It's a very useful book for learning statistics focused on business. Based on a solid theory, it gives you many practical case studies. The approach is not superficial, but the beginners will find a safe path for improvement and for the comprehension of the subjects.

I've used this textbook in the Athabasca University MBA program. The "Quantitative Analysis" course is an introductory course in statistics, with a focus on business applications. This text is perfect, as the examples used are directly relevant to businesspeople. There are also many questions and exercises, with the answers provided in the back of the book for all the odd-numbered questions. This allows students to practice and check their answers, while still allowing the professor to assign even-numbered questions as practice work to be checked by her later. The text starts with basic statistics, moves into probability, and continues all the way into multiple regression and non-parametric statistics. Real data sets are used liberally, and there are tutorials for SAS, SPSS, Minitab and ASP. Each section clearly defines critical terms offset from the body of the text, and the over 1000 exercises use data from real-world examples. Each chapter has exercises divided into "Learning the Mechanics" (straightforward applications of new concepts) and "Applying the Concepts" (applications of concepts to the solution of real-world problems). If you're a business student having difficulty with statistics, I'd recommend this book as an excellent primary or companion text.

Great book for beginners, and a good reference for more advanced statistical professionals. Logically structured, and written in real-world terms, this book is easy to read and follow. Sincich provides the reader with an understanding of statistical application in the every day world, bringing statistics out of the textbook and into reality. A superb guide to fundamental statistical principles.

Book comes with great condition, although the shipping speed is somehow disappointing.

This is a very demanding, thorough, and clear description of just about everything anyone could want to know on the subject. The second edition is considerably more rigorous than the first. Agresti stresses that logistic models are one kind of generalized linear model. This offers solid connections to many other models, but places corresponding demands on the reader. In particular, Chapter 4 is difficult going, but might be skipped or skimmed on first reading.

Given the mathematical level and rigor, this is a remarkably clear book. Anyone who analyzes categorical data on a regular basis should read it and have it on his or her shelf.

When this book came out in 1990 it was the first book to provide a truely modern treatment of categorical data analysis for both ordinal and nominal data. It provides an excellent treatment of the asymptotic theory for binary and multinomial data. It is extremely well written and is still a favorite of statisticians and practitioners. Because of its popularity and continued value, it should soon be added to the Wiley Classic series.

This is the first book to take the regression approach to categorical data analysis tieing the subject to the methods and theory of the generalized linear models. It also was one of the first to show the modern practicality of exact permutation methods.

The only drawback of this book is that it is 11 years old and there have been many interesting and relevant research developments in computer-intensive methods, analysis of missing data and mixed effects linear models to make a revision useful. Some of the latest developments can be found in Lloyd's new book "Statistical Analysis of Categorical Data" that was recently published by Wiley.

Agresti provides clear advice and also gives a nice historical perspective on the development of the subject. The book is authoritative and includes numerous relevant references. Each chapter contains many exercises and a wealth of practical examples for illustration of the techniques. This is a good text from both practical and theoretical perspectives. It is excellent for a graduate level course on categorical data analysis.

easy steps approaching to statistics and good examples to practice the text.

I have read many statistics books but never one I understand on the first read. This book is for the true beginner. Excellent.

This books has great presentations, diagrams, and examples of concepts in elementary statistics and probability. It is thorough, yet easy to read. Students will enjoy examples that they can relate to.

Dear Sirs I do not know if I can use this part as a tool for contacting the autour or not; however please excuse me if I have not used it properly: I am a student of epidemiology, and presently we are studying the following book: The Design and Analysis of Clinical Experiments; Joseph L. Fleiss; John Wiley & Sons; 1986. But on page 67 it seems that there is some misunderstandings: When we do a log transformation, in fact we are changing (or shifting) the previous distribution to a normal distribution. Without any doubts in this transformation the means of our previous distributions do not transfer to the means of the new distributions! In fact these are the medians of the previous distributions which are transferred to the place of the means of new distributions (of course if we presume that the new distributions are almost perfectly normal) and by finding the confidence interval of the difference of the means of new distributions (lambda1 - lambda2) we are finding the CI of ratio Median1/Median2. In this way it seems reasonable that the formula 3.25 be changed to Median1/ Median2. It would be very kind of you if you help me in this problem!

With best regards Dr. Shahrokh Izadi

This book was first published in 1986. As Fleiss states in his preface, the intention is to fill a gap in the standard texts on experimental designs by emphasizing and illustrating those that are useful in clinical studies. This book was clearly marketed for the rapidly growing and highly regulated pharmaceutical industry. In addition to the classic experimental designs, Fleiss covers cross-over designs and repeated measure designs that are important in clinical trials. He writes clearly and deals with the important issues in clinical trials including potential biases, blinding, randomized controls, multiple comparisons and repeated measures. The book starts off with a chapter that emphasizes the effect of measurement error and also provides some simple experiments on reliability of measurements.

