Environmental Data Analysis

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

Autorentext

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.

Inhalt
Preface.- The technical side: selecting a statistical software.- 1 Sample statistics.- 2 Sample statistics in R.- 3 Distributions, parameters and estimators.- 4 Distributions, parameters and estimators in R.- 5 Correlation and association.- 6 Correlation and association in R.- 7 Regression - Part I.- 8 Regression in R - Part I.- 9 Regression - Part II.- 10 Regression in R - Part II.- 11 The linear model: t-test and ANOVA.- 12 The linear model: t-test and ANOVA in R.- 13 Hypotheses and tests.- 14 Experimental Design.- 15 Multiple Regression.- 16 Multiple Regression in R.- 17 Outlook.- Index.