This is an introductory textbook for learning statistics. It helps the reader develop a conceptual understanding of statistics. New concepts are introduced with examples designed to be familiar to the reader, serving as a useful starting point for exploring more abstract concepts.
This is an introductory textbook for learning statistics. It helps the reader develop a conceptual understanding of statistics. New concepts are introduced with examples designed to be familiar to the reader, serving as a useful starting point for exploring more abstract concepts.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
A. Bradley Duthie is a Lecturer in Environmental Modelling at the University of Stirling, Scotland, UK. He completed undergraduate degrees in Biology and Philosophy at Southern Illinois University Edwardsville. He earned his PhD in Ecology and Evolutionary Biology from Iowa State University with a graduate minor in Statistics. His research focuses primarily on theory and modelling in ecology and evolutionary biology with particular interests in evolutionary ecology and community ecology. He contributes to several research projects as a biostatistician and wrote and maintains two R packages for agentbased modelling.
Inhaltsangabe
Preface 1. Background mathematics 2. Data organisation 3. Practical. Preparing data 4. Populations and samples 5. Types of variables 6. Accuracy, precision, and units 7. Uncertainty propagation 8. Practical. Introduction to jamovi 9. Decimal places, significant figures, and rounding 10. Graphs 11. Measures of central tendency 12. Measures of spread 13. Skew and Kurtosis 14. Practical. Plotting and statistical summaries in jamovi 15. Introduction to probability models 16. The Central Limit Theorem (CLT) 17. Practical. Probability and simulation 18. Confidence intervals (CIs) 19. The t-interval 20. Practical. z- and t- intervals 21. What is hypothesis testing? 22. The t-test 23. Practical. Hypothesis testing and t-tests 24. Analysis of variance 25. Multiple comparisons 26. Kruskall-Wallis H test 27. Two-way ANOVA 28. Practical. ANOVA and associated tests 29. Frequency and count data 30. Correlation 31. Practical. Analysis of counts and correlations 32. Simple linear regression 33. Multiple regression 34. Practical. Using regression 35. Randomisation Appendix A. Answers to chapter exercises B. Uncertainty derivation Bibliography Index
Preface 1. Background mathematics 2. Data organisation 3. Practical. Preparing data 4. Populations and samples 5. Types of variables 6. Accuracy, precision, and units 7. Uncertainty propagation 8. Practical. Introduction to jamovi 9. Decimal places, significant figures, and rounding 10. Graphs 11. Measures of central tendency 12. Measures of spread 13. Skew and Kurtosis 14. Practical. Plotting and statistical summaries in jamovi 15. Introduction to probability models 16. The Central Limit Theorem (CLT) 17. Practical. Probability and simulation 18. Confidence intervals (CIs) 19. The t-interval 20. Practical. z- and t- intervals 21. What is hypothesis testing? 22. The t-test 23. Practical. Hypothesis testing and t-tests 24. Analysis of variance 25. Multiple comparisons 26. Kruskall-Wallis H test 27. Two-way ANOVA 28. Practical. ANOVA and associated tests 29. Frequency and count data 30. Correlation 31. Practical. Analysis of counts and correlations 32. Simple linear regression 33. Multiple regression 34. Practical. Using regression 35. Randomisation Appendix A. Answers to chapter exercises B. Uncertainty derivation Bibliography Index
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