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

Prerequisites

Before attending this course, you should:;

Have experience executing SAS programs and creating SAS data sets, which you can gain from the SAS(R) Programming I: 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 I: Introduction to ANOVA, Regression, and Logistic Regression course. SAS Products Covered

SAS Viya

Course Outline

Introduction to SAS Viya

SAS Analytics Platform. Loading files into SAS Viya.Linear RegressionFitting models in the REGSELECT procedure. Model assessment.Quantile RegressionWhat is quantile regression?Logistic RegressionLogistic regression models. Model assessment for logistic regression.Generalized Linear ModelsGeneralized linear models. Poisson regression. Tweedie regression.Generalized Additive ModelsIntroduction to generalized additive models. Using the GAMSELECT procedure to fit generalized additive models.Survival AnalysisIntroduction to survival analysis. Cox Proportional Hazards model. Discrete time survival models.Mixed ModelsIntroduction to mixed modeling. Fitting a mixed model in the LMIXED procedure.Nonlinear RegressionIntroduction to nonlinear regression models. Fitting nonlinear regression models using the NLMOD procedure.Partial Least SquaresIntroduction to partial least squares. Fitting partial least squares models in the PLSMOD procedure.