Statistical Data Analysis Explained - Clemens Reimann

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Résumé :
Statistical Data Analysis Explained Applied Environmental Statistics with R Statistical Data Analysis Explained provides an accessible guide to practical data analysis for applied environmental sciences, using many of today's advanced Statistical. Written in a concise and logical manner, the book avoids complex Statistical jargon and can be easily understood by non-statisticians. Mathematical formulae is avoided where possible with explanations carried out using relevant examples. Opening with the simplest of statistical concepts, the book carefully moves on to introduce the reader to a more comprehensive understanding of he use of statistics within the environmental sciences. Clearly structured throughout, the book links the application of Statistical and other computer methods to the management, analysis and presentation of spatial data. Many of the examples used in the book are taken from applied geochemistry,although he principles and ideas apply equally to other natural sciences, for example, environmental science, hydrology, geography, forestry and ecology. The book will be an invaluable reference to anyone working with spatially-dependent data. * Approaches statistics without excessive formulae and avoids statistical jargon for the non-statistician * Accompanying website includes example data and the software package, DAS+R, as well as GUI and R-scripts. http://www.statistik.tuwien,ac.at/StatDA/ * Features an abundance of examples the reader can follow and duplicate, using R software * Takes an interdisciplinary approach combining the expertise of two geochemists and two statisticians * Focuses on exploratory data analysis for spatial data

Sommaire:
Preface. Acknowledgements. About the Authors. 1. Introduction. 2. Preparing the Data for Use in R and DAS+R. 3. Graphics to Display the Data Distribution. 4. Statistical Distribution Measures. 5. Mapping Spatial Data. 6. Further Graphics for Exploratory Data Analysis. 7. Defining Background and Threshold, Identification of Data Outliers and Element Sources. 8. Comparing Data in Tables and Graphics. 9. Comparing Data Using Statistical tests. 10. Improving Data Behaviour for Statistical Analysis: Ranking and Transformations. 11. Correlation. 12. Multivariate Graphics. 13. Multivariate Outlier Detection. 14. Principal Component Analysis (PCA) and Factor Analysis (FA). 15. Cluster Analysis. 16. Regression Analysis (RA). 17. Discriminant Analysis (DA) and Other Knowledge-Based Classification Methods. 18. Quality Control (QC). 19. Introduction to R and Structure of the DAS+R Graphical User Interface. References. Index.