Probability, Statistics, and Reliability for Engineers and Scientists, Second Edition Review

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(More customer reviews)In preparing this book, we strove to achieve the following educational objectives: (1) introducing probability, statistics, and reliability methods to engineering students and practicing engineers, (2) emphasizing the practical use of these methods, and (3) establishing the limitations, advantages, and disadvantages of the methods. Although, the book was developed with emphasis on engineering and technological problems, the methods can also be used to solve problems in other fields of sciences.
Problems that are commonly encountered by engineers require decision making under conditions of uncertainty. The uncertainty can be in the definition of a problem, the available information, the alternative solution methodologies and their results, and the random nature of the solution outcomes. Studies show that in the future engineers will need to solve more complex design problems with decisions made under conditions of limited resources, thus necessitating increased reliance on the proper treatment of uncertainty. Therefore, this book is intended to better prepare future engineers, as well as assist practicing engineers, in understanding the fundamentals of probability, statistics, and reliability methods, especially their applications, limitations, and potentials.
STRUCTURE, FORMAT, AND MAIN FEATURES
We have developed this book with a dual use in mind, as both a self-learning guidebook and as a required textbook for a course. In either case, the text has been designed to achieve important educational objectives.
The nine chapters of the book cover of the following subjects: (1) an introduction to the text that covers uncertainty types, decision analysis, and Taylor series expansion; (2) graphical analysis of data, and the computation of important characteristics of sample measurements and basic statistical characteristics; (3) the fundamentals of probability; (4) the joint behavior of random variables and the probabilistic characteristics of functions of random variables; (5) statistical analyses that include parameter estimation, hypothesis testing, confidence-interval estimation, sample-size determination, and probability-model selection; (6) curve fitting or model development based on data using regression analysis; (7) a formal presentation of Monte Carlo simulation; (8) reliability, risk, and decision analysis; and (9) the use of Bayesian methods in engineering. The book was designed for an introductory course in probability, statistics, and reliability with emphasis on applications. In developing the book, a set of educational outcomes as detailed in Chapter 1 motivated the structure and content of this text. Ultimately, serious readers will find the content of the book to be very useful in engineering problem solving and decision making. One of the most difficult to grasp aspects of probability and statistics is the concept of sampling variation. In engineering practice, an engineer typically has only one sample of data. It is important to recognize that the statistical results would be somewhat different if he or she had collected a different sample, even if that sample were equally likely to have occurred. Simulation is a means of demonstrating the sample-to-sample, or sampling, variation that can be expected. For this reason, we have incorporated a section on simulation at the end of each chapter (Chapters 1 to 6). Performing some simulations is one way of generating a better appreciation for sampling variation that is inherent in statistical problems presented in Chapters 1 to 6. Omitting the sections on simulation does not diminish a reader's understanding of the other sections or chapters. In each chapter of the book, computational examples are given in the individual sections of the chapter, with more detailed engineering applications given in a concluding section. Also, each chapter includes a set of exercise problems that cover the materials of the chapter. The problems were carefully designed to meet the needs of instructors in assigning homework and the readers in practicing the fundamental concepts. The book can be covered in one or two semesters depending the level of a course or the time allocated for topics covered in the book. The chapter sequence can be followed as a recommended sequence. However, if needed, instructors can choose a subset of the chapters for courses that do not permit a complete coverage of all chapters or a coverage that cannot follow the presented order. After completing Chapters 1, 2, and 3, the readers will have sufficient background to follow and understand the materials in the following tracks of chapters: Chapter 4; Chapters 5 and 6; Chapters 7 and 8; and Chapter 9 according to the indicated sequence.

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Virtually every engineer and scientist needs to be able to collect, analyze, interpret, and properly use vast arrays of data. This means acquiring a solid foundation in the methods of data analysis and synthesis. Understanding the theoretical aspects is important, but learning to properly apply the theory to real-world problems is essential.The second edition of this bestselling text introduces probability, statistics, reliability, and risk methods with an ideal balance of theory and applications. Clearly written and firmly focused on the practical use of these methods, it places increased emphasis on simulation, particularly as a modeling tool, applying it progressively with projects that continue in each chapter. It also features expanded discussions of the analysis of variance including single- and two-factor analyses and a thorough treatment of Monte Carlo simulation. The authors clearly establish the limitations, advantages, and disadvantages of each method, but also show that data analysis is a continuum rather than the isolated application of different methods.Probability, Statistics, and Reliability for Engineers and Scientists, Second Edition, was designed as both a reference and as a textbook, and it serves each purpose well. Ultimately, readers will find its content of great value in problem solving and decision making, particularly in practical applications.

