Course description
Includes the following on-demand courses:
1. Fitting Poisson Regression Models Using the GENMOD Procedure
This course is for those who analyze the number of occurrences of an event or the rate of occurrence of an event as a function of some predictor variables. For example, the rate of insurance claims, colony counts for bacteria or viruses, the number of equipment failures, and the incidence of disease can be modeled using Poisson regression models.
This course includes practice data and exercises.
2. Mixed Models Analyses Using SAS
This course teaches you how to analyze linear mixed models using the MIXED procedure. A brief introduction to analyzing generalized linear mixed models using the GLIMMIX procedure is also included.
3. Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS
This course teaches how to identify complex and dynamic patterns within multilevel data to inform a variety of decision-making needs. The course provides a conceptual understanding of multilevel linear models (MLM) and multilevel generalized linear models (MGLM) and their appropriate use in a variety of settings.
The self-study e-learning includes:
- Annotatable course notes in PDF format.
- Virtual lab time to practice.
4. Statistical Analysis with the GLIMMIX Procedure
This course focuses on the GLIMMIX procedure, a procedure for fitting generalized linear mixed models.
5. Advanced Statistical Modeling Using the NLMIXED Procedure
This course teaches you how to use the NLMIXED procedure to fit statistical models.
6. Robust Regression Techniques in SAS/STAT
This course 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. The two procedures addressed in the course are the ROBUSTREG procedure and the QUANTREG procedure.
This course includes practice data.
7. Structural Equation Modeling Using SAS
This course introduces the experienced statistical analyst to structural equation modeling (SEM) in the CALIS procedure in SAS/STAT software. The course also introduces the PATHDIAGRAM statement in the CALIS procedure, which draws path diagrams based on fitted models.
Structural equation modeling is a statistical technique that combines elements of traditional multivariate models, such as regression analysis, factor analysis, and simultaneous equation modeling. These models are often represented as matrices, equations, and/or path diagrams and can explicitly account for uncertainty in observed variables and for estimation bias due to measurement error. Competing models can be compared to one another, providing information about the complex drivers of the outcome variables of interest. Many applications of SEM can be found in the social, economic, and behavioral sciences, where measurement error and uncertain causal conditions are commonly encountered. This course does not address models containing categorical endogenous variables or multilevel SEM, as these methods are not supported in the CALIS procedure.
