Since the publication of the Second Edition of this popular textbook, new standards have changed the focus of reliability engineering, which introduced new concepts and terminology. Consequently, the Third Edition of System Reliability Theory: Models, Statistical Methods, and Applications has been thoroughly rewritten and updated to meet current standards. With an updated practical focus, incorporation of industry feedback, and many new examples based on real-world industry problems and data, this book begins with an introduction on reliability engineering and is followed by coverage on failures and failure analysis. The authors address failure models and qualitative system analysis and present new coverage on state space models. In addition, a new chapter on component reliability and availability is followed by a chapter on systems of independent components. Component importance is covered followed by a chapter on dependent failures, which now includes a discussion on causes of common cause failures, explicit versus implicit modeling, and the Beta-factor model. The authors also discuss counting processes and Markov Processes. In addition, the authors provide new sections on: maintenance assessment and optimization; advanced models failure rates; human errors; software bugs; CCFs (ICED + method in IEC 61508); generic failure rate databases; FRACAS data; application-specific data; frequency of dangerous failures (PFH); and reliability prediction. The book is supplemented with a companion website, which contains an Instructor Solutions Manual, lecture slides, reliability data sources, sample exam questions, and a terminology review.
Описание: "Applied Linear Statistical Models", 5e, is the long established leading authoritative text and reference on statistical modeling. For students in most any discipline where statistical analysis or interpretation is used, ALSM serves as the standard work. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. Applications used within the text and the hallmark problems, exercises, and projects are drawn from virtually all disciplines and fields providing motivation for students in virtually any college. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.
Автор: Kohler Название: Data Analysis Using Stata, Third Edition ISBN: 1597181102 ISBN-13(EAN): 9781597181105 Издательство: Taylor&Francis Рейтинг: Цена: 13248 р. Наличие на складе: Невозможна поставка.
Описание: Data Analysis Using Stata, Third Edition is a comprehensive introduction to both statistical methods and Stata. Beginners will learn the logic of data analysis and interpretation and easily become self-sufficient data analysts. Readers already familiar with Stata will find it an enjoyable resource for picking up new tips and tricks. The book is written as a self-study tutorial and organized around examples. It interactively introduces statistical techniques such as data exploration, description, and regression techniques for continuous and binary dependent variables. Step by step, readers move through the entire process of data analysis and in doing so learn the principles of Stata, data manipulation, graphical representation, and programs to automate repetitive tasks. This third edition includes advanced topics, such as factor-variables notation, average marginal effects, standard errors in complex survey, and multiple imputation in a way, that beginners of both data analysis and Stata can understand. Using data from a longitudinal study of private households, the authors provide examples from the social sciences that are relatable to researchers from all disciplines. The examples emphasize good statistical practice and reproducible research. Readers are encouraged to download the companion package of datasets to replicate the examples as they work through the book. Each chapter ends with exercises to consolidate acquired skills.
Описание: Serves as a comprehensive treatment of the fundamentals of probability and statistical inference. This textbook helps readers to advance to topics such as sampling, linear models, design of experiments, statistical computing, survival analysis, and bootstrapping. It discusses modes of convergence of sequences of random variables.
Описание: Applications of Finite Element Methods for Reliability Studies on ULSI Interconnections provides a detailed description of the application of finite element methods (FEMs) to the study of ULSI interconnect reliability. Over the past two decades the application of FEMs has become widespread and continues to lead to a much better understanding of reliability physics.To help readers cope with the increasing sophistication of FEMs’ applications to interconnect reliability, Applications of Finite Element Methods for Reliability Studies on ULSI Interconnections will:introduce the principle of FEMs;review numerical modeling of ULSI interconnect reliability;describe the physical mechanism of ULSI interconnect reliability encountered in the electronics industry; anddiscuss in detail the use of FEMs to understand and improve ULSI interconnect reliability from both the physical and practical perspective, incorporating the Monte Carlo method.A full-scale review of the numerical modeling methodology used in the study of interconnect reliability highlights useful and noteworthy techniques that have been developed recently. Many illustrations are used throughout the book to improve the reader’s understanding of the methodology and its verification. Actual experimental results and micrographs on ULSI interconnects are also included.Applications of Finite Element Methods for Reliability Studies on ULSI Interconnections is a good reference for researchers who are working on interconnect reliability modeling, as well as for those who want to know more about FEMs for reliability applications. It gives readers a thorough understanding of the applications of FEM to reliability modeling and an appreciation of the strengths and weaknesses of various numerical models for interconnect reliability.
Описание: Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation. Drawing on data sets from the author’s many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author’s website.
