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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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