The Analysis of Means
A Graphical Method for Comparing Means, Rates, and Proportions
Published on 31. August 2005
Book
Paperback/Softback
259 pages
978-0-89871-592-7 (ISBN)
Description
The analysis of means (ANOM) is a graphical procedure used to quantify differences among treatment groups in a variety of experimental design and observational study situations. The ANOM decision chart allows one to easily draw conclusions and interpret results with respect to both statistical and practical significance. It is an excellent choice for multiple comparisons of means, rates, or proportions and can be used with both balanced and unbalanced data. Key advances in ANOM procedures that have appeared only in technical journals during the last 20 years are included in this first comprehensive modern treatment of the ANOM containing all of the needed information for practitioners to understand and apply ANOM.
This book contains examples from a wide variety of fields adapted from real-world applications and data with easy-to-follow, step-by-step instructions. It is front loaded, so potential ANOM users can find solutions to standard problems in the first five chapters. An appendix contains several SAS (R) examples showing the system's ANOM capabilities and how SAS was used to produce selected ANOM decision charts in the book.
Given these features, the lack of any other book on ANOM, and the recent inclusion of ANOM in SAS, this book will be a welcome addition to practitioners' and statisticians' bookshelves, where it will serve both as a primer and reference.
This book contains examples from a wide variety of fields adapted from real-world applications and data with easy-to-follow, step-by-step instructions. It is front loaded, so potential ANOM users can find solutions to standard problems in the first five chapters. An appendix contains several SAS (R) examples showing the system's ANOM capabilities and how SAS was used to produce selected ANOM decision charts in the book.
Given these features, the lack of any other book on ANOM, and the recent inclusion of ANOM in SAS, this book will be a welcome addition to practitioners' and statisticians' bookshelves, where it will serve both as a primer and reference.
Reviews / Votes
'The book provides a well-written, comprehensive discussion of the analysis of means applied to almost all standard statistical methods.' William J. Wilson, Professor of Statistics, University of North Florida 'The book is a very practical comprehensive guide to the ANOM procedure. It has widespread application and is written at a level that can be comprehended by those who do not have a background in statistics. The ANOM procedures presented in the book provide the reader a means of communicating data to management, industry regulators, and peers in simple, graphical terms. The procedures combine the power of statistics with the simplicity of basic plotting techniques.' Sheri L. Meredith, Senior Quality Engineer, Vistakon, Division of Johnson & Johnson Vision Care, Inc., Jacksonville, Florida 'This book provides an excellent introduction to ANOM, and it should be part of every statistician's library. It is well written and covers a wide range of methods ... In addition to the lucid presentation of the different types of analyses, the authors also provide excellent explanations of statistical concepts, as they are needed.' Gudmund R. Iverson, MAA ReviewsMore details
Series
Language
English
Place of publication
New York
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Dimensions
Height: 247 mm
Width: 170 mm
Thickness: 13 mm
Weight
458 gr
ISBN-13
978-0-89871-592-7 (9780898715927)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Peter R. Nelson (1949 - 2004), formerly of Clemson University, played a key role in the development of ANOM methods. In addition to authoring journal articles, books, and book chapters, he served as editor and as editorial review board member of the Journal of Quality Technology and as editorial review board member of the American Journal of Mathematical and Management Sciences. Peter was a Fellow of the ASA and ASQ. Peter S. Wludyka is Associate Professor of Statistics at the University of North Florida, Jacksonville, and Director of the UNF Center for Research and Consulting in Statistics. He is also Biostatistical Consultant to the Office of the Dean at the University of Florida Health Sciences Center in Jacksonville, Florida. His paper on analysis of means for variances, co-authored with Peter Nelson, won the Wilcoxon Prize as best applications paper in Technometrics in 1997. Among his publications are a half dozen methodological papers on the ANOM. He has presented talks to SAS user groups on ANOM and routinely uses ANOM in collaborative research and consulting. Karen A. F. Copeland is currently the principal statistician of Boulder Statistics, which provides statistical consulting services to clients in a variety of industry sectors, including the medical device, chemical, medical diagnostic, environmental, consumer product, food product, and tourism sectors. She held academic and industrial positions before becoming an independent consultant. Karen is a co-author of Introductory Statistics for Engineering Experimentation (Academic Press, 2003) as well as an author of peer-reviewed papers. She has developed JMP scripts for using ANOM.
Content
Preface
Introduction
Chapter 1: One-Factor Balanced Studies
Chapter 2: One-Factor Unbalanced Studies
Chapter 3: Testing for Equal Variances
Chapter 4: Complete Multi-Factor Studies
Chapter 5: Incomplete Multi-Factor Studies
Chapter 6: Axial Mixture Designs
Chapter 7: Heteroscedastic Data
Chapter 8: Distribution-Free Techniques
Appendix A: Figures
Appendix B: Tables
Appendix C: SAS Examples
References
Index.
Introduction
Chapter 1: One-Factor Balanced Studies
Chapter 2: One-Factor Unbalanced Studies
Chapter 3: Testing for Equal Variances
Chapter 4: Complete Multi-Factor Studies
Chapter 5: Incomplete Multi-Factor Studies
Chapter 6: Axial Mixture Designs
Chapter 7: Heteroscedastic Data
Chapter 8: Distribution-Free Techniques
Appendix A: Figures
Appendix B: Tables
Appendix C: SAS Examples
References
Index.