Timeline for Why does minimizing absolute value and squares of residuals in a regression give different answers?
Current License: CC BY-SA 4.0
4 events
| when toggle format | what | by | license | comment | |
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| Dec 6, 2018 at 21:03 | vote | accept | badmax | ||
| Dec 6, 2018 at 21:03 | vote | accept | badmax | ||
| Dec 6, 2018 at 21:03 | |||||
| Dec 6, 2018 at 16:40 | comment | added | James Phillips | Consider two points, one with an absolute error of 5.0 and one with an absolute error of 1.0. When squared, these values become 25.0 and 1.0 which is why outliers can dominate a standard sum-of-squared-error regression - their squared errors become very large. | |
| Dec 6, 2018 at 16:31 | history | answered | Peter Flom | CC BY-SA 4.0 |