Applied statistics for environmental science with R

Applied statistics for environmental science with R

by Alkarkhi, Abbas F. M.

Type Book
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Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing s…

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Title Statements

  • Full title :Applied statistics for environmental science with R / Abbas F.M. Alkarkhi, Wasin A.A. Alqaraghuli.

Summary

Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day-to-day research problems.

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Edition Statements

  • Publication Date:©2020

Publication Statements

  • Publisher(s):Elsevier
  • Place of Publication:Amsterdam, Netherlands
  • Language:eng
  • Publication Date:©2020

The Physical Object

  • Description:1 online resource (ix, 229 pages) : : illustrations (black and white)

Edition Identifiers

  • ISBN:9780128186220, 0128186232, 9780128186237, 0128186224
  • oclc:on1120698268
  • LC Classification:GE45.S73
  • Dewey Decimal:550.2855133

Table of Contents

Multivariate data -- R statistical software -- Statistical notions -- Measures of center and variation -- Statistical hypothesis testing -- Multivariate analysis of variance -- Regression analysis -- Principal components -- Factor analysis -- Discriminant analysis -- Clustering approaches.

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