This course teaches you how to use the IML procedure via the programming language. You benefit from this course if you plan to use SAS/IML for manipulating matrices, simulating data, writing custom statistical analyses, or working with R. The programs in this course require SAS/IML 12.3 or later to run.

The self-study e-learning includes:

• Annotatable course notes in PDF format.
• Virtual lab time to practice.

Learn how to

• Create and manipulate matrices.
• Move data between IML matrices and SAS data sets.
• Simulate data.
• Write, store, and retrieve IML modules.
• Run SAS procedures from within IML.
• Interface with R from within IML.

Who should attend

SAS programmers, statisticians, econometricians, engineers, or others who want to use matrix algebra, simulate data, write custom statistical analyses, or work with R from IML

Formats available	Standard Duration
Live Web Classroom:	6 half-day session(s)	System Requirements
e-Learning:	21 hours/180 day license	System Requirements

Before attending this course, you should:

• Have completed the SAS® Programming 1: Essentials course or understand the material within.
• Have completed the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course or understand the material within.
• Possess an understanding of elementary matrix algebra or linear algebra concepts and operations, including matrix dimensions, matrix addition and multiplication, matrix inversion, and scalars.

This course addresses SAS/IML, SAS/STAT software.

This course also addresses R software.

Fundamentals of IML

• Creating and manipulating matrices.
• Assignment statements and operators
• accessing submatrices.

Modules, Logic, and Data

• Transferring data between matrices and data sets.
• Using IML modules.
• Loops and conditional logic.

Program Development

• Writing IML modules.
• Storage and memory.
• Efficiency and error handling.
• Calling SAS procedures from IML.

Simulating Data

• Random number generators.
• Monte Carlo simulation.

Working with R

• Setting up SAS and R.
• Transferring data between SAS and R.
• Running R analyses from IML.
• Creating R graphics from IML.