Timeline for Box-Cox transformation for residuals in R
Current License: CC BY-SA 3.0
8 events
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| Aug 16, 2013 at 18:53 | history | edited | Nick Cox | CC BY-SA 3.0 |
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| Aug 16, 2013 at 18:21 | vote | accept | GK89 | ||
| Aug 13, 2013 at 21:03 | comment | added | Nick Cox | I don't know any confidence interval procedure defined that way; I think you are confusing standard deviations and standard errors. But if the residuals are not symmetric, it is likely that confidence intervals should not be either. See an extra bullet point just added to my answer. (Hint on last point: How do take logarithms or square roots of negative residuals?) | |
| Aug 13, 2013 at 21:01 | history | edited | Nick Cox | CC BY-SA 3.0 |
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| Aug 13, 2013 at 20:56 | comment | added | GK89 | Unfortunately the model is something that I cannot change - so it is what it is. A 95% confidence interval from my understanding would by 1.96*standard deviation - using this wouldn't be entirely descriptive of the data. Thank you for the comment on positive/negative residuals, I didn't know Box-Cox had difficulty with that | |
| Aug 13, 2013 at 18:08 | comment | added | Nick Cox | Thanks for that point. Normality of residuals is indeed not a problem, while severe non-normality may be. | |
| Aug 13, 2013 at 18:06 | comment | added | whuber♦ | Good advice. I just want to suggest that some of it is context-dependent; for instance, non-normality of residuals can have critical effects on prediction limits. Thus there can be merit in achieving residual distributions that are not wildly skewed, at least. | |
| Aug 13, 2013 at 18:03 | history | answered | Nick Cox | CC BY-SA 3.0 |