a new approach to statistics in plain English, and walks readers through basic concepts, as well as some of the most complex statistical models in use.
a new approach to statistics in plain English, and walks readers through basic concepts, as well as some of the most complex statistical models in use.
Dana K. Keller, PhD, has explored Eastern philosophies for almost five decades, including a journey to China and Tibet. He embraces two very different worlds: the West's scientific approach to knowledge and the East's more intuitive and experiential approach. In The Tao of Statistics, he presents a way that the two worlds can mutually benefit. After supervising the research for over 100 doctoral dissertations, he joined the Delmarva Foundation as its chief statistician. During his seven years there, the Centers for Medicare & Medicaid Services named him as a national resource for the nation's managed care organizations for sampling and research methodology. His almost unique ability to explain statistical and methodological constructs in everyday language has resulted in his being frequently requested as a presenter and technical expert panel member. As president of Halcyon Research, Inc., he continues to bring his ability to explain statistical concepts simply to an ever-widening audience.
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Acknowledgments About the Author Introduction to the Second Edition 1. The Beginning - The Question 2. Ambiguity - Statistics 3. Fodder - Data 4. Data - Measurement 5. Data Structure - Levels of Measurement 6. Simplifying - Groups and Clusters 7. Counts - Frequencies 8. Pictures - Graphs 9. Scatterings - Distributions 10. Bell-Shaped - The Normal Curve 11. Lopsidedness - Skewness 12. Averages - Central Tendencies 13. Two Types - Descriptive and Inferential 14. Foundations - Assumptions 15. Murkiness - Missing Data 16. Leeway - Robustness 17. Consistency - Reliability 18. Truth - Validity 19. Unpredictability - Randomness 20. Representativeness - Samples 21. Mistakes - Error 22. Real or Not - Outliers 23. Impediments - Confounds 24. Nuisances - Covariates 25. Background - Independent Variables 26. Targets - Dependent Variables 27. Inequality - Standard Deviations and Variance 28. Prove - No, Falsify 29. No Difference - The Null Hypothesis 30. Reductionism - Models 31. Risk - Probability 32. Uncertainty - p Values 33. Expectations - Chi-Square 34. Importance vs. Difference - Substantive vs. Statistical Significance 35. Strength - Power 36. Impact - Effect Sizes 37. Likely Range - Confidence Intervals 38. Association - Correlation 39. Predictions - Multiple Regressions 40. Abundance - Multivariate Analysis 41. Differences - t Tests and Analysis of Variance 42. Differences that Matter - Discriminant Analysis 43. Both Sides Loaded - Canonical Covariance Analysis 44. Nesting - Hierarchical Models 45. Cohesion - Factor Analysis 46. Ordered Events - Path Analysis 47. Digging Deeper - Structural Equation Models 48. Abundance - Big Data 49. Scarcity - Small Data 50. Fiddling - Modifications and New Techniques 51. Epilogue
