From: David Winsemius <dwinsemius_at_comcast.net>

Date: Sun, 20 Jun 2010 21:14:29 -0400

Date: Sun, 20 Jun 2010 21:14:29 -0400

On Jun 20, 2010, at 8:17 PM, (Ted Harding) wrote:

> On 20-Jun-10 19:54:02, David Winsemius wrote:

*>> On Jun 20, 2010, at 1:38 PM, Ekaterina Pek wrote:
**>>> Hi, Ted.
**>>> Thanks for your reply. It helped. I have further a bit of questions.
**>>>
**>>>> It may be that lm(log(b) ~ log(a)) is, from a substantive point of
**>>>> view, a more appropriate model for whetever it is than lm(b ~ a).
**>>>> Or it may not be. This is a separate question. Again, Spearman's
**>>>> rho is not definitive.
**>>>
**>>> How one determines if one linear model is more appropriate than
**>>> another ?
**>>> And : linear model "log(b) ~ log(a)" is okay ? I hesitated to use
**>>> such
**>>> thing from the beginning, because it seemed to me like it would have
**>>> meant a nonlinear model rather than linear.. (Sorry, if the question
**>>> is stupid, I'm not that good at statistics)
**>>
**>> Your earlier description of the plots made me think both "a" and "b"
**>> were right-skewed. Such a situation (if my interpretation were
**>> correct) would seriously undermine the statistical validity of an
**>> analysis like lm(a ~ b) .
**>> --
**>> David Winsemius, MD
**>
**> That doesn't follow. If b is linearly related to a: b = A + B*a +
**> error,
**> and if the distribution of a is highly skewed, then so also will be
**> the distribution of b, even if the error is a nice Gaussian error
**> with constant variance (and small compared with the dispersion
**> of a & b).
*

Yes, but that was not what was suggested in the OP's description of the scatterplot of a and b.

-- David Winsemius, MD West Hartford, CT ______________________________________________ R-help_at_r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.Received on Mon 21 Jun 2010 - 01:20:19 GMT

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