Robust Regression Techniques in SAS/STAT

Duration: 1 half-day session CEU: 0.3

Presented by Jill Tao, statistical services specialist in the Education Division of SAS Institute

This lecture is designed for analysts, statisticians, modelers, and other professionals who have experience and knowledge in regression analysis and who want to learn available procedures in SAS/STAT software for robust regression and nonparametric regression techniques.

Learn how to

use PROC ROBUSTREG to fit a regression model less sensitive to outliers
use PROC LOESS to fit a robust nonparametric regression model
use PROC GLIMMIX to fit a radial smoother model.

Who should attend

Analysts, statisticians, and modelers

Prerequisites

Before attending this course, you should be familiar with

DATA step programming
basic SAS procedures for producing summary statistics and graphs, such as the MEANS and GPLOT procedures. You can gain this experience by completing the SAS Programming I: Essentials course.
basic knowledge and experience in linear regression models (PROC REG). You can gain this experience by completing the Statistics I: Introduction to ANOVA, Regression, and Logistic Regression course.

Course Materials

Students attend Live Web classes using a Web browser and a telephone and interact with their instructor and fellow classmates in real time. Each student receives an e-mail with instructions on how to join the class three business days before the class begins. The instructions e-mail includes a link to download the course materials, including the exercise files. Students need to download and print the course materials prior to class.

System Requirements

For Live Web, you must

review and meet the general system requirements.