Intuitive biostatistics : a nonmathematical guide to statistical thinking
著者
書誌事項
Intuitive biostatistics : a nonmathematical guide to statistical thinking
Oxford University Press, 2010
Completely rev. 2nd ed
- : pbk
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
Thoroughly revised and updated, the second edition of Intuitive Biostatistics retains and refines the core perspectives of the previous edition: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes. Intuitive Biostatistics, Completely Revised Second Edition, provides a clear introduction to statistics for undergraduate and graduate students and also serves as a statistics refresher for working scientists. NEW TO THIS EDITION: * Chapter 1 shows how our intuitions lead us to misinterpret data, thus explaining the need for statistical rigor. * Chapter 11 explains the lognormal distribution, an essential topic omitted from many other statistics books. * Chapter 21 contrasts testing for equivalence with testing for differences. * Chapters 22, 23, and 40 explore the pervasive problem of multiple comparisons. * Chapters 24 and 25 review testing for normality and outliers. * Chapter 35 shows how statistical hypothesis testing can be understood as comparing the fits of alternative models.
* Chapters 37 and 38 provide a brief introduction to multiple, logistic, and proportional hazards regression. * Chapter 46 reviews one example in great depth, reviewing numerous statistical concepts and identifying common mistakes. * Chapter 47 includes 49 multi-part problems, with answers fully discussed in Chapter 48. * New "Q and A" sections throughout the book review key concepts.
目次
- PART A. INTRODUCING STATISTICS
- 1. Statistics and Probability Are Not Intuitive
- 2. Why Statistics Can Be Hard to Learn
- 3. From Sample to Population
- PART B. CONFIDENCE INTERVALS
- 4. Confidence Interval of a Proportion
- 5. Confidence Interval of Survival Data
- 6. Confidence Interval of Counted Data
- 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 SIGNIFICANCE
- 15. Introducing P Values
- 16. Statistical Significance and Hypothesis Testing
- 17. Relationship Between Confidence Intervals and Statistical Significance
- 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. Multiple Comparison Traps
- 24. Gaussian or Not?
- 25. Outliers
- PART F. STATISTICAL TESTS
- 26. Comparing Observed and Expected Distributions
- 27. Comparing Proportions: Prospective and Experimental Studies
- 28. Comparing Proportions: 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. Models
- 35. Comparing Models
- 36. Nonlinear Regression
- 37. Multiple, Logistic, and Proportional Hazards Regression
- 38. Multiple Regression Traps
- PART H. THE REST OF STATISTICS
- 39. Analysis of Variance
- 40. Multiple Comparison Tests After ANOVA
- 41. Nonparametric Methods
- 42. Sensitivity Specificity and Receiver-Operator Characteristic Curves
- 43. Sample Size
- PART I. PUTTING IT ALL TOGETHER
- 44. Statistical Advice
- 45. Choosing a Statistical Test
- 46. Capstone Example
- 47. Review Problems
- 48. Answers to Review Problems
- APPENDICES
- A. STATISTICS WITH GRAPHPAD
- B. STATISTICS WITH EXCEL
- C. STATISTICS R
- D. VALUES OF THE T DISTRIBUTION NEEDED TO COMPUTE CIS
- E. A REVIEW OF LOGARITHMS
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