I think also that an experiment is the human attempt to make sense
out of the chaotic world:
the method is you assume chaos, H0 and then disprove it..
so you don't need controls because the experiment can be run to
prove maybe that the equation for velocity is valid..
(validation experiment).. (
> 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
[EMAIL PROTECTED] (Holger Boehm) wrote in message
news:<[EMAIL PROTECTED]>...
> Hi,
>
> I have calculated correlation coefficients between sets of parameters
> (A) and (B) and beween (A) and (C).
> Now I would like to determine the correlation between (A) and (B
> combined with C). How can I com
> You should take note that R^2 is *not* a very good measure
> of 'effect size.'
Hi Rich, you asked to see my data, i've posted the visual at the
following location http://www.accessv.com/~joemende/insulin2.gif note
that the r^2 is low despite the fact that it agrees with common sense:
Insuli
http://www.accessv.com/~joemende/insulin2.gif
Appologies, i also forgot to divide the KCAL in food by the 31 as this
represents kcal. It seems to me logical to advise decreasing food
intake and increasing physical activity to improve insulin
sensitivity. I would probably avoid reporting the R^2
> 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
"Jay Tanzman" <[EMAIL PROTECTED]> wrote in message
news:<a42e88$1bthp5$[EMAIL PROTECTED]>...
> Wuzzy <[EMAIL PROTECTED]> wrote in message
> [EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
> > It is because I am validating a 24hr dietary rec
> And that sounds impossible. I suspect a programming error.
>
> -Jay
you're right i programmed a food database incorrectly but i've redone
it and yep the correlation was only 0.20 for kcal or so.
it is hard to program a database *into* another database easy to make
errors..
i've made many err
Hi Rich, okay i'll post the reason why I ask:
It is because I am validating a 24hr dietary recall questionnaire
using
a food frequency questionnaire:
as someone else pointed out i got an error, also a perfect correlation
for pearsons.
it is much more complicated than this but that is the scoo
In my own defense:
I was asking a simple question:
will highly correlated cause an irregularly high R^2.
My answer to my own question is "no" it can't..
No-one here was able to give me this answer and I believe it is
correct: if your sample is large enough,(as mine is) then "no",
multicolline
> You made a model with the "exact same exposure in different units",
> which is something that no one would do,
Hehe, translation is don't post messages until you've thought them
through.
Anyway, turns out that the answer to my question is "No"..
Multicollinearity cannot force a correlation.
Is it possible that multicollinearity can force a correlation that
does not exist?
I have a very large sample of n=5,000
and have found that
disease= exposure + exposure + exposure + exposure R^2=0.45
where all 4 exposures are the exact same exposure in different units
like ug/dL or mg/dL or m
>
> In biostatistical studies, either version of beta is pretty worthless.
> Generally speaking.
If I may be permitted to infer a reason:
if you have
bodyweight= -a(drug) - b(exercise) + food
Then the standardized coefficients will affect bodyweight but they
will also affect each other. The
> Walter Willett has a whole chapter on this subject in his book Nutritional
> Epidemiology. It should be considered required reading before attempting to
> model anything that has to do with diet.
Thanks this is a really good book, not just for ppl wanting to study
nutrition but surveys in gen
> [ ... ]
> > 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 extreme
Pretend you want to see how fat relates to cancer risk
fat Kcalcancer
1 2 100
2 4 120
3 6 130
4 8 140
5 10 150
6 12 160
7 14 170
8 16 180
9 18 190
10 20 200
You have to adjust
also if you ajdust by using residuals, do you still have to factor in
KCal in your final regression equation?
it would seem to me that you should if you have other variables that
might be confounded by KCal, but otherwise you wouldn't.
===
Rich Ulrich <[EMAIL PROTECTED]> wrote in message
Thanks Rich, most informative, I am trying to determine a method of
comparing apples to oranges - it seems an improtant thing to try to
do, perhaps it is impossible .
I am trying to
determine which is better, glycemic index or carbohydrate total
If your beta coefficients are on different scales: like
you want to know whether temperature or pressure are affecting
your bread baking more,
Is the way to do this using Beta coefficients calculated
as Beta=beta*SDx/SDy
(SDx=standard deviation of each x)
(SDy=standard deviation of the dependant
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