Environmental data analysis : an introduction with examples in R / Carsten Dormann.
Dormann, Carsten.
2020
GE45.S73
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Title Environmental data analysis : an introduction with examples in R / Carsten Dormann.
Author Dormann, Carsten.
ISBN 9783030550202 (electronic bk.)
3030550206 (electronic bk.)
9783030550202
DOI https://doi.org/10.1007/978-3-030-55020-2
Published Cham : Springer, 2020.
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-3-030-55020-2 doi
Call Number GE45.S73
Dewey Decimal Classification 333.7072/7
519.5
Summary 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
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Available in Other Form Environmental Data Analysis.
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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.

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