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Exploratory Data Analysis Paperback – January 1, 1977

ISBN-13: 978-0201076165 ISBN-10: 0201076160 Edition: 1st

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Product Details

  • Series: Behavioral Science
  • Paperback: 688 pages
  • Publisher: Pearson; 1 edition (1977)
  • Language: English
  • ISBN-10: 0201076160
  • ISBN-13: 978-0201076165
  • Product Dimensions: 9.2 x 6.5 x 1.5 inches
  • Shipping Weight: 2.4 pounds (View shipping rates and policies)
  • Average Customer Review: 4.4 out of 5 stars  See all reviews (8 customer reviews)
  • Amazon Best Sellers Rank: #224,013 in Books (See Top 100 in Books)

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53 of 53 people found the following review helpful By William A. Huber on September 29, 2008
Format: Paperback Verified Purchase
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".
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64 of 67 people found the following review helpful By Barry Lewis on August 23, 1998
Format: 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".
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24 of 24 people found the following review helpful By Howard Wainer on April 29, 2010
Format: Paperback
I reviewed this book when it first appeared in 1977 -- having been reading preliminary versions for about a decade before that.
At the time I offered the not-very-prescient opinion that it would quickly become a classic. It has.

The various reviews on this site are all correct. Yes there are still wonderful ideas to be mined (easy univariate transformations to symmetry, transforms to linearity to aid in curve fitting, the crucial importance of robustness, of looking at outliers and fringeliers, and the dominant role that graphics plays in forcing us to see what we never expected).
And yes, there is some material that is out-dated (e.g. using break points to estimate logs in your head).

But these are beside the point -- Scholars still study Talmud and Newton's Principia. Why? Obviously there are many reasons varying with the work and the reader.
For me a principal reason for reading (and rereading) EDA is to get a close look at how a first class mind works, with the hope that when faced with a similar problem we can then try to emulate him. To this day as I read EDA I can see Tukey's smiling face patiently explaining to me how to look at data and exhorting me not to miss subtle hints.

This was a book to be treasured when it first became available, and I see no reason for that judgment to change now, or in the future.
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16 of 18 people found the following review helpful By Peter Hiscock on October 20, 1999
Format: Paperback Verified Purchase
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.
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