Visualizing Data [Hardcover]

William S. Cleveland (Author)

Book Description

ISBN-10: 0963488406 | ISBN-13: 978-0963488404 | Publication Date: March 1, 1993 | Edition: 1

Visualizing Data is about visualization
tools that provide deep insight into the
structure of data. There are graphical
tools such as coplots, multiway dot plots,
and the equal count algorithm. There are
fitting tools such as loess and bisquare
that fit equations, nonparametric curves,
and nonparametric surfaces to data.
But the book is much more than just a
compendium of useful tools. It conveys a
strategy for data analysis that stresses
the use of visualization to thoroughly
study the structure of data and to check
the validity of statistical models fitted
to data. The result of the tools and the
strategy is a vast increase in what you can
learn from your data. The book demonstrates
this by reanalyzing many data sets from the
scientific literature, revealing missed
effects and inappropriate models fitted
to data.

Editorial Reviews

Review

This book presents a set of graphical
methods for displaying data. The methods are
shown in use in practical examples and their
construction and properties are explained in
this context. The book presents a powerful
display of their use, not in isolation, but
in conjunction with other graphics and in
the context of a serious effort to produce
a coherent data analysis. --Journal of the Amerian Statistical Association

This is a terrific book --- in my opinion,
a pathbreaking book. Get it. Read it.
Practice what it preaches. You will improve
the quality of your data analysis. --Technometrics

Visualizing Data should be required
reading for every scientist and always
should be kept in easy reach. Anyone
with a familiarity with basic statistical
techniques and least-squares methods of
fitting regression lines to data should have
no trouble with the material presented. --BioScience

Visualizing Data should be required
reading for every scientist and always
should be kept in easy reach. Anyone
with a familiarity with basic statistical
techniques and least-squares methods of
fitting regression lines to data should have
no trouble with the material presented. --BioScience

This book presents a set of graphical
methods for displaying data. The methods are
shown in use in practical examples and their
construction and properties are explained in
this context. The book presents a powerful
display of their use, not in isolation, but
in conjunction with other graphics and in
the context of a serious effort to produce
a coherent data analysis. --Journal of the Amerian Statistical Association

About the Author

William S. Cleveland is the Shanti
S. Gupta Distinguished Professor at
Purdue University, and splits his time
between the Statistics and Computer Science
Departments. Throughout his career, he has
worked in research areas --- statistical
model building, local machine learning,
visualization, time series, and data
mining --- that have broadened the scope
of research in learning from data. He has
developed theories and methods that are
now part of the fundamental knowledge base
of data visualization. He has developed
fundamental tools of machine learning that
were subsequently intensively studied by
researchers both in statistics and computer
science, and widely used by the scientific
community.

Cleveland has published over 100 papers on
his research in a wide range of scientific
journals, books, and proceedings. His two
books, The Elements of Graphing Data and
Visualizing Data have been reviewed in many
journals from a wide variety of disciplines,
and Elements was selected for the Library
of Science.

Cleveland has twice won the Wilcoxon
Prize and once won the Youden prize from
the statistics journal Technometrics. He
is a Fellow of the American Statistical
Association, the Institute of Mathematical
Statistics, and the American Association
of the Advancement of Science, and is
an elected member of the International
Statistical Institute. In 1996 he was
chosen Statistician of the Year by the
Chicago Chapter of the American Statistical
Association.

Product Details

• Hardcover: 360 pages
• Publisher: Hobart Press; 1 edition (March 1, 1993)
• Language: English
• ISBN-10: 0963488406
• ISBN-13: 978-0963488404
• Product Dimensions: 9.8 x 7.4 x 1.2 inches
• Shipping Weight: 2.2 pounds (View shipping rates and policies)
• Average Customer Review: 4.8 out of 5 stars  See all reviews (5 customer reviews)
• Amazon Best Sellers Rank: #381,055 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

61 of 64 people found the following review helpful:

5.0 out of 5 stars A Valuable Tool, May 25, 2000

By A Customer

This review is from: Visualizing Data (Hardcover)

This book was recommended highly to me by a former university professor (and now consultant). It exceeds my expectations. The figures and acompanying explanations are very clear, as is the language throughout. Visualizing Data discusses several tools with which I was not familiar, and clarifies tools that I thought I understood (including box plots). I have taken several university statistics classes, but I believe this book would help anyone involved in displaying or interpreting data. A picture may be worth a thousand words, but when your business depends on it, a well-defined plot or graph can be worth much more. Visualizing Data enables you to produce well-defined plots and graphs with confidence.

29 of 29 people found the following review helpful:

4.0 out of 5 stars Wonderful for its intended audience, September 16, 2007

By 

Victoria Buckland (College Park, Maryland) - See all my reviews
(REAL NAME)   

This review is from: Visualizing Data (Hardcover)

First and foremost, this book has a definite audience: people who need to produce graphs for somewhat sophisticated audiences. This is not a book about producing graphs for mass marketing or other flashy arenas. While this point is implicit throughout the book, it is not often stated explicitly.

The biggest strength of this book, and what makes it worth the purchase, is Cleveland's discussion about the relationship between graphing and visual processing. We've all seen a thousand pie charts, for example, but it turns out that people are not good at visually processing pie charts. The way we process visually has implications for everything from line graph construction to color choices to deciding how to code data on XY scatter plots. Although this information does exist in other places, Cleveland brings it together concisely here. Some of the discussion can get a bit technical, however, so be warned.

This is a great first book to read to learn more about how to construct graphs, and it has enough references to point you to other sources if you feel you need more. I myself have purchased several other books about the visual representation of data (including Cleveland's other book "The Elements of Graphing Data"), but this is where I started, and the information in this book has enriched my understanding of those other books immeasurably.

21 of 53 people found the following review helpful:

5.0 out of 5 stars Behaviour Elucidation par Excellence! U didn't know this B4, January 27, 2005

By 

Mark A. Weiss (Germantown, MD United States) - See all my reviews
(REAL NAME)   

This review is from: Visualizing Data (Hardcover)

Behaviour elucidation is done amazingly well. This book is even more powerful than Cleveland's "Elements of Graphing Data". Key words for what you achieve: incisive, powerful, salient behaviour eludidation. The principles of graphical perception from "Elements" are great (and themselves powerful) but this book invents and emphasizes yet more incisive visualizations. These new visualizations involve considerable computation IN SUPPORT OF CONSTRUCTING the graphs. But the GRAPHS -- and the behaviours they make manifest/salient -- are the point. As in "Elements", Cleveland is not just about the techniques as if they were rote procedure; he helps you build perspective too. This book, in a very real sense, (even explicitly so stated by Cleveland himself) is an alternative paradigm to the pervasive statistical inference paradigm. No wonder, then, that another reviewer (a Statistics student) learned so much he had never even seen before. Boy was "Visualizing" useful for a project I had on univariate data in multiple categorical groups (folding durability; 6 groups of data); Chapter 2 of "Visualizing" TRULY had me seeing things I NEVER would've otherwise. The book also guides you in the computations you need to get to the visualizations.