Synopses & Reviews
This practical, highly readable work presents the latest methods in life data analysis both simple and sophisticated ones for predicting the time-to-failure of a product or living organism. Life data analysis is currently used to predict warranty costs, evaluate replacement policies, assess design changes, and compare alternate designs, vendors, materials, and manufacturing methods. It is also a valuable tool in the comparison of alternative medical treatments. In both organization and scope Applied Life Data Analysis is designed to serve the day-to-day user of statistics industrial statisticians, engineers, and other practitioners in fields where survival data are studied, such as medicine, biology, actuarial science, economics, business, and criminology. Its logical, step-by-step development of the methodology progresses from basic models and simple graphical analyses of data through advanced analytical methods, with each topic self-contained for easy reference. A wealth of practical numerical examples using real data simplify the application of the techniques. Derivations are omitted unless they prove helpful in understanding the material. Among the useful tools explained are:
- simple graphical methods for estimating a life distribution from complete and censored life data
- statistical models and analyses for data on competing failure modes and on series systems
- linear and maximum likelihood methods for estimating life distributions from complete and censored data
- methods for analyzing inspection data (quantal-response and interval data)
- and methods for comparing samples (hypothesis tests) and for pooling estimates from a number of samples.
A valuable working reference for test and reliability engineers, product designers, mechanical and electrical engineers, statisticians and biostatisticians, Applied Life Data Analysis also serves as a text for upper level undergraduate and graduate courses.
Synopsis
WILEY-INTERSCIENCE PAPERBACK SERIESThe Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.
"Many examples drawn from the author’s experience of engineering applications are used to illustrate the theoretical results, which are presented in a cookbook fashion...it provides an excellent practical guide to the analysis of product-life data."
–T.M.M. Farley
Special Programme of Research in Human Reproduction
World Health Organization
Geneva, Switzerland
Review in Biometrics, September 1983
Now a classic, Applied Life Data Analysis has been widely used by thousands of engineers and industrial statisticians to obtain information from life data on consumer, industrial, and military products. Organized to serve practitioners, this book starts with basic models and simple informative probability plots of life data. Then it progresses through advanced analytical methods, including maximum likelihood fitting of advanced models to life data. All data analysis methods are illustrated with numerous clients' applications from the author's consulting experience.
Synopsis
A valuable reference in life data analysis researchYour practical guide to statistical methods for predicting product life and reliability and comparing improvements in product manufacturing, design, and application. Describes the use of graphical methods, the method of maximum likelihood, censored data analysis, linear estimation, prediction methods, and methods for complete, singly censored, multiply censored, interval, and quantalresponse data. Techniques are illustrated with step-by-step, real-data numerical examples. This paperback edition features a new preface and includes a brief survey of commercially available software for the analysis of reliability data.
About the Author
DR. WAYNE NELSON IS AWARDED THE SHEWHART MEDALAmerican Society for Quality awarded Dr. Wayne Nelson of Schenectady, New York the 2003 Shewhart Medal. The Medal honors his outstanding technical leadership, particularly for innovative developments and applications of theory and methods for analyzing quality, reliability, and accelerated test data, and for widely disseminating such developments through his books and many publications, talks, and courses.
The Shewhart Medal for outstanding technical leadership is named after Dr. Walter A. Shewhart, who pioneered statistical methods for controlling and improving the quality of manufactured products. These methods contributed significantly to the United States' war effort in World War II. Subsequently taken to Japan by Dr. W. Edwards Deming, these methods revolutionized Japan's industries. Today these methods are part of widely used Six Sigma training on how to improve the quality of products and services.
The American Society for Quality is the world's largest professional society dedicated to the improved quality of products and services. It serves its members and the public through a variety of educational activities, including conferences, training courses, journals, and books.
Dr. Nelson is a graduate of the California Institute of Technology (Caltech) and the University of Illinois. Formerly with GE Research & Development, he now privately consults and gives courses for companies, professional societies, and universities. For his technical contributions, he was elected a Fellow of the American Society for Quality, the American Statistical Association, and the Institute of Electrical and Electronic Engineers. He recently spent four months in Argentina on a Fulbright Award, lecturing on analysis of product reliability data.
Table of Contents
Preface to the Paperback Edition.
Preface.
About the Author.
1.Overview and Background.
2. Basic Concepts and Distributions for Product Life.
3. Probability Plotting of Complete and Singly Censored Data.
4. Graphical Analysis of Multiply Censored Data.
5. Series Systems and Competing Risks.
6. Analysis of Complete Data.
7. Linear Methods for Singly Censored Data.
8. Maximum Likelihood Analysis of Multiply Censored Data.
9. Analyses of Inspection Data (Qualtal-Response and Interval Data).
10. Comparisons (Hypothesis Tests) For Complete Data.
11. Comparisons with Linear Estimators (Singly Censored and Complete Data).
12. Maximum Likelihood Comparisons (Multiply Censored and Other Data).
13. Survey of Other Topics.
Appendix A. Tables.
References.
Index.