Lukas Meier – författare

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.FeaturesAccessible to readers with a basic background in probability and statisticsCovers fundamental concepts of experimental design and cause-effect relationshipsIntroduces classical ANOVA models, including contrasts and multiple testingProvides an example-based introduction to mixed modelsFeatures basic concepts of split-plot and incomplete block designsR code available for all stepsSupplementary 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.

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.FeaturesAccessible to readers with a basic background in probability and statisticsCovers fundamental concepts of experimental design and cause-effect relationshipsIntroduces classical ANOVA models, including contrasts and multiple testingProvides an example-based introduction to mixed modelsFeatures basic concepts of split-plot and incomplete block designsR code available for all stepsSupplementary 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.

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.

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.

Dieses Open-Access-Buch gibt eine anwendungsorientierte Einführung in die logistische Regression. Ausgehend von Grundkenntnissen der linearen Regression wird diese zuerst als zweistufiges Modell interpretiert, was den Übergang zur logistischen Regression vereinfacht. Neben einer kompakten Einführung der entsprechenden Theorie liegt der Fokus auch auf der Umsetzung mit der Statistiksoftware R und der richtigen Formulierung der entsprechenden Ergebnisse. Alle Schritte werden anhand zahlreicher Beispiele illustriert. Hinzu kommt eine Einführung in die Klassifikation mit den entsprechenden Begriffen.

Dieses Lehrbuch führt in die zentralen Begriffe und Konzepte der Wahrscheinlichkeitsrechnung, der beschreibenden sowie der schließenden Statistik ein. Der Fokus liegt dabei auf einem intuitiven Verständnis der grundlegenden Konzepte, Zusammenhänge und Methoden. Trotzdem wird der Stoff mathematisch präzise präsentiert.Innerhalb jedes Kapitels finden Sie verschiedene didaktische Elemente, die Ihnen den Zugang und das Verständnis, aber auch die Orientierung deutlich erleichtern:Kurzübersicht der wichtigsten Begriffe am KapitelanfangAnschauliche Erklärungen und BeispieleZentrale Sachverhalte und DefinitionenWarnungen vor häufigen MissverständnissenSeparate Textabschnitte zur IntuitionGrafische oder tabellarische Übersichten am KapitelendeAm Ende der meisten Abschnitte finden Sie kurze Lernkontrollen – hier können Sie Ihr Wissen überprüfen, indem Sie verschiedene Aussagen alswahr oder falsch einordnen. Die Lösungen finden Sie im Anhang, je nach Schwierigkeitsgrad mit kurzem Lösungsweg. Am Ende der Kapitel finden Sie außerdem Verständnisfragen, die noch einmal die wichtigsten Inhalte abfragen und so Ihren Lernfortschritt zeigen.