Understanding statistics and statistical myths: how to become a profound learner
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, FL
CRC Press
[2016]
|
| Schlagworte: | |
| Beschreibung: | "A Productivity Press book." Vendor-supplied metadata |
| Beschreibung: | 1 online resource |
| ISBN: | 9781498727464 1498727468 |
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| 100 | 1 | |a Castañeda-Méndez, Kicab |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Understanding statistics and statistical myths |b how to become a profound learner |c Kicab Castañeda-Méndez |
| 264 | 1 | |a Boca Raton, FL |b CRC Press |c [2016] | |
| 300 | |a 1 online resource | ||
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| 500 | |a "A Productivity Press book." | ||
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| 505 | 8 | |a Chapter 1. Myth 1 : Two types of data : attribute/discrete and measurement/continuous -- chapter 2. Myth 2 : proportions and percentages are discrete data -- chapter 3. Myth 3 : s = v (xi -- x)2/(n -1) is the correct -- chapter 4. Myth 4 : sample standard deviation -- chapter 5. Myth 5 : variances can be added but not standard deviations -- chapter 6. Myth 6 : parts and operators for an MSA do not have to be randomly selected -- chapter 7. Myth 7 : %study (% contribution, number of distinct categories) is the best criterion for evaluating a measurement system for process improvement -- chapter 8. Myth 8 : only sigma can compare different processes and metrics -- chapter 9. Myth 9 : capability is not percent/proportion of good units -- chapter 10. Myth 10 : p = probability of making an error -- chapter 11. Myth 11 : need more data for discrete data than continuous data analysis -- chapter 12. Myth 12 : nonparametric tests are less powerful than parametric tests -- | |
| 505 | 8 | |a chapter 13. Myth 13 : sample size of 30 is acceptable (for statistical significance) -- chapter 14. Myth 14 : can only fail to reject Ho, can never accept Ho -- chapter 15. Myth 15 : control limits are +3 standard deviations from the center line -- chapter 16. Myth 16 : control chart limits are empirical limits -- chapter 17. Myth 17 : control chart limits are not probability limits -- chapter 18. Myth 18 : +3 sigma limits are the most economical control chart limits -- chapter 19. Myth 19 : statistical inferences are inductive inferences -- chapter 20. Myth 20 : there is one universe or population if data are homogeneous -- chapter 21. Myth 21 : control charts are analytic studies -- chapter 22. Myth 22 : control charts are not tests of hypotheses -- chapter 23. Myth 23 : process needs to be stable to calculate process capability -- chapter 24. Myth 24 : specifications don't belong on control charts -- | |
| 505 | 8 | |a chapter 25. Myth 25 : identify and eliminate assignable or assignable causes of variation -- chapter 26. Myth 26 : process needs to be stable before you can improve it -- chapter 27. Myth 27 : stability (homogeneity) is required to establish a baseline -- chapter 28. Myth 28 : a process must be stable to be predictable -- chapter 29. Myth 29 : adjusting a process based on a single defect is tampering, causing increased process -- chapter 30. Myth 30 : no assumptions required when the data speak for themselves | |
| 650 | 7 | |a MATHEMATICS / Applied |2 bisacsh | |
| 650 | 7 | |a MATHEMATICS / Probability & Statistics / General |2 bisacsh | |
| 650 | 7 | |a Problem solving / Statistical methods |2 fast | |
| 650 | 7 | |a Statistics |2 fast | |
| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Statistics |a Problem solving |x Statistical methods | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Castaneda-Mendez, Kicab |t Understanding Statistics and Statistical Myths : How to Become a Profound Learner |d Boca Raton : CRC Press,c2015 |z 9781498727457 |
| 912 | |a ZDB-4-NLEBK | ||
| 999 | |a oai:aleph.bib-bvb.de:BVB01-029762718 | ||
Datensatz im Suchindex
| _version_ | 1804177607262994432 |
|---|---|
| any_adam_object | |
| author | Castañeda-Méndez, Kicab |
| author_facet | Castañeda-Méndez, Kicab |
| author_role | aut |
| author_sort | Castañeda-Méndez, Kicab |
| author_variant | k c m kcm |
| building | Verbundindex |
| bvnumber | BV044360087 |