There is a wealth of methods included, many different designs and various parametric and nonparametric analysis techniques. It is aimed at biostatisticians in the pharmaceutical industry and the medical research field. The book is very much suited for an advanced undergraduate or graduate level course for students majoring in statistics or biostatistics. The level of mathematics is high but not excessively used. Mathematical results on sample size determination are deferred to an appendix.

The Wiley editors choose only successful books to be included in their Classics Library series. The intent of the Classics series is to take popular books by distinguished authors and create a paperback edition that may be more affordable than the hardcovered edition still in print. It is not a revision of the book. This book entered the Classics series in 1999.

It is a great reference source and I plan to consult it a great deal in the future. The only drawback to it that I see is that it is not up to date. The last 15 years has seen many advances in group sequential methods, Bayesian designs and longitudinal data analysis that this text misses. So Fleiss' book is not one stop shopping for a clinical biostatistician but it does offer a lot and presents it eloquently.

With regard to reviewer Izadi's comments on Amazon, I think the appropriate way to ask this question is really to write the author. Since it is here in print for the readers, I will attempt a reply. I think there is a misinterpretation of the terminology. When Fleiss refers to mean logarithms he is not referring to the population means on the log scale but rather the logarithm of the population mean on the original scale. With the latter interpretation equation 3.25 makes perfect sense. It is the former interpretation of the parameters that the reviewers point addresses. The importance in the example is to demonstrate the lack of robustness of t or F tests to non-normal (e.g. lognormal) data and to show that tests and confidence intervals can still be accomplished using the normal theory after the transformation. The key point is that it is the ratio of the parameters that is transformed into differences of the log of the parameters.

This is the standard text on this subject. Recommend you have at least a Master's degree in statistics to take full advantage of this book. This book is too technical for non-statisticians, although they may get some useful infomation from the non-statistical discussions.

This is a solid and inexpensive book on the subject. There are lots of examples and many exercise solutions. It is useful for self-study to the well motivated.

This book can be used as a reference, or as an introduction to Fourier series. It is clearly written, and gives the rigorous foundation as well as some practical applications of Fourier series.

A good, thorough treatment of Fourier series and their applications to PDEs. I was impressed by the rigorous, yet clear explanatations. The book is advanced material but very easy to read. Definitely recommended, and it's very cheap too!

The book is handy. It has SAS commands needed for running your analysis. Nonetheless, you will need to refer to other texts to understand your choices.

Fast and superb. Will buy again.

I've used the first edition of the book for my own data analysis more than once. It is an invaluable tool for translating statistical theory into practical knowledge. I often read statistical texts, think I understand them, but have no idea how to use that knowledge with a statistical package. This book takes the reader step by step through the theory, with each chapter adding a logical next layer of complexity. All along are SAS examples interspersed with theory so writing programs and interpreting output are greatly simplified. It may be necessary to read the entire book if your data is relatively complicated and the appropriate analysis is covered towards the end of the book, but the effort is well worth it. I have never so well understood the rationale for the statistical tests I've done. I have had a class in SAS, so this book may be too difficult for someone totally unfamiliar with SAS, but I am by no means an expert and have been able to write programs to do everything I wanted. I used this book in conjunction with the SAS/STAT user's guide and found both together totally adequate for my needs.

Lounesto's book is replete with geometric and physical applications. The treatment is informal and non-rigorous and appears to have been designed with developing intuition in the reader. The text starts slowly working through many examples of particular Clifford algebras of interest and their relevence to physical problems. Towards the end Lounesto investigates general Clifford algebras and their associated spin groups as well as some specialized topics.

This is a good introductory text but fails to give the reader a firm mathematical basis of the material. Most striking is the almost total lack of proofs of any kind - the author is content merely to state the most important results but seldom leaves the reader with any mathematical justification. As such it is really a primer and the student of Clifford algebras must after working through the material move beyond to a rigorous algebraic text.

A very clear and comprehensive introduction to a somewhat esoteric subject; essential for anyone in theoretical physics (especially field theory). It is possible to teach yourself the subject from this book alone--a rare feature in mathematics texts. Bravo!

