A First Course in Structural Equation Modeling Review

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(More customer reviews)If you haven't been exposed to SEM , this book is right for you. Basic, straightforward, well planned and nonmathematical. However, this book cannot be the "only" book in your library if you are planning to go deep in SEM. This book is a good step for moving to intermediate or advanced levels.
The book is well desinged. It starts with fundamentals of SEM and EQS and LISREL programs. This book is NOT an EQS or LISREL reference, however, in each chapter, Raykov and Marcoulides cover an example showing the syntax and output of each program. The syntax and the output are deeply explained.
After introducing SEM and EQS and LISREL, the authors start to cover different approaches in SEM. Path analysis, Confirmatory Factor Analysis, Structural Regression Models and finally there is chapter about Latent Change Analysis.
In overall, the book is useful for only beginners as the authors' claim.

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This book is designed to introduce students to the basics of structural equation modeling through a conceptual, nonmathematical approach. The few mathematical formulas included are used in a conceptual or illustrative nature, rather than a computational one. The book features examples from LISREL and EQS. For that reason, the book can also be used as a beginning guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. Intended as an introduction for graduate students or researchers in psychology, education, business, and other applied social and health sciences. The only prerequisite is a basic statistics course.

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A MINITAB Guide to Statistics (2nd Edition) Review

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(More customer reviews)The Minitab Guide to Statistics covers Minitab Release 11, not Release 13 as advertised. Therefore the book is not very useful. The dialog boxes and output screens have changed so it is hard to find what you are looking for and if you do find something, there is a good chance that it is not accurate.

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For a one or two term course in introductory or business statistics using the Minitab statistical software package.This well known, Minitab author team has written a Minitab manual that can be used with any introductory or business statistics text. The manual provides statistics students with a complete introduction to Minitab and the tools they need to run statistical procedures, from descriptive statistics to time series analysis, using Minitab. Interpretation of numerical and graphical output is included. The order of topics is quite similar to that of a traditional introductory statistics text. Minitab Release 13.0 for Windows is used for examples and exercises, but commands and output are very similar to previous releases. The manual is also adapted for the Student Release 12.0 Minitab.

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Graphical Models in Applied Multivariate Statistics (Wiley Series in Probability & Statistics) Review

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(More customer reviews)I was recommended this (very expensive!) book for supervised reading on graphical models by my university supervisor in statistics. As a physician who might have liked to be a statistician in another life I found it thorough but (very) challenging. If you want lemmas & proofs this is where you will get them! If you want end user example material there is some but less than I would like. From the style I think the author would have been an excellent lecturer and the book reads as if a collected lecture series which I think it was. The index, although very helpful, could do with a re-revision (for example deviance is introduced way before its indexed first entry) and the book precedes the availability of more modern computerized graphical model search programs.. To be fair the author does show that you can create an individualized study program by skipping around chapters but this would be helped my making each module more self contained . If it was reissued making these sorts of changes and the price was reduced it could be a best seller (well for people interested in graphical models!)

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Graphical models--a subset of log-linear models--reveal the interrelationships between multiple variables and features of the underlying conditional independence. Following the theorem-proof-remarks format, this introduction to the use of graphical models in the description and modeling of multivariate systems covers conditional independence, several types of independence graphs, Gaussian models, issues in model selection, regression and decomposition. Many numerical examples and exercises with solutions are included.

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Introduction to Stochastic Control Theory (Mathematics in Science and Engineering, Volume 70) Review

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(More customer reviews)I bought this book to use it as a reference book, but never had the chance to apply the stuff in it, which is sort in the early stages of this theory.

