The Tao of Statistics
eBook - ePub

The Tao of Statistics

A Path to Understanding (With No Math)

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

The Tao of Statistics

A Path to Understanding (With No Math)

About this book

This Second Edition of The Tao of Statistics: A Path to Understanding (With No Math) provides a reader-friendly approach to statistics in plain English. Unlike other statistics books, this text explains what statistics mean and how they are used, rather than how to calculate them. The book walks readers through basic concepts as well as some of the most complex statistical models in use. The Second Edition adds coverage of big data to better address its impact on p-values and other key concepts; material on small data to show readers how to handle data with fewer data points than optimal; and other new topics like missing data and effect sizes. The book's two characters (a high school principal and a director of public health) return in the revised edition, with their examples expanded and updated with reference to contemporary concerns in the fields of education and health.

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Yes, you can access The Tao of Statistics by Dana K. Keller in PDF and/or ePUB format, as well as other popular books in Social Sciences & Social Science Research & Methodology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Publisher Note
  4. Title Page
  5. Copyright Page
  6. Contents
  7. Acknowledgments
  8. About the Author
  9. Introduction to the Second Edition
  10. 1. The Beginning — The Question
  11. 2. Ambiguity—Statistics
  12. 3. Fodder—Data
  13. 4. Data—Measurement
  14. 5. Data Structure—Levels of Measurement
  15. 5.A. Nominal
  16. 5.B. Ordinal
  17. 5.C. Interval
  18. 5.D. Ratio
  19. 6. Simplifying—Groups and Clusters
  20. 7. Counts—Frequencies
  21. 8. Pictures—Graphs
  22. 9. Scatterings—Distributions
  23. 10. Bell-Shaped—The Normal Curve
  24. 11. Lopsidedness—Skewness
  25. 12. Averages—Central Tendencies
  26. 12.A. Mean
  27. 12.B. Median
  28. 12.C. Mode
  29. 13. Two Types—Descriptive and Inferential
  30. 14. Foundations—Assumptions
  31. 15. Murkiness—Missing Data
  32. 16. Leeway—Robustness
  33. 17. Consistency—Reliability
  34. 18. Truth—Validity
  35. 19. Unpredictability—Randomness
  36. 20. Representativeness—Samples
  37. 21. Mistakes—Error
  38. 22. Real or Not—Outliers
  39. 23. Impediments—Confounds
  40. 24. Nuisances—Covariates
  41. 25. Background—Independent Variables
  42. 26. Targets—Dependent Variables
  43. 27. Inequality—Standard Deviations and Variance
  44. 28. Prove—No, Falsify
  45. 29. No Difference—The Null Hypothesis
  46. 30. Reductionism—Models
  47. 31. Risk—Probability
  48. 32. Uncertainty—p Values
  49. 33. Expectations—Chi-Square
  50. 34. Importance vs. Difference—Substantive vs. Statistical Significance
  51. 35. Strength—Power
  52. 36. Impact—Effect Sizes
  53. 37. Likely Range—Confidence Intervals
  54. 38. Association—Correlation
  55. 39. Predictions—Multiple Regression
  56. 40. Abundance—Multivariate Analysis
  57. 41. Differences—t Tests and Analysis of Variance
  58. 41.A. ANOVA
  59. 41.B. ANCOVA
  60. 41.C. MANOVA
  61. 41.D. MANCOVA
  62. 42. Differences That Matter—Discriminant Analysis
  63. 43. Both Sides Loaded—Canonical Covariance Analysis
  64. 44. Nesting—Hierarchical Models
  65. 45. Cohesion—Factor Analysis
  66. 46. Ordered Events—Path Analysis
  67. 47. Digging Deeper—Structural Equation Models
  68. 48. Abundance—Big Data
  69. 49. Scarcity—Small Data
  70. 50. Fiddling—Modifications and New Techniques
  71. 51. Epilogue
  72. Publisher Note