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Environmental data analysis: an introduction with examples in R

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 likel...

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Detalles Bibliográficos
Autor principal: Dormann, Carsten
Lenguaje:eng
Publicado: Springer 2020
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-030-55020-2
http://cds.cern.ch/record/2749365
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author Dormann, Carsten
author_facet Dormann, Carsten
author_sort Dormann, Carsten
collection CERN
description 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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spelling cern-27493652021-04-21T16:44:01Zdoi:10.1007/978-3-030-55020-2http://cds.cern.ch/record/2749365engDormann, CarstenEnvironmental data analysis: an introduction with examples in RMathematical Physics and MathematicsEnvironmental 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. .Springeroai:cds.cern.ch:27493652020
spellingShingle Mathematical Physics and Mathematics
Dormann, Carsten
Environmental data analysis: an introduction with examples in R
title Environmental data analysis: an introduction with examples in R
title_full Environmental data analysis: an introduction with examples in R
title_fullStr Environmental data analysis: an introduction with examples in R
title_full_unstemmed Environmental data analysis: an introduction with examples in R
title_short Environmental data analysis: an introduction with examples in R
title_sort environmental data analysis: an introduction with examples in r
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-030-55020-2
http://cds.cern.ch/record/2749365
work_keys_str_mv AT dormanncarsten environmentaldataanalysisanintroductionwithexamplesinr