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Foundations of Time Series Analysis and Prediction Theory (Wiley Series in Probability and Statistics) Review

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(More customer reviews)This is an advanced text that covers the fundamentals of time series theory. It is split into three part with overlap to allow each part to be read independently. As Pourahmadi points out in his preface, the theory consists of time series modeling going back to the work of Schuster, Yule, Wold and others and the prediction theory developed by Wold, Kolmogorov, Cramer and others. The first part, chapters 1-4 covers most of the aspect of time series data analysis and is a mixture of theoretical and applied statistics. The rest of the book is very heavy on theroy and light on applications with part 2 covering chapters 5-8 which covers probability theory and the structure of stationary time series. This part is very theoretical but not extremely abstract. Part 3 is chapters 9 and 10 which are very abstract and cover the theory of Hilbert space, projections and Banach spaces (including a special function space defined on the open unit disc in the complex plane called a Hardy space). I am familiar with much of this abstract analysis from my graduate years in mathematics at the University of Maryland. But I have to confess that this having been 32 years ago, I don't remember much of it. Also I don't think I had ever heard of a Hardy space before.
The book is loaded with over 200 references going from the early 1930s to 2000.
If you have a graduate degree in mathematics or something comparable and are interested in the theory then I can recommend this book. But if you are just interested in learning the fundamentals of time series without abstract mathematics this book is not for you.

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Foundations of time series for researchers and studentsThis volume provides a mathematical foundation for time series analysis and prediction theory using the idea of regression and the geometry of Hilbert spaces. It presents an overview of the tools of time series data analysis, a detailed structural analysis of stationary processes through various reparameterizations employing techniques from prediction theory, digital signal processing, and linear algebra. The author emphasizes the foundation and structure of time series and backs up this coverage with theory and application.End-of-chapter exercises provide reinforcement for self-study and appendices covering multivariate distributions and Bayesian forecasting add useful reference material. Further coverage features:Similarities between time series analysis and longitudinal data analysisParsimonious modeling of covariance matrices through ARMA-like modelsFundamental roles of the Wold decomposition and orthogonalizationApplications in digital signal processing and Kalman filteringReview of functional and harmonic analysis and prediction theoryFoundations of Time Series Analysis and Prediction Theory guides readers from the very applied principles of time series analysis through the most theoretical underpinnings of prediction theory. It provides a firm foundation for a widely applicable subject for students, researchers, and professionals in diverse scientific fields.

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Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) Review

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(More customer reviews)This is similar to McCullagh and Nelder's "Generalized Linear Models" except that it includes models with random effects. In time it should become the standard that McCullagh and Nelder's book is.

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Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors.Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.

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Discovering Statistics Using SPSS for Windows: Advanced Techniques for Beginners (Introducing Statistical Methods series) Review

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(More customer reviews)The advent of statistical software for the personal computer in the 1980s can be argued to be both a great advancement as well as a critical liability for the field of research. Statistical software has certainly made difficult analytical tasks easier to accomplish, enabling more people to benefit from the use of quantitative techniques. The ever-increasing speed of the personal computer allows researchers to conduct complex analysis in minutes that would have taken days to complete manually. However, with this increased speed and usability comes pitfalls. The ease and speed of statistical software has encouraged some researchers to take a shotgun approach to analysis by running large numbers of analyses instead of strategically selecting analyses guided by theory. Statistical packages also make it possible to run complex procedures that may be misapplied or misinterpreted by researchers without a solid understanding of statistical principles.
No book can stop unscrupulous researchers from supporting their hypotheses by cherry picking favorable results from hundreds of analyses conducted. Well-intentioned students and researchers, however, can turn to Andy Field's book Discovering Statistics using SPSS for Windows for the statistical background and guidance needed to appropriately select, execute, and interpret results.
Field's book bridges the gap between introductory/intermediate statistical textbook and software manual. This engaging, easy to read book leads the reader through:
·Introduction to statistical models
·Exploring data
·Correlations
·Regression
·Logistic regression
·Comparing means
·ANOVA
·Complex ANOVA
·Repeated measures design
·MANOVA
·Exploratory factor analysis.
Each topic of the book begins with an overview of the applicable statistical theory. The theory is presented in non-technical language and references numerous sources for readers wanting a more in-depth review. When background material does become technical, these sections are specially labeled to alert non-technical readers. Field focuses particularly on the statistical assumptions of each statistical technique and how to use SPSS to test for them.
Following the review of statistical theory, each topic includes one or more exercises. Readers are guided step by step through each analysis from dummy coding data, through entering in the necessary SPSS commands, to interpreting SPSS output. An accompanying CD furnishes the data sets used in each exercise, allowing readers to work though each exercise and check results against the book. Through these exercises, Field shows how results can be misinterpreted without a thorough investigation of the data. Great pains are taken throughout the exercises to demonstrate the perils of blindly trusting SPSS output without understanding the theory and underlying statistical assumptions.
From this review, it is obvious that I greatly enjoyed reading the book. I assume that this book is intended primarily for students and practitioners without an extensive background in statistics. However, I found this book to be beneficial in reviewing concepts studied in graduate school more than ten years ago. Field accomplishes the difficult task of simplifying complex topics into everyday language without talking down to his readers. This book is perfectly suited for the non-statistical expert looking for guidance with running analyses with SPSS.

