This book provides straightforward conceptual explanations of statistical methods for the life sciences, specially designed for students lacking a strong mathematical background.
This book provides straightforward conceptual explanations of statistical methods for the life sciences, specially designed for students lacking a strong mathematical background.
Steve McKillup is an Associate Professor of Biology in the School of Medical and Applied Sciences at Central Queensland University, Rockhampton. He has received several tertiary teaching awards, including the Vice-Chancellor's Award for Quality Teaching and an Australian Learning and Teaching Council citation 'for developing a highly successful method of teaching complex physiological and statistical concepts, and embodying that method in an innovative international textbook' (2008). He has gained a further citation for Outstanding Contributions to Student Learning, in the latest Australian Awards for University Teaching 2014. The citation has been awarded for 'developing resources that engage, empower and enable environmental science students to understand and use biostatistics', which includes his books on statistics that are being used worldwide. He is the author of Geostatistics Explained: An Introductory Guide for Earth Scientists (Cambridge, 2010).
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. Probability explained 8. Using the normal distribution to make statistical decisions 9. Comparing the means of one and two samples of normally distributed data 10. Type 1 and Type 2 error, power and sample size 11. Single factor analysis of variance 12. Multiple comparisons after ANOVA 13. Two-factor analysis of variance 14. Important assumptions of analysis of variance, transformations and a test for equality of variances 15. More complex ANOVA 16. Relationships between variables: correlation and regression 17. Regression 18. Analysis of covariance 19. Non-parametric statistics 20. Non-parametric tests for nominal scale data 21. Non-parametric tests for ratio, interval or ordinal scale data 22. Introductory concepts of multivariate analysis 23. Choosing a test Appendix: critical values of chi-square, t and F 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. Probability explained 8. Using the normal distribution to make statistical decisions 9. Comparing the means of one and two samples of normally distributed data 10. Type 1 and Type 2 error, power and sample size 11. Single factor analysis of variance 12. Multiple comparisons after ANOVA 13. Two-factor analysis of variance 14. Important assumptions of analysis of variance, transformations and a test for equality of variances 15. More complex ANOVA 16. Relationships between variables: correlation and regression 17. Regression 18. Analysis of covariance 19. Non-parametric statistics 20. Non-parametric tests for nominal scale data 21. Non-parametric tests for ratio, interval or ordinal scale data 22. Introductory concepts of multivariate analysis 23. Choosing a test Appendix: critical values of chi-square, t and F References Index.
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