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Free eBook Statistical Methods for Reliability Data download

by Luis A. Escobar,William Q. Meeker

Free eBook Statistical Methods for Reliability Data download ISBN: 0471143286
Author: Luis A. Escobar,William Q. Meeker
Publisher: Wiley-Interscience; 1 edition (July 24, 1998)
Language: English
Pages: 712
Category: Engineering & Transportation
Subcategory: Engineering
Size MP3: 1201 mb
Size FLAC: 1957 mb
Rating: 4.8
Format: mbr azw lit docx


Bringing statistical methods for reliability testing in line with the computer age This volume presents . LUIS A. ESCOBAR, PhD, is a Professor in the Department of Experimental Statistics at Louisiana State University.

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.

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Reliability and survival analysis both deal with time to failure data. He said he did not plan to do it because Meeker and Escobar had just finished a work that would be as good as any revision he might want to produce. Much of the methodology is essentially the same. The term reliability is generally used to apply to hardware or software whereas survival analysis is a term for biological systems such as animals or humans. This book includes the standard nonparametric and parametric methods for estimating reliability functions and parameters. Other topics include failure time regression models including the popular Cox proportional hazards model and accelerated life test models.

William Q. Statistical Methods for Reliability Data was among those chosen.

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Reliability in statistics and psychometrics is the overall consistency of a measure. A measure is said to have a high reliability if it produces similar results under consistent conditions. It is the characteristic of a set of test scores that. It is the characteristic of a set of test scores that relates to the amount of random error from the measurement process that might be embedded in the scores. Scores that are highly reliable are accurate, reproducible, and consistent from one testing occasion to another.

Bringing statistical methods for reliability testing in line with the computer age This volume presents state-of-the-art, computer-based statistical . Additional Product Features. Place of Publication.

Additional Product Features.

Statistical Methods for Reliability Data was among those chosen.

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

Bringing statistical methods for reliability testing in linewith the computer age This volume presents state-of-the-art,computer-based statistical methods for reliability data analysisand test planning for industrial products. Statistical Methodsfor Reliability Data updates and improves establishedtechniques as it demonstrates how to apply the new graphical,numerical, or simulation-based methods to a broad range of modelsencountered in reliability data analysis. It includes methods forplanning reliability studies and analyzing degradation data,simulation methods used to complement large-sample asymptotictheory, general likelihood-based methods of handling arbitrarilycensored data and truncated data, and more. In this book, engineersand statisticians in industry and academia will find:

A wealth of information and procedures developed to giveproducts a competitive edgeSimple examples of data analysis computed with the S-PLUSsystem-for which a suite of functions and commands is availableover the InternetEnd-of-chapter, real-data exercise setsHundreds of computer graphics illustrating data, results ofanalyses, and technical concepts

An essential resource for practitioners involved in productreliability and design decisions, Statistical Methods forReliability Data is also an excellent textbook for on-the-jobtraining courses, and for university courses on applied reliabilitydata analysis at the graduate level.An Instructor's Manual presenting detailed solutions to all theproblems in the book is available upon requestfrom the Wileyeditorial department.

User reviews
Qucid
This book is great iff:
1) You enjoy doing math by hand on paper
Or
2) You travel back in time to a point where computers don't exist
Or
3) You have lots of time to reinvent the wheel
Or
4) You plow a field with an ox and feel the old ways are the best
Or
5) You're Amish or a Luddite

For the rest of us, don't bother. This book wold have been something special 40 years ago. Now, it's as outdated as the chapter on linearizing functions (so you can plot them on log paper.)

Many of the questions in this book remind me of conversations I have with my grand mother about the "old days". I think my grandmother is the target audience.
Lost Python
Relevant techniques, well organized, clear writing, and most importantly, useful . Need a solid background in statistics to get through most of the material.
Inth
This book might be of use to someone who's interested in all of the math behind reliability estimation, but it's of little practical use to anyone who doesn't have a year to study it. It's full of little gems like: "This [equation] can be used to parametrically adjust the nonparametric estimate of probability plot shown in...". You get equation after equation with no explicit way to get from the abstract math to the plots and conclusions you really need.

Not for anyone who needs a quick, practical guide to reliability analysis.
Xar
Reliability and survival analysis both deal with time to failure data. Much of the methodology is essentially the same. The term reliability is generally used to apply to hardware or software whereas survival analysis is a term for biological systems such as animals or humans. This book includes the standard nonparametric and parametric methods for estimating reliability functions and parameters. It includes system reliability and repairable systems and deals with recent developments with repairable systems including Nelson's mean cumulative function. A couple of years ago I asked Wayne Nelson if and when he might revise his popular text "Applied Life Data Analysis". He said he did not plan to do it because Meeker and Escobar had just finished a work that would be as good as any revision he might want to produce. Other topics include failure time regression models including the popular Cox proportional hazards model and accelerated life test models. It also includes modern topics such as bootstrap confidence intervals (both semi-parametric and nonparametric) for reliability parameters. The book is comprehensive and up-to-date. It also includes discussion of Bayesian methods. Some case studies are also included. The only topics it misses are reliability growth and warranty and service contracts. These topics are covered in the recent book by Blischke and Murthy "Reliability Modeling, Prediciton, and Optimization" also published by John Wiley and Sons, Inc.
Numerical examples are done using the SPlus software from MathSoft. An ftp site is available to download data sets to use with SPlus.
Andronrad
The purpose of this book was supposed to serve very broad groups of people: students, statisticians and engineers. Unfortunately, I found this book not quite suitable in engineering practice.
From practical point of view, when dealing with reliability estimations, one has to connect mathematical theory with real-life data. It appears that to accomplish this task it is necessary to understand some basic statistical ideas, plus specifics of the subject under consideration. Sometimes common sense knowledge can come in handy. Strangely enough but many fundamental principles are in fact surprisingly simple, elegant and thus beautiful. What is missing in the book is the lack of clear explanations of fundamental statistical concepts that certainly can be presented in a complicated form but in reality they are not. On the other side, the book could serve as a solid textbook to students, statisticians and mathematicians.
Buridora
One of the best books on Reliability Data analysis with an excellent set of examples and clear writing style.