This course covers nine regression methods. The models include linear, logistic, quantile, generalized linear, generalized additive, mixed, survival, nonlinear, and partial least squares. The applications, strengths, and weaknesses of each method are discussed, along with how the methods are implemented in SAS Viya. A comparison of the SAS Viya procedures and SAS/STAT procedures for each method is also shown. Examples in the course show applications in banking, financial services, direct marketing, insurance, telecommunications, medical, and academic fields.

The self-study e-learning includes:

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

Learn how to

• Use SAS Viya to fit various regression models.
• Assess model performance in SAS Viya.
• Score new data with the fitted regression models in SAS Viya.

Who should attend

Business analysts, social scientists, epidemiologists, and statisticians who want to see what SAS Viya has to offer in regression methods

Formats available	Standard Duration
Classroom:	2 days
Live Web Classroom:	4 half-day session(s)	System Requirements
e-Learning:	14 hours/180 day license	System Requirements

Before attending this course, you should:

• Have experience executing SAS programs and creating SAS data sets, which you can gain from the SAS® Programming 1: Essentials course.
• Have experience building statistical models using SAS software.
• Have completed a statistics course that covers linear regression and logistic regression, such as the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.

This course addresses SAS Viya software.

Introduction to SAS Viya

• SAS Analytics Platform.
• Loading files into SAS Viya.

Linear Regression

• Fitting models in the REGSELECT procedure.
• Model assessment.

Quantile Regression

• What is quantile regression?

Logistic Regression

• Logistic regression models.
• Model assessment for logistic regression.

Generalized Linear Models

• Generalized linear models.
• Poisson regression.
• Tweedie regression.

Generalized Additive Models

• Introduction to generalized additive models.
• Using the GAMSELECT procedure to fit generalized additive models.

Survival Analysis

• Introduction to survival analysis.
• Cox Proportional Hazards model.
• Discrete time survival models.

Mixed Models

• Introduction to mixed modeling.
• Fitting a mixed model in the LMIXED procedure.

Nonlinear Regression

• Introduction to nonlinear regression models.
• Fitting nonlinear regression models using the NLMOD procedure.

Partial Least Squares

• Introduction to partial least squares.
• Fitting partial least squares models in the PLSMOD procedure.