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In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation;methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; andmethods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory.As a result, the book represents a blend of new methods in general computational analysis,and specific, but also generic, techniques for study of systems theory ant its particularbranches, such as optimal filtering and information compression. - Best operator approximation,- Non-Lagrange interpolation,- Generic Karhunen-Loevetransform- Generalised low-rank matrix approximation- Optimal data compression- Optimal nonlinear filtering

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Forecasting in Business and Economics, Second Edition (Economic Theory, Econometrics, and Mathematical Economics) Review

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(More customer reviews)This is a great book, if you are looking for a graduate level experience -- but beware. There are 2 texts from 1986 with the same ISBN number. The first is a Hardcover book by Granger and is an undergraduate text. The second is a Paperback book by Granger and Newbold and is the real deal for grad students interested in time series analysis. So if you want this book, be sure to order the Paperback by Newbold. Good Luck! I received the Hardback copy twice in error before receiving the great paperback.

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This thoroughly revised second edition of an upper-level undergraduate/graduate text describes many major techniques of forecasting used in economics and business. This is the only time series book to concentrate on the forecasting of economic data and to cover such a broad range of topics. Key Features* Gives a complete description, with applications, of the Box-Jenkins single series modeling techniques* Extends the Box-Jenkins techniques to multivariate cases* Compares forecasts from purely statistical and econometric models* Pays careful attention to such problems as how to evaluate and compare forecasts* Covers nonstationary and nonlinear models, co-integration and error-correction models

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Fundamentals of Modern Statistical Methods: Substantially Improving Power and Accuracy Review

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(More customer reviews)This is a marvelous introductory statistics book. In the first five chapters Wilcox presents the classical statistical methods with simplicity and great care. Emphasis is properly placed on the assumptions of the methods and the fact that many times important assumptions are violated in practice. This is all done in the first five chapters. Then in chapters 6 - 12 he provides alternative modern methods that can work better than the classical ones when the assumptions fail.
Chapter 6 is on bootstrap. Chapter 8 covers robust measures of location. Chapter 9 covers statistical inference based on robust measures of location. Chapter 10 deals with nonparametric and robust measures of association. Chapter 11 covers robust estimates of location and Chapter 12 "Alternative Strategies" includes rank test, permutation tests and multiple comparison issues.
At the beginning of Wilcox's book there are three precious quotes that you should appreciate. 1. "If at first the idea is not absurd, then there is not hope for it" Albert Einstein. 2. "Everyone believes in the [normal] law of errors, the experimenters because they think it is a mathematical theorem, the mathematicians because they think it is an experimental fact." Henri Poincare. 3. "Each generation that discovers something from its experience must pass that on, but it must pass that on with a delicate balance of respect and disrespect, so that the race ... does not inflict its errors too rigidly on its youth, but it does pass on the accumulated wisdom plus the wisdom that it may not be wisdom." Richard Feynman.
These quotes were well chosen to fit the theme of the book and represent the wisdom of three great men from the 19th and 20th Centuries.

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Conventional statistical methods have a very serious flaw. They routinely miss differences among groups or associations among variables that are detected by more modern techniques - even under very small departures from normality. Hundreds of journal articles have described the reasons standard techniques can be unsatisfactory, but simple, intuitive explanations are generally unavailable. Improved methods have been derived, but they are far from obvious or intuitive based on the training most researchers receive. Situations arise where even highly nonsignificant results become significant when analyzed with more modern methods. Without assuming any prior training in statistics, Part I of this book describes basic statistical principles from a point of view that makes their shortcomings intuitive and easy to understand. The emphasis is on verbal and graphical descriptions of concepts. Part II describes modern methods that address the problems covered in Part I. Using data from actual studies, many examples are included to illustrate the practical problems with conventional procedures and how more modern methods can make a substantial difference in the conclusions reached in many areas of statistical research.Rand Wilcox is a professor of psychology at the University of Southern California. He is a fellow of the Royal Statistical Society and the American Psychological Society. Dr. Wilcox currently serves as an associate editor of Computational Statistics & Data Analysis and Psychometrika. He has published over 165 articles in a wide range of statistical journals and he is the author of three other books on statistics.|"The volume is a jewel of direct explanations and information necessary for a good understanding of analysis of data, aimed at ordinary researchers who must try to present reasonable interpretable accounts of their data or judge when to abandon a particular strategy..."|- Perceptual and Motor Skills, 2002

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A First Course in Structural Equation Modeling Review

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(More customer reviews)This book does a good job of explaining the basic concepts involved in SEM, path analysis, and CFA in easy to understand terms. Also, it lays out syntax and output for running popular SEM programs, such as LISREL, EQS, and Mplus. This book is perfect for the person who is trying to learn SEM on their own. I think it gives you enough to be able to know how to run a SEM, but it isn't enough to really make you a pro at it.

