I had the good fortune to take a short course from Bernie Silverman on density estimation just after this book came out in 1986. It is one of the clearest treatments of the subject and I found it particularly good on the coverage of optimal kernels. It is also filled with good practical examples and advice. For instance, the Old Faithful data provides an excellent example of a bimodal distribution where kernel density estimation provides a way to detect the two modes.

The author was also very perceptive in recognizing the value of projection pursuit techniques and bootstrap methods and the way density estimation techniques relate to these methods.

The book has the virtue of being clear and concise.

Quite a few books have been written since 1986, but this book is still the best. Very intuitive and very readable. It is written with a mastery of the subject and an excellent style of pedagogy. I remember of the joy and refreshness of reading this book around 1987 and it has served me well on a very important introductory of mordern statistics without having to go through tedious "math" notations and a shining example that statistics can be full of intuitive ideas and beautiful. For people unfamiliar with this book, it deals with probability density estimation using the idea of "local averages", and so it does not deal with other techniques such as splines. Also it is purely a density estimation book, and does not deal with another important problem, namely regression estimation (on which there are many other books). In summary, this book introduces the ideas and sense of "smoothing", a large (perhaps a little overblown) area of modern statistics. If you want to learn statistical smoothing, besides from Steve Marron, this one is the way to go.

This is a great book. It is not difficult to read. The style is very informal and at times actually humourous. It does not follow the definition-lemma-proof way of doing things at the expense of leaving simple definitions out, but these can be easily found somewhere else. The book contains an enormous amount of information, and the authors are clearly men of great knowledge and depth. The book is very nicely produced (from a 1st edition) by Cambridge U Press. Very clearly printed, and at a low price for the volume. I highly recommend both volumes to anyone who works in stochastic processes, or mathematical finance (assuming one wants to learn things, rather than just talk about them).

Although not an easy read, this book contains a wealth of information on diffusion, martingales and Ito calculus. Reading difficulty is comparable to Karatzas/Shreve. Mastery of topics included enables the reader to get understanding of most of the current research papers in this field.

Nowadays every spreadsheet program such as Excel or 123 come with a complete Statistics module that can crunch the numbers in an instant and give you a complete analysis of any set of numbers, leaving those who grew up with MINITAB in awe.

However, what these powerful programs won't and can't help you, is in grasping the meaning it. When to decide when to reject or accept a null hypothesis. Let alone what all that means. This is where this CD-ROM/ book set come into play.

The program provides a very comprehensive introductory course in Statistics. It has some very interesting videos on real life applications to Statistics, more than 200 self score test questions and a very comprehensive and logically structured text.

Perhaps this product won't replace a tutor, but it definitely help Students, Home Schoolers, or anybody interested in Statistics to increase their understanding of this some times obscure art. I think it is definitely a winner.

I know when I earn a score of 100% on a section, that I really do understand the material, and have not simply memorized it. I am able to apply those concepts to real-world situations, analyze data, and form conclusions. If this is what a statistician does, I'm seriously considering pursuing a career in this field. I never thought math could be "fun"...until now! This is a great product!

This is a pretty good book for whom doesn't have strong background in financial engeering. It begins from probability and I think this is a very good point to start. On the other hand, you also can know the where the formula comes from because the author use a small space to explain it. It is pretty funny!
One problem, this book doesnot cover all of the importnat topics such as Ito calculus but it is still a good book.

This reference starts with the basics and builds to the main subjects introductory students would want to see, for example, the derivation of the Black-Scholes equation. This is a very good book to read first since the author explains his subject very clearly, and the book is self-contained.

Excellent description of the purpose and procedures of basic statistical techniques. Uses simplified formulas, and does not get lost in the math. Easy to read and understand. Good for social science and public policy courses, including public administration. Only drawback: does not effectively incorporate computer applications.

One of the best statistics books I can think of for social scientists who are not well aquainted with statistical procedures. The book is structured in several chapters, each concentrating on one statistical problem. In the first part of each chapter the theoretical background is given, followed by examples. Then, other examples are given, so that it becomes easily understandable how to correctly use the formulas respectively when they can't be used. Finally, there is a part of exercises where the reader may apply his new knowledge, before proceeding to the next chapter. However, it is just an introduction, not dealing with more complex statistical problems. Very recommendable for anyone who needs a solid basic knowledge in statistic or who wants how to start processing existing data.

