Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition

Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition

• Used Book in Good Condition

Thoroughly revised and updated, the third edition of <i>Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking</i> retains and refines the core perspectives of the previous editions: 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.<br><br>With its engaging and conversational tone, this unique book provides a clear introduction to statistics for undergraduate and graduate students in a wide range of fields and also serves as a statistics refresher for working scientists. It is especially useful for those students in health-science related fields who have no background in biostatistics.<br><br> <div></div><div><div><b>CONTENTS</b></div><div><b></b></div><div><b></b></div><div><b><br>Part A: Introducing Statistics </b></div><div></div><div> 1. Statistics and Probability Are Not Intuitive</div><div> 2. The Complexities of Probability</div><div> 3. From Sample to Population </div><div></div><div><b>Part B: Confidence Intervals </b></div><div></div><div> 4. Confidence Interval of a Proportion </div><div> 5. Confidence Interval of Survival Data </div><div> 6. Confidence Interval of Counted Data </div><div></div><div><b>Part C: Continuous Variables </b></div><div></div><div> 7. Graphing Continuous Data</div><div> 8. Types of Variables </div><div> 9. Quantifying Scatter </div><div>10. The Gaussian Distribution </div><div>11. The Lognormal Distribution and Geometric Mean</div><div>12. Confidence Interval of a Mean </div><div>13. The Theory of Confidence Intervals</div><div>14. Error Bars </div><div></div><div><b>PART D: P Values and Significance </b></div><div></div><div>15. Introducing P Values </div><div>16. Statistical Significance and Hypothesis Testing</div><div>17. Relationship Between Confidence Intervals and Statistical Significance </div><div>18. Interpreting a Result That Is Statistically Significant </div><div>19. Interpreting a Result That Is Not Statistically Significant </div><div>20. Statistical Power</div><div>21. Testing for Equivalence or Noninferiority</div><div></div><div><b>PART E: Challenges in Statistics </b></div><div></div><div>22. Multiple Comparisons Concepts </div><div>23. The Ubiquity of Multiple Comparison</div><div>24. Normality Tests</div><div>25. Outliers </div><div>26. Choosing a Sample Size</div><div></div><div><b>PART F: Statistical Tests </b></div><div></div><div>27. Comparing Proportions</div><div>28. Case-Control Studies</div><div>29. Comparing Survival Curves </div><div>30. Comparing Two Means: Unpaired t Test</div><div>31. Comparing Two Paired Groups</div><div>32. Correlation </div><div></div><div><b>PART G: Fitting Models to Data </b></div><div></div><div>33. Simple Linear Regression</div><div>34. Introducing Models </div><div>35. Comparing Models </div><div>36. Nonlinear Regression</div><div>37. Multiple Regression </div><div>38. Logistic and Proportional Hazards Regression</div><div></div><div><b>PART H The Rest of Statistics </b></div><div></div><div>39. Analysis of Variance </div><div>40. Multiple Comparison Tests After ANOVA </div><div>41. Nonparametric Methods</div><div>42. Sensitivity and Specificity and Receiver-Operator Characteristic Curves </div><div>43. Meta-analysis</div><div></div><div><b>PART I Putting It All Together </b></div><div></div><div>44. The Key Concepts of Statistics</div><div>45. Statistical Traps to Avoid</div><div>46. Capstone Example </div><div>47. Review Problems </div><div>48. Answers to Review Problems </div><div></div><div><b> </b></div></div>