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Informationen zum Autor 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). Klappentext This book provides straightforward conceptual explanations of statistical methods for the life sciences, specially designed for students lacking a strong mathematical background. Zusammenfassung Uses a clear and encouraging reader-friendly approach to help students improve their confidence in designing experiments and choosing appropriate statistical tests. Even complex topics are explained clearly! using a pictorial approach with a minimum of formulae and terminology. End-of-chapter exercises! new to this edition! allow self-testing. Inhaltsverzeichnis 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....
