Table Of Contentebook
THE GUILFORD PRESS
Elementary Statistics for Geographers
Elementary Statistics
for Geographers
Third Edition
JAMES E. BURT
GERALD M. BARBER
DAVID L. RIGBY
THE GUILFORD PRESS
New York London
© 2009 The Guilford Press
A Division of Guilford Publications, Inc.
72 Spring Street, New York, NY 10012
www.guilford.com
All rights reserved
No part of this book may be reproduced, translated, stored in a retrieval system, or
transmitted, in any form or by any means, electronic, mechanical, photocopying,
microfilming, recording, or otherwise, without written permission from the Publisher.
Printed in the United States of America
This book is printed on acid-free paper.
Last digit is print number: 9 8 7 6 5 4 3 2 1
Library of Congress Cataloging-in-Publication Data
Burt, James E.
Elementary statistics for geographers / James E. Burt, Gerald M. Barber,
David L. Rigby. — 3rd ed.
p. cm.
Includes bibliographical references and index.
ISBN 978-1-57230-484-0 (hardcover)
1. Geography—Statistical methods. I. Barber, Gerald M. II. Rigby, David L.
III. Title.
G70.3.B37 2009
519.5024′91—dc22 2008043409
Preface
Readers who know our book will quickly see that this edition represents a significant
revision, containing both a substantial amount of new material and extensive reorgan-
ization of topics carried over from the second edition. However, our purpose remains
unchanged: to provide an accessible algebra-based text with explanations that rely on
fundamentals and theoretical underpinnings. Such an emphasis is essential if we ex-
pect students to utilize statistical methods in their own research or if we expect them
to evaluate critically the work of others who employ statistical methods. In addition,
when students understand the foundation of the methods that are covered in a first
course, they are far better equipped to handle new concepts, whether they encounter
those concepts in a more advanced class or through reading on their own. We ac-
knowledge that undergraduates often have a limited mathematical background, but
we do not believe this justifies a simplified approach to the subject, nor do we think
that students are well served by learning what is an inherently quantitative subject
area without reference to proofs and quantitative arguments. It is often said that
today’s entering students are less numerate than previous generations. That may be.
However, in our 20-plus years of teaching undergraduates we have seen no decrease
in their ability or in their propensity to rise to an intellectual challenge. Like earlier
versions, this edition of Elementary Statistics for Geographersis meant for instruc-
tors who share this outlook, and for their students, who—we trust—will benefit from
that point of view.
The Descriptive Statistics section of this edition greatly expands the coverage
of graphical methods, which now comprise a full chapter (Chapter 2). This reflects
new developments in computer-generated displays in statistics and their growing use;
also, students increasingly seem oriented toward visual learning. It is likely, for ex-
ample, that a student who obtains a good mental image of skewness from Chapter 2
can use that visual understanding to grasp more readily the quantitative measures
presented in Chapter 3. A second new chapter appearing in the descriptive section is
Chapter 4, Statistical Relationships. It introduces both concepts of and measures for cor-
relation and regression. This is somewhat nonstandard, in that most books postpone
these topics until after the discussion of univariate methods. We have found that earlier
v
vi PREFACE
introduction of this material has several advantages. First, correlation and regression
are large topics, and some students do better learning them in two parts. Second, the
concept of association is useful when explaining certain aspects of probability theory
such as independence, conditional probability, and joint probability. Finally, it is easier
to discuss nonparametric tests such as chi-square when the idea of statistical associ-
ation has already been presented. Of course, instructors who prefer to cover correla-
tion and regression in one section of their course can postpone Chapter 4 and cover
it as part of a package with Chapters 12 and 13.
The Inferential Statistics section has also been heavily revised. We merged
basic probability theory with the treatment of random variables to create more stream-
lined coverage in a single chapter (Chapter 5, Random Variables and Probability Dis-
tributions). Gone is the Computer-Intensive Methods chapter, with much of that
material incorporated into the Nonparametric Methods chapter. As bootstrapping and
related techniques have become mainstream, it is appropriate to locate them in their
natural home with other nonparametric methods. Chapter 11, Analysis of Variance, is
a new chapter, which covers both single- and two-factor designs. Also new is Chap-
ter 13, Extending Regression Analysis, which treats diagnostics as well as transforma-
tions and more advanced regression models (including multiple regression).
