Before attending this course, you should

• have some experience creating and managing SAS data sets, which you can gain from the SAS Programming 1: Essentials course
• be able to fit simple and multiple linear regression models using the REG procedure
• be able to analyze a one-way analysis of variance using the GLM procedure
• understand the statistical concepts of normal distribution, sampling distributions, hypothesis testing, and estimation
• have completed a graduate-level course in regression and analysis of variance methods or the Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression course.

Students should have completed the SAS Programming 1: Essentials and Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression courses, or have equivalent experience.

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

You benefit from this course even if SAS/GRAPH software is not installed at your location.

Who should attend
Data analysts and researchers with some statistical training

use the ODS Graphics facility and the new SG graphical procedures in SAS 9.3 to:

• fit polynomial regression models using the REG procedure
• select models based on several statistics and automatic model selection methods using PROC REG
• evaluate model fit and model assumptions using the REG, GLM, GENMOD, and UNIVARIATE procedures
• fit Poisson, negative binomial, and gamma regression models using the GENMOD procedure
• perform analysis of variance using the GLM procedure
• write CONTRAST and ESTIMATE statements in PROC GLM
• fit regression models with dummy variables using PROC REG and ANCOVA models using PROC GLM
• fit models with random effects using the MIXED procedure
• create a variety of statistical graphs.

Regression

• building and evaluating multiple polynomial regression models
• dealing with violations of model assumptions

Analysis of Variance

• performing n-way ANOVA
• interpreting significant interactions
• writing CONTRAST and ESTIMATE statements
• understanding issues associated with unbalanced data

Regression Using Indicator Variables and Analysis of Covariance

• using and interpreting indicator variables in the REG procedure
• building and interpreting analysis of covariance models using the GLM procedure
• comparing regression using indicator variables with analysis of covariance
• Generalized Linear Models
• using the GENMOD procedure to fit Poisson, negative binomial, and gamma regression models

Linear Mixed Models

• performing linear mixed model analysis