Geostatistics Explained

Subtitle:: An Introductory Guide for Earth Scientists
Author:: Stephen McKillup; Melinda Darby Dyar
Format:: Softcover

Now:	R621.95
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United Kingdom

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Short description

A clear and simple introduction to geostatistics, demystifying complex concepts and making formulas and statistical tests easy to apply.

Long description

A reader-friendly introduction to geostatistics for students and researchers struggling with statistics. Using simple, clear explanations for introductory and advanced material, it demystifies complex concepts and makes formulas and statistical tests easy to apply. Beginning with a critical evaluation of experimental and sampling design, the book moves on to explain essential concepts of probability, statistical significance and type 1 and type 2 error. An accessible graphical explanation of analysis of variance (ANOVA) leads onto advanced ANOVA designs, correlation and regression, and non-parametric tests including chi-square. Finally, it introduces the essentials of multivariate techniques, multi-dimensional scaling and cluster analysis, analysis of sequences and concepts of spatial analysis. Illustrated with wide-ranging examples from topics across the Earth and environmental sciences, Geostatistics Explained can be used for undergraduate courses or for self-study and reference. Worked examples at the end of each chapter reinforce a clear understanding of the statistical tests and their applications.

Product details

Publisher:: Cambridge University Press
ISBN:: 9780521746564
Length:: 229mm
Width:: 152mm
Thickness:: 23mm
Weight:: 658g
Pages:: 396
Illustrations:: Illustrated
Illustrated:: Illustrated

Preface
1. Introduction
2. 'Doing science' - hypotheses, experiments and disproof
3. Collecting and displaying data
4. Introductory concepts of experimental design
5. Doing science responsibly and ethically
6. Probability helps you make a decision about your results
7. Working from samples - data, populations and statistics
8. Normal distributions - tests for comparing the means of one and two samples
9. Type 1 and type 2 error, power and sample size
10. Single factor analysis of variance
11. Multiple comparisons after ANOVA
12. Two-factor analysis of variance
13. Important assumptions of analysis of variance: transformations and a test for equality of variances
14. Two-factor analysis of variance without replication, and nested analysis of variance
15. Relationships between variables: linear correlation and linear regression
16. Linear regression
17. Non-parametric statistics
18. Non-parametric tests for nominal scale data
19. Non-parametric tests for ratio, interval or ordinal scale data
20. Introductory concepts of multivariate analysis
21. Introductory concepts of sequence analysis
22. Introductory concepts of spatial analysis
23. Choosing a test
Appendix A. Critical values of chi-square, Student's t and F
Appendix B. Answers to questions
References
Index.