The crux of what Mike originally asked is "how good is my measurement
system." My (limited) understanding is that the concept of correlation is
not relevant to this application. Refer to some of the papers on the
calibration problem, such as those by Eberhardt & Mee (there are several
calibration references at the NIST web site, http://www.nist.gov ) or some
of the many articles warning against the use of r as a metric for a
measurement system (such as Analytical Methods Committee (1988) Analyst.
113:1469-1471).
Most measurements have a known functional relationship between the measurand
and the response (or measured) variable - we're not trying to see if they
correlate, we know they do. So the concepts of "goodness" typically
ascribed to a correlation coefficient do not apply here. We need to go
further to address issues related to fitness of a measurement system, such
as precision and accuracy. The techniques and references of my earlier post
address these concerns.
Regards,
Eric Scharin
-----Original Message-----
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED]]On Behalf Of dennis roberts
Sent: Thursday, May 18, 2000 12:08 PM
To: Hassane ABIDI
Cc: [EMAIL PROTECTED]
Subject: Re: Correlation
At 09:45 AM 5/18/00 +0200, Hassane ABIDI wrote:
>When the IDV variable (X) is random is it correct to use simple linear
>regression ????? It is very important question in it's general context,
>that you have not really responded.
in what way would it make sense to say that X is random (and what do you
mean by this?) and Y is fixed (and what do you mean by this?)?
all regression analysis does (in the linear case) is to find a least
squares line (and the equation for it) through the data plot ... that
minimizes squared deviations around the regression line ...
now, if one were doing an analysis of experimental data ... where X is a
treatment variable ... that has dosage levels of 3, 6, and 9 miligrams ...
you might (???) consider that fixed in that these are the ONLY levels of
the treatment you are interested in ... so you could plot the means on some
criterion variable against these 3 dosage levels ... here X might be
'fixed' and Y not ...
i don't really see how you can fix Y ... if that is the criterion ... ONLY
in the sense that you might have a measure that ONLY gives you 'fixed'
possible values .... but, that is a stretch ...
in what sense would height and weight be fixed or not fixed if we did a
regression using height to estimate or predict weight, or vice versa?
notion of 'fixed' really makes no sense here ... and regression does not
care
but, my point to the original poster was simple:
make a plot of the data ... see what you see ... and go from there
this is not about using pearson or spearman, it is about seeing if the data
look linear ... and IF they do ... then summarize it via a regression
equation ... this will give the means to 'link' X to Y ... proxy to true
if they don't ... you have to think about a different strategy
>|=======================================================|
>| Hassane ABIDI (PhD) |
>| Unite d'Epidemiologie; Centre Hospitalier Lyon-Sud |
>| Pavillon 1.M, 69495 Pierre Benite Cedex, France |
>| Tel: (33) 04 78 86 56 87 ; Fax: (33) 04 78 86 33 31 |
>| E. mail: [EMAIL PROTECTED] |
>|=======================================================|
Dennis Roberts, EdPsy, Penn State University
208 Cedar Bldg., University Park PA 16802
Email: [EMAIL PROTECTED], AC 814-863-2401, FAX 814-863-1002
WWW: http://roberts.ed.psu.edu/users/droberts/drober~1.htm
FRAMES: http://roberts.ed.psu.edu/users/droberts/drframe.htm
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