2005 / xii + 247 pages / Softcover / ISBN: 978-0-898715-92-7 / List Price $97.50 / ASA-SIAM Member Price $68.25 / Order Code SA18
ASA-SIAM Series on Statistics and Applied Probability 18
"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.
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.
The Analysis of Means: A Graphical Method for Comparing Means, Rates, and Proportions 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™ 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.
Applied statisticians, particularly consulting statisticians, will find that the graphical aspect of ANOM makes it easy to convey results to nonstatisticians. Industrial, process, and quality engineers will find that the ANOM decisions charts offer an ideal interface with management and can be instrumental in selling research conclusions. The ANOM procedures are great for comparing the rates and proportions found in managed health care settings, and for comparing outcomes in multiarm studies done by statistical researchers in medicine.
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.
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