This book provides a comprehensive introduction to the most commonly used statistical methods in atmospheric research. It is written at a level that is suitable for advanced undergraduates and beginning graduate students with only a moderate level of mathematical ability, but is suitable also as a basic text book for researchers with moderate experience in statistics. It lacks some depth in some sections, such as in discussions of multivariate methods, but for students the coverage is ideal, and I would strongly recommend the book as a standard text for any course in research methods for atmospheric scientists. Topics covered include empirical distributions and exploratory data analysis, data distributions, hypothesis testing, forecasting and verification methods, time series, and multivariate analyses.

Statistical methods are introduced in the context of their application. The emphasis is on solutions to meteorological problems rather than on the statistical methods per se. Although I see this approach as a major strength of the book, one result is that the book may be of less interest to non-atmospheric scientists. Limitations of the methods are discussed, and the reader is given considerable assistance in interpreting the statistical results of the methods covered. The mathematical back ground is kept at a level that should be digestible by most students. Equations are relatively few, but not lacking, so the mathematically shy should be able to gain a lot from the book. The text is excellently written: very clear and the logical development is very smooth. I think in time this book will prove to appeal to a wide range of atmospheric scientists.

Wilks brilliantly covers the fundamentals of statistical methods for atmospheric data with clarity and precision. This book is a classic, and a much-needed successor to the 1958 text by Panofsky and Brier. Moreover, the book's coverage is applicable to other geophysical fields, such as hydrology, which broadens its usefulness for researchers, practitioners, and students.

Need more be said? A comprehensive guide to the most useful statistical methods in the fields of oceanographic, meteorological and climatological research. This is the bible for atmospheric research and a neccessity on every research scientist's bookshelf.

This is a good reference text for the fundamentals, It is used at the Georgia Institute of Technology for a graduate math class.

Amazon can't get copies of this book so don't even try ordering it from them. They said they ordered this title from the publisher twelve weeks ago and told me I would have it in 4-6 weeks

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I used this book in my AP Statistics class my senior year of high school. The Workshop Statistics book was really easy to follow and understand. I have always had a hard time with math, but when I used this book, I picked up on the concepts quicker. The only problem I had with this book is the fact that explanations of the formulas and procedures were not expained as clearly as I would have liked. This book is also a great way to orient yourself with your TI-83 scientific calculator (Texas Instraments).

This is an excellent book. Its lab-style approach with practical / tangible sets of data coupled with on-target follow-up questions makes this a wonderful book to teach and learn from. This version of the textbook focuses on using a TI-83 calculator to organize, display and analyze data in most of the problems. Managing the data lists for the TI-83 calculators in class can be difficult. I recommend that you ... download the program files that Al Coons put together. He placed all the individual data lists for each problem into programs. This makes it easy to manage and transfer these files to other calculators. Especially between TI-83+ and TI-83 calculators. Solutions to the "Activities" problems and various supplementary materials can be downloaded.... I recommend using Adobe Acrobat to download the "Activities" solutions. I made the mistake of using an old web-browser and wasted a whole summer copying and pasting. This may sound obvious, but it must be stressed....Answer all of the problems that you plan to work on in class or assign for homework prior to the actual class. Some problems require advance preparation or data collected from previous class activity problems. You need to find this out before walking into a classroom. This is the first time that I've taught an AP-Statistics course and the approach of this book takes a little getting used to. I'm having a ball! I rely heavily upon this book and the Barron's AP-Statistics study guide. Good Luck. It's a great book!

I read this book during my senior AP Bio class. My Biology teacher yelled at me for reading it during her class. So I left, because I didn't want to be yelled at. I then received a detention. Then I couldn't do my statistics homework and I failed the class. The book was stimulating if somewhat bland. I am writing this in a Starbucks in Toronto.

Weisberg's book is a good introduction/overview of the principal techniques used in linear models. However, in many situations, he leaves out many details and derivations and either directs the reader to the references or says nothing. I would have liked it if he had put in a few more mathematical derivations and had been more thorough in the discussion about collinearity and variable selection.

This book provides an excellent introduction to the application of linear regression models. Starting with the idea of studying "relationships between measurable variables", it develops the concepts behind simple and multiple linear regression, discusses interpretations of the output, proscribes diagnostics, discusses model building techniques, and touches other topics. It covers most of the material contained in Draper & Smith's _Applied Regression Analysis_, but in half the number of pages.

I would wholeheartedly endorse this book as a primary or supplemental text to an introductory course, or as a text for someone who wants to learn about linear regression on their own.

