this book is really helpful if you're learning winbugs and mcmc for the first time, and handy even if you're not new. There are lots of examples, with code and explanation.

This is an extremely good way to become familiar with Bayesian methods. Most books don't help you through things to the degree that this one does. It starts with really basic principles necesary to understand Bayesian principles and goes all the way through MCMC techniques like Gibbs Sampling and simulated annealing. I especially like the way many of the intermediate steps are included rather than assuming the reader has the time to work through all of them. A nice addition to my methodology bookshelf.

This is a incredibly well-presented introduction to Bayesian methods and Bayesian posterior simulation. Gill goes from the bare-bones introduction through Gibbs, Metropolis, and annealing. Every chapter has a set of examples with data, code, and interesting results. The technical level is spot-on: detailed but not overwhelming. As an industry practitioner (finance) rather than someone at university, this book has been very helpful in getting me started in this area. What I've found this summer is that this book leads nicely into more advanced work which I've been exploring: the Congdon book, Carlin and Louis, and even Chen, Shao, and Ibrahim. I'm also new to winbugs and R, having been a SAS user for quite some time, and the worked examples in the Gill book are quite helpful in that one can run them immediately with code supplied. Superbly one does not even have to type them in as they are supplied on the net (along with some software links, errata, and other tidbits). My only real complaint here is that I would have liked to see an extended section on empirical Bayes. But as this is featured in Carlin and Louis, its not too large of an issue. If you're interested in Bayesian statistics, this is a "must-own". While there seems to be plenty of high-level works out there, particularly in say biostats, there are relatively few that get you started and provide such extensive detail.

This text was used in a graduate course on the Design of Experiments. It has a great wealth of examples that span many different fields of industry, from pharmaceuticals to "tool and die" manufacturing. This text, when used along with a statistical software package, made research projects easy to design, implement, analyze and present to others. I found the chapters on reliability, determining sample size for mixed models (random and fixed variables), very helpful in my research.

Doug Montgomery got excellent statistical training at VPI. He also has a wealth of practical experience from his consulting work. His books on regression, statistical design and response surfaces are all well written and understandable to engineers. This 4th edition published in 1997 still contains all the useful information on factorial and fractional factorial designs. Chapter 14 is a wonderful up-to-date chapter that covers important process optimization topics not often covered in traditional design of experiments books. This includes response surface methods, mixture experiments, evolutionary operation and Taguchi methods. It does not require high level mathematics.

Doug Montgomery is an engineer with great statistical training. This enabled him to be a great teacher for statistical practitioners and an author of several excellent applied books in the design and analysis of experimental data and statistical process control. This book is one of the best sources for Design of Experiments for engineers, currently a very important topic in industry. He provides a wealth of information on classical designs and the analysis of variance models. In addition he provides a balanced perspective on the value of Taguchi designs. Other topics covered include response surface methods, evolutionary operation and mixture experiments.

I used this book when I took OR class and I think that the book is quite good but it lack of theory calculation and also scientific calculation about each problem. In general I still believe that this is a good book.

I own both editions where I belive the 1st edition is better than their second. I don't particular like their new approach in teaching network modeling; it's non-intuitive. The first edition was a bit more 'logical' and easier to set up than the approach use in their 2nd edition. Secondly, their use of the Palisade @Risk is definitely a good choice, but the time lock (1 year if registered online) somewhat deflates the enthusiasm knowing that at some future date your software will cease to work. I bought the @129 upgrade but jumped through hoops with palisades (their authorization code did not undo the time lock and tech support argued that it was a publisher issue--in the long run they discovered a problem with the software where it was not taking the time lock off the application as designed.) I do like the 2nd edition's choice of larger fonts and the broader examples. I am still yet disappointed that unlike many other quality text books, the text does not include answers to 'odd' or even selected problems. I don't think the authors realize that their text is used by professionals looking to develop workplace skills and are not necessarily enrolled in a formal academic setting. Nevertheless, I bought the second edition because I think management science is a terribly valuable skill to have and that the authors have published the best book in the area of MS.

I am a business unit manager who frequently purchases books and software to maintain my level of competence in operations management, and I recently purchased the second edition of this book, copyright 2001. I have found it to be a great value, and agree with the reviewers the earlier edition who found it to be a very fine text on Operations Research. It comes with the standard version of Palisade's Decision Tools software, which is just outstanding. I wouldn't hesitate to recommend this book to anyone. Additionally, there's an option to upgrade to a fully licensed edition of this software at a greatly reduced price; the student version that comes with the book is fully functional, but is only licensed one year for students, 30 days for professionals. Microsoft doesn't give their software away for free either.

