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). Ted. -------------------------------------------------------------------- E-Mail: (Ted Harding) <ted.hard...@manchester.ac.uk> Fax-to-email: +44 (0)870 094 0861 Date: 21-Jun-10 Time: 01:17:34 ------------------------------ XFMail ------------------------------ ______________________________________________ R-help@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.