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100   $a20201220d2020      u|         0
101 0 $aeng
135   $aurnn#008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aEnvironmental Data Analysis $eAn Introduction with Examples in R /$fby Carsten Dormann
205   $a1st ed. 2020.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020.
215   $a1 online resource (XIX, 264 p. 136 illus., 27 illus. in color.)
311 08$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   $aBiometry
606   $aEcology
606   $aStatistics
606   $aBioinformatics
606   $aForests and forestry
606   $aBiostatistics
606   $aEcology
606   $aStatistical Theory and Methods
606   $aBioinformatics
606   $aForestry
615  0$aBiometry.
615  0$aEcology.
615  0$aStatistics.
615  0$aBioinformatics.
615  0$aForests and forestry.
615 14$aBiostatistics.
615 24$aEcology.
615 24$aStatistical Theory and Methods.
615 24$aBioinformatics.
615 24$aForestry.
676   $a363.70072
700   $aDormann$b Carsten$0996171
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9910483973303321
996   $aEnvironmental data analysis$92282990
997   $aUNINA