The Tao of Statistics: A Path to Understanding (With No Math)

By:

Keller, Dana K

Contributor(s):

Cardiff, Helen [Illustrator]

Material type: Text

TextPublication details: Sage Publications India Pvt. Ltd. 2016 New DelhiEdition: 2nd edDescription: 171pISBN:

9781483377926

Subject(s):

Statistics - Study and Teaching

DDC classification:

519.5 KEL

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Holdings
Item type	Current library	Item location	Collection	Call number	Status	Date due	Barcode	Item holds
Book	NIMA Knowledge Centre	9th Floor Reading Zone	General	519.5 KEL (Browse shelf(Opens below))	Available		M0032707

Total holds: 0

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Previous	519.5 JOH Advanced Business Statistics	519.5 KAN 100 Statistical Tests	519.5 KAP Mathematical Statistics	519.5 KEL The Tao of Statistics: A Path to Understanding (With No Math)	519.5 KHM Statistical Methods with Applications to Demography and Life Insurance	519.5 KLU Statistics: The Essentials for Research	519.5 KNA Learning Statistics through Playing Cards	Next

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 - Model
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

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