Applied Discriminant Analysis/Book and Disk (Wiley Series in Probability and Statistics) - Hardcover

Über die Autorin bzw. den Autor

About the author CARL J HUBERTY is Professor of Education at the University of Georgia, where he has also taught in the Department of Statistics. He earned an MS in mathematics from the University of Wisconsin and a PhD in education (applied statistics) from the University of Iowa. He is the author of numerous articles for professional journals and is a contributor to several books on applied statistics. He has conducted numerous workshops on discriminant analysis in Australia, Belgium, and the United States.

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This book/diskette set provides a complete introduction to the application of discriminant analysis data sets and programs included. Most books on discriminant analysis focus on statistical theory. This unique text/reference emphasizes applications and describes the numerous ways in which discriminant analysis can be useful, both to students who must read and understand published research and to professionals conducting original research. Numerous examples, exercises, and detailed explanations help to clarify difficult concepts and enhance your understanding of the subject. Data sets and programs on the accompanying diskette enable you to conduct multiple data analyses, both within and across computer packages. This book provides all the information you need to:

• Understand the kinds of research questions that are best answered by discriminant analysis
• Learn the terms and grasp the underlying concepts
• Read, understand, and interpret various computer package printouts that pertain to discriminant analysis
• Evaluate reports of applied discriminant analysis
• Design a study, analyze resulting data sets, and write a report based on your findings

Aus dem Klappentext

Most books on discriminant analysis focus on statistical theory. But properly applied, discriminant analysis methods can be enormously useful in the interpretation of data. This book is the first ever to offer a complete introduction to discriminant analysis that focuses on applications. It provides numerous examples, explained in great detail, using current statistical discriminant analysis algorithms. It also develops several themes that will be useful to researchers and students regardless of the analytical methods they employ. They are the careful examination of data prior to final analysis; the application of critical judgment and common sense to all analyses and interpretations; and conducting multiple analyses as a matter of routine. To encourage and enable readers to conduct multiple analyses of their data, the accompanying diskette contains the four complete data sets and five special computer programs that are referred to repeatedly in the text and are the subjects of numerous exercise problems. This enables the reader to carry out package analyses on the data sets using a variety of procedural options both within and across computer packages. The term "discriminant analysis" means different things to different people. For statisticians and researchers in the physical sciences, it usually denotes the process through which group membership is predicted on the basis of multiple predictor variables. Behavioral scientists, on the other hand, often use discriminant analysis to describe group differences across multiple response variables. Though closely related, predictive discriminant analysis (PDA) and descriptive discriminant analysis (DDA) are used for different purposes and should be approached in different ways. To accentuate these differences and distinguish clearly between the two, Applied Discriminant Analysis presents these topics separately. For graduate students, this book will expand your background in multivariate data analysis methods and facilitate both the reading and the conducting of applied empirical research. It will also be of great use to experienced researchers who wish to enhance or update their quantitative background, and to methodologists who want to learn more about the details of applied discriminant data analysis, and some still unresolved problems, as well.