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Statistical Methods for Reliability Data

 
 
 
 
Statistical Methods for Reliability Data
Author: William Q. Meeker - Luis A. Escobar
ISBN 13: 9780471143284
ISBN 10: 471143286
Edition: 1
Publisher: Wiley-Interscience
Publication Date: 1998-07-24
Format: Hardcover
Pages: 712
List Price: $200.95
 
 

Bringing statistical methods for reliability testing in line with the computer age This volume presents state-of-the-art, computer-based statistical methods for reliability data analysis and test planning for industrial products. Statistical Methods for Reliability Data updates and improves established techniques as it demonstrates how to apply the new graphical, numerical, or simulation-based methods to a broad range of models encountered in reliability data analysis. It includes methods for planning reliability studies and analyzing degradation data, simulation methods used to complement large-sample asymptotic theory, general likelihood-based methods of handling arbitrarily censored data and truncated data, and more. In this book, engineers and statisticians in industry and academia will find:

  • A wealth of information and procedures developed to give products a competitive edge

  • Simple examples of data analysis computed with the S-PLUS system for which a suite of functions and commands is available over the Internet

  • End-of-chapter, real-data exercise sets

  • Hundreds of computer graphics illustrating data, results of analyses, and technical concepts

An essential resource for practitioners involved in product reliability and design decisions, Statistical Methods for Reliability Data is also an excellent textbook for on-the-job training courses, and for university courses on applied reliability data analysis at the graduate level.

Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Methods for Reliability Data was among those chosen.

Booknews

Explains computer-based statistical methods for reliability data analysis and test planning for industrial products. Demonstrates how to apply the latest graphical, numerical, and simulation-based methods to a broad range of models found in reliability data analysis, and covers areas such as analyzing degradation data, simulation methods used to complement large-sample asymptotic theory, and data analysis computed with the S-PLUS system. Includes chapter exercises using real data sets. For professionals in product reliability and design, and for graduate students in courses in applied reliability data analysis. Annotation c. by Book News, Inc., Portland, Or.