This course teaches you how to analyze continuous response data and discrete count data. Linear regression, Poisson regression, negative binomial regression, gamma regression, analysis of variance, linear regression with indicator variables, analysis of covariance, and mixed models ANOVA are presented in the course.
Learn how to
Use the ODS Graphics facility and the new SG graphical procedures in SAS to:
- Fit polynomial regression models using the GLMSELECT and REG procedures.
- Select models based on several statistics and automatic model selection methods using PROC GLMSELECT.
- Evaluate model fit and model assumptions using the GLMSELECT, REG, GLM, GENMOD, and UNIVARIATE procedures.
- Fit Poisson and negative binomial models using the GENMOD procedure, and fit gamma regression models using the GLIMMIX procedure.
- Perform analysis of variance using the GLM procedure.
- Write LSMESTIMATE statements in PROC GLM.
- Fit ANCOVA models using PROC GLM.
- Fit models with random effects using PROC GLIMMIX.
- Create a variety of statistical graphs.
Who should attend
Data analysts and researchers with some statistical training
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. This course is compatible within SAS Studio.
Multiple Linear Regression
Regression Diagnostics and Remedial Measures
- Review of general linear models.
- Simple polynomial regression.
- Polynomial regression and multicollinearity.
- Modeling nonlinear relationships.
Analysis of Variance
- Regression model diagnostics.
- Remedial measures.
Analysis of Covariance
- ANOVA review.
- Postfitting analyses.
- Evaluations of model assumptions and remedial measures.
Introduction to Generalized Linear Models
- Introduction to analysis of covariance (ANCOVA).
- Least squares means for ANCOVA models.
- Diagnostics and remedial measures for ANCOVA models.
Introduction to Linear Mixed Models
- Introduction to generalized linear models.
- Poisson regression and negative binomial regression.
- Introduction to gamma regression.
- Basics of general linear models.
- Fitting linear mixed models.