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Computational Statistics II

Objectives

At the end of this course, students should be able to:
1. Redirect Log and Output for external files
2. Manage data sets and libraries
3. Produce reports in HTML, PDF, RTF and Listing formats using the ODS - Output Delivery System
4. Produce linear regression analysis using SAS procedures
5. Manipulate data and matrices using SAS/IML language
6. Perform statistical analysis and linear regression using SAS/IML language
7. Perform text replacement in SAS code
8. Automate and customize SAS code
9. Build SAS code conditionally or iteratively
10. Use macro variables and macro functions

General characterization

Code

400008

Credits

6.0

Responsible teacher

Ana Cristina Marinho da Costa

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

Some experience in SAS programming, or frequency of the course Computational Statistics I.

Bibliography

  • Base SAS® 9.2 Procedures Guide: Statistical Procedures, Third Edition. Cary, NC: SAS Institute Inc. 2010.
  • SAS/STAT® 9.22 User's Guide. NC: SAS Institute Inc. 2010.
  • SAS® 9.2 Output Delivery System: User’s Guide. Cary, NC: SAS Institute Inc., 2009.
  • SAS® 9.2 Macro Language: Reference. NC: SAS Institute Inc. 2009.
  • W. E. Griffiths, R. C. Hill, G. G. Judge, Learning and Practicing Econometrics. New York : John Wiley & Sons, 1993.

Teaching method

The course is based on theoretical and practical lessons that include exposure to concepts and syntax of the SAS programming language and resolution examples. Some practical sessions will focus on problem solving and exercises, including discussion of alternative resolutions.

Evaluation method

Final exam (100%) in both evaluation calls.

Subject matter

The course is organized in five Learning Units (LU):
LU1. Topics on data management in SAS
LU2. ODS - Output Delivery System
LU3. Linear regression
LU4. Introduction to SAS/IML
LU5. SAS/Macro Language