Timeline for In linear regression, why are raw least squares residuals heteroskedastic?
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Apr 22, 2019 at 15:01 | history | tweeted | twitter.com/StackStats/status/1120341779411288072 | ||
| Apr 21, 2019 at 22:01 | answer | added | Michael Hardy | timeline score: 1 | |
| Apr 21, 2019 at 19:42 | answer | added | kjetil b halvorsen♦ | timeline score: 5 | |
| Apr 19, 2019 at 1:22 | comment | added | Glen_b | There's a derivation of the variance of a residual for the multiple regression case and some additional explanation here | |
| Apr 18, 2019 at 21:46 | comment | added | kjetil b halvorsen♦ | The answer can be found here: stats.stackexchange.com/questions/212656/… | |
| Apr 18, 2019 at 3:18 | comment | added | Glen_b | For residuals, the variance is smaller at the extremes. This is because the more extreme observations have more influence over the regression function. ('more extreme' = further from the mean in x-space, as measured by Mahalanobis' distance) [Specific formulas are readily derived or may be found in other answers on site.] | |
| Apr 17, 2019 at 15:10 | history | asked | Kuku | CC BY-SA 4.0 |