A clear and simple introduction to geostatistics, demystifying complex concepts and making formulas and statistical tests easy to apply.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Steve McKillup is an Associate Professor in the Department of Biosystems and Resources at Central Queensland University. He has received several tertiary teaching awards, including the Vice-Chancellor's Award for Quality Teaching and a 2008 Australian Teaching and Learning Council citation 'For developing a highly successful method of teaching complex physiological and statistical concepts, and embodying that method in an innovative international textbook'. He is the author of Statistics Explained: An Introductory Guide for Life Scientists (Cambridge, 2006). His research interests include biological control of introduced species, the ecology of soft-sediment shores and mangrove swamps.
Inhaltsangabe
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.
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.
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