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While other books either concentrate on statistical theory or on the functions of the popular computer program SPSS, Andy Field integrates the two to provide the student with a thorough grounding in statistics through learning to use SPSS.The book is designed to answer all the questions a student asks when using SPSS to analyse their data. Each statistical test is introduced in a clear and accessible way, with examples of how to run an analysis in SPSS.The book covers in detail the following:Exploring data, Correlation, Regression, LOGISTIC Regression,Comparing Means (t-tests), ANOVA, Complex ANOVA (GLM), MANOVA, Factor AnalysisAndy Field has written an up-to-date student-oriented text book with the aim of making the learning of advanced statistics and using SPSS as painless as possible.The book includes a CD-Rom with SPSS datasets and examples from the book and the author provides a website for further help and updates. The book is suitable for use with SPSS versions 7.0, 7.5, 8.0 and above.

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Subset Selection in Regression, Second Editon Review

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(More customer reviews)This book describes techniques for finding good predictors of some phenomenon (assuming linear dependence). For example, suppose you want to know what factor determine IQ. You do a survey that includes 500 factors such as "family income", "length of hair", "father's IQ", etc. Now determine which are the 20 best determinants of IQ! Thats impossible (practically speaking) because you'd have to do an exhaustive search of 500 choose 20 variables which would take to much computing power. The book describes known techniques which identify factors that are good predictors (but not necessarily the best).
In my searches, I found this book to be the best available on the subject. I found it difficult to read -- it is math heavy with emphasis on linear algebra.
The author is certainly one of the most authoritative subject matter experts. He supports free fortran source code which are available on his WEB site. He also maintains software (for MATHLAB I think) which use the techniques.
I would like the book to be targetted at three levels of audiences (I know - I want to world!). The book should include the math - for the theoretician. The book should be organized so persons not interested in the theory can figure out what the techiques do (at a high level). Finally, the book should identify which methods are supported by the major commerical statistical packages (e.g., SPSS, MATLAB, etc).

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Originally published in 1990, the first edition of Subset Selection in Regression filled a significant gap in the literature, and its critical and popular success has continued for more than a decade. Thoroughly revised to reflect progress in theory, methods, and computing power, the second edition promises to continue that tradition. The author has thoroughly updated each chapter, incorporated new material on recent developments, and included more examples and references. New in the Second Edition:"A separate chapter on Bayesian methods"Complete revision of the chapter on estimation"A major example from the field of near infrared spectroscopy"More emphasis on cross-validation"Greater focus on bootstrapping"Stochastic algorithms for finding good subsets from large numbers of predictors when an exhaustive search is not feasible "Software available on the Internet for implementing many of the algorithms presented"More examplesSubset Selection in Regression, Second Edition remains dedicated to the techniques for fitting and choosing models that are linear in their parameters and to understanding and correcting the bias introduced by selecting a model that fits only slightly better than others. The presentation is clear, concise, and belongs on the shelf of anyone researching, using, or teaching subset selecting techniques.

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Plane Answers to Complex Questions: The Theory of Linear Models (Springer Texts in Statistics) Review

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(More customer reviews)This is a pretty good textbook for the linear model. If you have backgrounds in experimental design and matrix theory. Then this book will help you a lot. Some people may recommend the Searle's linear model. But Searle's book may be too focus on the theories therefore not too many applications.
If you are looking for a book for your linear model class. You might choose this one. Since it will help for your first step on the Linear model.

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This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The authors emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas. Every chapter comes with numerous exercises and examples that make it ideal for a graduate- level course. All of the standard topics are covered in depth. In addition, the book covers topics that are not usually treated at this level, but which are important in their own right. The author, Ronald Christensen, is a Professor of Statistics at the University of New Mexico.

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Multivariate Statistical Modelling Based on Generalized Linear Models (Springer Series in Statistics) Review

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(More customer reviews)Back in 2000 Stephen Fienberg gave a talk at the University of California at Irvine on the 2000 census and his book "Who Counts". After the talk I went to dinner with him, my colleague Bob Newcomb and Anita Iannucci. Driving to dinner Bob ask Steve for a recommendation on a multivariate textbook. A number of choice were mentioned. Bob's favorite was Cooley and Lohnes but that was a bit dated. He was definitely looking for an applied text and not a theoretical one. I learned my multivariate analysis out of the first edition of Ted Anderson's book. But that is traditional multivariate Gaussian theory and is not at all an applied text. I always liked Gnanadesikan's book and I mentioned that. Srivastava and carter is an applied text that I like and there are many other choices.
I don't recall many of Fienberg's suggestions but I do distinctly recall that he did say that now you can teach it as a special case of the generalized linear models. The idea seemed to make sense to me but I couldn't picture the details. This book is apparently the book Fienberg had in mind. He might have been thinking about the first edition because this second edition was not out then.
The book is very applied and modern and covers many important topics for biostatisticians. Coverage includes multicategorical responses, semi and nonparametric modelling, time series and longitudinal data, random effects models, state space models including Kalman Filters and nonlinear models, and survival analysis. This is not traditional multivariate data but covers many type of multivariate data and models that do not fit the standard multivariate Gaussian theory.
Chapter 4 on selecting and checking models seems to deal with the classical linear models taking a non-standard approach through the methods of generalized linear models.
Excellent text for an applied course and for a reference book. It also covers hidden Markov models and Bayesian methods (including the MCMC implementation and the WinBugs software).

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