425 pages, 200 b/w illustrations, 93 tables
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, Geostatistics Explained 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.
"I can highly recommend this book based on my own experience with it, and can also imagine it being very useful for those teaching statistical methods to geoscientists."
- Nina Kirchner, Stockholm University
"[...] provides a lifeline for students and researchers across the Earth and environmental sciences who until now have struggled with statistics [...] demystifies complex concepts and makes formulas and statistical tests easy to understand and apply."
- The Eggs
"[...] reading this book will stop students, researchers and working geologists from using statistics packages as 'black boxes'. There's now no excuse for not having a basic grasp of what you're doing, why you're doing it and how to interpret (but not over-interpret) the results."
- Geological Magazine
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
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Steve McKillup is an Associate Professor in the Department of Biosystems and Resources at Central Queensland University. Melinda Darby Dyar is an Associate Professor of Geology and Astronomy at Mount Holyoke College, Massachusetts.