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Join us to learn advanced regression techniques for continuous or count data outcomes, multilevel data patterns, and more. You’ll learn which technique to use when.
Learn how to:
Before taking these courses, it is recommended that you take the SAS Programming 1: Essentials and Statistics 1: Introduction to ANOVA, Regression, and Logistic Regression courses, which are available in instructor-led or free online e-learning formats. The Statistics 2: ANOVA and Regression course or equivalent knowledge is also helpful.
Level 1
COURSE
Fitting Poisson Regression Models Using the GENMOD Procedure |
This course includes practice data and exercises.
COURSE
Mixed Models Analyses Using SAS® |
COURSE
Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS® |
Level 2
COURSE
Statistical Analysis with the GLIMMIX Procedure |
COURSE
Advanced Statistical Modeling Using the NLMIXED Procedure |
COURSE
Robust Regression Techniques in SAS/STAT® |
This course includes practice data.
COURSE
Structural Equation Modeling Using SAS® |
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.
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