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.

A. John Bailer, PhD, PStat®, 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).

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.

1. Structuring, implementing, and debugging programs to learn about data Statistical Programming Learning from Constructed, Artificial Data Good Programming Practice SAS Program Structure What Is a SAS Data Set? Internally Documenting SAS Program Basic Debugging Getting Help Exercises

2. Reading, Creating and Formatting Data Sets What does a SAS Data Step do? Reading Data from External Files Reading CSV, Excel and TEXT files Temporary versus Permanent Status of Data Sets Formatting and Labeling Variables User-defined Formatting Recoding and Transforming Variables in a DATA Step Writing Out a File or Making a Simple Report Exercises

3. Programming a DATA step Writing Programs by subdividing tasks Ordering How Tasks are Done Index-able Lists of variables, aka arrays Functions associated with Statistical Distributions Generating Variables Using Random Number Generators Remembering Variable Values across Observations Processing multiple observations for a single observation Case Study 1: Is the Two-Sample t-Test Robust to Violations of the Heterogeneous Variance assumption? Efficiency considerations - how long does it take? Case Study 2: Monte Carlo Integration to Estimate an Integral Case Study 3: Simple Percentile-Based Bootstrap Case Study 4: Randomization Test for the Equality of Two Populations Exercises

4. Combining, extracting and reshaping data Adding observations by SET-ing data sets Adding variables by MERGE-ing data sets Working with tables in PROC SQL Converting wide to long formats Converting long to wide formats Case Study: Reshaping a World Bank data set Building training and validation data sets Exercises Self-Study lab

5. Macro Programming What Is a Macro and Why Would You Use It? Motivation for Macros: Numerical Integration to Determine P(0

6. Customizing Output and Generating Data Visualizations Using the Output Delivery System…