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.

Davenport's "Probability and Random Processes" is an excellent, readable text on this subject for engineers/scientists/etc with an intermediate to advanced level of mathematics background. The important chapters are: 3) Probability, 4) Random variables, 5) random vectors, 6) functions of rv's, 7) statistical averages, 8) estimation, sampling, and prediction, 9) random processes (a wide variety), 10) linear transforms, 11) spectral analysis, 12) sums of independent rv's, 13) the poisson process, 14) the gaussian process.

Clearly this book covers a wide breadth which is the attraction for me. His approach is rigorous yet approachable through many interesting examples. Many examples deal with digital communcation of binary data. What this book is not is a highfalutin, axiomatic, terse text for those with PhD's in mathematics. McGraw-Hill recently republished this classic 1970 volume in its "classic textbook reissue" series. The author, Davenport, was in fact of Prof. at MIT.

I highly recommend this text for those who want a rigorous understanding of random processes.

the axioms probability,random variable,functions of random variable,transformation of random variables,sequences of random variable,stochastic processes

I have had no formal class in statistics or probability, so I bought this book in the hope that it would help me with an intro to bioinformatics class I am taking. It assumes no prior knowledge of the subject, just some basic algebra. The first chapter doesn't even get into much math, it explains (in English) what probability is.

There are worked problems throughout each section to aid with understanding the current concept; there are also study questions at the end of each section (answers are in the back.)

I have been very impressed with this book. It gives a good explanation of concepts, clear examples, and useful study questions. Even the paper and binding are high quality! I highly recommend it to anyone new to, or wishing to brush up on, probability.

A must have for the industrial engineer and quality professional. Montgomery and Hines present an excellent piece that is well organized, comprehensive and stocked full of real world examples. A must have for your reference books and library.

Galen Shorack is a statistics professor at the University of Washington. He specializes in empirical processes and probability theory. He and Wellner wrote a mammoth treatise on empirical processes that was well received.

This book is very well written. It covers the basics for a standard advanced probability course very well. What sets it apart from most of its competition is its emphasis on applications to statistical inference.

Probability theory and empirical process theory in particular, are useful in proving consistency results about the bootstrap. So it is therefore no surprise that the bootstrap is covered in this book. Shorack provides a very lucid introduction to bootstrapping and on page 432 covers both the bootstrap principle and the weak bootstrap principle. In cases where the bootstrap principle can be verified, we are assured that the Monte Carlo approximation to the bootstrap works with probability one (i.e. it can only fail for data sets with zero probability of occurrence, fails on sets of probability measure zero in the jargon of probabilists). The practical implications of this is that you can apply it to the particular data set that you use the bootstrap on. The weak bootstrap applies a weaker convergence concept and is less useful because it only guarantees that the Monte Carlo approximation will work on most data sets that are drawn at random from a population with distribution F. It is less desirable because it provides no guarantee for the particular data set that you actually draw!

This book is the standard for all who seek detailed information about stochastics in Banach spaces. Written from two of the top researchers in this area, it provides a broad area of topics, a concise treatment of deep results and exhaustive references. Sadly enough, it is out of print now, but I hope for a 2nd edition.

Notwithstanding a record-setting incidence of typographical errors, this is the very best book of its kind extant. Brilliant, deep, rich, suggestive: a responsible, disciplined, psycho-spiritual classic, part poetry, part science. Do not miss it!