Overview

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.

Full Product Details

Author:   Lukas Meier
Publisher:   Taylor & Francis Ltd
Imprint:   Chapman & Hall/CRC
Weight:   0.371kg
ISBN:  

9780367704209

ISBN 10:   036770420
Pages:   187
Publication Date:   04 November 2022
Audience:   General/trade ,  General
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

"1. Learning from Data. 1.1. Cause-Effect Relationships. 1.2. Experimental Studies. 2. Completely Randomized Designs. 2.1. One-Way Analysis of Variance. 2.2. Checking Model Assumptions. 2.3. Nonparametric Approaches. 2.4. Power or ""What Sample Size Do I Need?"". 2.5. Adjusting for Covariates. 2.6. Appendix. 3. Contrasts and Multiple Testing. 3.1. Contrasts. 3.2. Multiple Testing. 4. Factorial Treatment Structure. 4.1. Introduction. 4.2. Two-Way ANOVA Model. 5. Complete Block Designs. 5.1. Introduction. 5.2. Randomized Complete Block Designs (RCBD). 5.3. Nonparametric Alternatives. 5.4. Outlook: Multiple Block Factors. 6. Random and Mixed Effects Models. 6.1. Random Effects Models. 7. Split-Plot Designs. 7.1. Introduction. 7.2. Properties of Split-Plot Designs. 7.3. A More Complex Example in Detail: Oat Varieties. 8. Incomplete Block Designs. 8.1. Introduction. 8.2. Balanced Incomplete Block Designs (BIBD). 8.3. Analysis of Incomplete Block Designs. 8.4. Outlook. 8.5. Concluding Remarks. Bibliography. Index"

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Author Information

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.

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