The last section, Patterns in Space and Time, contains a revised version of the
Time Series Analysis chapter from the second edition, and the entirely new Chapter 14,
Spatial Patterns and Relationships. The latter is an overview of spatial analysis, and
covers point patterns (especially nearest neighbor analysis), spatial autocorrelation
(variograms, join counts, Moran’s I, LISA, and G-statistics), and spatial regression
(including an introduction to geographically weighted regression).
Additionally, there are lesser changes too numerous to itemize. We’ve placed
greater emphasis on worked examples, often with accompanying graphics, and the
datasets that we refer to throughout the book are available on the website that ac-
companies this book. On the website, readers can also find answers to most of the
end-of-chapter exercises. See www.guilford.com/pr/burtfor the online resources.
We have said already that this new edition adheres to the previous editions’ em-
phasis on explanation, rather than mere description, in its presentation of quantitative
methods. Several other aspects are also unchanged. We have retained the coverage of
time series, which of course is seldom covered in this type of book. Time series data
are extremely common in all branches of geography; thus, geographers need to be
equipped with at least a few tools of analysis for temporal data. Also, once students
get to linear regression, they are well positioned to understand the basics of time se-
ries modeling. In other words, ability to handle time series can be acquired at little
additional cost. Because time series are so common, geographers will likely have
occasion to deal with temporal data regardless of their formal training in the subject.
We believe that even simple operations like running means should not be undertaken
by individuals who do not appreciate the implications of the procedure. Because most
students will not take a full course in time series, minimal coverage, at least, is es-
sential in an introductory text. Also, we’ve received strong positive feedback on this
material from instructors.
PREFACE vii
We have continued our practice from the second edition, of not tying the book
to any particular software package. We believe that most instructors use software for
teaching this material, but no package has emerged as an overwhelming favorite. We
might gain a friend by gearing the book to a particular package, but we would alien-
ate half a dozen more. Also, since statistical software is becoming increasingly easy
to use, students require less in the way of instruction. And we want the book to stay
current. We have found that even minor changes in the formatting of output can con-
found students who have been directed to look for particular tables of values or par-
ticular terminology in software packages.
Finally, in keeping with the trend from edition to edition, what was a long book
is even longer. Unless it is used in a year-long course, instructors will have to be very
selective with regard to what they assign. With this in mind, we have attempted to
make the chapters as self-contained as possible. Except for the chapter on probabil-
ity and sampling theory, a “pick-and-choose” approach will work reasonably well. For
example, we know from experience that some instructors leave out the Nonparametric
Methods chapter altogether, with no downstream effects, whereas others skip various
chapters andsubsections within chapters. If some students complain about having to
skip around so much, most appreciate a book that covers more than what is taught in
the course. Later, when confronted with an unfamiliar method in readings or on a re-
search project, they can return to a book whose notational quirks have already been
mastered, and can understand the new technique in context with what was presented
in the course. As we reflect on our own bookshelves, it is precisely that kind of book
that has proved most useful to us over the years. We wouldn’t presume to claim that
our work will have similar lasting utility, but we offer it in the belief that it is better
to cover too much than too little.
Many people deserve our thanks for their help in preparing this book. We are
particularly grateful to students and teaching assistants at UCLA, Queen’s University,
and the University of Wisconsin–Madison for telling us what worked and what d idn’t.
Thanks also to the panel of anonymous reviewers for their comments on previous ver-
sions of the manuscript. You improved it greatly. We also very much appreciate the
hard work by everyone at The Guilford Press involved with the project, especially our
editor, the ever-patient and encouraging Kristal Hawkins. Our production editor
William Meyer also deserves particular mention for his careful attention to both the
print and digital components of the project. Most of all, we thank our families for so
willingly accepting the cost of our preoccupation. To them we dedicate the book.