William Cleveland clearly describes how data can be presented to great effect. His description of visual perception spell out the "how to's" of graphing data. While many graphing programs are available in today's high technology environment, Cleveland's descriptions of how data can be presented into graphical format is enlightening. The book provides great examples of both superior and poor graphing presentation, focusing on how to encode graphs to allow for straightforward data analysis.

"The Elements of Graphiing Data" is a must for those who graph scientific data.

Tufte shows you why it's important to do graphs well. Cleveland shows you _how_.

The last quarter of the book details experiments in human visual perception that rank how well we detect certain things: relative angles not on a common baseline (i.e. pie charts) justly come out at the bottom of the list.

One of a only handful of books I've labelled "JXH ONLY". If I loan you my copy, know that you are special.

This is by far the best introductory text to statistics for students in the behavioral sciences I have come across.

The conceptual layout makes this textbook especially engaging. Unlike most statistics textbooks this one presents more difficult concepts in a step-by-step manner, which allows for better understanding.

The authors have done a superb job of explaining the logic behind the statistical procedures. This is important, for without this understanding one will likely misapply statistics and/or misread statistics.

In short, here one finds not only an introduction to descriptive and inferential statistics for behavioral scientists, but also a text that will give the reader a firm grounding in the logic behind statistics.

As I read this book three things became apparent: one, the authors know their subject inside and out; second, they understand their beginning audience; last and most importantly they know how to teach. I strongly recommend this well written book to anyone needing to learn stats.

Excellent! Even if you fear probability and statistics, you can learn so much from this book. Dr. Serrano provides a crawl, walk, run approach to probability, statistics, and uncertainty. Any academic discipline could gain from this book, but the examples used tend toward engineering and its applications. Not only will the reader gain a firm foundation on the subject matter, the latter chapters take you to the upper level of stats, probability, and modeling of the unknown. As a bonus, Dr. Serrano inserts helpful hints and example programs to use in the very powerful C based Maple mathmatics software.

Excellent! I knew nothing of statistics or probability until this book. It takes you through a crawl, walk, run of these needlessly feared subjects. The practical applications are uncanny. Although focused on engineering applications, any academic discipline would gain from this book. Serrano also teaches you the basic ins and outs of the powerful C based Maple mathmatics software.

Arrived so fast! Good job

This is an excellent first course in statistics for people who want to understand how to perform and interpret statistics. Contains no calculus or higher math. The first half of the book covers the prerequisite probability theory (including the binomial and geometric distributions), and the second half covers statistics proper, with the empahasis placed on the normal and Student's distributions. The Chi-Square distribution is also covered. Linear regression, confidence intervals and hypothesis testing are given very heavy emphasis. The concentration is on practical problems and their solutions rather than with an abstract mathematical development of the theory. Highly recommended for people who have no prior knowledge of the subject.

When I was a graduate student at Stanford in the late 1970s Sid Resnick was an assistant professor there. I took a course in stochastic processes from him. His presentation was engaging and humorous as is his writing style. This is more readily appreciated in one of his other books "A Probability Path".

This is one of several good texts on the theory of extremes that came out in the 1980s. The book is rigorous and formal and deals primarily with the iid case (the exception is extremes of moving averages). It differs from the others in that it treats the relationship between extremes and record values.

Resnick also deals with a characterization of tail behavior called regular variation that is a very useful tool in developing some of the theory. Like Leadbetter et al. he uses the point process approach but he does not exploit its application to stationary processes the way they do.

In the 1980s Resnick went on to Colorado State University where he had a very fruitful collaboration with Richard Davis. That is where he was at teh time of this text and the results of their joint research is reflected in the text. It also includes material on multivariate extremes and extremal processes.

Currently Resnick is a professor at Cornell University.

superbly written, lucid , clear text communicating fundamental concepts of basic stochastic processes, probability theory in a fashion, books designed for those subjects fall woefully short relative to resnick's exposition. extreme value theory is clearly presented in a fashion which makes it look easy!