# Applied Statistics for Environmental Science with R

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. Includes step-by-step tutorials to aid in understanding the process and implementation of unique data Presents statistical theory in a simple way without complex mathematical proofs Shows how to analyze data using R software and provides R scripts for all examples and figures

Produk Detail:

• Author : Abbas F. M. Al-Karkhi
• Publisher : Elsevier
• Pages : 240 pages
• ISBN : 0128186232
• Rating : 4/5 from 21 reviews

## Applied Statistics for Environmental Science with R

• Author : Abbas F. M. Al-Karkhi,Wasin A. A. Alqaraghuli
• Publisher : Elsevier
• Release : 13 September 2019

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

## Statistical Data Analysis Explained

• Author : Clemens Reimann,Peter Filzmoser,Robert Garrett,Rudolf Dutter
• Publisher : John Wiley & Sons
• Release : 31 August 2011

Few books on statistical data analysis in the natural sciences are written at a level that a non-statistician will easily understand. This is a book written in colloquial language, avoiding mathematical formulae as much as possible, trying to explain statistical methods using examples and graphics instead. To use the book efficiently, readers should have some computer experience. The book starts with the simplest of statistical concepts and carries readers forward to a deeper and more extensive understanding of the use

## Foundational and Applied Statistics for Biologists Using R

• Author : Ken A. Aho
• Publisher : CRC Press
• Release : 09 March 2016

Full of biological applications, exercises, and interactive graphical examples, Foundational and Applied Statistics for Biologists Using R presents comprehensive coverage of both modern analytical methods and statistical foundations. The author harnesses the inherent properties of the R environment to enable students to examine the code of complica

## Applied Statistics in Agricultural, Biological, and Environmental Sciences

• Author : Barry Glaz,Kathleen M. Yeater
• Publisher : John Wiley & Sons
• Release : 22 January 2020

Better experimental design and statistical analysis make for more robust science. A thorough understanding of modern statistical methods can mean the difference between discovering and missing crucial results and conclusions in your research, and can shape the course of your entire research career. With Applied Statistics, Barry Glaz and Kathleen M. Yeater have worked with a team of expert authors to create a comprehensive text for graduate students and practicing scientists in the agricultural, biological, and environmental sciences. The contributors

## Statistics and Data with R

• Author : Yosef Cohen,Jeremiah Y. Cohen
• Publisher : John Wiley & Sons
• Release : 20 November 2008

R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels – from simple to advanced – and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up

## Environmental and Ecological Statistics with R

• Author : Song S. Qian
• Publisher : CRC Press
• Release : 03 November 2016

Emphasizing the inductive nature of statistical thinking, Environmental and Ecological Statistics with R, Second Edition, connects applied statistics to the environmental and ecological fields. Using examples from published works in the ecological and environmental literature, the book explains the approach to solving a statistical problem, covering model specification, parameter estimation, and model evaluation. It includes many examples to illustrate the statistical methods and presents R code for their implementation. The emphasis is on model interpretation and assessment, and using several

## Applied Statistical Genetics with R

• Author : Andrea S. Foulkes
• Publisher : Springer Science & Business Media
• Release : 28 April 2009

Statistical genetics has become a core course in many graduate programs in public health and medicine. This book presents fundamental concepts and principles in this emerging field at a level that is accessible to students and researchers with a first course in biostatistics. Extensive examples are provided using publicly available data and the open source, statistical computing environment, R.

## Learn R for Applied Statistics

• Author : Eric Goh Ming Hui
• Publisher : Apress
• Release : 30 November 2018

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will work through data visualizations such as histograms and boxplot charting, descriptive statistics, and inferential statistics such as t-test, chi-square test, ANOVA, non-parametric test, and linear regressions. Learn R for Applied Statistics

## Applied Statistics

• Author : Dieter Rasch,Rob Verdooren,Jürgen Pilz
• Publisher : John Wiley & Sons
• Release : 14 August 2019

Instructs readers on how to use methods of statistics and experimental design with R software Applied statistics covers both the theory and the application of modern statistical and mathematical modelling techniques to applied problems in industry, public services, commerce, and research. It proceeds from a strong theoretical background, but it is practically oriented to develop one's ability to tackle new and non-standard problems confidently. Taking a practical approach to applied statistics, this user-friendly guide teaches readers how to use methods

## Modern Applied Statistics with S-PLUS

• Author : William N. Venables,Brian D. Ripley
• Publisher : Springer Science & Business Media
• Release : 11 November 2013

A guide to using the power of S-PLUS to perform statistical analyses, providing both an introduction to the program and a course in modern statistical methods. Readers are assumed to have a basic grounding in statistics, thus the book is intended for would-be users, as well as students and researchers using statistics. Throughout, the emphasis is on presenting practical problems and full analyses of real data sets, with many of the methods discussed being modern approaches to topics such as

## EnvStats

• Author : Steven P. Millard
• Publisher : Springer Science & Business Media
• Release : 16 October 2013

This book describes EnvStats, a new comprehensive R package for environmental statistics and the successor to the S-PLUS module EnvironmentalStats for S-PLUS (first released in 1997). EnvStats and R provide an open-source set of powerful functions for performing graphical and statistical analyses of environmental data, bringing major environmental statistical methods found in the literature and regulatory guidance documents into one statistical package, along with an extensive hypertext help system that explains what these methods do, how to use these methods, and

## Applied Spatial Data Analysis with R

• Author : Roger S. Bivand,Edzer Pebesma,Virgilio Gómez-Rubio
• Publisher : Springer Science & Business Media
• Release : 21 June 2013

Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more

## An R Companion for Applied Statistics I

• Author : Danney Rasco
• Publisher : SAGE Publications
• Release : 28 January 2020

An R Companion for Applied Statistics I: Basic Bivariate Techniques breaks the language of the R software down into manageable chunks in order to help students learn how to use it. R is a powerful, flexible, and free tool. However, the flexibility—which eventually becomes a great asset—can make the initial learning curve appear steep. This book introduces a few key aspects of the R tool. As readers become comfortable with these aspects, they develop a foundation from which

## An R Companion to Applied Regression

• Author : John Fox,Sanford Weisberg
• Publisher : SAGE Publications
• Release : 27 September 2018

An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied regression analysis. John Fox and Sanford Weisberg provide a step-by-step guide to using the free statistical software R, an emphasis on integrating statistical computing in R with the practice of data analysis, coverage of generalized linear models, and substantial web-based support materials. The Third Edition has been reorganized and includes a new chapter on mixed-effects models, new and updated

## Applied Econometrics with R

• Author : Christian Kleiber,Achim Zeileis
• Publisher : Springer Science & Business Media
• Release : 10 December 2008

R is a language and environment for data analysis and graphics. It may be considered an implementation of S, an award-winning language initially - veloped at Bell Laboratories since the late 1970s. The R project was initiated by Robert Gentleman and Ross Ihaka at the University of Auckland, New Zealand, in the early 1990s, and has been developed by an international team since mid-1997. Historically, econometricians have favored other computing environments, some of which have fallen by the wayside, and