Intuitive biostatistics : a nonmathematical guide to statistical thinking

著者

書誌事項

Intuitive biostatistics : a nonmathematical guide to statistical thinking

Harvey Motulsky

Oxford University Press, c2018

4th ed

大学図書館所蔵 件 / 全7件

この図書・雑誌をさがす

注記

Includes bibliographical references (p. 533-547) and index

内容説明・目次

内容説明

Intuitive Biostatistics takes a non-technical, non-quantitative approach to statistics and emphasizes interpretation of statistical results rather than the computational strategies for generating statistical data. This makes the text especially useful for those in health-science fields who have not taken a biostatistics course before. The text is also an excellent resource for professionals in labs, acting as a conceptually oriented and accessible biostatistics guide. With an engaging and conversational tone, Intuitive Biostatistics provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists.

目次

Part A. Introducing Statistics 1. Statistics and Probability are not Intuitive 2. The Complexities of Probability 3. From Sample to Population Part B. Introducing Confidence Intervals 4. Confidence Interval of a Proportion 5. Confidence Interval of Survival Data 6. Confidence Interval of Counted Data (Poisson Distribution) Part C. Continuous Variables 7. Graphing Continuous Data 8. Types of Variables 9. Quantifying Scatter 10. The Gaussian Distribution 11. The Lognormal Distribution and Geometric Mean 12. Confidence Interval of a Mean 13. The Theory of Confidence Intervals 14. Error Bars Part D. P Values and Statistical Significance 15. Introducing P Values 16. Statistical Significance and Hypothesis Testing 17. Comparing Groups with Confidence Intervals and P Values 18. Interpreting a Result That Is Statistically Significant 19. Interpreting a Result That Is Not Statistically Significant 20. Statistical Power 21. Testing For Equivalence or Noninferiority Part E. Challenges in Statistics 22. Multiple Comparisons Concepts 23. The Ubiquity of Multiple Comparisons 24. Normality Tests 25. Outliers 26. Choosing a Sample Size Part F. Statistical Tests 27. Comparing Proportions 28. Case-Control Studies 29. Comparing Survival Curves 30. Comparing Two Means: Unpaired t Test 31. Comparing Two Paired Groups 32. Correlation Part G. Fitting Models to Data 33. Simple Linear Regression 34. Introducing Models 35. Comparing Models 36. Nonlinear Regression 37. Multiple Regression 38. Logistic and Proportional Hazards Regression Part H. The Rest of Statistics 39. Analysis of Variance 40. Multiple Comparison Tests after ANOVA 41. Nonparametric Methods 42. Sensitivity, Specificity, and Receiver-Operating Characteristic Curves 43. Meta-Analysis Part I. Putting It All Together 44. The Key Concepts of Statistics 45. Statistical Traps to Avoid 46. Capstone Example 47. Statistics and Reproducibility 48. Checklists for Reporting Statistical Methods and Results Part J. Appendices

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