Timeline for In R, how to detect possible outliers in right skewed data assuming Poisson distribution?
Current License: CC BY-SA 4.0
27 events
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| Mar 28, 2020 at 15:41 | comment | added | Vesanen | Thank you, I received your file. | |
| Mar 28, 2020 at 3:40 | comment | added | Carl | Link to file paarking.nb good for 30 days. easyupload.io/ace0tn File is a bit messy, I'm not good at annotating what I do with code. You may have to execute code mostly sequentially but sometimes out of sequence to follow what I was playing with. Will delete in 30 days. | |
| Mar 27, 2020 at 22:54 | vote | accept | Vesanen | ||
| Mar 27, 2020 at 22:54 | comment | added | Vesanen | nb file will work well. Thank you. I've already voted up your answer. | |
| Mar 26, 2020 at 20:26 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 26, 2020 at 20:03 | comment | added | Carl | Do you have a copy of Mathematica to read .nb files, or want a .pdf file or snippets of code in the answer? It's a bit lengthy to put everything into an answer. | |
| Mar 26, 2020 at 19:14 | comment | added | Carl | OK, working on it, but accept the answer and vote for it. I up voted your question, so you have enough reputation points to up vote. | |
| Mar 26, 2020 at 9:28 | comment | added | Vesanen | Thank you again for your swift, illustrative answers. I would appreciate the Mathematica code, if you have the time to post it. | |
| Mar 26, 2020 at 3:01 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 26, 2020 at 2:35 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 26, 2020 at 2:11 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 26, 2020 at 2:05 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 26, 2020 at 1:24 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 26, 2020 at 1:02 | comment | added | Carl | Yes, Mathematica. If you want the "notebook" i.e., the Mathematica code, I will figure out how to post it. | |
| Mar 26, 2020 at 0:35 | comment | added | Carl | The only outliers are the 99+ answers. Everything else is good data. The CDF was used for noise reduction. The "noise" in the data is due to human nature as in "give me a minute" or "wait a second." The choice of 5, 10, 15,... minutes is due to clock divisions which is how most people, even in this digital age, learn to tell time. Wait times have heavy tails to the right, that is not "outlier" activity, it is data. | |
| Mar 25, 2020 at 19:14 | comment | added | Vesanen | Thank you for your thorough answer. I can't, however, sort out your conclusion to my problem. Would you kindly elaborate how the CDF helps in detecting the outliers in my skewed data? Are all the plots you display made in Mathematica? | |
| Mar 25, 2020 at 3:31 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 25, 2020 at 3:03 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 25, 2020 at 2:27 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 25, 2020 at 2:17 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 25, 2020 at 1:39 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 25, 2020 at 1:31 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 25, 2020 at 0:13 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 25, 2020 at 0:02 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 24, 2020 at 23:43 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 24, 2020 at 23:23 | history | edited | Carl | CC BY-SA 4.0 |
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| Mar 24, 2020 at 23:09 | history | answered | Carl | CC BY-SA 4.0 |