The analysis of biological data
著者
書誌事項
The analysis of biological data
Roberts and Co. Publishers, c2009
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内容説明・目次
内容説明
The Analysis of Biological Data is a new approach to teaching introductory statistics to biology students. To reach this unique audience, Whitlock and Schluter motivate learning with interesting biological and medical examples; they emphasize intuitive understanding; and they focus on real data. The book covers basic topics in introductory statistics, including graphs, confidence intervals, hypothesis testing, comparison of means, regression, and designing experiments. It also introduces the principles behind such modern topics as likelihood, linear models, meta-analysis and computer-intensive methods. Instructors and students consistently praise the book's clear and engaging writing, strong visualization techniques, and its variety of fascinating and relevant biological examples.
目次
PART 1 INTRODUCTION TO STATISTICS 1. Statistics and samples INTERLEAF 1 Biology and the history of statistics 2. Displaying data 3. Describing data 4. Estimating with uncertainty INTERLEAF 2 Pseudoreplication 5. Probability 6. Hypothesis testing INTERLEAF 3 Why statistical significance is not the same as biological importance PART 2 PROPORTIONS AND FREQUENCIES 7. Analyzing proportions INTERLEAF 4 Correlation does not require causation 8. Fitting probability models to frequency data INTERLEAF 5 Making a plan 9. Contingency analysis: associations between categorical variables PART 3 COMPARING NUMERICAL VALUES 10. The normal distribution INTERLEAF 6 Controls in medical studies 11. Inference for a normal population 12. Comparing two means INTERLEAF 7 Which test should I use? 13. Handling violations of assumptions 14. Designing experiments INTERLEAF 8 Data dredging 15. Comparing means of more than two groups INTERLEAF 9 Experimental and statistical mistakes PART 4 REGRESSION AND CORRELATION 16. Correlation between numerical variables INTERLEAF 10 Publication bias 17. Regression INTERLEAF 11 Using species as data points PART 5 MODERN STATISTICAL METHODS 18. Multiple explanatory variables 19. Computer-intensive methods 20. Likelihood 21. Meta-analysis: combining information from multiple studies Answers to practice problems Literature cited Statistical tables Photo credits Index
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