Timeline for How to interpret Residuals vs. Fitted Plot
Current License: CC BY-SA 4.0
10 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Nov 7, 2019 at 14:35 | comment | added | Carl Witthoft |
You probably should mention that you're using the R language, for those few folks at this site who don't recognize it. :-)
|
|
| Nov 7, 2019 at 0:40 | history | became hot network question | |||
| Nov 6, 2019 at 21:00 | history | tweeted | twitter.com/StackStats/status/1192185015326724097 | ||
| Nov 6, 2019 at 18:26 | answer | added | Student | timeline score: 15 | |
| Nov 6, 2019 at 17:48 | history | edited | Nick Cox | CC BY-SA 4.0 |
deleted 25 characters in body
|
| Nov 6, 2019 at 17:43 | answer | added | BruceET | timeline score: 8 | |
| Nov 6, 2019 at 17:23 | comment | added | BruceET | Probability plots often 'wobble' towards the extremes because data is relatively sparse there. | |
| Nov 6, 2019 at 16:58 | answer | added | Bernhard | timeline score: 4 | |
| Nov 6, 2019 at 16:57 | comment | added | Student | Visitors is a count variable with support $\{0, 1, \ldots\}$ but the OLS assumes it is normal with support $(-\infty,\infty)$. For low predicted (fitted) visitor counts, the prediction error (residual) can only get so low, hence the cutoff in the plot. A more apt specification might be a Poisson regression or another regression model based on a count outcome. | |
| Nov 6, 2019 at 16:29 | history | asked | Daniël Lutjens | CC BY-SA 4.0 |