Clifford algebras make geometry and its applications to advanced physics incredibly simple, and this book is one of the best that I have read on this topic and on spinors. Readers outside physics should also study this book, if necessary with the help of a consultant or tutor to translate into more or less ordinary English, because most fields of science or industry are in need of tremendous simplification. Lounesto's approach is algebraic, as is Okubo's (see my review of his book) and Chisholm's (likewise), and Cambridge University Press as usual is at the head of the field in publishing the deepest and yet most simple topics. Spinors (the word comes from spin plus the ending -ors) describe spin in quantum theory, and Lounesto has the most detailed division of the types of spinors that I have seen in a book together with their physical applications. Weyl and Majorana spinors describe the neutrino (of the weak force, although the former only describe massless neutrinos), flagpole spinors appear to describe the strong nuclear force/interaction and appear to be related to quark confinement, Dirac spinors describe the electron (Weyl and Majorana spinors, unlike Dirac spinors, are singular with a light-like pole/current). Penrose flags (see my reviews of Roger Penrose's books) are related to Weyl and Majorana spinors. Penrose has an interesting theory of twistors which is well reviewed in some of the popular science books.

This book, although comprehensive, does not have great appeal. It does not explain all the topics completely by giving several examples. The cover is the best part.

This is an excellent textbook on probability. i especially like the introduction for each topic as it presents the practicality of its use in real life. Also the examples makes the topic very clear and one should come away with a good understanding of each chapter. Highly recommended!

Yo. I'm one of the writer's sons. If I would of read the book, I'm sure it would be great.

Tom Ferguson's book is the standard at the UCLA Department of Statistics and for good reason. The book follows a logical format, essentially proving a different limit theorem/approximation in each chapter. The book is good for an advanced graduate 1 quarter/semester course in asymptotic theory, although some of the topics may have to be omitted. I wouldn't recommend reading this book by yourself since I find it to be very compact/concise. However, if you've taken a similar course already it makes an invaluable reference.

It covers important topics, and it has a clear exposition

It is almost impossible not to recommend a book by Professor Ferguson, and this book is no exception. I will deviate slightly from typical book reviewers to mention a few noteworthy things common to Professor Ferguson's books. First of all, he writes mathematics clearly, concisely, logically, and in an organized manner. He is therefore an exception to the typical mathematics researcher whose writings look like running notes from a gauntlet runner or a gladiator running from a lion in an ancient Roman arena. I first learned graduate statistics from his 1966 book which I believe is titled Decision Theory or Statistical Decision Theory, and that book is as up to date in its information (aside from incorporating intervening studies) as though it were written today. Readers even outside mathematics should demand a reprint of that book if they want to learn real statistics. Professor Ferguson's character (I have met him) is as honest and open and logical as his books. His books do involve Lebesgue integration, as some other reviewers have mentioned, and I recommend that even non-statisticians hire a consultant or tutor to either teach them Lebesgue integration or to translate into approximate English or at least elementary mathematical language what Lebesgue integration does. I will try to discuss it myself either in a later addition to this book review or in another book review. My only criticism of Ferguson's books concerns the lack of representation of probabilistic alternatives to Bayesian methods (which I have been developing since 1980) in which, instead of dividing probabilities one substracts them and adds a constant. These have the advantage of being defined even when events have probability zero, unlike (Bayesian) conditional probability, and probability zero events are surprisingly common (e.g., lower dimensional events, extremely rare events assuming continuous random variables, etc.)unlike most people's impression - precisely because of arguments involving Lebesgue type integration. You can find abstracts of some of my papers on this at the Institute for Logic of the University of Vienna (on the internet).

Without any doubt, this is a comprehensive book in this field. However, because the authors tried to demonstrate their superior english writting abilities, they made the whole text hard to understand for students. From my point of view, as a new professor, I don't believe that recommending this book is appropriate.

This is a practitioner's book, not a scolar's. I'm not a scolar -- never have been and never will be. I love this book. It's lucid, it's sensible and it's great statistics. It goes thoroughly into the logic of linear model ANOVAs yet the bulk of the exposition is in the simple English language -- it has no calculus and no eigenvectors and almost no matrix algebra. You need an acquaintance with elementary one-way ANOVA but no more background than that. The authors pace it carefully and are not afraid of a bit of repetition for the sake of clarity (something I always appreciate when I'm reading new technical material). It's one of the best $105 dollars I've ever spent. I got loads out of it all the way through and it's a big book. The explanation of multivariate repeated measures is worth the price alone.

I continue to select this text for a graduate course in advanced experimental design. The book is not easy, and that is one reason why I like it. It presents a thoughtful scholarship on controversial issues pertaining to the use of inferential statistics as a tool to assist the researcher in making decisions about the validity and strength of functional relationships present in the data. The book eliminates the naive overconfidence that sometimes results from cookbook applications of algorithms to the results of experiments. It shows that the process of discovery and data analysis is never complete and that the general linear model is an imperfect technique by which to discern orderliness in nature where intersubject variability is a given.