ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and researcher a compact introduction to the analysis of data from the most popular experimental designs. Based on knowledge from an introductory course on probability and statistics, the theoretical foundations of the most important models are introduced. The focus is on an intuitive understanding of the theory, common pitfalls in practice, and the application of the methods in R. From data visualization and model fitting, up to the interpretation of the corresponding output, the whole workflow is presented using R. The book does not only cover standard ANOVA models, but also models for more advanced designs and mixed models, which are common in many practical applications.
Features
Accessible to readers with a basic background in probability and statistics
Covers fundamental concepts of experimental design and cause-effect relationships
Introduces classical ANOVA models, including contrasts and multiple testing
Provides an example-based introduction to mixed models
Features basic concepts of split-plot and incomplete block designs
R code available for all steps
Supplementary website with additional resources and updates available at https://stat.ethz.ch/~meier/teaching/book-anova/
This bookis primarily aimed at students, researchers, and practitioners from all areas who wish to analyze corresponding data with R. Readers will learn a broad array of models hand-in-hand with R, including the applications of some of the most important add-on packages.
ANOVA and Mixed Models: A Short Introduction Using R provides both the practitioner and researcher a compact introduction to the analysis of data from the most popular experimental designs. Based on knowledge from an introductory course on probability and statistics, the theoretical foundations of the most important models are introduced. The focus is on an intuitive understanding of the theory, common pitfalls in practice, and the application of the methods in R. From data visualization and model fitting, up to the interpretation of the corresponding output, the whole workflow is presented using R. The book does not only cover standard ANOVA models, but also models for more advanced designs and mixed models, which are common in many practical applications.
Features
Accessible to readers with a basic background in probability and statistics
Covers fundamental concepts of experimental design and cause-effect relationships
Introduces classical ANOVA models, including contrasts and multiple testing
Provides an example-based introduction to mixed models
Features basic concepts of split-plot and incomplete block designs
R code available for all steps
Supplementary website with additional resources and updates available at https://stat.ethz.ch/~meier/teaching/book-anova/
This bookis primarily aimed at students, researchers, and practitioners from all areas who wish to analyze corresponding data with R. Readers will learn a broad array of models hand-in-hand with R, including the applications of some of the most important add-on packages.
Renowned statistician R.G. Miller set the pace for statistics students with Beyond ANOVA: Basics of Applied Statistics. Designed to show students how to work with a set of "real world data,"...
The information contained in this book has served as the basis for a graduate-level biostatistics class at the University of North Carolina at Chapel Hill. The book focuses in the General Linear...
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