These past two semesters I've been enrolled in a graduate-level analysis course. The book we used, by Folland (see my review) was not a good book, in my opinion. After reading the first several chapters of Folland, I just did not understand what the point was to Lebesgue integration, or why we had to develop all this machinery that goes along with it.

Sometime during the semester, I got hold of this book, by Saxe, and started reading the chapters on Lebesgue integration. After doing that, I began to develop an understanding of what it was, how it was used, and why it was necessary to cover all these theorems. The book gave me perspective on the subject; (and hence motivation) something which Folland did not do.

Saxe's book isn't without it's faults; I had some issues with her proof of the Baire Category theorem (in this case, I actually found Follands proof much more believable) and she got the year of Hermann Minkowski's death wrong. Other than that, I could not find any problems with the book.

In summary, this book fills a much needed void in the literature: a readable book which introduces the student to functional analysis beyond the undergraduate "advanced calculus level." If you are in a graduate-level real analysis course and haven't a clue what a sigma algebra is or why you should care (but would like to), then buy this book.

I sure as heck never had before. I am dyslexic, math phobic, and a graduate student in Psych who must take 1 full year of advanced stat. Terrifed of this prospect, I bought this book. It is, without doubt, the best text book, ever. It is funny (to the point of being downright cheeky), laid out exceedingly well, progresses from one idea to another naturally, and explains all these confusing concepts easily. I actually looked forward to reading it in my spare time, and I promise you, I am not a math geek! There simply is not a better Intro to Statistics book out there. Better than cartoon guide, stats for dummies, etc. Save your money and time and go right to the source.

In my field I must read many research studies dealing with various topics. I've taken research classes and have learned to recognize key statistical concepts but I must admit that I've never fully understood how they work, what they really mean (no pun intended) or how they were derived. Don't get me wrong. I've studied, but most texts were just too advanced right from the start. Now that I've read and worked through the sample questions in this text I finally feel ready to tackle more advanced texts. The language used is clear and concise with clearly presented examples and applications of concepts. The sequencing is well thought out and quite seamless. I highly recommend it for anyone just starting research classes who has had little or no preparation in statistics. However, knowledge of basic algebra is a must. If you haven't had algebra for some time, you may want to brush up a bit.

Rupert Miller was the best instructor I had as a Stanford graduate student. This book came out of lecture notes that he produced for the required applied statistics course in the Statistics Department at Stanford. I took both quarters of that course. He was an excellent lecturer and writer. The book emphasizes practical aspects of the analysis of variance and deals with issues of robustness and modifications when the basic assumptions fail. It is set out in a very logical manner and deals with ANOVA and regression. It deals with transformations and is unique in its treatment of ratios. Discussion of departures from model assumptions is both useful and insightful. The book was published in 1986 prior to Miller's death and was updated by Brown posthumously in 1996. Brown worked closely with Miller on biostatistical applications at Stanford University. Other related books that Miller wrote or helped with included the Biostatistics Casebook, Survival Analysis (both published by Wiley) and Simultaneous Statistical Inference (published in its second edition by Springer-Verlag).

It astounds me how these guys, while being so very handsome fitting thoroughly into the category of boy band, can pull off such funny and satiriacl songs. Brilliant!

John Lachin is Professor and Director of the graduate program in biostatistics at George Washington University. The book is intended as a first advanced course for students in that program. The book emphasizes methods for problems in biostatistics. To Lachin this means an emphasis on binary, categorical and survival data that relate to the assessment of risk and relative risk through clinical research. Consequently much of the standard parametric and nonparametric modeling of continuous response data is not considered.

A variety of methods are covered on a number of subjects. The first half of the book deals with classical approaches to single and multiple 2x2 contigency tables used in cross-sectional, prospective and case-control studies. In the second half, the more modern likelihood or model-based approach is presented. Technical mathematical details are covered in the appendix which is referenced throughout the text. The appendix deals with statistical theory (stochastic convergence results and other theory) but does not provide rigorous proofs of the theorems. Real probelms are presented and analyses are illustrated using procedures in SAS.

