> My tentative conclusion is that your 2% effect really
> is a small one; it should be difficult to discern among
> likely artifacts; and therefore, it is hardly worth mentioning....
I agree to me it makes sense as well: fasting insulin should have more
to do with error and genetics than food and exercise, I'm not giving
up though. I've tried transforming Insulin as I noted odd error
behavior on my residuals but it only improved R^2 marginally.
Also I don't know if the fact that my population is so large is making
a difference. I note that most published studies usually study
percentiles of serum levels. This makes more sense I think as maybe
10,000 people will have "normal" serum levels whereas 400 might have
abnormal, and so would this have an effect on r^2.
I think I am breaking the assumption of regression that you can't
repeat the same points over and over. I will try to Consolidate
people into groups and then re-run the data. I'm not sure if this
will make a difference, but this is how i see it done in the
literature.
Statistics is interesting, it is hard to find information on the
problems you come across and they can only be tackled by running more
queries from different angles..
an exception : i asked a while ago whether standardized beta
coefficients are
valid and the answer was shown to be "no", curiously i came across a
journal article on this very topic, if anyone was following the
article is "A heuristic method for estimating the relative weight of
predictor variables in multiple regression" (Multivr behav res. 35 1
1-19, 2000) This article is very intereting to read... much to
comment..
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