The Tao of Statistics: A Path to Understanding (With No Math)
Autor Dana K. Kelleren Limba Engleză Paperback – 27 apr 2015
The Second Edition contains new chapters on “big data” on the one hand, and on small data situations at the other end of the spectrum; on missing data; and on effect sizes. The two "characters" in the text (a high school principal, and a director of public health) return in the Second Edition, and their needs to make sense of the data available to them have been updated with reference to contemporary concerns in the fields of education and health.
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Specificații
ISBN-13: 9781483377926
ISBN-10: 148337792X
Pagini: 192
Dimensiuni: 140 x 216 x 11 mm
Greutate: 0.26 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
ISBN-10: 148337792X
Pagini: 192
Dimensiuni: 140 x 216 x 11 mm
Greutate: 0.26 kg
Ediția:Second Edition
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
Recenzii
This Second Edition retains all the charm and conciseness which made the original so tremendously helpful, and even enjoyable to read. I would not hesitate to recommend it to any student struggling to comprehend statistics. The Tao of Statistics' key benefit lies in its ability to overcome the anxiety which numbers provoke for too many beginners, or even for those struggling with more advanced statistical methodologies. The succinct, single-focus chapters compel readers, by asking that they just understand the intent and usefulness of each aspect, and no more. Thus, it maintains focus on the individual components rather than allowing them to become overwhelmed by the whole. One might say the individual trees can be appreciated once fear of the dark forest is allayed.
"The Tao of Statistics: A Path to Understanding (With No Math) appears in its second edition to provide a user-friendly guide to statistics that explains what they mean, with a difference - there's no math involved. Most books are loaded with formulas; this is loaded with explanation. The basic concepts are covered with attention to how and why they are applied, offering important keys to understanding. This second edition adds new coverage of 'big data' and its impact and concepts, material contrasting it with 'small data', and discussions on missing data and more. The result is a pick for any interested in more than math formulas."
“'For most people, the concept of statistics begins as a shadowy mathematical nightmare....' The author opens his introduction to the second edition with these words, and undoubtedly many engaged in statistical research would agree. Unfortunately, these persons sometimes find themselves gathering, processing, and interpreting data with a great sense of discomfort as they blindly follow a mathematical procedure without really understanding what the results actually mean. Keller (president, Halcyon Research, Inc.) clearly has this audience in mind. Purists may be startled by a statistics book without numbers, graphs, or formulas, but they should appreciate Keller’s brief, insightful discussions designed to clarify each of his fifty topics. Especially helpful are illustrations of how a high school principal, a public health director, or a sociologist might use the concept at hand. In some cases the author even explains why these individuals might not need a particular procedure at all, a refreshingly honest approach that reflects his sense of tao. Of course, statistics without graphs or data is not realistic, but the user of statistics has to have a basic understanding of what is being accomplished and, in that sense, the author has definitely succeeded."
"The Tao of Statistics: A Path to Understanding (With No Math) appears in its second edition to provide a user-friendly guide to statistics that explains what they mean, with a difference - there's no math involved. Most books are loaded with formulas; this is loaded with explanation. The basic concepts are covered with attention to how and why they are applied, offering important keys to understanding. This second edition adds new coverage of 'big data' and its impact and concepts, material contrasting it with 'small data', and discussions on missing data and more. The result is a pick for any interested in more than math formulas."
“'For most people, the concept of statistics begins as a shadowy mathematical nightmare....' The author opens his introduction to the second edition with these words, and undoubtedly many engaged in statistical research would agree. Unfortunately, these persons sometimes find themselves gathering, processing, and interpreting data with a great sense of discomfort as they blindly follow a mathematical procedure without really understanding what the results actually mean. Keller (president, Halcyon Research, Inc.) clearly has this audience in mind. Purists may be startled by a statistics book without numbers, graphs, or formulas, but they should appreciate Keller’s brief, insightful discussions designed to clarify each of his fifty topics. Especially helpful are illustrations of how a high school principal, a public health director, or a sociologist might use the concept at hand. In some cases the author even explains why these individuals might not need a particular procedure at all, a refreshingly honest approach that reflects his sense of tao. Of course, statistics without graphs or data is not realistic, but the user of statistics has to have a basic understanding of what is being accomplished and, in that sense, the author has definitely succeeded."
Cuprins
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
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
Notă biografică
Descriere
This text concentrates on what statistics mean and how they are used, rather than how to calculate them, and this Second Edition contains new chapters on big data, small data situations, missing data and effect sizes.