In the model-based sections, topics include logistic regression, Poisson regression, proportional hazard and multiplicative intensity models. The book is modern, well written, provides a good list of references, has extensive problem sets at the end of the chapters and employs case studies to illustrate the application of the methods. It is not a book for beginners. It is a great reference source for biostatisticians and epidemiologists as well as a fine text for a graduate-level course in biostatistics.

If you ever wanted to know what the odds are of your being fired, getting hit by a bus, or being injured by your lawn mower, then your at the right place. This book gets a ten because it includes the sources for all of its trivia facts. That's a very classy touch in the trivia book world.

George Box is one of the statistical giants of the 20th Century. He started his career in chemical engineering in England where he learned the importance of experimental design and statistical methods. He came to the US in 1953 and spent time at North Carolina State College and later came back to be part of the statistics group at Princeton. After that he founded the Department fo Statistics at the University of Wisconsin. This history and other important career decisions icluding the founding of Technometrics are detailed in the brief section "My Professional Life" that he wrote for this volume.

Box's contributions to statistics are diverse and large. He developed many practical statistical designs including the central composite design. He is responsible for evolutionary operation and wrote a book on it with Norman Draper. He has also made major contributions to response surface methodology.

With Gwilym Jenkins he systematized the application of the ARIMA models and led the development of software for easy application of these model building techniques. He championed the concept of parsimonious models and insisted that model building should be an iterative and continually evolving technique. He contributed to the area of control through his stochastic time series models and found ways to incorporate it in manufacturing process control.

With David Cox he developed the Box-Cox family of transformations. These simple power transformation can be used to make the data have an approximate normal shape. he gave a prescription for how to estimate or pick the power to use based on the data.

These enormous contributions can be found in the volumes of collected works that Tiao and others have edited. His contributions can also be seen from his books on evolutionary operation, time series analysis, automated process control, empirical model building and response surfaces, and practical experimental designs ("Statistics for Experimenters").

However in the decades of the 80s and 90s from age 60 to 80, instead of retiring, George Box took on the challenge of developing a center for quality and productivity at the University of Wisconsin. This volme, edited by Tiao, Bisgaard, Hill, Pena and Stigler provides a collection of articles by Box. These are mostly articles written in the 1990s covering the subjects of A) continuous process improvement, B) designing experiments to gain quality information, C) sequential investigation and discovery (including response surface methods), D) quality control and E) learning how to identify and reduce variation or be less sensitive to it by constructing robust processes (i.e. processes not sensitive to minor changes in process parameters). The articles are mostly directed toward quality issues and are mostly articles that were published in the 1990s or 2000 with a few from the 80s. Some are important technical contributions but many are also very philosophical.

George Box is one of the great thinkers of the 20th century and his philosophy on statistics and scientific inference is as important as his many technical contributions. There are 46 articles in total 4 on topic A, 12 on B, 10 on C, 11 on D and 9 on E. Each topic area has a brief introduction identifying a unifying theme in the papers in that section.

Box has a terrific sense of humor that often comes out in his lectures and sometimes in his writings. One gets a good appreciation of it by reading the three songs on statistics that are included in Part F of the book. This is only a sample of several that he has written that are parodies of familiar tunes. Of these three my favorite is "There's no theorem like Bayes theorem" to the tune of "There's no business like show business."

There is a nice bibliography in the back of the book that is followed by a biography on Box and a list of his books and articles published between 1982 and 1999. This includes 3 books and 91 articles! Believe it or not he published even more in his earlier years.

I read this book just before starting an MBA course called "Basic Statistics". I found the book to be clear, intuitive and it certainly covered all topics we discussed in class. In my opinion, this book presents an easy way to understand Statistics by applying them to real business situations. I still use it as reference at work. Highly recommendable for business people interested in Statistics and with no much background in Math (just make sure you know how to add)

A colleague had this book and we all used it on a regular basis because, as engineers, our job requires us to present everything with data. Therefore, we need to understand the statistics behind the data. This book is very easy to read and understand for those of us that have some statistical background but are not experts. It was a great reference for our group. Now that that colleague has left, we needed to get another copy!!

Everything you need as an MBA student. Comprehensive, straight forward, and with many examples. Couldn't find an other book that comes even close...