Advances in Physical Organic Chemistry, Volume 37 (APOC) (Advances in Physical Organic Chemistry)

Book Description

Advances in Physical Organic Chemistry provides the chemical community with authoritative and critical assessments of the many aspects of physical organic chemistry. The field is a fast developing one, with results and methodologies finding application from biology to solid state physics.

This latest volume deals comprehensively with investigations that can be traced back to the birth of the field but which are still proving critical to the understanding of the stability of organic molecules and the mechanisms for their reactions.

. Volume 37 of this hugely successful Advances in Physical Organic Chemistry series
. Comprehensive review articles covering various topics of interest within the physical organic chemistry field

Advances in Physical Organic Chemistry, Volume 37 (APOC) (Advances in Physical Organic Chemistry)

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Advances in Physical Organic Chemistry, Volume 37 (APOC) (Advances in Physical Organic Chemistry)
John Richard
Manufacturer: Academic Press
ProductGroup: Book
Binding: Paperback
ASIN: B000OH5E1O

Book Description

This practical resource presents modern, statistical methods for accelerated testing including test models, analyses of data, and plans for testing. Each topic is self-contained for easy reference. Coverage is broad and detailed enough to serve as a text or reference. This handy book features real test examples along with data analyses, computer programs, and references to the literature.

Customer Reviews:

Should be on every Developer and Engineer's shelf........2006-11-01

Dr. Taylor does his usual great job covering a difficult subject - ties in well with his "Applied Life Data Analysis", the revered reliability resource. Because of his GE background, he emphasizes experimental plans and analysis methods (i.e.: partially censored) which permit you to make good predictions regarding a product or system while you are still collecting data (e.g.: management wants an answer NOW, even if the experiment is not completed). A few more examples of how "typical" systems behave would be helpful, but otherwise a great resource.

Great companion volume to Applied Life Data Analysis.......2004-01-30

I agree with the previous reviewer that this is the definitive classic in accelerated life testing. It's a great companion volume to Dr. Nelson's "Applied Life Data Analysis" which remains one of the best reliability textbooks ever written. I have noticed that all major reliability and accelerated testing programs use this book as their main reference - proof of the books usefulness. My only compaint is with the publishers for the steep price - now that they're bringing out a lower cost version of Applied Life Data Analysis, I hope they'll bring out a low-cost or paperback version of this classic as well.

best statistical account of accelerated testing.......2000-06-22

Nelson is a private consultant who has worked on countless practical problems in reliability from his consulting practice and previous employment at General Electric. He is an excellent lecturer and writer. His earlier book "Applied Life Data Analysis" was considered to be one of the best texts on reliability.

Book Description

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.

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.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon requestfrom the Wiley editorial department.

Customer Reviews:

Not very useful in practice.......2001-09-21

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.

comprehensive modern account of reliability.......2000-06-13

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.

A superlative resource for understanding reliability........1998-08-16

Reliability data isn't amenable to treatment with the ubiquitous normal distribution, a fact which should catch the attention of any practicing engineer with only that bullet in his gun. Reliability data has other distinguishing features. The data are usually censored, which means the exact failure times are not known so the observations can only provide bounds on the actual failure times. Inferences and predictions usually require extrapolations, making engineering and physics-based modeling an important adjunct to statistical methods. Whereas many *statistical* problems focus on parameter estimation (e.g.: mean, standard deviation), these are not of primary interest to engineers who need specific measures of product reliability (e.g.: failure probabilities, life distribution quintiles, failure rates).

The chapter headings provide an overview of the book:

1) Reliability Concepts and Reliability Data 2) Models, Censoring, and Likelihood for Failure-Time Data 3) Nonparametric Estimation 4) Location-Scale-Based Parametric Distributions 5) Other Parametric Distributions 6) Probability Plotting 7) Parametric Likelihood Fitting Concepts: Exponential Distribution 8) Maximum Likelihood for Log-Location-Scale Distributions 9) Bootstrap Confidence Intervals 10) Planning Life Tests 11) Parametric Maximum Likelihood: Other Models 12) Prediction of Future Quantiles 13) Degradation Data, Models, and Data Analysis 14) Introduction to the Use of Bayesian Methods for Reliability Data 15) System Reliability Concepts and Methods 16) Analysis of Reparable System and Other Recurrence Data 17) Failure-Time Regression Analysis 18) Accelerated Life Tests 21) Accelerated Degradation Tests 22) Case Studies and Further Applications Appendix A - Notation and Acronyms Appendix B - Some Results from Statistical Theory

