Exploratory Data Analysis [Paperback]

John W. Tukey (Author)

Book Description

ISBN-10: 0201076160 | ISBN-13: 978-0201076165 | Publication Date: 1977 | Edition: 1

The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator.

Product Details

• Paperback: 688 pages
• Publisher: Addison Wesley; 1 edition (1977)
• Language: English
• ISBN-10: 0201076160
• ISBN-13: 978-0201076165
• Product Dimensions: 9.6 x 6.7 x 1.3 inches
• Shipping Weight: 2.5 pounds (View shipping rates and policies)
• Average Customer Review: 4.3 out of 5 stars  See all reviews (7 customer reviews)
• Amazon Best Sellers Rank: #228,794 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

39 of 41 people found the following review helpful:

5.0 out of 5 stars An extraordinarily rich and creative book! Brilliant!, August 23, 1998

By 

Barry Lewis (Champaign, Illinois) - See all my reviews
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This review is from: Exploratory Data Analysis (Paperback)

Tukey's EDA is a ground-breaking text, one that is as rich in extraordinary ideas and approaches to data analysis in 1998 as it was in 1977.

An earlier reviewer on this web page dismissed the EDA book as a pre-PC contribution, a dinosaur of the slide rule era. The comment recalled for me the experience of a colleague who, years ago, had the opportunity to visit the UIUC office of John Bardeen, who took Nobel Prizes in Physics in 1956 and again in 1972. He said that he went to the interview expecting to be impressed by the computing technology in this guy's office, but was dismayed to find that Bardeen's main tools were pencils, good quality graph paper, and a slide rule. Good enough for him to co-invent the transistor....

As my friend's experience reminds us, it's the ideas, not the tools, that really count. Tukey and his EDA book are gold mines of fresh ideas and approaches to data analysis. I recommend it strongly and without any reservation to every researcher who is interested in data analysis. It is a marvelous read.

...and lest anyone who is unfamiliar with Tukey's contributions feels that he is a light-weight on the computer side of things, he _did_ give computerese such fundamentals as the word "bit".

25 of 25 people found the following review helpful:

5.0 out of 5 stars A classic, still relevant, September 29, 2008

By 

William A. Huber (Rosemont, PA USA) - See all my reviews
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This review is from: Exploratory Data Analysis (Paperback)

In the preface, Tukey writes, "this book ... exists to expose its readers and users to a considerable variety of techniques for looking more effectively at one's data." It succeeds remarkably well and is still relevant: after over 40 years, many of the techniques still have not been incorporated in commercial software and some of those that have (such as box-and-whisker plots and robust smoothing) are often emasculated.

This book has served me well for decades: I have used most of its techniques in my statistical consulting practice and, more recently, have used it as a foundation for courses in data analysis that range from a few hours to an entire semester. Students always appreciate the practical experience and set of tools they acquire. The more experienced ones comment on the insight: "I never fully understood what the box-and-whisker plot really did until now" is a recent example from a mid-career engineering professional.

Nevertheless, it is true that some of the material is outmoded due to its focus on manual calculation and some of the rest may be too idiosyncratic for most. What remains--which is plenty--can be studied on its own, because this book is designed for self-study: most of the chapter groups are independent of all but the introductory material, they provide detailed examples, ask many thought-provoking questions, and supply many datasets for practice. Tukey's methods speak for themselves through the gains in insight they provide, so he is content to show *how* to do them and to provide copious examples. What he does not do is supply the mathematical theory. If you like, you can read about that in Hoaglin, Mosteller, and Tukey's "Understanding Robust and Exploratory Data Analysis".

The highlights of this book, in terms of techniques, are:

* Chapters 1-4 on graphing data and on basic, useful data summaries: stem-and-leaf plots and n-letter summaries. Most statistical software now provides these. They are of primary interest as building blocks in more advanced analyses.

* "Straightening out plots:" simple, effective techniques to re-express the independent and dependent variables in a bivariate scatterplot so that the relationship becomes approximately linear. (Chapters 5 and 6.) I am not aware of any software that does this, but the techniques are so simple and elegant you can still carry them out with pencil and paper (or a spreadsheet) even with huge datasets.

* Smoothing sequences (chapters 7, 8, and 16). The "3rssh" method has largely been displaced by Lowess smooths in software, but the principles and ideas of smoothing, "roughing," and "re-roughing" are timeless.

* Analyses of two-way tables using median polish. This technique has recently been exploited in various fields, including spatial statistics, but only in the most elementary way. Think of this as a robust version of Analysis of Variance, with a focus on finding the effects, but without any of the mathematical apparatus. (Chapters 10 and 11.) One of the best tools for performing median polish is a spreadsheet.

* Advanced fitting of two-way tables: adding an interaction term; transforming the dependent variable; plotting the fits. (Chapters 12 and 13.)

* Techniques for re-expressing and analyzing counts and fractions. (Chapters 15, 17, and 18.)

Additional material covers "delineations" of scatterplots (chapters 8, 9, and 14) and assessing distributions (chapters 19 and 20). The latter suffers in retrospect from not using probability plotting methods. The former is worth learning but there does not seem to be any simple way to use modern statistical software to create these delineations.

In brief, this book requires no more mathematical prerequisite than facility with arithmetic, but after working through it, the diligent reader will come away with a body of techniques for understanding almost any kind of data set, including methods of time series analysis, regression, analysis of variance, and contingency table analysis.

14 of 16 people found the following review helpful:

5.0 out of 5 stars Fabulous, October 20, 1999

By 

Peter Hiscock (Australian National University) - See all my reviews
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This review is from: Exploratory Data Analysis (Paperback)

Wow. I had assumed this was out of print, but here it is. This is simply a remarkable book about the basics of thinking numerically. Even if you do your stats on a computer I recommend this. I use it to show Arts students that quantitative analysis can be fun, powerful and simple.