Lenth, Russell  V.

English

Journal of Statistical Software

JSS Journal of Statistical SoftwareJanuary 2009, Volume 29, Book Review 5. http://www.jstatsoft.org/Reviewer: Russell V. LenthUniversity of IowaDesign and Analysis of Experiments: Classical and Regression Approacheswith SASLeonard C. OnyiahChapman & 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 regressionand design, or selected topics could be used for a one-semester graduate course in design forstatistics majors, and there is more than one semester’s worth for non-statistics majors. Thereare 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 multipleregression, polynomial models, and orthogonal polynomials. Chapter 2 discusses one-factordesigns, with modeling, ANOVA, some follow-up tests, checking model assumptions, con-trasts, and regression formulations. Chapters 3 and 4 extend these materials to two-factordesigns, and introduce random effects, mixed models, and expected mean squares. Chapters 5and 6 discuss block designs, Latin and Graeco-Latin squares. Chapters 7–9 cover 2k and 3kdesigns, fractional replication, and confounding. Chapter 10 presents materials on BIB, lat-tice, and nested designs. Chapter 11 introduces response-surface methods and analysis ofcovariance. Chapter 12 is on MANOVA.The back cover and promotional materials for this text characterize it as being “unlike otherbooks . . . it not only covers classical experimental design theory, it also explores regressionapproaches.” Actually, lots of design books discuss regression approaches; but after examiningthe presentation of topics in this text, I gather this to mean that considerable attention is givento how ANOVA models can be expressed and analyzed in terms of dummy variables. Thisis done in considerable detail. To obtain unique parameter estimates, the book emphasizeseffects 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 usingboth codings. In all the accompanying SAS examples I examined, these dummy variablesare inputted explicitly as part of the data. I found no discussion of how SAS deals withestimability, or of its Type I–IV estimable functions.2 Design and Analysis of Experiments: Classical and Regression Approaches with SASIndeed, I have to say that if you seek a text where SAS is used as a vital tool to replace thetedium of hand calculations and to aid in exploring and graphing data, you will be sorelydisappointed. The role of SAS is confined to producing regression coefficients and sums ofsquares. Almost all examples are confined 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 couldnot find an instance where residuals are saved in an output dataset, or where SAS is used toproduce diagnostic plots. This is especially puzzling in that there is a fair amount of emphasison 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 wherean INFILE statement or PROC IMPORT is used to read in data, or where a SAS binary data fileis created or used; all SAS examples incorporate the data in the program using DATALINESor CARDS (!) statements. I did not see an example where a design is actually produced orrandomized, 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-file-related example, another prominent non-feature of this book isthe lack of an accompanying CD-ROM or Web site where textbook data files 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, includingmatrix notations and even generalized inverses. However, the statistical level is often low,with insufficient discussion of important inferential ideas. For instance, follow-up tests arepresented 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 endof the section, we have but one paragraph (four lines long) that in any way contrasts thesethree methods. It states that the HSD yields narrower confidence intervals than the LSD(which is false, and at odds with the numerical example just presented), more conservativethan the Duncan method (true), and the latter“has a type I error rate of α for all comparisonson an experimentwise basis” (questionable, depending on meaning of “experimentwise” whichis not defined). That’s it; end of discussion.The tone of this textbook is that of a very routinized approach to data analysis, focusingon obtaining numerical answers with small datasets. One of the routines evident in the SASexamples is the inputting of data in unaligned free format, with several observations perline; and the use of PROC PRINT with an accompanying SUM statement in almost all SASexamples. I have to admit that I didn’t know about the SUM statement before reading thisbook. Somehow, I have managed to get by all these years without displaying column sums inmy data printouts.The instructional goals are spelled out clearly on the back cover: “the author uses bothmanual methods and SAS programs to carry out analyses.” It is important to take this veryliterally. There is considerable emphasis (and no relief from) hand calculations, with additionalmaterials on SAS. That is, SAS programming is not treated as an integral tool in statisticalanalysis; it is a source of additional numerical examples and end-of-chapter exercises, andreally not much more. This is very far from what I had expected (or hoped) to see. I goback far enough to remember the original version of SAS, and using PROC REGR before it wassupplanted by PROC GLM. Nevertheless, I disagree with the use of the word “modern” in theback-cover description of this book.Journal of Statistical Software – Book Reviews 3Reviewer:Russell V. LenthThe University of IowaDepartment of Statistics and Actuarial ScienceIowa City, IA 52242, United States of AmericaE-mail: russell-lenth@uiowa.eduJournal of Statistical Software http://www.jstatsoft.org/published by the American Statistical Association http://www.amstat.org/Volume 29, Book Review 5 Published: 2009-01-03January 2009

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

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

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