Steve Mckillup
Statistics Explained
Steve Mckillup
Statistics Explained
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This book provides straightforward conceptual explanations of statistical methods for the life sciences, specially designed for students lacking a strong mathematical background.
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This book provides straightforward conceptual explanations of statistical methods for the life sciences, specially designed for students lacking a strong mathematical background.
Produktdetails
- Produktdetails
- Verlag: Cambridge University Press
- 2. Auflage
- Seitenzahl: 420
- Erscheinungstermin: 2. August 2012
- Englisch
- Abmessung: 235mm x 157mm x 27mm
- Gewicht: 758g
- ISBN-13: 9781107005518
- ISBN-10: 1107005515
- Artikelnr.: 34229106
- Verlag: Cambridge University Press
- 2. Auflage
- Seitenzahl: 420
- Erscheinungstermin: 2. August 2012
- Englisch
- Abmessung: 235mm x 157mm x 27mm
- Gewicht: 758g
- ISBN-13: 9781107005518
- ISBN-10: 1107005515
- Artikelnr.: 34229106
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).
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.
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.
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.