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In this book, authors Tenko Raykov and George A. Marcoulides introduce students to the basics of structural equation modeling (SEM) through a conceptual, nonmathematical approach. For ease of understanding, the few mathematical formulas presented are used in a conceptual or illustrative nature, rather than a computational one. Featuring examples from EQS, LISREL, and Mplus, A First Course in Structural Equation Modeling is an excellent beginner's guide to learning how to set up input files to fit the most commonly used types of structural equation models with these programs. The basic ideas and methods for conducting SEM are independent of any particular software. Highlights of the second edition include:• review of latent change (growth) analysis models at an introductory level; •coverage of the popular Mplus program; •updated examples of LISREL and EQS; and •a CD that contains all of the text's LISREL, EQS, and Mplus examples.A First Course in Structural Equation Modeling is intended as an introductory book for students and researchers in psychology, education, business, medicine, and other applied social, behavioral, and health sciences with limited or no previous exposure to SEM. A prerequisite of basic statistics through regression analysis is recommended. The book frequently draws parallels between SEM and regression, making this prior knowledge helpful.

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Data Analysis with Microsoft Excel: Updated for Office 2007 (Book Only) Review

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(More customer reviews)Usefulness of this reference book depends on being able to download StatPlus and supporting files from the[...] website. These files are locked on the website and in order to access them you need to either: (1) open an account by validating you are a faculty member OR (2) have an access card. If you are not a faculty member or you don't have an access card, you will not be able to access the files on which much of this text relies. EXERCISE CAUTION WHEN BUYING THIS TEXT.

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This popular best-selling book shows students and professionals how to do data analysis with Microsoft? Excel. DATA ANALYSIS WITH MICROSOFT EXCEL teaches the fundamental concepts of statistics and how to use Microsoft Excel to solve the kind of data-intensive problems that arise in business and elsewhere. Even students with no previous experience using spreadsheets will find that this text?s step-by-step approach, extensive tutorials, and examples make it easy to learn how to use Excel for analyzing data. A downloadable StatPlus? add-in for Microsoft Excel, data sets for exercises, and interactive concept tutorials are available on the Book Companion Website.

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Analyzing Linguistic Data: A Practical Introduction to Statistics using R Review

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(More customer reviews)This book is very well organized, and has a wealth of information. That being said, it is not for absolute beginners. It assumes that you already know a little bit about statistics, R, and linguistics. You should have some basic knowledge of t-tests, ANOVA and the like before reading. You should also go through some of the R tutorials if you have not used R before. Once you have that basic knowledge, this book will really help you learn how to do some advanced statistical analyses of your linguistic data. There are many code samples, including some R packages which you can download.

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Statistical analysis is a useful skill for linguists and psycholinguists, allowing them to understand the quantitative structure of their data. This textbook provides a straightforward introduction to the statistical analysis of language. Designed for linguists with a non-mathematical background, it clearly introduces the basic principles and methods of statistical analysis, using 'R', the leading computational statistics programme. The reader is guided step-by-step through a range of real data sets, allowing them to analyse acoustic data, construct grammatical trees for a variety of languages, quantify register variation in corpus linguistics, and measure experimental data using state-of-the-art models. The visualization of data plays a key role, both in the initial stages of data exploration and later on when the reader is encouraged to criticize various models. Containing over 40 exercises with model answers, this book will be welcomed by all linguists wishing to learn more about working with and presenting quantitative data.

