ANOVA and Mixed Models

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 are available here.

This book is 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.

Author Biography

Lukas Meier is a senior scientist at the Seminar für Statistik at ETH Zürich. His main interests are teaching statistics at various levels, the application of statistics in many fields of applications using advanced ANOVA or regression models, and high-dimensional statistics. He co-leads the statistical consulting service at ETH Zürich and is the director of a continuing education program in applied statistics.