Geostatistics explained
Subtitle: an introductory guide for earth scientists
By: Stephen McKillup, Melinda Darby Dyar.
Contributors: Dyar, M. Darby
Publisher: Cambridge University Press
Published: 2010
Place of publication: New York
Language: eng
Number of pages: 396
ISBN-13: 9780521763226
Subjects: Geology -- Statistical methods
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"--Provided by publisher.

"Earth scientists face special challenges because the things they study - the rock formations, ore bodies, deposits of minerals and fossil species - are often very large, widely dispersed and/or difficult to access. Therefore, it is usually impossible for an earth scientist to study more than a small fraction of any geological phenomenon"--Provided by publisher.
Table of contents: title: Machine generated contents note: 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.