Timeline for Calculating weight as 1/(stnd error) for weighted regression if stnd error = 0
Current License: CC BY-SA 3.0
9 events
| when toggle format | what | by | license | comment | |
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| Dec 1, 2015 at 18:32 | comment | added | RobertF | @AndrewM - Yes, weighting by stnd error, although it's been 3 years so I'd have to search thru my files for the SAS code to be absolutely certain. | |
| Dec 1, 2015 at 18:16 | comment | added | Andrew M | Just checking: you are weighting by the standard error of the mean for the $i$th observation: $\hat{s_i}/\sqrt{n_i}$, right? The $\sqrt{n_i}$ factor could be more important than the $\hat{s_i}$ factor | |
| Dec 1, 2015 at 15:51 | answer | added | Ogaday | timeline score: 2 | |
| Sep 19, 2012 at 21:07 | history | edited | whuber♦ | CC BY-SA 3.0 |
appended answer 37621 as supplemental
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| Sep 19, 2012 at 19:21 | comment | added | whuber♦ | This is, at bottom, an issue of identifying and quantifying separate components of variance. Your weighting is appropriate when all variation can be attributed to the measurement error in LOS. Because you're doing a regression, there will likely be residuals: they will include a separate (independent) variance component. If, eyeballing the scatterplot, it appears the regression residuals will be larger than typical LOS SE's, then you are probably OK not weighting anything. The problem is more challenging otherwise, so first it would be good to do this check! What does it tell you? | |
| Sep 19, 2012 at 18:03 | answer | added | Michael R. Chernick | timeline score: 5 | |
| S Sep 19, 2012 at 15:52 | history | suggested | jonsca | CC BY-SA 3.0 |
Removed signature, reworked title
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| Sep 19, 2012 at 15:51 | review | Suggested edits | |||
| S Sep 19, 2012 at 15:52 | |||||
| Sep 19, 2012 at 15:42 | history | asked | RobertF | CC BY-SA 3.0 |