Timeline for How should outliers be dealt with in linear regression analysis?
Current License: CC BY-SA 2.5
5 events
| when toggle format | what | by | license | comment | |
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| Mar 2, 2023 at 17:15 | comment | added | Agustin Barrachina | I like how it was serious until the " but hey, it's a crazy world, no system is perfect" | |
| Aug 24, 2022 at 9:00 | comment | added | mkt | (-1) Summarising data with extreme values does not imply removing data that is not easily summarised by a simple model. Improve your model, don't throw out (valid) data. | |
| Apr 6, 2017 at 11:46 | comment | added | drevicko | Do consider other models though. The world if full of removed "outliers" that were real data, resulting in failing to predict something really important. Many natural processes have power-law like behaviour with rare extreme events. Linear models may seem to fit such data (albeit not too well), but using one and deleting the "outliers" means missing those extreme events, which are usually important to know about! | |
| May 5, 2015 at 12:59 | comment | added | bartektartanus | "hey, it's a crazy world, no system is perfect" +1 for that my friend! :) | |
| Jul 21, 2010 at 7:51 | history | answered | Chris Beeley | CC BY-SA 2.5 |