Summary

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.

Author Biography

Clemens Reiman (born 1952) holds an M.Sc. in Mineralogy and Petrology from the University of Hamburg (Germany), a Ph.D. in Geosciences from Leoben Mining University, Austria, and a D.Sc. in Applied Geochemistry from the same university. he has worked as a lecturer in Mineralogy and Petrology and Environmental Sciences at Leoben Mining University, as an exploration geochemist in eastern Canada, in contract research in environmental sciences in Austria and managed the laboratory of an Austrian cement company before joining the Geological Survey of Norway in 1991 as a senior geochemist. From March to October 2004 he was director and professor at the German Federal Environment Agency (Unweltbundesamt, UBAS), responsible for the Division II, Environmental Health and Protection of Ecosystems. At present he is chairman of the EuroGeoSurveys geochemistry expert group, acting vice president of the International Association of GeoChemistry (IAGC), and associate editor of both Applied Geochemistry and Geochemistry: Exploration, Environment, Analysis.

Peter Filzmoser (born 1968) studies Applied Mathematics at the Vienna University of Technology, Austria, where he also wrote his doctoral thesis and habilitation devoted to the field of multivariate statistics. His research led him to the area of robust statistics, resulting in many international collaborations and various scientific papers in this area. His interest in applications of robust methods resulted in the development of R software packages. He was and is involved in the Organisation of several scientific evens devoted to robust statistics. Since 2001 he has been dozent at the Statistics Department at Vienna University of Technology. He was visiting professor at the universities of Vienna, Toulouse and Minsk.

Robert G. Garrett (Bob Garrett) studied Mining Geology and Applied Geochemistry at Imperial College, London, and joined the Geological Survey of Canada (GSC) in 1967 following post-doctoral studies at Northwestern University, Evanston. For the next 25 years his activities focused on regional geochemical mapping in Canada, and overseas for the Canadian International Development Agency, to support mineral exploration and resource appraisal. Throughout his work there has been a use of computers and statistics to manage data, assess their quality, and maximise the knowledge extracted from them. In the 1990s he commenced collaboration crops. Since then he has been involved in various Canadian Federal and university-based research initiatives aimed at providing sound science to support Canadian regulatory and international policy activities concerning risk assessments and risk management for metals. he retired in March 2005 but remains active as an Emeritus Scientist.

Rudolf Dutter is senior statistician and full professor at Vienna University of Technology, Austria. he studies Applied Mathematics in Vienna (M.Sc.) and Statistics at Universite de Montreal, Canada (Ph.D.). He spent three years as a post-doctoral fellow at ETH, Zurich, working on computational robust statistics. research and teaching activities followed at the Graz University of Technology, and as a full professor of statistics at Vienna University of Technology, both in Austria. he also taught and consulted at Leoben Mining University, Technology, both in Austria. he also taught and consulted at Leoben Mining University, Austria; currently he consults in many fields of applied statistics with main interests in computational and robust statistics, development of statistical software, and geostatistics. He is author and coauthor of many publications and several books, e.g., an early booklet in German on geostatistics.

Preface
Introduction
The Kola Ecogeochemistry Project
Preparing the Data for Use in R and DAS+R
Required Data Format for Import in R and DAS+R
The Detection Limit Problem
Missing Values
Some "Typical" Problems Encountered When Editing a Laboratory Data Report
Appending and Linking Data Files
Requirements for a Geochemical Database
Summary
Graphics to Display the Data Distribution
The One-Dimensional Scatter Plot
The Histogram
The Density Trace
Plots of the Distribution Function
Boxplots
Combination of Histogram, Density Trace, One-Dimensional Scattergram, Boxplot, and ECDF-plot
Combination of Histogram, Boxplot or Box-and-Whisker plot, ECDF-plot, and CPplot
Summary
Statistical Distribution Measures
Central Value
Measures of Spread
Quartiles, Quantiles and Percentiles
Skewness
Kurtosis
Summary Table of Statistical Distribution Measures
Summary
Mapping Spatial Data
Map Coordinate Systems (Map Projection)
Map Scale
Choice of the Base Map for Geochemical Mapping
Mapping Geochemical Data With Proportional Dots
Mapping Geochemical Data Using Classes
Surface Maps Constructed with Smoothing Techniques
Surface Maps Constructed with Kriging
Colour Maps
Some Common Mistakes in Geochemical Mapping
Summary
Further Graphics for Exploratory Data Analysis
Scatterplots (xy-plots)
Linear Regression Lines
Time Trends
Spatial Trends
Spatial Distance Plot
Spiderplots (Normalised Multi-Element Diagrams)
Scatterplot Matrix
Ternary Plots
Summary
Defining Background and Threshold, Identification of Data Outliers and Element Sources
Statistical Methods to Identify Extreme Values and Data Outliers
Detecting Outliers and Extreme Values in the ECDF- or CP-Plot
Including the Spatial Distribution in the Definition of Background
Methods to Distinguish Geogenic from Anthropogenic Element Sources
Summary
Comparing Data in Tables and Graphics
Comparing Data in Tables
Graphical Comparison of the Data Distributions of Several Data Sets
Comparing the Spatial Data Structure
Subset Creation - a Mighty Tool in Graphical Data Analysis
Data Subsets in Scatterplots
Data Subsets in Time and Spatial Trend Diagrams
Data Subsets in Ternary Diagrams
Data Subsets in the Scatterplot Matrix
Data Subsets in Maps
Summary
Comparing Data Using Statistical Tests
Tests for Distribution (Kolmogorov-Smirnov and Shapiro-Wilk Tests)
The One-Sample t-Test (Test for the Central Value)
Wilcoxon Signed-rank Test
Comparing Two Central Values of the Distributions of Independent Data Groups
Comparing Two Central Values of Matched Pairs of Data
Comparing the Variance of Two Data Sets
Comparing Several Central Values
Comparing the Variance of Several Data Groups
Comparing Several Central Values of Dependent Groups
Summary
Improving Data Behaviour for Statistical Analysis: Ranking and Transformations
Ranking/Sorting
Non-linear Transformations
Linear Transformations
Preparing a Dataset for Multivariate Data Analysis
The Special Case of Closed Number Systems
Summary
Correlation
Pearson Correlation
Spearman Rank Corr
Table of Contents provided by Publisher. All Rights Reserved.

Supplemental Materials

What is included with this book?

The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.

The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.