This book is too technical and has too much math. A better book for the elementary learner is "Practical Statistics: Simply Explained," by Russell Langley. Bulmer's book may be useful to me later on after I understand basic statistics. But don't waste your money if you know little about statistics and you don't like complex mathematical explanations.

This brief book gives an understandable introduction to the main ideas of statistics, with problems at the end of each chapter, and answers in the back. There is a bit of calculus. The book is nicely written, with interesting examples.

This modest little book is both a masterpiece and a gem! I can't praise it enough! It is different from any other statistics book I have ever read in that it puts you in the place of famous historical figures in statistics and helps you rediscover their findings. His use of original source material is very well done. The book is self-contained and the author proves almost everything of importance(some of the proofs are more intuitive than rigorous at times, but that's the point). Bulmer has a knack of making the most difficult concepts (hyperspace, degrees of freedom) seem natural. He covers a very broad terrain from distributions, tests of significance, inference, Bayesian methods, etc. Written on many levels, this is useful for a novice or intermediate student but I suspect professional statisticians will find much to keep them thinking about. While reading through this book you will often say "aha, so that's why they do that". For the price it is the best value possible; you won't regret picking up a copy of this book and if you enjoy the inner workings of statistical theory you will refer to it again and again.

This is the best introductory SAS book I have seen. It introduces the basic SAS structure and programming statements. A good and detailed explanation is provided for DATA step vs. PROC, a topic that always proves to be difficult to grasp for SAS newbies.

The book starts with SAS terminology and logic, i.e., how SAS works its magic. It moves on to simple tasks such as reading in data, summarizing it, and displaying it. Then there are chapters on SAS programming elements (expressions, logic flows, etc.) as well as intermediate-advanced statements. The MERGE statement is explained very clearly.

Examples abound, although I feel that they are presented in a very confusing format -- often you can't tell if a block of text belongs to the main text or an example. The organization of the book is also a little confusing, esp. if you want to use it as a reference book (not recommended; stick with SAS manual set instead).

In short, this book provides an excellent ovewview of SAS. Read the chapters in sequence and you'll soon be on your way to happy SAS'ing!

I went to the Fundamentals of SAS course held at the SAS Institute and was left in dire need of more information. This book was a first step. The exercises in the book were helpful and reinforcing. If you need more than brginner's knowledge, then you'll have to purchase another book but this is good to get started.

If you are in need of data management or analysis, SAS is likely big on your list. This book gives extremely clear and useful introductions to how SAS is structured, how to manipulate and organize data, and some basic statistical procedures (descriptive statistics, ANOVA, OLS models, etc.) are also presented.

Official SAS manuals are usually bulky, complicated, and difficult to understand, particularly if you have little/no experience with SAS. This book can take a complete beginner through data set creation and manipulation into substantially more complex and important procedures and functions. As this is an introduction to the SAS system, the more advanced functions in SAS, such as SAS STAT, are not covered in any detail. However, in order to understand how to actually use SAS and manipulate data so as to employ these higher level procedures, this book is excellent.

This is the absolute best introduction on how to use the SAS system I have seen, but it has lots of useful information for people with some basic SAS knowledge as well. It is presented in the style of a textbook,with each section concluding with problems (with answers provided) that the aspiring SAS programmer can solve.

If you are just starting to look into SAS, I cannot recommend this book highly enough. 'If I was able to do it, anyone can.'

I work as an actuary, and I have passed the first actuarial exam (the calculus and stat exam with a <30% pass ratio). My friend uses this book for her stat class, and I have found it to be a terrible book.

I hope that any teacher reads this, and looks at the book before assigning it to a class.

There are very few examples of problems, very little discussion of theory, and it is structured in a way that does not allow you to easily reference other texts.

If you want a good text for challenging problems and relevant examples, try Hogg and Tanis.

This book is a book on mathematical statistics and hence goes further than most entry-level statistics books, which mostly deal with business statistics. This book does not reinvent the wheel, but is goes further where other books end. You will find a mathematical explanation and theorems are proved as well. Soms more advanced topics are moment generating functions and power tests. The consequention is that the learning curve is steeper. At my university, econometrics students use this book, and they couldn't have made a better choice. Logically, since econometricians don't have enough on a book with only a minor focus on formulas.

