Statistical Programming in SAS Second Edition provides a foundation for programming to implement statistical solutions using SAS, a system that has been used to solve data analytic problems for more than 40 years. The author includes motivating examples to inspire readers to generate programming solutions. Upper-level undergraduates, beginning graduate students, and professionals involved in generating programming solutions for data-analytic problems will benefit from this book. The ideal background for a reader is some background in regression modeling and introductory experience with computer programming.
The coverage of statistical programming in the second edition includes
Getting data into the SAS system, engineering new features, and formatting variables
Writing readable and well-documented code
Structuring, implementing, and debugging programs that are well documented
Creating solutions to novel problems
Combining data sources, extracting parts of data sets, and reshaping data sets as needed for other analyses
Generating general solutions using macros
Customizing output
Producing insight-inspiring data visualizations
Parsing, processing, and analyzing text
Programming solutions using matrices and connecting to R
Processing text
Programming with matrices
Connecting SAS with R
Covering topics that are part of both base and certification exams.
About the Author: A. John Bailer, PhD, PStat(R), is a University Distinguished Professor and a founding chair of the Department of Statistics and an affiliate member of the Departments of Biology and Sociology and Gerontology as well as the Institute for the Environment and Sustainability at the Miami University in Oxford, Ohio. He is President of the International Statistical Institute (2019-2021). He previously served on the Board of Directors of the American Statistical Association. He is a Fellow of the American Statistical Association, the Society for Risk Analysis, and the American Association for the Advancement of Science. His research has focused on the quantitative risk estimation but has collaborations addressing problems in toxicology, environmental health, and occupational safety. He received the E. Phillips Knox Distinguished Teaching Award in 2018 after previously receiving the Distinguished Teaching Award for Excellence in Graduate Instruction and Mentoring and the College of Arts and Science Distinguished Teaching Award. He is also the co-founder and continuing panelist on the Stats+Stories podcast (www.statsandstories.net).
