Environmental Data Analysis
An Introduction with Examples in R
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Beschreibung
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.
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
Autor*in
Carsten Dormann
Themen in »Environmental Data Analysis«
maximum likelihood Generalized Linear Models ANOVA regression design of experiments hypothesis testing cluster analysis model selection multiple regression data visualisation environmetrics principal component analysis