Index. |
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Sommario/riassunto |
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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 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 |
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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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