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Book Reviews(Chapter 5), the design of experiments (Chapters 6and 7) and regression analysis (Chapter 8). Everybody who has ever browsed a ‘six sigma black belt’training curriculum will agree that these topics area need, at least in the ﬁeld of quality improvement,
and probably one of the more important application areas in engineering.
Personally, I liked the blend of good expositionof mathematical concepts, motivating real world
applications and anecdotes (‘. . . one of us (Hogg)
took a quality fact-ﬁnding tour to more than 20companies. . . ’, page 331), the latter showing theauthors’ deep involvement in questions outside thepure academic world. The book has found its
place in my hand library—and I have already recommended it to more than one of the colleagues
from engineering initially quoted.
Thomas Hochkirchen
Aachentical problem and here they are encouraged to get
into the habit of organizing solutions in a clear andmethodical manner.
Figures and graphs are very accessible andplentiful in all chapters. Data ﬁles of examples andexercises are included in an appendix, allowing
students to gain practical knowledge. In addition,
one can download data from the author’s Web
page at http://iwu.edu/∼bleekley. All oddnumbered exercises are fully worked out with ﬁgures
presented in an extensive appendix. These solutions
are certainly very helpful for students of statistics
and encouraging for instructors. This book is highly
recommended as a textbook for business statistics
and it can also be used as a manual for self-study.
Morteza Aalabaf-Sabaghi
Economic Cooperation Organization
College of Insurance
TehranE-mail: aalabaf@eco.ac.ir

Applied Statistics for Business and Economics
R. M. Leekley, 2010
Boca Raton, CRC Pressxx+ 476 pp., £49.99
ISBN 978-1-439-80568-8

Statistical Methods for Disease Clustering
T. Tango, 2010
New York, Springer
x+ 248 pp., €69.95
ISBN 978-1-441-91571-9

In this excellent textbook Professor Leekley takes
the reader, as if they were his students, throughevery detail of examples working all steps with greatpatience. This generous approach is even extended
to explaining how mathematical and statistical notation and symbols are read; a very rare and valuable
education.
The text includes 14 chapters that take a typicalsocial science student through the basics of descriptive statistics, standard probability distributions,hypothesis testing, regression analysis and timeseries. Mathematical statistics and derivations ofprobability distributions are not included as these
are not usually required for business-related disciplines. But the highlight of this book is its treatment of hypothesis testing which is explored in fourchapters. The text covers one and two-sample tests,
contingency tables, goodness of ﬁt, analysis ofvariance and homogeneity of variances. Classicalhypothesis testing, which was the norm in older
texts, is explained and contrasted with current practice of using p-values and operating characteristics
in calculating type I and II errors.
The use of spreadsheet computer programs isused in most applications throughout the book. Thisis very useful for students learning statistics andspreadsheet manipulations at the same time. Often
students do not know how to tackle a given statis-

This is an updated book that provides statisticalmethods for detecting disease clustering andlocalized clusters. The methods provided are very
useful in epidemiology where it is important to evaluate whether a disease is randomly distributed acrossspace, time or both, as this may give clues on thedisease aetiology.
This concise monograph is structured in ninechapters. Chapter 1 just gives an introduction on
the subject and presents the organization of thefollowing chapters. Chapter 2 provides insight into
the deﬁnition of clustering or clusters showing
several spatial patterns in a hypothetical squared
area. Chapter 3 deals with statistical techniques fordisease mapping. The maps show the spatial variation in risks, providing a visual summary of complex geographical patterns, and could also highlightareas with apparent high risks. The author recommends use of smoothed map risks (estimated by
Bayesian models based on Markov chain MonteCarlo techniques) together with statistical tests fordisease clustering (that are described in the rest ofthe chapters) because the maps could help the interpretation of the test results. Although I agree withthe author on suggesting both procedures for
detecting clustering, I do not necessarily agree on
the necessity to smooth the maps by using Markov chain Monte Carlo methods, as there are also

