This is perhaps the best book at simply explaining the statistics of risk and uncertainty I have run across. I have even used what the author calls the illusion of certainty in analyzing the highest and best use of real estate. This book shows how medical experts and criminologists can be misled, not so much by innumeracy, as by what might better be called an illusion of expertise. Experts in any field may find this book useful in view of the U.S. Supreme Court's Daubert Rule that expert courtroom testimony must follow the scientific method.
A couple of caveats are in order however, and they are, shall we say, doozies. Gigerenzer states that there is ample evidence that smoking causes lung cancer. But he fails to consider why people from Asian and Pacific-Island cultures have some of the highest smoking rates in the world, but some of the lowest cancer rates. Any why do longitudinal studies show that people from these same cultures have much higher rates of cancer once they migrate to modern countries? Is it diet, smoking, a combination of the two, or something else that "causes" cancer? Likewise, Gigerenzer states that there is strong evidence that secondhand smoke is harmful to health. But he fails to mention the cardinal rule of toxicology: the dose, or concentration of a substance, makes the poison, not the substance itself. It is only in modern energy efficient air-tight buildings that smoke can be sufficiently concentrated so as to become an irritant, let alone a perceived health hazard. Thus, it may not be secondhand smoke, but the environment of tight buildings that is the source of the problem.
Thus, Gigerenzer fails to point out that all statistics and numbers must be actively interpreted and are relative in meaning to the interpreter. This involves a social filtering process not discussed in the book. Also, government may legitimate some health and crime statistics, when they may be bogus. As an aficionado of Gigerenzer's books, maybe he will write a sequel on the interpretation, misinterpretation, and social and political construction of statistics.

Gerd Gigerenzer has written several books dealing with "bounded rationality"--how humans use their brains to understand the world around them, make decisions, and determine the risks associated with a given course of action. This book is easily his most accessible. It is clear and easy to read, with most(but not all)the examples drawn from the field of personal health.

Gigerenzer provides the simple mental tools that allow anyone to make sense of the statistics that bombard us daily in the media. It is exactly his point that one does not need to be a rocket scientist (or professional statistician) to understand the numbers used by professionals, from personal physicians to DNA experts, that affect our lives and livelihoods.

If I could recommend only one book to address "numerical illiteracy," this would be it. You will learn some essential skills in a clearly informative and entertaining way.

Heading for a medical exam? Wonder if those uncomfortable, expensive tests really make a difference? Skip the medical libraries and talk to the statisticians.

Gigerenzer bares the truth that doctors conceal because of ignorance or greed. Every woman should read his chapter on the risks and benefits of mammograms. The rate of false positives for mammograms is a whopping ninety percent. The cost is not measured by x-ray charges alone (although a radiologist huffed out of a meeting with a gynecologist who stopped recommending mammograms -- they make big bucks from those tests!). Think of the unnecessary biopsies -- and the unnecessary surgery because biopsies have error rates too.

Cancer tests do not cure or prevent cancer. They may reduce the risk of death, although a comparison between screened and unscreened populations shows that very few lives are actually saved this way. And there is no risk reduction unless early detection affords access to a cure.

AIDS tests also carry risks. The rate of false positives among a healthy, "safe-sex" population is about fifty percent. The author describes horror stories of disease-free people who were mis-diagnosed. They lost jobs, homes and friends; some sued for recovery but at least one committed suicide.

Our health care system spends millions on tests because both patients and doctors are ill-informed. We demand a cure and the medical system finds a way to give us the illusion of progress.

It's not just the US. The author found ignorance of false positives for AIDS tests in Germany. When I lived in Canada, the provincial health system bombarded us with propaganda for mammograms.

Gigerenzer has done the world a great service by writing this book and presenting data in a reader-friendly fashion. I suspect there is a human tendency to look for certainty and today's medical tests seems to be the equivalent of divining rods and astrology of three hundred years ago. Now I wish he'd take a look at academic and career tests, most of which also give a form of "false positives." We'd like a yes or no in this world, but alas, mostly we have to learn to live with the maybes.

This book explained topics I was interested in above my personal expectations. All the mathematics and probabilistic models were explained in detail with a practical approach. I was even able to refine some of those models for specific needs without much previous experience nor knowledge. I highly recommend this book, it is one of the best I ever read.