| collection | ZDB-4-NLEBK |
| contents | Chapter 1. Myth 1 : Two types of data : attribute/discrete and measurement/continuous -- chapter 2. Myth 2 : proportions and percentages are discrete data -- chapter 3. Myth 3 : s = v (xi -- x)2/(n -1) is the correct -- chapter 4. Myth 4 : sample standard deviation -- chapter 5. Myth 5 : variances can be added but not standard deviations -- chapter 6. Myth 6 : parts and operators for an MSA do not have to be randomly selected -- chapter 7. Myth 7 : %study (% contribution, number of distinct categories) is the best criterion for evaluating a measurement system for process improvement -- chapter 8. Myth 8 : only sigma can compare different processes and metrics -- chapter 9. Myth 9 : capability is not percent/proportion of good units -- chapter 10. Myth 10 : p = probability of making an error -- chapter 11. Myth 11 : need more data for discrete data than continuous data analysis -- chapter 12. Myth 12 : nonparametric tests are less powerful than parametric tests -- chapter 13. Myth 13 : sample size of 30 is acceptable (for statistical significance) -- chapter 14. Myth 14 : can only fail to reject Ho, can never accept Ho -- chapter 15. Myth 15 : control limits are +3 standard deviations from the center line -- chapter 16. Myth 16 : control chart limits are empirical limits -- chapter 17. Myth 17 : control chart limits are not probability limits -- chapter 18. Myth 18 : +3 sigma limits are the most economical control chart limits -- chapter 19. Myth 19 : statistical inferences are inductive inferences -- chapter 20. Myth 20 : there is one universe or population if data are homogeneous -- chapter 21. Myth 21 : control charts are analytic studies -- chapter 22. Myth 22 : control charts are not tests of hypotheses -- chapter 23. Myth 23 : process needs to be stable to calculate process capability -- chapter 24. Myth 24 : specifications don't belong on control charts -- chapter 25. Myth 25 : identify and eliminate assignable or assignable causes of variation -- chapter 26. Myth 26 : process needs to be stable before you can improve it -- chapter 27. Myth 27 : stability (homogeneity) is required to establish a baseline -- chapter 28. Myth 28 : a process must be stable to be predictable -- chapter 29. Myth 29 : adjusting a process based on a single defect is tampering, causing increased process -- chapter 30. Myth 30 : no assumptions required when the data speak for themselves |
| ctrlnum | (ZDB-4-NLEBK)ocn928883548 (OCoLC)928883548 (DE-599)BVBBV044360087 |
| dewey-full | 519.5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5 |
| dewey-search | 519.5 |
| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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Myth 1 : Two types of data : attribute/discrete and measurement/continuous -- chapter 2. Myth 2 : proportions and percentages are discrete data -- chapter 3. Myth 3 : s = v (xi -- x)2/(n -1) is the correct -- chapter 4. Myth 4 : sample standard deviation -- chapter 5. Myth 5 : variances can be added but not standard deviations -- chapter 6. Myth 6 : parts and operators for an MSA do not have to be randomly selected -- chapter 7. Myth 7 : %study (% contribution, number of distinct categories) is the best criterion for evaluating a measurement system for process improvement -- chapter 8. Myth 8 : only sigma can compare different processes and metrics -- chapter 9. Myth 9 : capability is not percent/proportion of good units -- chapter 10. Myth 10 : p = probability of making an error -- chapter 11. Myth 11 : need more data for discrete data than continuous data analysis -- chapter 12. Myth 12 : nonparametric tests are less powerful than parametric tests -- </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">chapter 13. Myth 13 : sample size of 30 is acceptable (for statistical significance) -- chapter 14. Myth 14 : can only fail to reject Ho, can never accept Ho -- chapter 15. Myth 15 : control limits are +3 standard deviations from the center line -- chapter 16. Myth 16 : control chart limits are empirical limits -- chapter 17. Myth 17 : control chart limits are not probability limits -- chapter 18. Myth 18 : +3 sigma limits are the most economical control chart limits -- chapter 19. Myth 19 : statistical inferences are inductive inferences -- chapter 20. Myth 20 : there is one universe or population if data are homogeneous -- chapter 21. Myth 21 : control charts are analytic studies -- chapter 22. Myth 22 : control charts are not tests of hypotheses -- chapter 23. Myth 23 : process needs to be stable to calculate process capability -- chapter 24. Myth 24 : specifications don't belong on control charts -- </subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">chapter 25. Myth 25 : identify and eliminate assignable or assignable causes of variation -- chapter 26. Myth 26 : process needs to be stable before you can improve it -- chapter 27. Myth 27 : stability (homogeneity) is required to establish a baseline -- chapter 28. Myth 28 : a process must be stable to be predictable -- chapter 29. Myth 29 : adjusting a process based on a single defect is tampering, causing increased process -- chapter 30. 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| illustrated | Not Illustrated |
| indexdate | 2024-07-10T07:50:46Z |
| institution | BVB |
| isbn | 9781498727464 1498727468 |
| language | English |
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| publisher | CRC Press |
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| spelling | Castañeda-Méndez, Kicab Verfasser aut Understanding statistics and statistical myths how to become a profound learner Kicab Castañeda-Méndez Boca Raton, FL CRC Press [2016] 1 online resource txt rdacontent c rdamedia cr rdacarrier "A Productivity Press book." Vendor-supplied metadata Chapter 1. Myth 1 : Two types of data : attribute/discrete and measurement/continuous -- chapter 2. Myth 2 : proportions and percentages are discrete data -- chapter 3. Myth 3 : s = v (xi -- x)2/(n -1) is the correct -- chapter 4. Myth 4 : sample standard deviation -- chapter 5. Myth 5 : variances can be added but not standard deviations -- chapter 6. Myth 6 : parts and operators for an MSA do not have to be randomly selected -- chapter 7. Myth 7 : %study (% contribution, number of distinct categories) is the best criterion for evaluating a measurement system for process improvement -- chapter 8. Myth 8 : only sigma can compare different processes and metrics -- chapter 9. Myth 9 : capability is not percent/proportion of good units -- chapter 10. Myth 10 : p = probability of making an error -- chapter 11. Myth 11 : need more data for discrete data than continuous data analysis -- chapter 12. Myth 12 : nonparametric tests are less powerful than parametric tests -- chapter 13. Myth 13 : sample size of 30 is acceptable (for statistical significance) -- chapter 14. Myth 14 : can only fail to reject Ho, can never accept Ho -- chapter 15. Myth 15 : control limits are +3 standard deviations from the center line -- chapter 16. Myth 16 : control chart limits are empirical limits -- chapter 17. Myth 17 : control chart limits are not probability limits -- chapter 18. Myth 18 : +3 sigma limits are the most economical control chart limits -- chapter 19. Myth 19 : statistical inferences are inductive inferences -- chapter 20. Myth 20 : there is one universe or population if data are homogeneous -- chapter 21. Myth 21 : control charts are analytic studies -- chapter 22. Myth 22 : control charts are not tests of hypotheses -- chapter 23. Myth 23 : process needs to be stable to calculate process capability -- chapter 24. Myth 24 : specifications don't belong on control charts -- chapter 25. Myth 25 : identify and eliminate assignable or assignable causes of variation -- chapter 26. Myth 26 : process needs to be stable before you can improve it -- chapter 27. Myth 27 : stability (homogeneity) is required to establish a baseline -- chapter 28. Myth 28 : a process must be stable to be predictable -- chapter 29. Myth 29 : adjusting a process based on a single defect is tampering, causing increased process -- chapter 30. Myth 30 : no assumptions required when the data speak for themselves MATHEMATICS / Applied bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Problem solving / Statistical methods fast Statistics fast Statistik Statistics Problem solving Statistical methods Erscheint auch als Druck-Ausgabe Castaneda-Mendez, Kicab Understanding Statistics and Statistical Myths : How to Become a Profound Learner Boca Raton : CRC Press,c2015 9781498727457 |
