Timeline for What theories should every statistician know?
Current License: CC BY-SA 3.0
8 events
| when toggle format | what | by | license | comment | |
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| Mar 10, 2014 at 21:25 | comment | added | Scortchi♦ | Fair enough: I'd have said measurement systems analysis (inter-rater agreement, gauge reproducibility & repeatability studies), statistical process control, reliability analysis (a.k.a. survival analysis), & experimental design ((fractional) factorial designs, response-surface methodology) were characteristic of industrial statistics. | |
| Mar 8, 2014 at 17:24 | history | edited | StasK | CC BY-SA 3.0 |
refs to big data
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| Mar 8, 2014 at 17:22 | comment | added | StasK | @Scortchi, I was unable to get past these terminology differences, frankly. I also know that normal approximations are close to being useless in the tails, so the 6 sigma probability $10^{-9}$ may be off by a factor of 100 or 1000. | |
| Mar 7, 2014 at 16:02 | comment | added | Scortchi♦ | I'm puzzled by these "peculiar Six Sigma paradigms", "remotely connected to mainstream Statistics" with which you say Industrial Statistics operates. It seems entirely orthodox to me, putting aside the differences in terminology found between all of these sub-fields. | |
| Feb 22, 2012 at 22:48 | vote | accept | bnjmn | ||
| Feb 17, 2012 at 20:56 | comment | added | bnjmn | Great answer. Thank you for highlighting the some of the big differences between statisticians within industry. This helps motivate my question because I believe many people have a different idea of what a statistician is/does. I guess I was trying to find out where these all intersect from a basic understanding. Also, I really appreciate your last paragraph about business topics and how essential they are. Great points but I would still like to see if anyone can add to the conversation before accepting. | |
| S Feb 14, 2012 at 19:39 | history | answered | StasK | CC BY-SA 3.0 | |
| S Feb 14, 2012 at 19:39 | history | made wiki | Post Made Community Wiki by StasK |