Statistical programming in SAS. (2019)

• Record Type: Book
• Further Information: Note: A. John Bailer.
• Authors: Bailer, A. John
• Contents: 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<Z<1.645); Processing Macros; Macro Variables, Parameters, and Functions; Conditional Execution, Looping, and Macros; Saving Macros; Functions and Routines for Macros; Case Study: Macro for constructing training and test data set for Model Comparison; Case Study: Processing Multiple Data Sets; Exercises 6. Customizing Output and Generating Data Visualizations ; Using the Output Delivery System; Graphics in SAS; ODS Statistical Graphics; Modifying Graphics Using the ODS Graphics Editor; Graphing with Styles and Templates; Statistical Graphics—Entering the Land of SG Procedures; Case Study: Using the SG Procedures; Enhancing SG displays – options with SG procedure statements; Using Annotate Data Sets to enhance SG displays; Using Attribute Maps to enhance SG displays; Exercises 7. Processing Text ; Cleaning and Processing Text Data; Starting with Character Functions; Processing Text; Case Study: Sentiment in State of the Union addresses; Case Study: Reading Text from a Web Page; Regular Expressions; Case Study (revisited) – Applying Regular Expressions; Exercises 8. Programming with Matrices and Vectors ; 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 Study 1: Monte Carlo Integration to Estimate π; Case Study 2: Bisection Root Finder; Case Study 3: Randomization Test Using Matrices Imported from PROC PLAN; Case Study 4: SAS/IML Module to Implement Monte Carlo Integration to Estimate π; Storing and loading SAS/IML modules; SAS/IML and R; Exercises References
• Edition: Second edition
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2019
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 005.55; Statistics -- Data processing
• Languages: English
• ISBNs: 9781000735420; 9781000734928; 9781000735178; 9780429341847
• Related ISBNs: 9780367358006; 9780367357979
• Notes: Note: Description based on CIP data; resource not viewed.
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