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Explanatory Item Response Models: A Generalized Linear and Nonlinear Approach (Statistics for Social and Behavioral Sciences) Review

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(More customer reviews)Excerpts of my review from Psychometrika are above. Unfortunately half the quotes really apply to the other book I was reviewing at the time, the title by Skrondal and Rabe-Hesketh (which is also a nice book, but rather different). Anyway, this book very much deserves a five star rating. It shows how many IRT models fit together as regression models in the generalized linear (or nonlinear) mixed model framework. SAS code is provided for nearly all models in the book. The breadth of talented authors assembled by the editors is substantial. This is an excellent book for "advanced" users of IRT or people who are proficient with GLMMs and want to use them as measurement models. It is not a "beginner's book" by any stretch of the imagination, but a very good title to own if you want to start doing projects with real data. In addition to the SAS code, one of the must useful features of the book is the fact that the authors keep to only a few datasets, which means you get a very intensive look at them. Clever!

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This book offers a way of thinking about psychometric models of the item-response type (IRT) as logistic mixed models. There are three clear advantages to this approach. First, one can see how the many different IRT models relate to one another, as they are built up from a common set of principles. Second, the user can formulate her or his models, selecting among these principles and modifying them according to one's modeling needs. Third, the approach is closely linked withthe possibilities of PROC NLMIXED from SAS, so that estimation is mostly not difficult.

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Mapping Species Distributions: Spatial Inference and Prediction (Ecology, Biodiversity and Conservation) Review

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(More customer reviews)I used this book for my MSc thesis and found very helpful. The first chapters focus on the data for species and environmental variables. Consequently an overview of different modelling techniques is given (statistical and machine learning). Finally evaluation statistics are discussed. All these chapters are backed up with ample references to the primary literature.
My conclusion is, that the whole task of modelling species distributions is set into a very nice framework in this book.

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Maps of species' distributions or habitat suitability are required for many aspects of environmental research, resource management and conservation planning. These include biodiversity assessment, reserve design, habitat management and restoration, species and habitat conservation plans and predicting the effects of environmental change on species and ecosystems. The proliferation of methods and uncertainty regarding their effectiveness can be daunting to researchers, resource managers and conservation planners alike. Franklin summarises the methods used in species distribution modeling (also called niche modeling) and presents a framework for spatial prediction of species distributions based on the attributes (space, time, scale) of the data and questions being asked. The framework links theoretical ecological models of species distributions to spatial data on species and environment, and statistical models used for spatial prediction. Providing practical guidelines to students, researchers and practitioners in a broad range of environmental sciences including ecology, geography, conservation biology, and natural resources management.

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A Step-by-Step Approach to Using SAS for Univariate and Multivariate Statistics, Second Edition Review

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(More customer reviews)This book allowed me to learn statistics and using SAS to do statistics without being overwhelmed. I particularly like the thorough, easy-to-follow explanations that accompany SAS outputs, i.e. the authors explain each section of the lengthy output well. What use is it if you get a bunch of output but don't know how to interpret? The authors try to help you with that, and I would say they are successful.
Sometimes I felt that certain sections are too step-by-step (like detailing the romance study at great depth), but I guess readers can gain some exposure to study designs by going through those paragraphs.

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Updated for SAS®9, this second edition is an easy-to-understand introduction to SAS as well as to univariate and multivariate statistics. Clear explanations and simple language guide you through the research terminology, data input, data manipulation, and types of statistical analysis that are most commonly used in the social and behavioral sciences. Providing practice data inspired by actual studies, this book teaches you how to choose the right statistic, understand the assumptions underlying the procedure, prepare the SAS program for the analysis, interpret the output, and summarize the analysis and results according to the format prescribed in the Publication Manual of the American Psychological Association. Step by step you will learn how to perform the following types of analysis: simple descriptive statistics, measures of bivariate association, t tests: independent samples and paired samples, NOVA and MANOVA, multiple regression, principal component analysis, and assessing scale reliability with coefficient alpha. This text is ideally suited to students who are beginning their study of data analysis, and to professors and researchers who want a handy reference on their bookshelf.

