> low-fat vegan diet" would be close). However, the incidence of heterozygous
> familal hypercholesterolemia is only 1:500,000, so this exposure contributes
> little to the variance in serum cholesterol in the population; its r^2 would
> be small.
>
> -Jay
Thanks,
This is similar to a problem I have come across: the measurement of a
serum value against exposure.
My theory is that they are correlated. But the data says that they
have an R^2 of 0.02 even though the p-value for the beta is p=1E-40
(ie. zero).
As you explain this is possible. My reasoning is that the exposure is
happening many hours before the measurement of serum and so that is
why R^2 is low. Nonetheless the strong beta might suggest a strong
effect of the exposure on the serum marker.
I've inserted time before exposure into the equation and it barely
explained the difference the reason is that not enough people had
their serum measured 2hrs after exposure.
basically the data is inadequate - but i'm crossing fingers that the
low p-value is useful.
anyway what i've learned is that R^2 does not measure slope. I knew
this but it hadn't sunk in. R^2 is very useful though, for example if
you want to know in the american population what is the highest source
of fat, you would use R^2 on the food frequencies, not the beta
coefficient.. because the R^2 would tell you the food that most
predicts, rather than the "strength" of the effect of the food.. ie.
low fat foods may be main source of fat in diet..
-just thinking outloud hehe..
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