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024 7 $a10.1007/978-3-030-55020-2
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035   $a(DE-He213)978-3-030-55020-2
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100   $a20210618d2020      uy             0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aEnvironmental data analysis $ean introduction with examples in R /$fCarsten Dormann
205   $a1st ed. 2020.
210  1$aCham, Switzerland :$cSpringer,$d[2020]
210  4$d©2020
215   $a1 online resource (XIX, 264 p. 136 illus., 27 illus. in color.)
311   $a3-030-55019-2 
327   $aPreface -- 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.
330   $aEnvironmental 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. .
606   $aEnvironmental sciences$xStatistical methods
606   $aR (Computer program language)
615  0$aEnvironmental sciences$xStatistical methods.
615  0$aR (Computer program language).
676   $a363.70072
700   $aDormann$b Carsten$0996171
801  0$bMiAaPQ
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801  2$bUtOrBLW
906   $aBOOK
912   $a996418194803316
996   $aEnvironmental data analysis$92282990
997   $aUNISA