This is definitely one of the best introductory statistical books around. The writing is lucid and masterly, and includes a wide variety of topics. The authors have included an abundant number of well-chosen exercises and these cover a wide spectrum showing the application of statistics to the engineering, biological, etc., sciences. My only complaints are that there is not enough on residuals and influence in the chapter on regression; the use of the hat matrix in this context would have been most beneficial, in my opinion. Also I would have very much liked to see the Fisher information in inference and the Smith-Satterthwaite procedure in hypothesis testing included. Nonethless both students and instructors will find the book extremely useful and it gets my highest recommendations!

This book is a condensed review of statistics, without laborious examples. It can be used as a refresher or supplement to a more verbose introductory primer. It is perfect for a high school or a one semester introductory stats course.

I am taking Stat 244 after taking the first class 5 years ago. I needed a review to catch up, but I am finding that this is much more comprehensible than the textbook and the instructor. Nice and straight forward... Makes you wonder why you need to spend $80+ for a used textbook in college when you can learn it more efficiently from this little guy... THANK YOU!!!!!!

Cliff's Quick Review of Statistics is a good supplement
to any Stat course, and should be kept as a reference.
Good intro text. It ought to be required.

For non-quantitative concepts, presented for the layman,
get the classic "How to Lie With Statistics" by Huff.

ERRATA

Page 7, beneath Eqn 2-1, this statement is completely erroneous and needs to be removed: "Note that Avg and ..."

Page 85, next to last paragraph, replace P=Avg with P-Avg.

John T. Thorngren

Powerful- a real sword to all those who believe in the tooth fairy

An excellent book that mathematically exposes "hot number", wheeling, and other "gimmicks" sold as sure fire methods to win the lottery -

This is truly an oustanding book on MCMC methods for Bayesian
computation. The authors present a nice balance between technical
developments and applications. It covers several topics not covered by other MCMC books, such as HPD regions, model selection, and density estimation. This book is world class.

This is an outstanding book on MCMC methods. The book presents
novel and sophisticated methods for carrying out posterior
computations and summarizing posterior quantities of interest using novel MCMC techniques. The authors present a lot of their
groundbreaking work as well as summarizing the work of many others. The book presents a number of complex models used in real and interesting applications in the biomedical sciences. Two of the authors also have wirtten another outstanding book titled Bayesian Survival Analysis (Ibrahim et al., 2001), which presents modern methods for Bayesian survival analysis and provides a comprehensive and thorough treatment of the subject. The authors are to be congratulated on writing two very fine books. Both books get 5 stars from me.

This is a great book by the authors, covering a wide range of
topics in MCMC. The coverage of the material is deep and novel.
Two of the authors also have published another outstanding book
titled Bayesian Survival Analyis, by Ibrahim et al., which presents
cutting edge and novel methods in the analysis of survival data.
Both books get 5 stars from me. A splendid job by the authors
in writing two very fine books.

This book has revolutionized the way psychologists think about interactions. It provides step-by-step instructions on how to probe the moderating effects after you find a significant interaction in a multiple regression.

The basic idea about interaction is that the relationship between two variables were different according to a third variable. For example, some risk factors (such as poor family income) may affect children's academic achievement in a negative way. However, if the parents provide enough support on their children's study, then it's possible that the risk factors will no longer influence their children test scores. Therefore, with low support, risk factors are very effective, but with high support, risk factors have not effects. This book teaches you how to probe this relationship in a systematic way, it covers 2-way, 3-way interactions and also quardratic relationships.

If you fully understand this book, the techniques you have will be enough for a masters thesis in your area.

This book takes a very practival approach to the analysis of interactions in regression. No other book I've used has covered these topics as clearly or as in depth. The extensive discussion of decomposing interactions is a prime example. With the push to replace old techniques of dichotomizing continuous variables with a continuous (regression) treatment of these variables (especially in psychology), this book is extremely important.

Back when the book was first published, I was completing doctoral research. Aiken & West provided the explanations and instructions that enabled me to complete my dissertation. Nowhere else have I seen the information they provide; seldom have I seen statistical treatments as clearly and easily explained. Like many in the social sciences, math was not my greatest intellectual ability. This book made computing and understanding regression interactions a relative breeze. One reviewer bemoaned the lack of information on interactions among categorical variables. I suppose he didn't read the preface that specifically explains the reason for the absence: such information is widely available in any good statistical text. What Aiken & West provide can't be found elsewhere in any real depth. I am ordering another copy of the book because I'm tired of loaning out my copy to colleagues, especially one who has now begun to copy whole chapters. Yes, it's that useful.