-----Original Message-----
From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED]]On
Behalf Of JJ Diamond
Sent: Saturday, April 06, 2002 9:12 AM
To: [EMAIL PROTECTED]
Subject: Re: linear regession
[EMAIL PROTECTED] (Shi) wrote in message
news:<[EMAIL PROTECTED]>...
> > taking you at your word, i assume you really mean "perpendicular" and
> > not vertical deviations. here's something that might help:
> >
> > www.mathpages.com/home/kmath110.htm
>
> Thanks : )
>
> Yes I do mean "perpendicular" deviations.
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What you (Shi) is doing is a regression in which errors of measurement occur
with both Y and X variables.
If the data set has only one X variable, then the technique is called
Orthogonal Regression. Since there is insufficient data, you have to assume
a numerical value for the ratio of the Y error variance to the X error
variance. Carroll and Ruppert (American Statistician, Feb 1996, page 1)
discuss this and how to obtain solutions. Although Shi states
"perpendicular" the angle depends on the variance ratio. Their references
are a good source for further information, including algorithms.
If there are more than one X variable, the method is called Total Least
Squares. It is all matrix stuff, and computer algorithms. It is dependent on
how the errors are distributed among the X variables. There are a whole
bunch of analytic approaches.
DAHeiser
.
.
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