Описание: Regression Models for Categorical Dependent Variables Using Stata, Third Edition shows how to use Stata to fit and interpret regression models for categorical data. The third edition is a complete rewrite of the book. Factor variables and the margins command changed how the effects of variables can be estimated and interpreted. In addition, the authors' views on interpretation have evolved. The changes to Stata and to the authors' views inspired the authors to completely rewrite their popular SPost commands to take advantage of the power of the margins command and the flexibility of factor-variable notation. The new edition will interest readers of a previous edition as well as new readers. Even though about 150 pages of appendixes were removed, the third edition is about 60 pages longer than the second. Although regression models for categorical dependent variables are common, few texts explain how to interpret such models; this text fills the void. With the book, Long and Freese provide a suite of commands for model interpretation, hypothesis testing, and model diagnostics. The new commands that accompany the third edition make it easy to include powers or interactions of covariates in regression models and work seamlessly with models estimated with complex survey data. The authors' new commands greatly simplify the use of margins, in the same way that the marginsplot command harnesses the power of margins for plotting predictions. The authors discuss how to use margins and their new mchange, mtable, and mgen commands to compute tables and to plot predictions. They also discuss how to use these commands to estimate marginal effects, averaged either over the sample or at fixed values of the regressors. The authors introduce and advocate a variety of new methods that use predictions to interpret the effect of variables in regression models. The third edition begins with an excellent introduction to Stata and follows with general treatments of the estimation, testing, fit, and interpretation of this class of models. New to the third edition is an entire chapter about how to interpret regression models using predictions—a chapter that is expanded upon in later chapters that focus on models for binary, ordinal, nominal, and count outcomes. Long and Freese use many concrete examples in their third edition. All the examples, datasets, and author-written commands are available on the authors' website, so readers can easily replicate the examples with Stata. This book is ideal for students or applied researchers who want to learn how to fit and interpret models for categorical data.
Описание: This book contains extended versions of 34 carefully selected and reviewed papers presented at the Third International Conference on Mathematical Methods
in Reliability, held in Trondheim, Norway in 2002. It provides a broad overview of current research activities in reliability theory and its applications. There are chapters on reliability
modelling, network and system reliability, reliability optimization, survival analysis, degradation and maintenance modelling, and software reliability.
The authors are all leading
experts in the field. A particular feature of the book is a historical review by Professor Richard E Barlow, well known for his pioneering research on reliability. The list of authors also
includes the plenary session speakers Odd O Aalen, Philip J Boland, Sallie A Keller-McNulty, and Nozer Singpurwalla.
Описание: This volume contains extended versions of 28 carefully selected and reviewed papers presented at The Fourth International Conference on Mathematical
Methods in Reliability in Santa Fe, New Mexico, June 21-25, 2004, the leading conference in reliability research. The meeting serves as a forum for discussing fundamental issues on
mathematical methods in reliability theory and its applications. A broad overview
of current research activities in reliability theory and its applications is provided with coverage on reliability modelling, network and system reliability, Bayesian methods, survival analysis,
degradation and maintenance modelling, and software reliability.
The contributors are all leading experts in the field and include the plenary session speakers, Tim Bedford,
Thierry Duchesne, Henry Wynn, Vicki Bier, Edsel Pena, Michael Hamada, and Todd Graves.
Описание: Devoted to Multi-State System (MSS) reliability analysis and optimization, this volume provides an historical overview of the field. It presents basic concepts of
MSS, defines MSS reliability measures and systematically describes the tools for MSS reliability assessment and optimization. Basic methods for MSS reliability assessment, such as a
Boolean methods extension, basic random process methods (both Markov and semi-Markov) and universal generating function models, are systematically studied.
genetic algorithm optimization technique and all details of its application are described. All the methods are illustrated by numerical examples. The book also contains many examples of
application of reliability assessment and optimization methods to real engineering problems.
The aim of this book is to give a comprehensive, up-to-date presentation of MSS
reliability theory based on modern advances in this field and provide a theoretical summary and examples of engineering applications to a variety of technical problems. From this point of
view the book bridges the gap between theoretical advances and practical reliabili
Описание: Updated to conform to Mathematica® 7.0, this second edition shows how to easily create simulations from templates and solve problems using Mathematica. Along with new sections on order statistics, transformations of multivariate normal random variables, and Brownian motion, this edition offers an expanded section on Markov chains, more example data of the normal distribution, and more attention on conditional expectation. It also includes additional problems from Actuarial Exam P as well as new examples, exercises, and data sets. The accompanying CD-ROM contains updated Mathematica notebooks and a revised solutions manual is available for qualifying instructors.
Описание: With amusing anecdotes and trivia, this text explains how statistical methods are used for data analysis and uses the elementary functions of R to perform the individual steps of statistical procedures. It introduces basic concepts of inference through a careful study of several important procedures, including parametric and nonparametric methods, analysis of variance, and regression. The text also presents many applications, supporting data sets, and end-of-chapter exercises. The R code and data sets are available for download online and a solutions manual is available for qualifying instructors.
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