Timeline for Regression on means of log-transformed variables
Current License: CC BY-SA 3.0
9 events
when toggle format | what | by | license | comment | |
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Apr 2, 2013 at 2:22 | vote | accept | Joe | ||
Apr 1, 2013 at 22:43 | comment | added | whuber♦ | For intuition, suppose $Y = X$ up to a relatively small (independent, homoscedastic) random error and that the values of $X$ are skewed. Then the values of $Y$, looking like those of $X$, will also be skewed: but you would have no reason to, and gain no benefit from, taking logarithms. For intuition about your method, consider that most of the averages $\bar{x}_i$ will be close to the mean of all the $x$'s and most of the $\bar{y}_i$ will be close to the mean of all the $y$'s. Thus any regression based on them will grossly extrapolate to the full range of $x$'s and probably be wildly wrong. | |
Apr 1, 2013 at 22:13 | answer | added | Glen_b | timeline score: 3 | |
Apr 1, 2013 at 18:45 | review | First posts | |||
Apr 1, 2013 at 18:46 | |||||
Apr 1, 2013 at 18:43 | comment | added | D L Dahly | It seems like you could simulate date under one "model" and then estimate the relationship with the other. | |
Apr 1, 2013 at 18:37 | comment | added | whuber♦ | Please explain how we are to construe "more or less": if that doesn't mean exactly the same, then how are we to determine how close is close enough? (We would have a built-in way to do that if your procedure provided accurate error estimates, but it does not.) | |
Apr 1, 2013 at 18:33 | comment | added | Joe | "equivalent" means the linear regression results of the two methods (coefficient estimates, etc.) will be more or less the same. In other words, they both arrive at the same relationship between X and Y. | |
Apr 1, 2013 at 18:29 | comment | added | whuber♦ | It is clear the two approaches are different, because your proposal fails to obtain (correct) information about the uncertainty in the estimates. So in what sense do you mean "equivalent"? | |
Apr 1, 2013 at 18:26 | history | asked | Joe | CC BY-SA 3.0 |