It is a good book about nonparametric statistics although there are some errors in it. Comparing books of the similar content, it is a high quality one.

Applied Nonparametric Statistics, second edition, by Wayne W. Daniel, published by Duxbury Press is now available in paperback.

The book is a good introduction to bayesian decision theory. The plenty examples in the book are helpful in the understanding of the subject, but one could wish a more detailed description of the bayesian paradigm. People with little experience with statistics should maybe consider another book.

Robert's defend the bayesian choice is one of the clearest and convincing in the last years. The book is concise and direct and the math kept at an appropriate level. I can only hope Springer republises it ASAP.

I had a chance to review this book. I think the authors can make us much better understand, particularly one who is about to learn the concept of statistics. You can apply this book with the SPSS program in order to interpret the results. Anyway, I still like the calculation parts which are removed in this edition (3rd) compared with the last one. I think they can help someone, who is very interested in statistics, to understand better before go ahead to use the computer. It's a great book for the beginner.

An excellent beginning reference book for anyone in need of understanding or interpreting univariate and multivariate statistics. Geared toward health care research, with many examples. A data set is provided with the book for students to use with different analyses. Reading audience is upper division undergraduate or graduate student.

A very well-written and helpful statistics book. It was required reading for a course in Quantitative Nursing Research last semester. Overall, the concepts such as t-tests, correlation and regression were explained in relatively simple terms. Examples with answers were found at the end of each chapter and the book came with a data disk. This was the most user-friendly statistics book that I have used so far!

Maxwell & Delaney is better conceptually. And Winer is just work whereas Maxwell & Delaney is a pleasure. I would give Winer more stars if I didn't have the better alternative book though.

This is the source for statistics related to experiments. Everything from single factor ANOVA to complex latin squared designs are covered. It is a hard read, but worth it. Not for the novice.

Could you tell me more about what to do about unequal sample sizes within groups? Since the F ratio becomes sensitive to the non equality of variances if the group sample sizes are unequal, this becomes a very important question for the validity of the ANOVA.

This text is more of a formula-reference with examples and is written for persons with substantial statistical / mathematical background. While I am sure that the text represents substantial value for those with substantial backgrounds in statistics / mathematics/ calculus, the rest of us may be somewhat less enlightened by the text. Make sure you have read over your Statistics 101, 102, 201, 202, 301, 302, 401, 402, 501, 502, Calculus 101, 102, 201, 202, and committed these texts to memory before reading this text. If you never met a formula you didn't like...this text is for you. The author does not hold your hand through the math and if you lack the background required, the book may have better use as a paperweight.

This is a wonderful source of information on methods for analyzing spatial data. It is still reasonably current for this rapidly developing area in statistics. You won't find some of the latest research on the joint modeling of space and time. But I expect that Cressie is working on a book that will cover it. It is an encyclopedia of references. The author covers the gamut of techniques. This explains why the book is so lengthy. I think that if he plans a revision he will be more selective and only cover those methods that have proved to be the most promising. Here you can finally learn what kriging is and even see how the bootstrap can be applied to spatial data.

The book covers very wide spectrum of different simulation and prediction methods and techniques. One can really merit from this book but under good mathematical background. The large number of practical examples helps for better understanding. The most of them can be recommended to apply for the development and testing of own statistical and mathematical software.

I read this one when I was a graduate student in the Institute of Statistics.I really love this book.At that time,our textbook is Box and Jenkins' Time series....I also love their book...But this one is especially a good one...I really get impression about the useful of time series...I had met the writer on a conference..He is really a gentleman...and a nice guy.....I think everyone wants to know time series better ...should not miss this book...There are two different approaches about time series: one is the time domain and the other is the frequency domain...This book contains both....

This book gives a thorough analysis of the basis of time series analysis from the basic AR and MA-processes up to ARMA, ARIMA-processes. It also gives you a brief introduction to intervention, seasonal, vector and interruption analyses. A drawback however is the inept use of relevant software to produce more accurate parameter-estimates. I used this book as one of my graduate courses and it gives a proper background to help you understand time series analysis.

This book provides a well-written and rigorous coverage of univariate time series, particularly the time domain models of Box and Jenkins. Its outstanding feature, however, is its treatment of multivariate time series modeling. It is the only book that I know of, that provides a clear and to the point picture of successful multivariate approaches. A little discussion of more recent multivariate advances can be found in Kennedy's 4th edition of his "A Guide to Econometrics".