| Description |
x, 342 pages : illustrations ; 24 cm |
| Contents |
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 factory analysis -- 13. Confirmatory factory analysis and covariance structure models |
| Summary |
"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."--BOOK JACKET |
| Notes |
Previous ed.: 1991 |
| Bibliography |
Includes bibliographical references and index |
| Subject |
Multivariate analysis.
|
|
Social sciences -- Statistical methods.
|
| Author |
Dunn, G. (Graham), 1949-
|
| LC no. |
2001276408 |
| ISBN |
0340741228 (paperback) |
|
