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Introduction to R for terrestrial ec...

Lakicevic, Milena.

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Introduction to R for terrestrial ecology = basics of numerical analysis, mapping, statistical tests and advanced application of R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to R for terrestrial ecology/ by Milena Lakicevic, Nicholas Povak, Keith M. Reynolds.
Reminder of title:	basics of numerical analysis, mapping, statistical tests and advanced application of R /
Author:	Lakicevic, Milena.
other author:	Povak, Nicholas.
Published:	Cham :Springer International Publishing : : 2020.,
Description:	xvii, 158 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	1. Types of Data in R -- 2. Numerical Analysis -- 3. Creating Maps -- 4. Basic Statistical Tests -- 5. Predictive Modeling with Machine Learning Applications -- Appendix.
Contained By:	Springer eBooks
Subject:	R (Computer program language) -
Online resource:	https://doi.org/10.1007/978-3-030-27603-4
ISBN:	9783030276034

Introduction to R for terrestrial ecology = basics of numerical analysis, mapping, statistical tests and advanced application of R /
Lakicevic, Milena.

Introduction to R for terrestrial ecologybasics of numerical analysis, mapping, statistical tests and advanced application of R /[electronic resource] :by Milena Lakicevic, Nicholas Povak, Keith M. Reynolds. - Cham :Springer International Publishing :2020. - xvii, 158 p. :ill. (some col.), digital ;24 cm.

1. Types of Data in R -- 2. Numerical Analysis -- 3. Creating Maps -- 4. Basic Statistical Tests -- 5. Predictive Modeling with Machine Learning Applications -- Appendix.

This textbook covers R data analysis related to environmental science, starting with basic examples and proceeding up to advanced applications of the R programming language. The main objective of the textbook is to serve as a guide for undergraduate students, who have no previous experience with R, but part of the textbook is dedicated to advanced R applications, and will also be useful for Masters and PhD students, and professionals. The textbook deals with solving specific programming tasks in R, and tasks are organized in terms of gradually increasing R proficiency, with examples getting more challenging as the chapters progress. The main competencies students will acquire from this textbook are: manipulating and processing data tables performing statistical tests creating maps in R This textbook will be useful in undergraduate and graduate courses in Advanced Landscape Ecology, Analysis of Ecological and Environmental Data, Ecological Modeling, Analytical Methods for Ecologists, Statistical Inference for Applied Research, Elements of Statistical Methods, Computational Ecology, Landscape Metrics and Spatial Statistics.

ISBN: 9783030276034

Standard No.: 10.1007/978-3-030-27603-4doiSubjects--Topical Terms:

784593
R (Computer program language)

LC Class. No.: QA276.45.R3 / L35 2020

Dewey Class. No.: 519.50285536

Introduction to R for terrestrial ecology = basics of numerical analysis, mapping, statistical tests and advanced application of R /
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300 $a xvii, 158 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a 1. Types of Data in R -- 2. Numerical Analysis -- 3. Creating Maps -- 4. Basic Statistical Tests -- 5. Predictive Modeling with Machine Learning Applications -- Appendix.
520 $a This textbook covers R data analysis related to environmental science, starting with basic examples and proceeding up to advanced applications of the R programming language. The main objective of the textbook is to serve as a guide for undergraduate students, who have no previous experience with R, but part of the textbook is dedicated to advanced R applications, and will also be useful for Masters and PhD students, and professionals. The textbook deals with solving specific programming tasks in R, and tasks are organized in terms of gradually increasing R proficiency, with examples getting more challenging as the chapters progress. The main competencies students will acquire from this textbook are: manipulating and processing data tables performing statistical tests creating maps in R This textbook will be useful in undergraduate and graduate courses in Advanced Landscape Ecology, Analysis of Ecological and Environmental Data, Ecological Modeling, Analytical Methods for Ecologists, Statistical Inference for Applied Research, Elements of Statistical Methods, Computational Ecology, Landscape Metrics and Spatial Statistics.
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650 0 $a Ecology $x Mathematical models. $3 545792
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