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Explaining Psychological Statistics Review

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(More customer reviews)For a while, I was looking for a good book on statistics, and finally, I found it! This book is excellent for those who are going to start learning statistics (every thing is explained very well from very beginning!) as well as for those (like me) who look for more advanced books. It is almost perfect! It has precise and clear explanations, simple brief examples, thoughtfully chosen questions and tasks at the end of each chapter, good overall organization. Every word (well, sentence) contributes to understanding of concepts, no unnecessary information, no waste of time. Every new concept is built on the previous ones, no gaps in explanation, no unanswered questions. It feels as if the author follows a reader's mind, if some questions arise they get answered in the next paragraphs. Pure pleasure! Ultimate book on statistics! Highly recommend.

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Principles and Practice of Structural Equation Modeling, Second Edition (Methodology In The Social Sciences) Review

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(More customer reviews)Kline's book provides a very readable introduction to and explanation of structural equation modeling. The book does not include statistical proofs, so it would not serve well as an advanced text. But if you are looking for a book that explains what SEM is and how it fits within the larger framework of inferential statistics, I recommend it.

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The bestselling text that has been so popular with graduate students and researchers for providing an accessible guide to the application, interpretation, and pitfalls of structural equation modeling (SEM) has now been carefully revised to be even more useful.New to this edition are: * The first SEM text web page, offering free access to data and program syntax files for many of the research examples in the book, electronic overheads that readers can download and print, and links to other useful sites. * Separate chapters that review fundamental statistical concepts: one on correlation and regression (providing a foundation for less advanced readers), and another on data preparation and screening. * More coverage of the relation between measurement models and structural models in Chapter 8, which directly compares both types of models. * New, separate chapters on nonrecursive models of multiple-sample SEM in Part III, including extensive explanations of latent growth models in Chapter 10 and multilevel SEM in Chapter 13.

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Foundations of Behavioral Statistics: An Insight-Based Approach Review

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(More customer reviews)This is a great book for all ages including elementary school kids. My fourth grader enjoyed reading the chapter on central tendendy -- mean, median and mode -- and she doubly enjoyed reading about these things through an easy to read graduate level textbook while learning them at school at the same time. She asked to keep that book for her. I wish I had that book when I was a fourth grader as well!
It includes many examples from real life studies -- including an example from the famous LibQUAL+(R) protocol that libraries all over the world use to evaluate their services. You get extra bonus marks if you can explain the LibQUAL+(R) example and you are a librarian!
Martha Kyrillidou, Director of Statistics and Service Quality Programs, Association of Research Libraries

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With humor, extraordinary clarity, and carefully paced explanations and examples, Bruce Thompson shows readers how to use the latest techniques for interpreting research outcomes as well as how to make statistical decisions that result in better research. Utilizing the general linear model to demonstrate how different statistical methods are related to each other, Thompson integrates a broad array of methods involving only a single dependent variable, ranging from classical and robust location descriptive statistics, through effect sizes, and on through ANOVA, multiple regression, loglinear analysis and logistic regression. Special features include SPSS and Excel demonstrations that offer opportunities, in the book’s datasets and on Thompson’s website, for further exploration of statistical dynamics.

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Blackwell Handbook of Research Methods in Industrial and Organizational Psychology (Blackwell Handbooks of Research Methods in Psychology) Review

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(More customer reviews)Steven's daughter is the coolest kid in the world... Everyone should buy this book because it is dedicated to Sasha and Sasha Rocks!!!!!!!

