Cieľová skupina
Táto časť nie je lokalizovaná
Statisticians, experienced data analysts, and researchers with sound statistical knowledgeŠtruktúra kurzu
Táto časť nie je lokalizovaná
Introduction to Mixed Models
- identifying fixed and random effects
- describing linear mixed model equations and assumptions
- fitting a linear mixed model for a randomized complete block design using the MIXED procedure
- writing CONTRAST and ESTIMATE statements to perform custom hypothesis tests
Examples of Mixed Models in Some Designed Experiments
- fitting a linear mixed model for two-way mixed models
- fitting a linear mixed model for nested mixed models
- fitting a linear mixed model for split-plot designs
- fitting a linear mixed model for crossover designs
Examples of Mixed Models with Covariates
- fitting analysis of covariance models with random effects
- performing random coefficient regression analysis
- conducting hierarchical linear modeling
Best Linear Unbiased Prediction
- explaining BLUPs and EBLUPs
- producing parameter estimates associated with the fixed effects and random effects
- explaining the difference between LSMEANS and EBLUPs
- computing LSMEANS and EBLUPs using the MIXED procedure
Repeated Measures Analysis
- discussing issues on repeated measures analysis, including modeling covariance structure
- analyzing repeated measures data using the four-step process with the MIXED procedure
Mixed Models Residual Diagnostics and Troubleshooting
- performing residual and influence diagnostics for linear mixed models
- troubleshooting convergence problems
Additional Information about Linear Mixed Models (Self-Study)
- discussing issues associated with unbalanced data, data with empty cells, estimation and inference of variance parameters, and different denominator degrees of freedom estimation methods
Introduction to Generalized Linear Mixed Models and Nonlinear Mixed Models
- discussing the situations where generalized linear mixed models and nonlinear mixed models analysis are needed
- performing the analysis for generalized linear mixed models using the GLIMMIX procedure