Statistical Data Analysis Explained ：Applied Environmental Statistics with R

Publication subTitle ：Applied Environmental Statistics with R

Author： Clemens Reimann  

Publisher： John Wiley & Sons Inc‎

Publication year： 2008

E-ISBN: 9780470987599

P-ISBN(Hardback):  9780470985816

Subject： X11 environmental mathematics

Language： ENG

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Description

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 of statistics in environmental sciences. The book concerns the application of statistical and other computer methods to the management, analysis and display of spatial data. These data are characterised by including locations (geographic coordinates), which leads to the necessity of using maps to display the data and the results of the statistical methods. Although the book uses examples from applied geochemistry, and a large geochemical survey in particular, the principles and ideas equally well apply to other natural sciences, e.g., environmental sciences, pedology, hydrology, geography, forestry, ecology, and health sciences/epidemiology.

The book is unique because it supplies direct access to software solutions (based on R, the Open Source version of the S-language for statistics) for applied environmental statistics. For all graphics and tables presented in the book, the R-scripts are provided in the form of executable R-scripts. In addition, a graphical user interface for R, called DAS+R, was developed for convenient, fast and interactive data analysis.

Statistical Data Analysis Explained: Applied Environmental Statistics with R provides, on an accompanying website, the software to undertake all the procedures discussed, and the data employed for their description in the book.

Chapter

Contents

pp.：  7 – 15

Preface

pp.：  15 – 17

Acknowledgements

pp.：  17 – 19

About the authors

pp.：  19 – 21

1 Introduction

pp.：  21 – 33

2 Preparing the Data for Use in R and DAS+R

pp.：  33 – 49

3 Graphics to Display the Data Distribution

pp.：  49 – 71

4 Statistical Distribution Measures

pp.：  71 – 83

5 Mapping Spatial Data

pp.：  83 – 111

6 Further Graphics for Exploratory Data Analysis

pp.：  111 – 127

7 Defining Background and Threshold, Identification of Data Outliers and Element Sources

pp.：  127 – 149

8 Comparing Data in Tables and Graphics

pp.：  149 – 169

9 Comparing Data Using Statistical Tests

pp.：  169 – 187

10 Improving Data Behaviour for Statistical Analysis: Ranking and Transformations

pp.：  187 – 201

11 Correlation

pp.：  201 – 213

12 Multivariate Graphics

pp.：  213 – 221

13 Multivariate Outlier Detection

pp.：  221 – 231

14 Principal Component Analysis (PCA) and Factor Analysis (FA)

pp.：  231 – 253

15 Cluster Analysis

pp.：  253 – 269

16 Regression Analysis (RA)

pp.：  269 – 289

17 Discriminant Analysis (DA) and Other Knowledge-Based Classification Methods

pp.：  289 – 301

18 Quality Control (QC)

pp.：  301 – 321

19 Introduction to R and Structure of the DAS+R Graphical User Interface

pp.：  321 – 341

References

pp.：  341 – 357

Index

pp.：  357 – 371

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