Understanding Statistics and Statistical Myths
eBook - PDF

Understanding Statistics and Statistical Myths

How to Become a Profound Learner

  1. 585 pages
  2. English
  3. PDF
  4. Available on iOS & Android
eBook - PDF

Understanding Statistics and Statistical Myths

How to Become a Profound Learner

About this book

Addressing 30 statistical myths in the areas of data, estimation, measurement system analysis, capability, hypothesis testing, statistical inference, and control charts, this book explains how to understand statistics rather than how to do statistics. Every statistical myth listed in this book has been stated in course materials used by the author

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Yes, you can access Understanding Statistics and Statistical Myths by Kicab Castaneda-Mendez in PDF and/or ePUB format, as well as other popular books in Business & Operations. We have over one million books available in our catalogue for you to explore.

Information

Publisher
CRC Press
Year
2015
Print ISBN
9781498727457
eBook ISBN
9781498727464
Edition
1
Topic
Business
Subtopic
Operations
Index
Business

Table of contents

  1. Front Cover
  2. Contents
  3. Preface
  4. Prologue
  5. Chapter 1: Myth 1: Two Types of Dataā€”Attribute/Discrete and Measurement/Continuous
  6. Chapter 2: Myth 2: Proportions and Percentages Are Discrete Data
  7. Chapter 3: Myth 3: ļæ¼Is the Correct Formula for Sample Standard Deviation
  8. Chapter 4: Myth 4: Sample Standard Deviation ļæ¼ Is Unbiased
  9. Chapter 5: Myth 5: Variances Can Be Added but Not Standard Deviations
  10. Chapter 6: Myth 6: Parts and Operators for an MSA Do Not Have to Be Randomly Selected
  11. Chapter 7: Myth 7: % Study (% Contribution, Number of Distinct Categories) Is the Best Criterion for Evaluating a Measurement System for Process Improvement
  12. Chapter 8: Myth 8: Only Sigma Can Compare Different Processes and Metrics
  13. Chapter 9: Myth 9: Capability Is Not Percent/Proportion of Good Units
  14. Chapter 10: Myth 10: p = Probability of Making an Error
  15. Chapter 11: Myth 11: Need More Data for Discrete Data than Continuous Data Analysis
  16. Chapter 12: Myth 12: Nonparametric Tests Are Less Powerful than Parametric Tests
  17. Chapter 13: Myth 13: Sample Size of 30 Is Acceptable (for Statistical Significance)
  18. Chapter 14: Myth 14: Can Only Fail to Reject Ho, Can Never Accept Ho
  19. Chapter 15: Myth 15: Control Limits Are Ā±3 Standard Deviations from the Center Line
  20. Chapter 16: Myth 16: Control Chart Limits Are Empirical Limits
  21. Chapter 17: Myth 17: Control Chart Limits Are Not Probability Limits
  22. Chapter 18: Myth 18: Ā±3 Sigma Limits Are the Most Economical Control Chart Limits
  23. Chapter 19: Myth 19: Statistical Inferences Are Inductive Inferences
  24. Chapter 20: Myth 20: There Is One Universe or Population if Data Are Homogeneous
  25. Chapter 21: Myth 21: Control Charts Are Analytic Studies
  26. Chapter 22: Myth 22: Control Charts Are Not Tests of Hypotheses
  27. Chapter 23: Myth 23: Process Needs to Be Stable to Calculate Process Capability
  28. Chapter 24: Myth 24: Specifications Donā€™t Belong on Control Charts
  29. Chapter 25: Myth 25: Identify and Eliminate Assignable or Assignable Causes of Variation
  30. Chapter 26: Myth 26: Process Needs to Be Stable before You Can Improve It
  31. Chapter 27: Myth 27: Stability (Homogeneity) Is Required to Establish a Baseline
  32. Chapter 28: Myth 28: A Process Must Be Stable to Be Predictable
  33. Chapter 29: Myth 29: Adjusting a Process Based on a Single Defect Is Tampering, Causing Increased Process Variation
  34. Chapter 30: Myth 30: No Assumptions Required When the Data Speak for Themselves
  35. Epilogue
  36. References
  37. About the Author
  38. Back Cover