Statistical Programming in SAS 1st Edition by A John Bailer Phd ISBN 1599946564 9781599946566

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ISBN 10: 1599946564 
ISBN 13: 9781599946566
Author: A John Bailer Phd

In Statistical Programming in SAS, author A. John Bailer integrates SAS tools with interesting statistical applications and uses SAS 9.2 as a platform to introduce programming ideas for statistical analysis, data management, and data display and simulation. Written using a reader-friendly and narrative style, the book includes extensive examples and case studies to present a well-structured introduction to programming issues.

This book has two parts. The first part addresses the nuts and bolts of programming, 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.

Statistical Programming in SAS 1st  Table of contents:

Part I: The Nuts and Bolts of Programming

• Chapter 1: Let’s Get Started—Preliminaries and a SAS Quick Start
  • Statistical Computing versus Programming versus Managing Data
  • Good Programming Practice
  • SAS Program Structure
  • What Is a SAS Data Set?
  • Internally Documenting SAS Programs
  • Basic Debugging
  • Getting Help
  • Exercises
• Chapter 2: Reading, Combining, and Managing Data for Later Analysis
  • Temporary versus Permanent Status of Data Sets
  • Reading Data into a SAS Data Set (from various sources like text, CSV, Excel)
  • Writing Out a File or Making a Simple Report
  • Concatenating Data Sets and Adding Observations
  • Merging Data Sets and Adding Variables
  • Database Processing with PROC SQL
  • Self-Study Laboratory Explorations
  • Exercises
• Chapter 3: Using SAS Procedures
  • SAS System Options
  • Statements That Can Modify the Output of Most Procedures
  • Defining Your Own Formats for Variable Values
  • Selecting or Stratifying an Analysis by Values of a Variable
  • Displaying Data Set Properties and Observations
  • Using PROC PRINT to List the Observations in a Data Set
  • Basic Graphical Displays (e.g., using SGPLOT, PROC PLOT)
  • Summarizing Categorical Variables (e.g., PROC FREQ)
  • Summarizing Numeric Variables (e.g., PROC MEANS, PROC UNIVARIATE)
  • Selecting a Simple Random Sample
  • Randomly Assigning Treatments to Observations
  • Exercises
• Chapter 4: Complex Table Construction and Output Control
  • Introducing PROC TABULATE
  • Building from Simple Specifications
  • Enhancing PROC TABULATE Output
  • Using the Output Delivery System (ODS)
    • Basic ideas
    • Destinations (RTF, HTML, PDF, etc.)
    • Selecting output objects
    • The ODS OUTPUT destination
  • Exercises

Part II: Programming in the Context of a DATA Step, Macros, and SAS/IML

• Chapter 5: Basic Models in SAS
  • Overview of Modeling
  • Linear Regression Models (e.g., PROC REG)
    • Simple Linear Regression
    • Multiple Regression
  • ANOVA Models (e.g., PROC GLM)
    • One-Way ANOVA
    • ANOVA with Two or More Factors
  • Examples using statistical methods not always encountered in basic statistics courses (e.g., bootstrapping, randomization tests, jittering)
  • Exercises
• Chapter 6: Customizing Output and Generating Data Visualizations
  • Using ODS Statistical Graphics (SG procedures)
  • Modifying Graphics Using the ODS Graphics Editor
  • Graphing with Styles and Templates
  • Enhancing SG displays (options, Annotate Data Sets, Attribute Maps)
  • Exercises
• Chapter 7: Programming a DATA Step
  • Writing Programs by Subdividing Tasks
  • Ordering How Tasks are Done
  • Index-able Lists of Variables (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 Studies (e.g., Monte Carlo Integration, Bootstrapping, Randomization Tests)
  • Efficiency Considerations
  • Exercises
• Chapter 8: Macro Programming
  • What Is a Macro and Why Would You Use It?
  • Motivation for Macros
  • Processing Macros
  • Macro Variables, Parameters, and Functions
  • Conditional Execution, Looping, and Macros
  • Saving Macros
  • Case Studies (e.g., constructing training and test data sets, processing multiple data sets)
  • Exercises
• Chapter 9: Programming with Matrices and Vectors (SAS/IML)
  • Defining a Matrix and Subscripting
  • Using Diagonal Matrices and Stacking Matrices
  • Using Elementwise Operations, Repeating, and Multiplying Matrices
  • Importing a Data Set into SAS/IML and Exporting Matrices from SAS/IML to a Data Set
  • Case Studies (e.g., Monte Carlo Integration, Bisection Root Finder, Randomization Test)
  • Connecting SAS with R (likely a brief introduction or mention)
  • Exercises

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Tags: A John Bailer Phd, Statistical, Programming