SAS for Mixed Models: Introduction and Basic Applications
- 13h 59m
- Elizabeth A. Claassen, George A. Milliken, Russell D. Wolfinger, Walter W. Stroup
- SAS Institute
- 2017
Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS for Linear Models, Fourth Edition, and SAS for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS.
This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics:
- Random-effect-only and random-coefficients models
- Multilevel, split-plot, multilocation, and repeated measures models
- Hierarchical models with nested random effects
- Analysis of covariance models
- Generalized linear mixed models
In this Book
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About This Book
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Mixed Model Basics
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Design Structure I—Single Random Effect
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Mean Comparisons for Fixed Effects
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Power, Precision, and Sample Size I—Basic Concepts
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Design Structure II—Models with Multiple Random Effects
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Random Effects Models
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Analysis of Covariance
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Analysis of Repeated Measures Data
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Best Linear Unbiased Prediction (BLUP) and Inference on Random Effects
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Random Coefficient Models
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Generalized Linear Mixed Models for Binomial Data
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Generalized Linear Mixed Models for Count Data
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Generalized Linear Mixed Models for Multilevel and Repeated Measures Experiments
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Power, Precision, and Sample Size II—General Approaches
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Mixed Model Troubleshooting and Diagnostics
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References
