Review

"The real world is multivariate. This update of the first iteration (undated) includes chapter revisions and exercises; new sections on neural networks and random effects models for longitudinal data; and appended information on software packages, and the problem of missing values. Familiarity with matrix algebra is helpful, though more technical material is confined to the tables. Suitable for an applied statistics course text or reference for applied researchers. Everitt is a behavioral statistics professor at the Institute of Psychiatry at King's College in London. Dunn teaches biomedical statistics at the U. of Manchester."--SciTech Book News

"This intermediate to advanced level data analysis book is appropriate for investigators who deal with complex data sets involving multiple response variables. Exploratory and confirmatory methodologies are explained and some mathematical justification for the methods is provided. ... [P]resumably [the] intention [of the authors] is to acquaint readers with the more useful aspects of multivariate analysis and display by updating the material from the earlier edition. There is a need for such information and this book makes a contribution in that direction. It appears to be written for a relatively nonmathematical audience. This could include students and practitioners particularly, but not exclusively, in the social sciences. ... The best thing about the book is that the authors have incorporated graphical display where possible. ... The coverage appears complete and I can't think of any major topic that has been ignored."--Doody's

Product Description

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.