This book is a very well written overview to hidden Markov models and context-free grammar methods in computational biology. The authors have written a book that is useful to both biologists and mathematicians. Biologists with a background in probability theory equivalent to a senior-level course should be able to follow along without any trouble. The approach the author's take in the book is very intuitive and they motivate the concepts with elementary examples before moving on to the more abstract definitions. Exercises also abound in the book, and they are straightforward enough to work out, and should be if one desires an in-depth understanding of the main text. In addition, there is a software package called HMMER, developed by one of the authors (Eddy) that is in the public domain and can be downloaded from the Internet. The package specifically uses hidden Markov models to perform sequence analysis using the methods outlined in the book.

Probabilistic modeling has been applied to many different areas, including speech recognition, network performance analysis, and computational radiology. An overview of probabilistic modeling is given in the first chapter, and the authors effectively introduce the concepts without heavy abstract formalism, which for completeness they delegate to the last chapter of the book. Bayesian parameter estimation is introduced as well as maximum likelihood estimation. The authors take a pragmatic attitude in the utility of these different approaches, with both being developed in the book.

This is followed by a treatment of pairwise alignment in Chapter Two, which begins with substitution matrices. They point out, via some exercises, the role of physics in influencing particular alignments (hydrophobicity for example). Global alignment via the Gotoh algorithm and local alignment via the Smith-Waterman algorithm, are both discussed very effectively. Finite state machines with accompanying diagrams are used to discuss dynamic programming approaches to sequence alignment. The BLAST and FASTA packages are briefly discussed, along with the PAM and BLOSUM matrices.

Hidden Markov models are treated thoroughly in the next chapter with the Viterbi and Baum-Welch algorithms playing the central role. HIdden Markov models are then used in Chapter 4 for pairwise alignment. State diagrams are again used very effectively to illustrate the relevant ideas. Profile hidden Markov models which, according to the authors are the most popular application of hidden Markov models, are treated in detail in the next chapter. A very surprising application of Voronoi diagrams from computational geometry to weighting training sequences is given.

Several different approaches, such as Barton-Sternberg, CLUSTALW, Feng-Doolittle, MSA, simulated annealing, and Gibbs sampling are applied to multiple sequence alignment methods in Chapter 6. It is very well written, with the only disappointment being that only one exercise is given in the entire chapter. Phylogenetic trees are covered in Chapter 7, with emphasis placed on tree building algorithms using parsimony. The next chapter discusses the same topic from a probabilistic perspective. This to me was the most interesting part of the book as it connects the sequence alignment algorithms with evolutionary models.

The authors switch gears starting with the next chapter on transformational grammars. It is intriguing to see how concepts used in compiler construction can be generalized to the probabilistic case and then applied to computational biology. The PROSITE database is given as an example of the application of regular grammars to sequence matching. This chapter is fascinating reading, and there are some straightforward exercises illustrating the main points.

The last chapter covers RNA structure analysis, which introduces the concept of a pseudoknot. These are not to be confused with the usual knot constructions that can be applied to the topology of DNA, but instead result from the existence of non-nested base pairs in RNA sequences. The authors discuss many other techniques used in RNA sequence analysis and take care to point out which ones are more practical from a computational point of view. Surprisingly, genetic algorithms and algorithms based on Monte Carlo sampling are not discussed in the book, but the authors do give references for the interested reader.

The best attribute of this book is that the authors take a pragmatic point of view of how mathematics can be applied to problems in computational biology. They are not dogmatic about any particular approach, but instead fit the algorithm to the problem at hand.

I am a physicist and had some interest in what these bio informatics actually do. I must say I am impressed both in the rigor and sharpness of the probabilistic reasoning. This book relies heavily on probability theory (especially hidden Markov models) and is clear enough to be read without a sharp pencil. Don't get me wrong it is not simple enough to be good late night bedtime entertainment. The biological and chemical background is also easy to grasp.
The authors are obviously very active in the field they describe. Their self citations seem absolutely reasonable.