This book is written for practitioners - engineers and statisticians - yet does not presume an undergraduate degree in statistics. More involved statistical ideas (Bayesian thought, censored observations, bootstrapping, et cetera) are all described to the user with the assumption that they have had little prior exposure. The book's concepts are presented in an unstuffy and intuitive manner. For example, for Meeker and Escobar likelihood is simply "the probability of the data," making a maximum likelihood estimator one which maximizes the probability that the experiment turned out the way it did. (Contrast this to the hushed tones in many "engineering statistics" texts which suggest that Likelihood is a profound concept beyond the (limited) capacity of the engineer and best left to the trained statisticians.) The wholesome, unpretentious, and practical approach taken by Meeker and Escobar is quite pleasing to this reviewer, a professional engineer whose formal statistical education began later in life.

The book should be interesting to statisticians too. It can be used as a two-semester graduate statistics course, a one-semester course for engineers and statisticians, or as the basis for workshops and short courses on selected topics for industry practitioners. Each chapter is suffused with examples using real data and ends with thought-provoking exercises. While this is a practical book, it does not neglect statistical theory (after all, the authors are well-known academic statisticians) - although it is interesting to note that for censored observations there may be no *exact* theory for statistical inference. While the book's emphasis is more on results than on theoretical proofs, I think the practicing statistician will be quite pleased with the book's balance.

Not only are its 680 pages chock-full of ideas, information, and techniques, _Statistical Methods for Reliability Data_ is a noteworthy paradigm for technical exposition: Even before each chapter's introduction, there is a brief statement of chapter objectives, followed by an overview which places the chapter in perspective, stating for example, that the material is a prerequisite for this or that future topic, or conditions under which it could be omitted, or why its is useful. This makes it easy for a practitioner to find his way around the text.

Product Description

This book is meant for postgraduate modules that cover lifetime data in reliability and survival analysis as taught in statistics, engineering statistics and medical statistics courses. It is helpful for researchers who wish to choose appropriate models and methods for analyzing lifetime data. There is an extensive discussion on the concept and role of ageing in choosing appropriate models for lifetime data, with a special emphasis on tests of exponentiality. There are interesting contributions related to the topics of ageing, tests for exponentiality, competing risks and repairable systems. A special feature of this book is that it introduces the public domain R-software and explains how it can be used in computations of methods discussed in the book.

Practical Methods for Reliability Data Analysis (Oxford Statistical Science Series)

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Practical Methods for Reliability Data Analysis (Oxford Statistical Science Series)
J. I. Ansell , and M. J. Phillips
Manufacturer: Oxford University Press, USA
ProductGroup: Book
Binding: Hardcover

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ASIN: 019853664X

Book Description

This is a practical text for those who wish to analyse data from Reliability studies. The emphasis is on clear explanation of the techniques used, supported by extensive mathematical and statistical background and nature of the data before it is analysed. There are chapters on survival analysis, using illuminating case studies.

Flowgraph Models for Multistate Time-to-Event Data (Wiley Series in Probability and Statistics)

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Flowgraph Models for Multistate Time-to-Event Data (Wiley Series in Probability and Statistics)
Aparna V. Huzurbazar
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Hardcover

