If you only own one statistics book, this should be it! In than 20 years of working with laypeople, engineering and biology undergraduates and grad. students and medical students, this has proven to be the most reliable, intelligible and accessible single volume on how to treat experimental and observational data in a sound, statistically valid, way. Of particular value is a table in the back (Guide to Significance Tests) which takes a "leaf identification" approach to selection of the most appropriate test. Many good examples and problems with solutions makes this an excellent self study text.

Jonathan Black, Professor Emeritus, Clemson University.

John Tukey was one of the great statisticians of the 20th Century. This book is a collection of essays written by former students and colleagues of Tukey and presented at a conference in honor of his 80th birthday in 1995. With his death just a few short weeks ago it is again a good time to read this book and reflect on his accomplishments. The book starts off with some biographical information on Tukey including introductory remarks from the editors and a short biography. This is followed by his curriculum vitae and a chronological list of his Ph.D. students. An interview of Tukey in the style found in the journal "Statistical Science" follows. This is then followed by the remarks of his wife at a dinner meeting. Many well known statisticians participated in the conference at Princeton and there are several nice pictures from the event. This is followed by a large collection of papers on topics near and dear to Tukey including robust regression, jackknife, the Princeton robustness year, outlier analysis, case studies and philosophy of statistics and data analysis.

In a New York Times obituary article for Tukey, it was mentioned that Tukey coined the terms software and bit which are now computer science terms. He was a pioneer in statistical computing and changed people's thinking about the value of exploratory data analysis in addtion to confirmatory analysis. He is famous for his work on data analysis, robust statistical methods, time series analysis and the fast Fourier transform and many other important advances. For beginners the box plot and the stem and leaf plot are now commonly taught to them in their first course. These were also clever little inventions of Tukey.

The first edition of this book was the most authoritative book on this subject 15 years ago. Now the author has provided us with a much-needed second edition since there are many developments since. Principal components related techniques are the main dimension-reduction methods in analysis of multivariate data. Since there is much redundancy with high throughput measurements such as spatial, spectra, or image data, thus the need to compress or decompose data. Related but somewhat different techniques include SVD, singular spectrum analysis (SSA), PC regression, shrinkage, EoF, etc.
Main consumers of PCA-related methods include chemometrics, climate analysis, and image analysis A very nice book in the
area of climate analysis is Principal Component Analysis in Meteorology and Oceanography by Rudolph W. Preisendorfer (1988).
The area of SSA has been developing fast and several
monographs have appeared already. The area of indpedent component analysis is another one that has attracted increasing
attention in recent years. Most of these recent developments have been covered and more. With this, I strongly recommend this book for readers who use multivariate analysis extensively and who need to keep abreast of the fast growing PCA tools which are as important as ever!

It's not banal or trivial, without being obscure. It has just the right amount of maths to allow an in-depth understanding of the various techniques, without slipping beyond intelligibility by a non-mathematician user. It seems a good reference book for people with already a basic knowledge of multivariate statistics and the principles underlying it, although an introductory chapter is provided for those who want to dust off their old knowledge of matrixes, etc.

The main theme of this book is the formulation and interpretation of probabilistic reliability models in analysis and the utilization of these techniques for reliability design. The book is written as a text for college and industrial courses in reliability and is intended for an engineering audience at the level of first-year graduate school or senior undergraduate. The book developes the basics from fundamental probabiity theory which is covered in detail in Chapter 2. The contents are:

Chapter 1 Introduction Chapter 2 Mathematical Background: The basis of probability theory is developed from fundamentals at the level of a Junior Undergraduate. Chapter 3 Combinatorial Reliability: Reliability of series, parallel, bridge, r:n configurations. General techniques in terms of event space and cut-sets and tie-sets. Chapter 4 Catastrophic Failure Models: Treatment of Failure Data, Density Functions, Distribution Functions, Failure Rate (hazard functions), Decreasing, Constant, Increasing, Weibull failure rates. Chapter 5 System Reliability: How Combinatorial Reliability Formulas and Failure Rate Models combine to form a System Reliability Model, Approximations, Markov Models for Repairable Systems, Availability. Chapter 6 Reliability Improvement: Reducing Component Failure Rate, System Redundancy, Component Redundancy, Repair, Availability, Digital Voting Systems. Chapter 7 Drift Failures, Component Tolerances, and Parameter Variations: Analysis of System Failures due to Aging, Drift, and Parameter Variations. Chapter 8 Reliability Physics Models and Statistical Parameter Estimation: Stress-Strength Models applicable to Structural Reliability Analysis Appendices A - K: Covering topics such as Monte Carlo Simulation, Fault-Tree Analysis, Event-Tree Models, Advanced Availability Modeling Techniques, Software Reliability, Fault-Tolerant Computing, Safety and Risk Analysis, and Failure Rate Data Sources. If you buy one book on reliability, this is the definitive one to get. Understandable presentation and development for the beginner or advanced analyst and system designer.

...It is a first class treatment of the ever important area of risk assessment. A number of countries including the UK are using, or proposing to use various risk assessment models for dealing with pollution and contaminated land issues. This book provides an excellent overview with a great deal of what was called common sense. It of use to anybody involved in risk assessment. It is written in a very clear style and would also be of use to the concerned non-specialist with a broad knowledge of such issues. I thoroughly recommend this book.

This is a classic, read and cited by many. In describing monographs on stochastic processes, Jacod and Shiryaev (1987) call it ``the very complete book", and Ethier and Kurtz (1986) - ``most recent presentation". Actually, it is even difficult to find a serious book on the subject that does not cite the volumes of Dellacherie and Meyer. Despite its age, this monograph is still almost a must for anybody who works in stochastic processes. It is too bad it is out of print.

Professor McGervey manages to present the practical application of probability and statistics to every day circumstances in an easily understandable way.

I have this book now and am starting to do the problems without really knowing anything about MAPLE but I am keen to learn. I have been studying the subject at school the past couple of years and it seems to be just right for the theory behind statistics. I hope it becomes a textbook. In fact, if I am teaching this subject soon I will use it.

I first used this text in the earlier version, which comprises the first half of the book, in a one-year course in Hilbert Spaces and Lebesgue Measure theory when in the first year of grad school. The material is presented in a clearly written manner and the exposition is some of the clearest mathematical writing I've seen in a subject which is replete with textbooks.

Anyone who wants to be inaugurated into the "mysteries" of measure theory and the fine points of the rigorous theory of stochastic processes and the Ito integral, will do himself or herself a favor by using this text. If it is not assigned to your class and you have the extra cash, order it anyway. It is also well-suited for self-study.