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This book is intended for use as the textbook in a second course in
applied statistics that covers topics in multiple regression and
analysis of variance at an intermediate level. Generally, students
enrolled in such courses are p- marily graduate majors or advanced
undergraduate students from a variety of disciplines. These
students typically have taken an introductory-level s- tistical
methods course that requires the use a software system such as SAS
for performing statistical analysis. Thus students are expected to
have an - derstanding of basic concepts of statistical inference
such as estimation and hypothesis testing. Understandably, adequate
time is not available in a ?rst course in stat- tical methods to
cover the use of a software system adequately in the amount of time
available for instruction. The aim of this book is to teach how to
use the SAS system for data analysis. The SAS language is
introduced at a level of sophistication not found in most
introductory SAS books. Important features such as SAS data step
programming, pointers, and line-hold spe- ?ers are described in
detail. The powerful graphics support available in SAS is
emphasized throughout, and many worked SAS program examples contain
graphic components.
The aim of this textbook (previously titled SAS for Data Analytics)
is to teach the use of SAS for statistical analysis of data for
advanced undergraduate and graduate students in statistics, data
science, and disciplines involving analyzing data. The book begins
with an introduction beyond the basics of SAS, illustrated with
non-trivial, real-world, worked examples. It proceeds to SAS
programming and applications, SAS graphics, statistical analysis of
regression models, analysis of variance models, analysis of
variance with random and mixed effects models, and then takes the
discussion beyond regression and analysis of variance to conclude.
Pedagogically, the authors introduce theory and methodological
basis topic by topic, present a problem as an application, followed
by a SAS analysis of the data provided and a discussion of results.
The text focuses on applied statistical problems and methods. Key
features include: end of chapter exercises, downloadable SAS code
and data sets, and advanced material suitable for a second course
in applied statistics with every method explained using SAS
analysis to illustrate a real-world problem. New to this edition: *
Covers SAS v9.2 and incorporates new commands * Uses SAS ODS
(output delivery system) for reproduction of tables and graphics
output * Presents new commands needed to produce ODS output * All
chapters rewritten for clarity * New and updated examples
throughout * All SAS outputs are new and updated, including
graphics * More exercises and problems * Completely new chapter on
analysis of nonlinear and generalized linear models * Completely
new appendix Mervyn G. Marasinghe, PhD, is Associate Professor
Emeritus of Statistics at Iowa State University, where he has
taught courses in statistical methods and statistical computing.
Kenneth J. Koehler, PhD, is University Professor of Statistics at
Iowa State University, where he teaches courses in statistical
methodology at both graduate and undergraduate levels and primarily
uses SAS to supplement his teaching.
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