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This is the best applied book on multivariate analysis I know. It clearly explains how to do statistical analyses and how to interpret the output. Clear examples throughout. Syntax supplied for each type of multivariate analysis in both SPSS and SAS, with LISREL notation for CFA. In addition to specific techniques (factor analysis, multiple regression, multiple discriminant analysis, MANOVA, conjoint analysis, canonical correlation, cluster analysis, multidimensional scaling, structural equation modeling/CFA) excellent sections on structuring data, cleaning data, and handling missing data. While mathematical sophistication always helps in stat, this book doesn't require it. No knowledge of matrix algebra needed to understand this book. Few if any formulas. Emphasis is on logic rather than math.

This book is an excellent source of information on multivariate analysis techniques. I especially like the flowcharts used for determining which analysis method to use as well as the flowcharts showing what steps to take for the analysis method chosen. If you have a good basic knowledge of statistics and a good head on your shoulders, you will have no problem understanding the methods presented.

I took multivariate in Ph.D. school. Our professor didn't have a book (just his notes), so I had no book to be loyal to when I got out. I was given this book once I graduated and consider it to be a fabulous reference. I can't speak to *learning* multivariate techniques out of it because that's not been my experience with this book.

I use it as a reference- to refresh myself on a technique, or to consult when I run into a problem- this book has yet to let me down and has been able to answer any question or solve any problem that I've had.

You see this book cited in academic behavioral research, but the book does a great job of explaining things in a managerial way as well.

Other of these reviews have criticized it for going on too long on an example or a technique- for that I PRAISE this book- I WANT that extra information. I'm reminded of that quote from "Amadeus"- "Too many notes." I WANT as many notes as I can get- that's what makes it so much more helpful.

If you are looking for a great reference book for multivariate techniques, look no further.

I have previously reviewed this book. My review pertains to the second edition as that is the only edition I have.

Recently I did some consulting for a colleague. He had some data that he wanted to test for the presence of a single outlier. I referred him to the procedures due to Grubbs and Dixon. I also mention the book by Barnett and Lewis which has the most detailed account of outlier methods. However, Barnett and Lewis is so detailed that it can be overwhelming for a beginner. Fortunately my friend has a copy of Sokal and Rohlf's book. I believe he has the same second edition that I have. They provide a good elementary treatment of these methods and have tables to use. Unfortunately, I discovered that the tables are in a separate supplement. My colleague has the supplement but I don't. The reader should be aware that the supplement is needed to implement some of the procedures in the book that require tables. It is not expensive but it is essential. I imagine that the same is true for the third edition but I am not sure. Regardless this is an excellent refer for biostatisticians and practitioners including regulatory affairs analysts and medical writers.

This book has served well as a reference source on biostatistical methods for statisticians and non-statisticians alike. It includes many of the important topics. It provides detailed descriptions of regression, correlation and analysis of variance. It emphasizes the required assumptions. It is written at an introductory level. It also covers aspects of biological data and special topics such as "combining probabilities' (i.e. meta-analysis), randomization tests (i.e. permutation methods such as Fisher's exact test), and the jackknife.

Important topics that are not included are survival analysis, sample size determination and Bayesian methods.

The book is based upon biostatistics courses taught by the authors. It is designed to be used as a self-tutorial if so desired. The explainations and examples are excellent.

First off, I am a very tough grade. I never give more than 3 stars unless a book is exceptional. This book is THE FINEST introductory book every written on information theory!! ... (Lemme explain briefly: In the golden era of information theory, there were many people who sought to "jump on the information theory bandwagon". In fact Claude Shannon actually wrote a brief paper about that. You had all kinds of people trying to apply information theory to the fields of investments and even psychology, ad nauseum. This book has chapters that deal with that... With respect to Pierce, it's junk bogus science... and he really shouldn't have sunk to that level.... even thought there continue to be many Thesis and Dissertations today which still try to use information theory to justify economics and group psychology... Believe that stuff if it makes you happy).

This book is practical, it get's straight to the point and tells you what information theory can actually (and is actually) used for. Alot of information theory books don't
have any practical value whatsoever (Reza, Ash, Khinchin) as they seem to be written more by mathemeticians than scientists/engineers... Pierce has written several books in the golden era, and he is one of the very best authors. His insight, knowledge and clarity of writing are almost unparalleled by no other author. Only Claude Shannon, Bernard Sklar and James Massey rival John Pierce in exceptionally simple writing style.

The book has very few mathematical equations. The ones he presents are so simple it's basic middle school mathematics. In lieiu of math equations, Pierce explains information theory in plain english. If you know nothing whatsoever about information theory... this is the book I would highly recommend first.... ...

