Understanding statistics and statistical myths : how to become a profound learner
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton :
CRC Press
2016
|
| Subjects: |
Table of Contents:
- 1. Two types of data : attribute/discrete and measurement/continuous
- 2. Proportions and percentages are discrete data
- 3. : s = v (xi
- x)2/(n -1) is the correct
- 4. Sample standard deviation
- 5. Variances can be added but not standard deviations
- 6. Parts and operators for an MSA do not have to be randomly selected
- 7. %study (% contribution, number of distinct categories) is the best criterion for evaluating a measurement system for process improvement
- 8. Only sigma can compare different processes and metrics
- 9. Capability is not percent/proportion of good units
- 10. p = probability of making an error
- 11. Need more data for discrete data than continuous data analysis
- 12. Nonparametric tests are less powerful than parametric tests
- 13. Sample size of 30 is acceptable (for statistical significance)
- 14. Can only fail to reject Ho, can never accept Ho
- 15. Control limits are +3 standard deviations from the center line
- 16. Control chart limits are empirical limits
- 17. Control chart limits are not probability limits
- 18. +3 sigma limits are the most economical control chart limits
- 19. Statistical inferences are inductive inferences
- 20. There is one universe or population if data are homogeneous
- 21. Control charts are analytic studies
- 22. Control charts are not tests of hypotheses
- 23. Process needs to be stable to calculate process capability
- 24. Specifications don't belong on control charts
- 25. Identify and eliminate assignable or assignable causes of variation
- 26. Process needs to be stable before you can improve it
- 27. Stability (homogeneity) is required to establish a baseline
- 28. A process must be stable to be predictable
- 29. Adjusting a process based on a single defect is tampering, causing increased process