Timeline for When (and why) should you take the log of a distribution (of numbers)?
Current License: CC BY-SA 3.0
22 events
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| Aug 19, 2020 at 13:30 | answer | added | Haotian Chen | timeline score: 2 | |
| Mar 19, 2020 at 3:19 | history | protected | kjetil b halvorsen♦ | ||
| Mar 19, 2020 at 3:08 | answer | added | Maddog74 | timeline score: 3 | |
| Oct 10, 2019 at 5:56 | comment | added | Ben | Are there some solid basic advices when not to use a log-transformation or what to consider when done? | |
| Dec 8, 2018 at 17:57 | comment | added | Deepak | This and this are providing a good explanation. | |
| Sep 6, 2018 at 4:23 | comment | added | Lakshay Dulani | so is it right to say that if a distribution is non linear, and its log is linear.. we use the log one since it is easy to model a linear distribution? | |
| S Sep 19, 2016 at 12:01 | history | suggested | Jim G. | CC BY-SA 3.0 |
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| Sep 19, 2016 at 11:28 | review | Suggested edits | |||
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| Mar 3, 2013 at 2:41 | comment | added | gung - Reinstate Monica | Readers here may also want to look at these closely related threads: interpretation-of-log-transformed-predictor, & How to interpret logarithmically transformed coefficients in linear regression. | |
| Jan 24, 2013 at 21:07 | answer | added | vector07 | timeline score: 146 | |
| Nov 30, 2011 at 5:09 | vote | accept | PhD | ||
| Nov 28, 2011 at 14:21 | history | tweeted | twitter.com/#!/StackStats/status/141159806563127297 | ||
| Nov 28, 2011 at 7:18 | history | edited | IrishStat | CC BY-SA 3.0 |
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| Nov 23, 2011 at 23:45 | comment | added | PhD | @whuber: Ah, I see. Time for some introspection, it seems :) | |
| Nov 23, 2011 at 22:48 | answer | added | IrishStat | timeline score: 133 | |
| Nov 23, 2011 at 21:29 | comment | added | whuber♦ | Ah, but you know much more than that, because after using logs in regression, you know that the results are interpreted differently and you know to take care in back-transforming fitted values and confidence intervals. I'm suggesting that you might not be confused and that you probably already know many of the answers to these four questions, even though you weren't initially aware of it :-). | |
| Nov 23, 2011 at 21:24 | comment | added | PhD | @whuber: I see...so I do understand the reasons for taking logs in regression, but only because I had been taught so - I understand it from the need to do so perspective i.e., to make sure the data fits within the assumptions of linear regression. That's my only understanding. Maybe what I'm missing is "real understanding" of the effect of taking logs and hence the confusion...any help? ;) | |
| Nov 23, 2011 at 21:15 | comment | added | whuber♦ | The clarification helps. You might want to ponder the fact, though, that regression with only a constant term (and no other independent variables) amounts to assessing the variation of the data around their mean. Therefore, if you really understand the effects of taking logs of dependent variables in regression, you already understand the (simpler) situation you are asking about here. In short, once you have answers to all four questions for regression, you don't need to ask them again about "the distribution in isolation." | |
| Nov 23, 2011 at 21:10 | history | edited | PhD | CC BY-SA 3.0 |
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| Nov 23, 2011 at 20:49 | history | edited | whuber♦ |
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| Nov 23, 2011 at 20:46 | comment | added | whuber♦ | Because this covers almost the same ground as previous questions here and here, please read those threads and update your question to focus on any aspects of this issue that haven't already been addressed. Note, too, #4 (and part of #3) are elementary questions about logarithms whose answers are readily found in many places. | |
| Nov 23, 2011 at 20:41 | history | asked | PhD | CC BY-SA 3.0 |