> [ ... ]
> > Is doing a univariate regression between the variable you want to
> > adjust for and your predictor the only way to adjust for values as
>
> Univariate? Absolutely not. *Multiple* regression gives
> "partial regression coefficients." Those "adjust."
>
I find it extremely difficult to interpret multivariate equations.
Are there any good books on conceptualizing the equation?
For instance:
If you are assessing whether protein, fat, or carbohydrate is
important in obesity independant of calories, do you do the following
model:
Disease=carb+proten+fat+calories
and if so, isn't the word "calories" meaningless as it is equal to the
sum of the other three.
Perhaps it should not be included in the model.
I have read of studies were they will use everything except "carb" as
follows:
disease=protein+fat+calories
and from here you can determine what substituting carb with protein or
fat will have on the disease.
It is very difficult to conceptualize and very difficult to understand
what the word "calories" means anymore in a multivariate model..
It seems if you use univariate adjusted values it is easier to model,
I have very little experience in statistics as everyone can tell..
Just commenting, no real question here.. I will probably understand
it with time..
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