| spellingShingle | Castañeda-Méndez, Kicab Understanding statistics and statistical myths how to become a profound learner Chapter 1. Myth 1 : Two types of data : attribute/discrete and measurement/continuous -- chapter 2. Myth 2 : proportions and percentages are discrete data -- chapter 3. Myth 3 : s = v (xi -- x)2/(n -1) is the correct -- chapter 4. Myth 4 : sample standard deviation -- chapter 5. Myth 5 : variances can be added but not standard deviations -- chapter 6. Myth 6 : parts and operators for an MSA do not have to be randomly selected -- chapter 7. Myth 7 : %study (% contribution, number of distinct categories) is the best criterion for evaluating a measurement system for process improvement -- chapter 8. Myth 8 : only sigma can compare different processes and metrics -- chapter 9. Myth 9 : capability is not percent/proportion of good units -- chapter 10. Myth 10 : p = probability of making an error -- chapter 11. Myth 11 : need more data for discrete data than continuous data analysis -- chapter 12. Myth 12 : nonparametric tests are less powerful than parametric tests -- chapter 13. Myth 13 : sample size of 30 is acceptable (for statistical significance) -- chapter 14. Myth 14 : can only fail to reject Ho, can never accept Ho -- chapter 15. Myth 15 : control limits are +3 standard deviations from the center line -- chapter 16. Myth 16 : control chart limits are empirical limits -- chapter 17. Myth 17 : control chart limits are not probability limits -- chapter 18. Myth 18 : +3 sigma limits are the most economical control chart limits -- chapter 19. Myth 19 : statistical inferences are inductive inferences -- chapter 20. Myth 20 : there is one universe or population if data are homogeneous -- chapter 21. Myth 21 : control charts are analytic studies -- chapter 22. Myth 22 : control charts are not tests of hypotheses -- chapter 23. Myth 23 : process needs to be stable to calculate process capability -- chapter 24. Myth 24 : specifications don't belong on control charts -- chapter 25. Myth 25 : identify and eliminate assignable or assignable causes of variation -- chapter 26. Myth 26 : process needs to be stable before you can improve it -- chapter 27. Myth 27 : stability (homogeneity) is required to establish a baseline -- chapter 28. Myth 28 : a process must be stable to be predictable -- chapter 29. Myth 29 : adjusting a process based on a single defect is tampering, causing increased process -- chapter 30. Myth 30 : no assumptions required when the data speak for themselves MATHEMATICS / Applied bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Problem solving / Statistical methods fast Statistics fast Statistik Statistics Problem solving Statistical methods |
| title | Understanding statistics and statistical myths how to become a profound learner |
| title_auth | Understanding statistics and statistical myths how to become a profound learner |
| title_exact_search | Understanding statistics and statistical myths how to become a profound learner |
| title_full | Understanding statistics and statistical myths how to become a profound learner Kicab Castañeda-Méndez |
| title_fullStr | Understanding statistics and statistical myths how to become a profound learner Kicab Castañeda-Méndez |
| title_full_unstemmed | Understanding statistics and statistical myths how to become a profound learner Kicab Castañeda-Méndez |
| title_short | Understanding statistics and statistical myths |
| title_sort | understanding statistics and statistical myths how to become a profound learner |
| title_sub | how to become a profound learner |
| topic | MATHEMATICS / Applied bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Problem solving / Statistical methods fast Statistics fast Statistik Statistics Problem solving Statistical methods |
| topic_facet | MATHEMATICS / Applied MATHEMATICS / Probability & Statistics / General Problem solving / Statistical methods Statistics Statistik Statistics Problem solving Statistical methods |
| work_keys_str_mv | AT castanedamendezkicab understandingstatisticsandstatisticalmythshowtobecomeaprofoundlearner |