On Jun 20, 2010, at 9:14 PM, David Winsemius wrote:
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.
Or rather I should say that is not the data picture that came to mind.
Your theory can be visualized as:
> a <- rlnorm(3000)
> b <- 1 + 2*a +rnorm(3000)
> plot(a,b)
Mine was a more heteroskedastic picture:
> a <- rlnorm(3000)
> b <- rlnorm(3000)
> plot(a,b)
--
David Winsemius, MD
West Hartford, CT
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