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ASIN: 0471265144

Book Description

A unique introduction to the innovative methodology of statistical flowgraphs
This book offers a practical, application-based approach to flowgraph models for time-to-event data. It clearly shows how this innovative new methodology can be used to analyze data from semi-Markov processes without prior knowledge of stochastic processes--opening the door to interesting applications in survival analysis and reliability as well as stochastic processes.
Unlike other books on multistate time-to-event data, this work emphasizes reliability and not just biostatistics, illustrating each method with medical and engineering examples. It demonstrates how flowgraphs bring together applied probability techniques and combine them with data analysis and statistical methods to answer questions of practical interest. Bayesian methods of data analysis are emphasized. Coverage includes:
* Clear instructions on how to model multistate time-to-event data using flowgraph models
* An emphasis on computation, real data, and Bayesian methods for problem solving
* Real-world examples for analyzing data from stochastic processes
* The use of flowgraph models to analyze complex stochastic networks
* Exercise sets to reinforce the practical approach of this volume
Flowgraph Models for Multistate Time-to-Event Data is an invaluable resource/reference for researchers in biostatistics/survival analysis, systems engineering, and in fields that use stochastic processes, including anthropology, biology, psychology, computer science, and engineering.

Download Description

A unique introduction to the innovative methodology of statistical flowgraphs This book offers a practical, application-based approach to flowgraph models for time-to-event data. It clearly shows how this innovative new methodology can be used to analyze data from semi-Markov processes without prior knowledge of stochastic processes--opening the door to interesting applications in survival analysis and reliability as well as stochastic processes. Unlike other books on multistate time-to-event data, this work emphasizes reliability and not just biostatistics, illustrating each method with medical and engineering examples. It demonstrates how flowgraphs bring together applied probability techniques and combine them with data analysis and statistical methods to answer questions of practical interest. Bayesian methods of data analysis are emphasized. Coverage includes: * Clear instructions on how to model multistate time-to-event data using flowgraph models * An emphasis on computation, real data, and Bayesian methods for problem solving * Real-world examples for analyzing data from stochastic processes * The use of flowgraph models to analyze complex stochastic networks * Exercise sets to reinforce the practical approach of this volume Flowgraph Models for Multistate Time-to-Event Data is an invaluable resource/reference for researchers in biostatistics/survival analysis, systems engineering, and in fields that use stochastic processes, including anthropology, biology, psychology, computer science, and engineering.

Guidelines for Improving Plant Reliability Through Data Collection and Analysis (Guidelines)

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Guidelines for Improving Plant Reliability Through Data Collection and Analysis (Guidelines)
Center for Chemical Process Safety (CCPS)
Manufacturer: Wiley-AIChE
ProductGroup: Book
Binding: Hardcover

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ASIN: 081690751X

Book Description

Written by reliability data experts, the book gives plant managers and supervisors the guidance they need to collect, and use with confidence, process equipment reliability data for risk-based decisions. Focusing on the process industries, it provides the protocol and techniques to collect and organize high quality plant performance, maintenance, and repair data from your own operations, and includes methods and examples on how the data can be converted into useful information for engineering, maintenance, safety, and loss prevention. This data can be used for: facility reliability/availability assessments; making decisions on the need for redundant systems; improving equipment designs; selecting the best equipment for specific tasks; estimating required work force; benchmarking current efforts both frequency and time; integrating predictive and preventive maintenance effort; integrating shutdowns with production needs; quantifying risks; and minimizing human reliability issues.

Qcpac: Statistical Quality Control on the IBM PC (Quality and Reliability, 3)

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Qcpac: Statistical Quality Control on the IBM PC (Quality and Reliability, 3)
Steven M. Zimmerman
Manufacturer: Marcel Dekker Inc
ProductGroup: Book
Binding: Hardcover

Quality Control | Management & Leadership | Business & Investing | Subjects | Books

Statistical Analysis of Reliability Data (Statistics Texts)

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Statistical Analysis of Reliability Data (Statistics Texts)
M. J. Crowder , A. C. Kimber , R. L. Smith , and T. J. Sweeting
Manufacturer: Chapman & Hall
ProductGroup: Book
Binding: Hardcover

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ASIN: 0412305607

Book Description

Written for those who have taken a first course in statistical methods, this book takes a modern, computer-oriented approach to describe the statistical techniques used for the assessment of reliability.

Bayesian Statistical Estimation of Reliability Data

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Bayesian Statistical Estimation of Reliability Data
V. P. Savchuk , and Chris P. Tsokos
Manufacturer: World Federation Pub Inc
ProductGroup: Book
Binding: Hardcover

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ASIN: 1885978081

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