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
On 18 Feb 2002 16:29:27 -0800, [EMAIL PROTECTED] (Wuzzy) wrote:
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 don't remember doing that -
i've posted the visual at
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
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:
Insulin
Wuzzy wrote:
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
[ snip, previous problem]
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
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
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
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 recall questionnaire
using
a food frequency questionnaire:
It was
Wuzzy [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
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
Wuzzy [EMAIL PROTECTED] wrote in message
[EMAIL PROTECTED]">news:[EMAIL PROTECTED]...
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:
It doesn't make sense to do that.
Amazingly I got a
In article [EMAIL PROTECTED],
Wuzzy [EMAIL PROTECTED] wrote:
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
On 5 Feb 2002 18:01:15 -0800, [EMAIL PROTECTED] (Wuzzy) wrote:
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
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
To: [EMAIL PROTECTED]
Date sent: 5 Feb 2002 18:15:00 -0800
From: [EMAIL PROTECTED] (Wuzzy)
Organization: http://groups.google.com/
Subject:Re: can multicollinearity force a correlation?
In my own defense:
I was asking a simple
I can't help it. the last paragraph in this post absolutely _demands_ a
response.
Wuzzy wrote:
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,
I'm curious to know why you're using the same exact exposure in different
units. I've included a dichotomized version of a continuous exposure
variable to look at potential threshold effects, but I've never heard of
anyone doing what you've described.
At 08:28 AM 2/5/02 -0800, Wuzzy wrote:
Title: RE: can multicollinearity 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
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. It
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,
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