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Handbook of Research Methods in Industrial and Organizational Psychology is a comprehensive and contemporary treatment of research philosophies, approaches, tools, and techniques indigenous to industrial and organizational psychology. In this volume, leading methodological and measurement scholars discuss topics spanning the entire organizational research process. Topics include, but are not limited to, research ethics, reliability and validity, research design, qualitative research paradigms, power analysis, computational modeling, confirmatory factor analysis, internet data collection, longitudinal modeling, modeling complex data structures, multilevel research, cross-cultural organizational research, and modeling nonlinear relationships. Chapters are written so that both the novice and the experienced researcher will gain new and useful practical and theoretical insights into how to systematically and pragmatically study work-related phenomena. This handbook will serve as an excellent modern complement to other more content-based handbooks of industrial/organizational psychology, organizational behavior, and human resources management.

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Probability and Statistics for Engineering and the Sciences (with Student Suite Online) Review

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(More customer reviews)Given any particular university level textbook, there's always a compromise between the author's need to write a professional text and concurrently make it easy to understand. This textbook is no exception, and it leans toward the professional aspect. It is therefore good reference or supplementary material for those who already have some background in statistics, but it is very difficult to learn from the text alone.
As for the book itself, it has strengths and weaknesses. On the good side, it has excellent examples that use real world data, albeit largely from esoteric sources. You can see right off that knowing the material will be very useful in real-world applications, which isn't something you get from many other textbooks.
If you happen to be interested in statistical theory, this book has everything you'd want to know and more. Some of the details get pretty gory, but if you like that sort of thing, it's all here. At the same time, the text is organized so you can easily skip those parts if it's not your ballgame.
The layout and organization, in general, are well thought out and implemented. Important formulas are boxed for easy identification, and key terms are well referenced. The book size and weight is also very reasonable for a textbook. This is attained by very concise, mathematical language. Also, a useful CD is included with the text, containing all the data used in the exercises (various program formats) so you don't have to type it all in manually. All-important tables are located in the back of the book, where you can always find them. Additionally, the appendix section has answers to the odd-numbered exercises. It's not that much of a problem that you only have half the solutions, because concurrent problems are usually similar.
Downsides to the text include the language, which is highly technical, relying heavily on symbols, terminology, and acronyms. Of course, statistics in general is like this, but this book really forces you to learn this rather distasteful aspect of the field. Anyway, it could certainly be more user-friendly, although as such it might be less concise. For those who are well accustomed to such things (e.g. statisticians, mathematicians, military people) the material might be an easy thing to pick up. For others, it can be frustrating when you have to flip back a few hundred pages to remind yourself what a particular Greek alphabet was supposed to represent. Personally, I feel a table of all the symbols with a brief description of each would have been a very welcome addition to the appendix.
Though the exercises are generally well done and challenging, I do have some issues with them. From time to time, one would refer to a problem or data set from way back in the textbook- with no executive summary. It would have been nice to be able to see what was being asked without flipping back hundreds of pages. Additionally, the answers provided in the appendix are often nearly useless, since a terse numerical answer says little about how you might arrive at it. For this purpose there is a solutions manual available, but you might be disinclined to pay for it.
The most prominent difficulty with the text- and I know it's not exclusively mine- is the simple fact that it is a professional work. If you have no knowledge of statistics beforehand, it can be an extremely difficult read. For a while I tried to browse the text before lectures, but I found that it wasn't worth the effort. It took so long to plow through to the 'moral of the story' that I ended up just using the book for review. Fortunately, I was lucky enough to have an excellent statistics professor (the author himself) and so was able to pick up the concepts by simply going to class. If, in the admittedly improbable event that you're new to statistics and are looking for an extra book to make up for a poor instructor, you might want to look at a different one. If you already know something about statistics and want a useful reference, then this is your resource.

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This comprehensive introduction to probability and statistics will give you the solid grounding you need no matter what your engineering specialty. Through the use of lively and realistic examples, the author helps you go beyond simply learning about statistics to actually putting the statistical methods to use. Rather than focus on rigorous mathematical development and potentially overwhelming derivations, the book emphasizes concepts, models, methodology, and applications that facilitate your understanding.

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