Claude Shannon died last year, and it's really disgraceful that his name is not a household word in the manner of Einstein and Newton. He really WAS the Isaac Newton of communications theory, and his master's thesis on Boolean logic applied to circuits is probably the most cited ever.

This is the ONLY book of which I am aware which attempts to present Shannon's results to the educated lay reader, and Pierce does a crackerjack job of it. Notwithstanding, this is not a book for the casual reader. The ideas underlying the theory are inherently subtle and mathematical, although there are numerous practical manifestations of them in nature, and in human "information transmission" behavior. On the other hand, this is a work which repays all effort invested in its mastery many times over.

Pierce is an accomplished scientist/engineer, and was influential in the development of information theory/signal processing. This book has some mathematics, but lays a solid qualitative foundation for understanding the material. This book is a classic, good for computer engineers/scientists (as is his book Signals: The Science of Telecommunications). The presentation is accessible, and first hand accounts of important discoveries motivates a real appreciation for Pierce's contributions.

However, the clarity of the presentation tends to obscure just how profound and deep the thinking involved really is. During the first reading, Pierce's insights made the material seem almost obvious. Later I would get doubts that such straightforward approaches could be correct, and then would think about the correctness of his assertions. This is why this is a great book, because it focuses on important stuff, and doesn't shy away from deep topics. This is a great book for those interested in the basis of information theory, on a side note Shannon's original papers are also quite readable.

I have used earlier versions of this book throughout my professional life. It is a handy quick reference for the more advanced practitioner who wants to explore techniques that they have not used before. Or, to refresh one's memory on techniques that have not been used for awhile. This book is also very useful for training junior practitioners in multivariate techniques. There is enough detail to satisfy those asking more detailed questions about techniques without overwelming those less inclined to pore over mathematical formula. Steps and tests are well laid out, with enough discussion so that the reader understands the value and importance of working through the standard routines and assumption checks. It also offers important pratical steps in how to design and run specific statistical procedures in the common statistical packages.

Given the increasingly user-friendly statistical software on the market, this book offers a quick antidote for the rising number of button pushers, by showing budding statisticians the implications of using a technique ignorantly. This is done without making the technique inscrutable to the reader.

While the new edition offers some additional information - the cost conscious buyer could easily find considerable value in older editions.

This book is a classic. It is the first major purchase you should make for multivariate techniques. In my opinion, it is not a theoretical book, yet it is not a cookbook either. For novices, it provides enough rigor and theory to prevent one from having too many unanswered questions. However, it does not bombard the reader with so much theory that practicality is lost. It's a great way to begin looking at multivariate stats. If your research necessitates deeper analysis, you can find it in other books, but without an adequate foundation, such as that provided for by this book, it will be difficult to tackle more advanced texts. The book costs too much, which is a drawback. It is a shame that books are getting in the ridiculous category for pricing. With that said, this is a book that you will be able to keep for quite a while. The techniques presented are durable and the conepts are timeless for the most part. New statistical methods that are currently being developed are generally available in small booklets ...for cheap prices. Reading this text will make most of those 'new' methods understandable. So, in that regard, it may be a good purchase. But, your best bet is to hit the college bookstore a week before the semester to find a used copy in excellent condition. For the reviewer from Brazil, he should put down the thesaurus and re-read the book. For the rest of us, let's be thankful for people like that for they often return these books to the bookstore in a huff of frustration, with the books barely opened and ripe for the taking at a used price. Great text, should stay on your shelf for years, just try to find a good deal on one.

I used this text for my multivariate stats class. T & F set up each chapter for describing an individual method and then gives several examples for each statistical method described. A floppy disc with the data sets used in the text is available with the text so one can use a computer, with the appropriate programs (SAS,SPSS,Lisrel), with the text as a guide to get to the correct answers. A step by step description and interpretation is given for each problem in case you get lost in the process. This helps immensely in understanding what's going on in the problem; as multivariate stats can get real confusing after awhile. The only complaint is that the previous edition's cover had the author in a pixelated belly dancing pose - I was hoping that the obscured cover would be in focus in this edition.