Book Reviews
computationally simpler approaches such as penalized quasi-likelihood that behave very well whenanalysing count data (see, for example, Ugarte et al.
(2009)).
The rest of the chapters (Chapters 4–9) have thesame structure. They start with a data section
describing the data and the notation used. The next
section states the null and alternative hypothesis of
the statistical tests proposed. A historical overview
of some methods existing in the literature is given in
the next section. The ‘Selected methods’ section isuseful as it discusses the methods which the author
thinks are widely known or widely used. In the next
section real data are analysed by using these selected
methods. A ‘power comparison’ and a discussionsection follow to compare the various methods.
The particular content of these six chapters is
brieﬂy described in what follows. Chapter 4 deals
with the problem of detecting disease clustering intime. The problem of detecting disease clustering inspace on the basis of regional count data is treatedin Chapter 5. General tests for detecting disease
clustering in space but now based on case–control
data are discussed in Chapter 6. Space–time clustering is the topic of Chapter 7. The tests for space–time clustering that are described in this chapter
are designed for evaluating whether cases tend tocome in groups or are located close to each other nomatter where they occur.
Focused tests designed to examine a raised riskof disease around putative sources are explained inChapter 8. The last chapter deals with space–timescan statistics designed for both detecting localized
clusters and evaluating their signiﬁcance. One of themethods selected in this chapter called the cylindrical space–time scan statistic (Kulldorff, 2001)
has been implemented in surveillance systems as amajor tool for the detection of outbreaks. Readers will ﬁnd it useful to know that the book also
contains information on the existing software for
implementing the procedures.
The topics presented in this book constitute animportant part of the area known as spatial epidemiology. Although I found the content of the bookvery useful for epidemiologists and public healthresearchers, I think that the author could have included a detailed discussion to clarify the distinction between clustering, cluster detection andspatial variation in risk. The distinction between
these three topics is often fuzzy. I recommend
Elliot et al. (2000) as a companion book that will
help to clarify this distinction and to enlarge interesting discussions on this fascinating ﬁeld.
References
Elliot, P., Wakeﬁeld, J. C., Best, N. and Briggs, D. J.
(2000) Spatial Epidemiology. Oxford: Oxford University Press.

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Kulldorff, M. (2001) Prospective time periodic geographical disease surveillance using a scan statistic.
J. R. Statist. Soc. A, 164, 61–72.
Ugarte, M. D., Goicoa, T. and Militino, A. F. (2009)
Empirical Bayes and fully Bayes procedures to detect
high-risks areas in disease mapping. Computnl Statist. Data Anal., 53, 2938–2949.

M. Dolores Ugarte
Universidad Pública de Navarra
Pamplona
E-mail: lola@unavarra.es

Statistics in Plain EnglishT. C. Urdan, 2010
New York, Routledgexii + 212 pp., £19.95
ISBN 978-0-415-87291-1
Statistics in Plain English opens with some anecdotes from Dr Urdan’s life, corroborating the ideathat‘many people have a general distrust of statistics,
believing that crafty statisticians can make statistics say whatever they want’(page ix) and with the suggestion that‘a better option is to gain an understanding of
how statistics work and then use that understanding to interpret the statistics one sees and hear for
oneself’(page ix). This book tries to help the reader who
is motivated to gain such an understanding by
providing a simple and accessible discussion of thestatistics that are most commonly employed insocial science research.
Chapter 1 is a concise but effective introduction
to research terminology and study designs. Chapters 2–5 discuss measures of central tendency andvariability and the normal distribution. Chapter 6 isdedicated to the standard error, which is a conceptthat‘is often difﬁcult to grasp the ﬁrst time it is encountered (or the second or the third)’(page 49). Chapter 7 explains the concept of ‘statistical signiﬁcance’ and contrasts it with the conceptof ‘practical signiﬁcance’. This leads to a superb
discussion of effect size and conﬁdence intervals.
This chapter will offer to statistical novices a muchneeded immunization against the misuse of statistical signiﬁcance which is so common in practice.
(Subsequent chapters on statistical tests will improve the immunization rate by repeatedly making
clear this concept with practical examples.) Chapters