 7491 Reviews star_rate star_rate star_rate star_rate star_half

# Mixed Models Analyses Using SAS(R)

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....

\$3,000 USD GSA  \$2,561.53
Course Code AGLM42
Available Formats Classroom

### Reviews

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.

## Skills Gained

• analyze data (including binary data) with random effects
• fit random coefficient models and hierarchical linear models
• analyze repeated measures data
• obtain and interpret the best linear unbiased predictions
• perform residual and influence diagnostic analysis

## Who Can Benefit

• Statisticians, experienced data analysts, and researchers with sound statistical knowledge

## Prerequisites

• Before attending this course, you should
• know how to create and manage SAS data sets
• have experience performing analysis of variance using the GLM procedure of SAS/STAT software
• have completed and mastered the Statistics 2: ANOVA and Regression course or completed a graduate-level course on general linear models
• have an understanding of generalized linear models and their analysis.

### Course Details

#### 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

• 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

Close

Close

Close
Close
Close

### SummerSavings

Save up to \$250-\$2500 Use Promo Code: SurfBoard

View Details Register by September 6, 2019

Close

#### Confirm

Close 