Wolfgang M. Hartmann

English

Research Papers in Economics

JSS Journal of Statistical Software
October 2008, Volume 28, Book Review 1. http://www.jstatsoft.org/
Reviewer: Wolfgang M. Hartmann
Heidelberg, Germany
SAS for Data Analysis: Intermediate Statistical Methods
Mervyn G. Marasinghe and William J. Kennedy
Springer-Verlag, New York, 2008.
ISBN 978-0-387-77371-1. 557 pp. USD 69.95.
http://mervyn.public.iastate.edu/Book/
The authors provide an easily readable introduction into the SAS language, some of its basic
statistical methods, and many applications for statistical linear modeling. With the rst three
chapters of this book the authors show their excellence in teaching, with the remaining three
chapters they show how to solve some more complicated problems in the statistical analysis
of linear models. The book is designed for beginners in both, SAS language and statistics but
assumes a steep learning curve or some prior knowledge of linear models. Very helpful are
the many exercises in each chapter which make this book valuable for teaching at universities
and colleges or just for teaching a very focussed reader. As of today, almost all test examples
and data sets are available from the Web page accompanying the book (see above).
The last chapters may also be useful for SAS consultants at colleges, universities, and research
facilities to look up a scienticly sound approach to a specic statistical problem. The book
does not cover how to write SAS macros. Only the BOXANNO macro by Friendly (1991) is men-
tioned in book and index. Also, I did not nd anything about the very important importing
or exporting of data from or into other environments, e.g., SPSS or Microsoft Excel.
There is, however, a discrepancy between the general titel and the specic content of this
book: The book covers only a small part of the SAS/BASE, SAS/GRAPH, and SAS/STAT
packages of SAS and not much is said about many other packages like SAS/ETS, SAS/OR,
SAS/IML, SAS/QC, SAS Enterprise Miner, or even the nancial software of SAS. Even for
SAS/STAT only some basic PROCs such as UNIVARIATE and FREQ and the linear modeling
PROCs REG, ANOVA, GLM, and MIXED are covered. Many other SAS/STAT PROCs, e.g., LOGISTIC,
GENMOD, PHREG, ROBUSTREG, FACTEX, OPTEX, NLIN, PRINCOMP, imputation, PROCs for life and
behavioral sciences etc. are not mentioned. A similar selection of SAS software is covered by
Littell, Freund, and Spector (1991) and Littell, Milliken, Stroup, and Wolnger (1996) but
assuming more knowledge of SAS and less of statistics by the reader.
The book is divided into the following chapters:
1. Introduction to the SAS language: This is written for beginners and should enable every
reader to write DATA and PROC steps in the SAS language. The text makes a good2 SAS for Data Analysis
eort to explain the SAS way of record-wise dealing with data which is so dicult to
understand for some beginners.
2. More on SAS programming and some applications: This chapter describes some neces-
sary tools for data processing and provide a number of examples for basic PROCs from
SAS/STAT: UNIVARIATE, FREQ, and from SAS/BASE: PLOT, CHART, and TABULATE.
3. Statistical graphics using SAS/GRAPH: The authors provide a thorough introduction
into writing those tedious SAS/GRAPH scripts with many examples. On a funny note,
two PROCs are considered \useful": GPLOT and GCHART. Also, a short introduction into
some high resolution graphics with SAS is given here.
4. Statistical analysis of regression models: The chapter starts with simple and multi-
ple linear regression models and illustrates the use of PROC REG with some examples.
Forward, backward, stepwise and all-subsets methods are explained using PROC REG.
5. Analysis of variance models: This and the last chapter are probably the most valuable
for readers who are already educated in SAS and statistics. Each of these two chapters
covers about 100 pages of text. Here, one-way and two-way classication for xed eects
analysis of variance modeling are explained with examples specied for PROCs ANOVA and
GLM.
6. Analysis of variance: Random and mixed eects models: One-way and two-way crossed
and nested random and mixed eects models are estimated using PROCs GLM and MIXED.
7. Appendix (with SAS/GRAPH statements and tables)
That means the rst three chapters are useful for readers inexperienced in both, the SAS
language and statistics. The last three chapters, however, assume some more experience with
SAS and a basic statistical education. Since the book contains so many examples, reading the
rst three chapters may enable the reader to go through the more advanced last chapters.
As a nal remark, SAS Institute, the owner and distributor of SAS, does no longer (for several
years) relate its name to the term \Statistical Analysis System" as noted by the authors.
References
Friendly M (1991). SAS System for Statistical Graphics. SAS Insitute, Carey, NC.
Littell RC, Freund RJ, Spector PC (1991). SAS System for Linear Models. SAS Insitute,
Carey, NC.
Littell RC, Milliken GA, Stroup WW, Wolnger RD (1996). SAS System for Mixed Models.
SAS Insitute, Carey, NC.Journal of Statistical Software { Book Reviews 3
Reviewer:
Wolfgang M. Hartmann
Heidelberg, Germany
E-mail: cmat.wolfgang@gmail.com
Journal of Statistical Software http://www.jstatsoft.org/
published by the American Statistical Association http://www.amstat.org/
Volume 28, Book Review 1 Published: 2008-10-26
October 2008

SAS for Data Analysis: Intermediate Statistical Methods

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