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…mehr

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Produktbeschreibung

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.

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

Produktdetails
Verlag: Taylor & Francis eBooks
Seitenzahl: 201
Erscheinungstermin: 4. November 2022
Englisch
ISBN-13: 9781000776737
Artikelnr.: 66009228

Produktdetails

Verlag: Taylor & Francis eBooks
Seitenzahl: 201
Erscheinungstermin: 4. November 2022
Englisch
ISBN-13: 9781000776737
Artikelnr.: 66009228
Herstellerkennzeichnung
Die Herstellerinformationen sind derzeit nicht verfügbar.

Autorenporträt

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.

Inhaltsangabe

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
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

Inhaltsangabe

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
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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ANOVA and Mixed Models (eBook, PDF)

A Short Introduction Using R

ANOVA and Mixed Models (eBook, PDF)

A Short Introduction Using R

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