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st: RE: RE: Regression across variables


From   "Wallace, John" <John_Wallace@affymetrix.com>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: RE: Regression across variables
Date   Tue, 11 Nov 2003 11:02:23 -0800

Thanks Nick - any implication of non-orthodoxy is purely my ignorance in
these matters.  My formal stat background is pretty weak.  What I was trying
to show is that there is in effect a variable orthogonal to the matrix of
observations (the Molarity value) that I would like to regress the row of
values for each observation against the row of Molarity values (rather than
the column of A values against the column of B values, for example).

The question would be how to introduce the molarity values into the dataset
(each variable corresponds to a concentration level that is being tested)
and how to tell stata to use it in the regression.

If the answer is the same, I'll just have to plug away and see if I can
figure out how my mental picture fits into what you said.

I appreciate the help!

-JW

-----Original Message-----
From: Nick Cox [mailto:n.j.cox@durham.ac.uk] 
Sent: Tuesday, November 11, 2003 9:59 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: RE: Regression across variables

As I understand it, this is more orthodox 
than you imply, and you could think 
of the analysis as a series of regressions, except that
you have no covariates, at least that you're 
showing us. That's not fatal, however. 

. regress A 

says in effect estimate the mean of A, 
and much of the output you get is based
on the assumption that A follows, or 
should follow, a normal (Gaussian, central)
distribution. 

Following that with 

. test _cons = 0.5 

is, perhaps, a long-winded way of going

. ttest A = 0.5 

except that if you do have covariates, 
the -regress- framework is the one on 
which you can build. Ronan Conroy's 
paper in SJ 2(3) 2002 is a very nice
example of this principle. 

Having said that, the assumption of normality
is important. It wouldn't surprise me if the 
distributions were skewed and (say) gamma-like, 
so that -glm- is then a better framework. 

Nick 
n.j.cox@durham.ac.uk 

Wallace, John
> 
> Hi Statalisters.  I'm trying to get Stata to perform a 
> regression in a data
> structure different from the usual yvar xvar arrangement.  
> I'll diagram the
> data set to show what I mean:
> 
> Molarity	0.5	1	2	3
> 
> Variable	A	B	C	D
> Observ1	.22	.45	.99	1.4
> Observ2	.23	.5	.98	1.5
> Observ3	.19	.38	1.1	1.42
> 
> Molarity in this case would be the constant associated with 
> each variable.
> The observations are measurements of the system attempting 
> to quantify the
> molarity.  The idea would be to generate additional 
> variables that contain
> the various regression results of the observations vs Molarity.
> 
> My data set at this point is just variable name against 
> observation number.
> I don't know how to associate each variable with the 
> corresponding molarity,
> or how to tell Stata to perform a regression in this way.  
> Do I have to
> -reshape- or is there another way?

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