Eco-Stats: Data Analysis in Ecology

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος:	Eco-Stats: Data Analysis in Ecology
Περιγραφή:	This book introduces ecologists to the wonderful world of modern tools for data analysis, especially multivariate analysis. For biologists with relatively little prior knowledge of statistics, it introduces a modern, advanced approach to data analysis in an intuitive and accessible way. The book begins by reviewing some core principles in statistics, and relates common methods to the linear model, a general framework for modeling data where the response is continuous. This is then extended to discrete data using generalized linear models, to designs with multiple sampling levels via mixed models, and to situations where there are multiple response variables via model-based approaches to multivariate analysis. Along the way there is an introduction to: important principles in model selection; adaptations of the model to handle non-linearity and cyclical variables; dependence due to structured correlation in time, space or phylogeny; and design-based techniques for inference that can relax some of the modelling assumptions. It concludes with a range of advanced topics in model-based multivariate analysis relevant to the modern ecologist, including fourth corner, latent variable and copula models. Examples span a variety of applications including environmental monitoring, species distribution modeling, global-scale surveys of plant traits, and small field experiments on biological controls. Math Boxes throughout the book explain some of the core ideas mathematically for readers who want to delve deeper, and R code is used throughout. Accompanying code, data, and solutions to exercises can be found in the ecostats R package on CRAN.
Συγγραφείς:	David I Warton
Resource Type:	eBook.
Θέματα:	Biometry, Ecology--Statistical methods--Data processing, Population biology
Categories:	MATHEMATICS / Probability & Statistics / General, COMPUTERS / Data Science / Bioinformatics, MEDICAL / Biostatistics, SCIENCE / Life Sciences / Ecology
Βάση Δεδομένων:	eBook Index

FullText	Text: Availability: 0
Header	DbId: edsebk DbLabel: eBook Index An: 3358199 RelevancyScore: 962 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 962.24267578125
IllustrationInfo
Items	– Name: Title Label: Title Group: Ti Data: Eco-Stats: Data Analysis in Ecology – Name: Abstract Label: Description Group: Ab Data: This book introduces ecologists to the wonderful world of modern tools for data analysis, especially multivariate analysis. For biologists with relatively little prior knowledge of statistics, it introduces a modern, advanced approach to data analysis in an intuitive and accessible way. The book begins by reviewing some core principles in statistics, and relates common methods to the linear model, a general framework for modeling data where the response is continuous. This is then extended to discrete data using generalized linear models, to designs with multiple sampling levels via mixed models, and to situations where there are multiple response variables via model-based approaches to multivariate analysis. Along the way there is an introduction to: important principles in model selection; adaptations of the model to handle non-linearity and cyclical variables; dependence due to structured correlation in time, space or phylogeny; and design-based techniques for inference that can relax some of the modelling assumptions. It concludes with a range of advanced topics in model-based multivariate analysis relevant to the modern ecologist, including fourth corner, latent variable and copula models. Examples span a variety of applications including environmental monitoring, species distribution modeling, global-scale surveys of plant traits, and small field experiments on biological controls. Math Boxes throughout the book explain some of the core ideas mathematically for readers who want to delve deeper, and R code is used throughout. Accompanying code, data, and solutions to exercises can be found in the ecostats R package on CRAN. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22David+I+Warton%22">David I Warton</searchLink> – Name: TypePub Label: Resource Type Group: TypPub Data: eBook. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Biometry%22">Biometry</searchLink><br /><searchLink fieldCode="DE" term="%22Ecology--Statistical+methods--Data+processing%22">Ecology--Statistical methods--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Population+biology%22">Population biology</searchLink> – Name: SubjectBISAC Label: Categories Group: Su Data: <searchLink fieldCode="ZK" term="%22MATHEMATICS+%2F+Probability+%26+Statistics+%2F+General%22">MATHEMATICS / Probability & Statistics / General</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+Bioinformatics%22">COMPUTERS / Data Science / Bioinformatics</searchLink><br /><searchLink fieldCode="ZK" term="%22MEDICAL+%2F+Biostatistics%22">MEDICAL / Biostatistics</searchLink><br /><searchLink fieldCode="ZK" term="%22SCIENCE+%2F+Life+Sciences+%2F+Ecology%22">SCIENCE / Life Sciences / Ecology</searchLink>
PLink	https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=3358199
RecordInfo	BibRecord: BibEntity: Classifications: – Code: 577.015195 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Biometry Type: general – SubjectFull: Ecology--Statistical methods--Data processing Type: general – SubjectFull: Population biology Type: general Titles: – TitleFull: Eco-Stats: Data Analysis in Ecology Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: David I Warton – PersonEntity: Name: NameFull: David I Warton IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2022 – D: 11 M: 08 Type: profile Y: 2022 Identifiers: – Type: isbn-print Value: 9783030884420 – Type: isbn-electronic Value: 9783030884437 Titles: – TitleFull: Eco-Stats: Data Analysis in Ecology Type: main
ResultId	1