A. Bradley Duthie (UK. University of Stirling)
Fundamental Statistical Concepts and Techniques in the Biological and Environmental Sciences
With jamovi
A. Bradley Duthie (UK. University of Stirling)
Fundamental Statistical Concepts and Techniques in the Biological and Environmental Sciences
With jamovi
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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.
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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.
Produktdetails
- Produktdetails
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 348
- Erscheinungstermin: 8. Oktober 2024
- Englisch
- Abmessung: 233mm x 249mm x 24mm
- Gewicht: 538g
- ISBN-13: 9781032687186
- ISBN-10: 1032687185
- Artikelnr.: 70360536
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
- Verlag: Taylor & Francis Ltd
- Seitenzahl: 348
- Erscheinungstermin: 8. Oktober 2024
- Englisch
- Abmessung: 233mm x 249mm x 24mm
- Gewicht: 538g
- ISBN-13: 9781032687186
- ISBN-10: 1032687185
- Artikelnr.: 70360536
- Herstellerkennzeichnung
- Libri GmbH
- Europaallee 1
- 36244 Bad Hersfeld
- gpsr@libri.de
A. Bradley Duthie is a Lecturer in Environmental Modelling at the University of Stirling. Brad completed undergraduate degrees in Biology and Philosophy at Southern Illinois University Edwardsville. He finished his PhD in Ecology and Evolutionary Biology at 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 also contributes to several research projects as a biostatistician and wrote and maintains two R packages for agent-based modelling.
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
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
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