Overview
- Offers an introduction to statistical methods relevant for environmental science
- Is centred on the GLM as a general and flexible statistical approach
- Includes a back-to-back structure, with practical R code chapters complementing each example-driven, theoretical chapter
- Provides an essential foundation for further reading on Bayesian and machine-learning approaches
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Table of contents (17 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Environmental Data Analysis is an introductory statistics textbook for environmental science. It covers descriptive, inferential and predictive statistics, centred on the Generalized Linear Model. The key idea behind this book is to approach statistical analyses from the perspective of maximum likelihood, essentially treating most analyses as (multiple) regression problems. The reader will be introduced to statistical distributions early on, and will learn to deploy models suitable for the data at hand, which in environmental science are often not normally distributed. To make the initially steep learning curve more manageable, each statistical chapter is followed by a walk-through in a corresponding R-based how-to chapter, which reviews the theory and applies it to environmental data. In this way, a coherent and expandable foundation in parametric statistics is laid, which can be expanded in advanced courses.The content has been “field-tested” in several years of courses on statistics for Environmental Science, Geography and Forestry taught at the University of Freiburg.
Authors and Affiliations
About the author
Carsten Dormann is a Professor of Biometry and Environmental System Analysis at the Faculty of Environment and Natural Resources, University of Freiburg, Germany. After completing his PhD in Plant Ecology at the University of Aberdeen, UK, he went on to become a statistical ecologist, with a research remit spanning from conservation ecology to the development of statistical methods, and from field experiments to population modelling. He currently teaches statistics at the BSc and MSc levels, from introductory classes to Bayesian statistics and machine learning.
Bibliographic Information
Book Title: Environmental Data Analysis
Book Subtitle: An Introduction with Examples in R
Authors: Carsten Dormann
DOI: https://doi.org/10.1007/978-3-030-55020-2
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-55019-6Published: 21 December 2020
Softcover ISBN: 978-3-030-55022-6Published: 21 December 2021
eBook ISBN: 978-3-030-55020-2Published: 20 December 2020
Edition Number: 1
Number of Pages: XIX, 264
Number of Illustrations: 109 b/w illustrations, 27 illustrations in colour
Topics: Statistics for Life Sciences, Medicine, Health Sciences, Ecology, Statistical Theory and Methods, Bioinformatics, Forestry