Russell V. Lenth

English

Research Papers in Economics

JSS Journal of Statistical Software
January 2009, Volume 29, Book Review 5. http://www.jstatsoft.org/
Reviewer: Russell V. Lenth
University of Iowa
Design and Analysis of Experiments: Classical and Regression Approaches
with SAS
Leonard C. Onyiah
Chapman & Hall/CRC, Boca Raton, FL, 2009.
ISBN 978-1-4200-6054-6. xxvii + 822 pp. USD 99.95 (H).
This book is aimed primarily at advanced undergraduate and beginning graduate students.
The preface indicates that it could be used as the text for a two-semester sequence in regression
and design, or selected topics could be used for a one-semester graduate course in design for
statistics majors, and there is more than one semester's worth for non-statistics majors. There
are probably fewer regression topics than most would want for a standard regression course,
however; so I question the idea of that two-semester regression-design sequence.
There are twelve chapters that follow a fairly traditional sequence. There is a chapter with re-
view materials (and beyond) on statistical methods and regression analysis, including multiple
regression, polynomial models, and orthogonal polynomials. Chapter 2 discusses one-factor
designs, with modeling, ANOVA, some follow-up tests, checking model assumptions, con-
trasts, and regression formulations. Chapters 3 and 4 extend these materials to two-factor
designs, and introduce random eects, mixed models, and expected mean squares. Chapters 5
and 6 discuss block designs, Latin and Graeco-Latin squares. Chapters 7{9 cover 2k and 3k
designs, fractional replication, and confounding. Chapter 10 presents materials on BIB, lat-
tice, and nested designs. Chapter 11 introduces response-surface methods and analysis of
covariance. Chapter 12 is on MANOVA.
The back cover and promotional materials for this text characterize it as being \unlike other
books ... it not only covers classical experimental design theory, it also explores regression
approaches." Actually, lots of design books discuss regression approaches; but after examining
the presentation of topics in this text, I gather this to mean that considerable attention is given
to how ANOVA models can be expressed and analyzed in terms of dummy variables. This
is done in considerable detail. To obtain unique parameter estimates, the book emphasizes
eects coding (where one treatment level's indicator is subtracted from each of the others),
and reference-cell coding (where one indicator is omitted); and often repeats examples using
both codings. In all the accompanying SAS examples I examined, these dummy variables
are inputted explicitly as part of the data. I found no discussion of how SAS deals with
estimability, or of its Type I{IV estimable functions.2 Design and Analysis of Experiments: Classical and Regression Approaches with SAS
Indeed, I have to say that if you seek a text where SAS is used as a vital tool to replace the
tedium of hand calculations and to aid in exploring and graphing data, you will be sorely
disappointed. The role of SAS is conned to producing regression coecients and sums of
squares. Almost all examples are conned to SAS PROCs REG, ANOVA, GLM, MIXED, and PRINT
(most often of all); and even then, these are used in very limited ways. For example, I could
not nd an instance where residuals are saved in an output dataset, or where SAS is used to
produce diagnostic plots. This is especially puzzling in that there is a fair amount of emphasis
on such plots and they are shown in many examples|just not in the SAS examples.
There are several other SAS topics that are notable by their absence. I saw no examples where
an INFILE statement or PROC IMPORT is used to read in data, or where a SAS binary data le
is created or used; all SAS examples incorporate the data in the program using DATALINES
or CARDS (!) statements. I did not see an example where a design is actually produced or
randomized, via PROC PLAN, FACTEX, OPTEX. I did not see a discussion of sample-size, power,
or the use of PROC POWER or GLMPOWER.
Related to the lack of data-le-related example, another prominent non-feature of this book is
the lack of an accompanying CD-ROM or Web site where textbook data les can be obtained.
It is surprising that this can even happen in a 2009 textbook with SAS in its title.
The mathematical level is high, with plenty of notation and detailed derivations, including
matrix notations and even generalized inverses. However, the statistical level is often low,
with insucient discussion of important inferential ideas. For instance, follow-up tests are
presented in Section 2.9. The methods given are the LSD, the Duncan multiple-range test
(presented as\another method..."), and the Tukey HSD (\yet another test ..."). At the end
of the section, we have but one paragraph (four lines long) that in any way contrasts these
three methods. It states that the HSD yields narrower condence intervals than the LSD
(which is false, and at odds with the numerical example just presented), more conservative
than the Duncan method (true), and the latter\has a type I error rate of  for all comparisons
on an experimentwise basis"(questionable, depending on meaning of\experimentwise"which
is not dened). That's it; end of discussion.
The tone of this textbook is that of a very routinized approach to data analysis, focusing
on obtaining numerical answers with small datasets. One of the routines evident in the SAS
examples is the inputting of data in unaligned free format, with several observations per
line; and the use of PROC PRINT with an accompanying SUM statement in almost all SAS
examples. I have to admit that I didn't know about the SUM statement before reading this
book. Somehow, I have managed to get by all these years without displaying column sums in
my data printouts.
The instructional goals are spelled out clearly on the back cover: \the author uses both
manual methods and SAS programs to carry out analyses." It is important to take this very
literally. There is considerable emphasis (and no relief from) hand calculations, with additional
materials on SAS. That is, SAS programming is not treated as an integral tool in statistical
analysis; it is a source of additional numerical examples and end-of-chapter exercises, and
really not much more. This is very far from what I had expected (or hoped) to see. I go
back far enough to remember the original version of SAS, and using PROC REGR before it was
supplanted by PROC GLM. Nevertheless, I disagree with the use of the word \modern" in the
back-cover description of this book.Journal of Statistical Software { Book Reviews 3
Reviewer:
Russell V. Lenth
The University of Iowa
Department of Statistics and Actuarial Science
Iowa City, IA 52242, United States of America
E-mail: russell-lenth@uiowa.edu
Journal of Statistical Software http://www.jstatsoft.org/
published by the American Statistical Association http://www.amstat.org/
Volume 29, Book Review 5 Published: 2009-01-03
January 2009

Design and Analysis of Experiments: Classical and Regression Approaches with SAS

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