I bought this book hoping to learn DOE using Taguchi Methods and apply what I learned to real life problems in my work. It was very helpful. Not only did it help me learn the methods I needed to perform my testing at work but I did it in only 8 days of reading. The book was very easy to read and the examples helped me understand everything I needed to know to apply this method to real situations. A great buy for anyone looking to learn Taguchi Methods.

The challenge that all research directors and senior scientists face is that virtually all scientists are originally trained to believe that the only proper way to conduct an experiment is to vary only one factor at a time while holding all other variables fixed. This belief, which can be shown mathematically to be totally incorrect, has been a drag on the scientific community for decades, causing experimental science to be much less productive than it should be. In fact, by properly designing experiments in which several factors are varied in a carefully thought out, predetermined pattern, much more information can be obtained with far fewer experiments.

What Dr. Roy has achieved in his book on experimental design is to clearly explain why this is so and to provide the tools which allows his readers to overcome their ingrained beliefs and adopt a more effective method of designing their own experimental programs.

Of particular interest is his discussion of experimental design using orthogonal arrays (Chapter 4) for it is in this part of the book that the full power of modern approaches to experimentation become most evident. Here, he illustrates how a set of as few as eight experiments can be used to determine how three presumable independent factors, such as time, temperature and concentration, can be studied to obtain detailed information not only on how each factor individually effects the quality of the final product, but also on the extent to which the factors interact.

Historically, learning this type of technique for designing experiments has been a daunting task since somewhat tedious mathematically manipulations are required for both the design of the experiment and the analyses of the resulting data. However, included with Dr. Roy's book is complete software which eliminates the need for the experimenter to either manually solve the handful of equations needed to extract the results in useful form, or to develop custom spreadsheets in an attempt to automate the process. The software is easy to use and includes all of the tables and data which are used in the book to illustrate the principles of experimental design. The software is also capable of assisting the experimenter in designing sets of up to eight concurrent experiments, rapidly analyzing the data and generating graphical and tabular presentations which greatly aid in the interpretation of the results.

This is an extremely useful book which can have a major beneficial effect on the productivity of any laboratory engaged in experimental process research such as crystal growth, chemical synthesis or manufacturing. By allowing the reader to overcome his innate aversion to varying more than one experimental factor at a time, the book makes it possible for the reader to become a much more productive scientist or engineer and become a role model for his coworkers to emulate.

First, I completely agree with all the good things mentioned in the other reviews, but wish to add the following comments:

1. The discussion group (usenet) hosted by Dr Roy is fantastic. If by chance you don't "get it" from reading the book, a simple question posted to the usenet frequently gets you an answer, often within a few hours (its a vocal crowd!!). Many times, the author himself responds to usenet questions. And, on at least one instance, after I repeatdly asked my question, he began exchanging emails with me, which then led to some pleasant phone disscussions.

2. User friendly software and support. See above.

3. Taguichi is frequently attacked by full blown Design of Experiments statistical types who miss the whole point. I strongly encourage all professionals interested in gauging quality control to try out his books and the free software downloads....

An excellent book with powerful software and support......

Reads like a winner, looks like a winner, performs likes a winner....... need I say more...

Auburn Alabama

This is a very well written book and the first of its kind
on Bayesian survival analysis. The authors have a very keen
sense of the important issues and models in this area, and they
do a wonderful job of presenting the various topics. The book
discusses state-of-the-art methods for fitting Bayesian survival
models. The content on the power prior and its uses in survival
analysis was very exciting. The motivating examples in Chapter 1
were novel and very appealing. The authors have a great deal
of experience in this area and in the applications they present.
I definitely recommend buying this book. It serves as an
exceptional reference or textbook.

This is a great book on Bayesian survival analysis. It presents
a very comprehensive account of modern Bayesian methods in
survival analysis. The applications that are addressed in the
book are excellent and present major modeling and computational
challenges that would be impossible to implement in the
frequentist paradigm. The book has some outstanding chapters
on model selection, cure rate models, joint models for
longitudinal and survival data, and Bayesian methods for
clinical trials. This book would serve as a great textbook
for a Ph.D. level course in Bayesian survival analysis. The
book contains a number of useful and challenging exercises
and it contains a very exhaustive bibliography. I definitely
recommend this book.

