Experimental Design and Data Analysis for Biologists

by Gerry P. Quinn, Michael J. Keough

Applying statistical concepts to biological scenarios, this established textbook continues to be the go-to tool for advanced undergraduates and postgraduates studying biostatistics or experimental design in biology-related areas. Chapters cover linear models, common regression and ANOVA methods, mixed effects models, model selection, and multivariate methods used by biologists, requiring only introductory statistics and basic mathematics. Demystifying statistical concepts with clear, jargon-free explanations, this new edition takes a holistic approach to help students understand the relationship between statistics and experimental design. Each chapter contains further-reading recommendations, and worked examples from today's biological literature. All examples reflect modern settings, methodology and equipment, representing a wide range of biological research areas. These are supported by hands-on online resources including real-world data sets, full R code to help repeat analyses for all worked examples, and additional review questions and exercises for each chapter.

Genres: Research, Science, Textbooks, Academic

412 pages, Paperback

First published January 28, 2002

Reviews

[Sara Grove]

About as good as it sounds. All you ever wanted to know about regression and variance!

[Jaime · Rating 5 out of 5]

Uno de los manuales sobre diseño de experimentos/muestreos y análisis estadísticos de datos más completos y accesibles que existen, y eso que se trata de un manual escrito hace más de 20 años.

Lo interesante es que sirve tanto para la biología de laboratorio como para la toma de datos y análisis ecológicos. Además, los autores han desarrollado una Web en la que actualizan el manual, lo completan con fe de erratas, aportan ejemplos y ejercicios prácticos apoyándose en GitHub, etc.

Dejo el enlace a la Web del manual: https://qkstats.com/

[kate]

very useful

[Jennifer · Rating 2 out of 5]

Spotty coverage. Doesn't explain interactions, model selection, or tricky back-transformations, but covers advanced topics like MANOVA and ordination. Introduces a wide range of techniques, but I'm guessing there are better books that specialize in each of the techniques. On the plus side, all of the examples use ecological datasets.