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Modeling Techniques
COURSE
Multivariate Statistics for Understanding Complex Data |
COURSE
Mixed Models Analyses Using SAS® |
COURSE
Multilevel Modeling of Hierarchical and Longitudinal Data Using SAS® |
COURSE
Longitudinal Data Analysis Using Discrete and Continuous Responses |
The self-study e-learning includes:
COURSE
Statistical Analysis with the GLIMMIX Procedure |
COURSE
Probability Surveys 1: Design, Descriptive Statistics, and Analysis |
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.
Integration with Open Source
COURSE
Using Python and R with SAS® Viya® for Advanced Analytics |
COURSE
SAS® Programming for R Users |
Principles and Best Practices
COURSE
Responsible Innovation and Trustworthy AI |
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This learning subscription is not currently associated with a SAS credential.
“I think the self-paced training was the BEST I’ve ever taken. The videos were short and segmented correctly to keep my attention and the activities and quizzes were just enough to help my confidence.”