Visualizing Data
by William S. Cleveland
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Book Summary Information
Author: William S. ClevelandEdition: Hardcover
Audio: English (Unknown); English (Original Language); English (Published)
Published: 1993-03-01
ISBN: 0963488406
Number of pages: 360
Publisher: Hobart Press
Book Reviews of Visualizing Data
Book Review: Wonderful for its intended audience
Summary: 4 Stars
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.
Summary of Visualizing Data
Visualizing Data is about visualizationtools 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.
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