This book is part of the SAS Press program.
Jorge G. Morel, Ph.D., is principal statistician for Procter and Gamble, where he specializes in SAS/STAT and SAS/IML. A SAS user since 1981, he received his Ph.D. in statistics from Iowa State University and holds two M.S. degrees in the same field. He is a member of the American Statistical Association, and he has been published widely in the Journal of Biopharmaceutical Statistics, Biometrical Journal, and Applied Statistics, among other publications.
Nagaraj K. Neerchal, Ph.D., is Professor of Statistics and Chair of the Department of Mathematics and Statistics at the University of Maryland, Baltimore County. A SAS user since 1982, he specializes in SAS/STAT and SAS/ETS. Coauthor of more than a dozen journal articles, he received his B.S. and M.S. from the Indian Statistical Institute and his Ph.D. from Iowa State University. Dr. Neerchal received the Distinguished Achievement Award and Medal from the American Statistical Association's Section on Statistics and the Environment in 2000.
This book has two parts. The first part addresses the nuts and bolts, including fostering good programming habits, getting external data sets into SAS to construct an analysis data set, generating basic descriptive statistical summaries, producing customized tables, generating more attractive output, and producing high-quality graphical displays. The second part emphasizes programming in the context of a DATA step, in macros, and in SAS/IML software.
Examples of statistical methods and concepts not always encountered in basic statistics courses (for example, bootstrapping, randomization tests, and jittering) are used to illustrate programming ideas. This book provides extensive illustrations of the new statistical graphics procedures in SAS, a description of the new ODS Graphics Editor, and an introduction to some of the capabilities of SAS/IML Studio, such as producing dynamically linked data displays and invoking R from SAS.
It is at a level also accessible to non-mathematicians, focusing on the methods and applications of various multilevel models and using the widely used statistical software SAS®. Examples are drawn from analysis of real-world research data.