This is truly a marvelous book on Bayesian survival analysis.
The authors, who are true experts in the field, have written
a gem that covers modern Bayesian methods in survival analysis.
They have a nice blend between modeling, theory, and applications
that truly makes this book the first of its kind. It has some
very nicely written chapters on semiparametric models based on
prior processes and frailty models. The book is very extensive
in its coverage and has a very long bibliography. This book is
going to be a best seller for a long time.

My AP statistics teacher wasn't the greatest teacher. She did not believe in assigning homework. As far as AP exams go, AP statistics is one of the easiest ones. This book helps you prepare wonderfully. The only problem with this book is it doesn't teach you how to fully utilize the statistics features of the ti-83 or ti-89 calculator (both allowable on the AP stats exam). In my class last year, only 2 people got 5s, 2 got 3s, and the rest failed. These were not stupid people. Two of the seniors are now at Cornell. But anyway, for AP statistics, this is THE BOOK TO GET. With this book, you can easily achieve a 5. The problems in this book are HARDER than the ones on the exam. This book REALLY PREPAREs you to get a 5. I did, and you can too.

Statistics is probably one of the easiest AP exams you can take if you are a math-minded person. I bought this book and went through it once, and without using anything else I was able to achieve a 5 on the Stat exam. Saved me a lot of trouble when I got to college by already having the credit, I would recommend anyone with skills in math take the Stat exam.

Well I didn't take AP statistics this year. I took normal statistics last year and took the exam this year. Although this book sort of slacked off in the end with the BETA-test topic, the rest of the topics were well-reviewed. Take it from me: you don't even need a text book to get a 5 on the AP test if you get this study guide.

This is good enough at what it does: illustrating mathematical concepts under the guise of Sherlock Holmes stories. However, I have one beef and that is that Bruce, perhaps through lack of information on his own part, makes Holmes less intelligent than he should be.

My main example is that throughout the book, Holmes and Watson make reference to the year 1900 (their present year) as being the beginning of a new century. I feel certain that Holmes at least would know that centuries do not begin until the year one, in this case 1901. When Watson mentioned it, I felt sure that Bruce was taking the normal tack of making him obviously less intelligent than his partner (the man *is* a doctor, for crying out loud, give him *some* credit), but when Holmes mentions it later, I was duly perturbed.

Bruce also uses characters purely to tack on surprise endings to his stories, one of which did not work for this reviewer. In one story, the pair meet the Reverend Charles Dodgson, which any bibliophile knows is the real name of Lewis Carroll, but does not present this information until the last paragraph of the story. The surprise ending, using the pseudonym, was therefore lost on me.

In another story, there is no solution presented to a murder. This irked me no end at first, but then I realized that there being no solution to the mystery better illustrated the mathematical principle being explained. I still prefer my murders to have solutions, however.

All in all, this is an entertaining book. Bruce's skills as a storyteller and his ability to mix lessons into his stories is commendable. The stories, as Holmes pastiches, ring true overall, only clunking during the details I have mentioned, such as certain actions that seem totally out of character. One other example is when Sherlock and Mycroft are explaining a principle and Sherlock pulls out a graph to illustrate. Bruce (as Watson) writes the following (to the best of my memory): 'I jumped up, knocking over my chair, and cried, 'I have a horror of algebra!'' I couldn't help but laugh! This behavior from one of the most beloved characters in literature?

But, as I said, as a whole the book succeeds, and if you can overlook these details and engross yourself in the superb storytelling, you will enjoy yourself, and probably be educated in the process.

If I could guarantee that the author of this book was as wise as his characters, I would marry him sight unseen.
Regardless, this is a book worthy of many readings.

Unlike some other reviewers, I am neither a statistitian nor a Sherlock Holms lover. I never cared much for murder mysteries perse, but as a tool for exploring such interesting concepts I thought it worked well. Yes he took a few liberties with history (as he pointed out in the end notes)--so what?

The stories were not designed to top those of doyle but to make some interesting probability and decision making concepts approachable, relevent, and enjoyable. This they did wonderfully. As someone who was turned off to math after years of dull, abstract school lecture, my interest arose from my work in business and computer science. Some of these concepts were not new to me, but all were from new angles. I found .the math easy to follow(depressingly difficult to predict!) and only wished I had not run out of pages. I plan not only to check out the author's other work, but some of the additional reading he kindly suggests in the notes. Thank you Mr. Bruce for and enjoyable read.