Solving in another way is beyond my knowledge at this point.
As i've stated, i'm no stats guru.  I can however come up with
an interface or something to perform the regression, and then
provide a RealMatrix based implementation.  Beyond that, i'm
afraid i'm not going to be able to do more than i have already.

-----Original Message-----
From: Phil Steitz [mailto:[EMAIL PROTECTED]
Sent: Wednesday, January 28, 2004 5:22 PM
To: Jakarta Commons Developers List
Subject: RE: [math] Re: - Multiple Linear Regression



--- [EMAIL PROTECTED] wrote:
> Here's a little snippet on linear regression:
>
http://www.mathworks.com/access/helpdesk/help/toolbox/stats/linear20.shtml#4
9537

Nice reference.  Unfortunately, it points out the fact that I think either
you, J or Al mentioned some time ago that solving the normal equations
directly gives bad numerics.  That means that it is probably *not* a good
idea to use RealMatrix directly, but maybe to first implement QR decomp. In
any case, we need to look into the numerics, select a good algorithm and
probably make it pluggable.
> 
> The first equation is the common form (in matrix terms) of a linear
> model.  It also defines residuals and other important points regarding
> multiple regression.
> 
> The nice thing about linear models, is not only are they used for
> regression analysis, but they are the basis for ANOVA as well.  So, I
> for one, would like to see a pure matrix based implementation for
> linear regression as it more likely reuseable for ANOVA.
> 
> Also, we can add a facade to the matrix implementation for those whole
> just want to deal with double[] and double[][] structures.  Much like
> we've done with the summary stats.
+1 
> 
> Take note of the hat matrix.  Some more advanced analysis like
> measuring the effect of an observation can be easily accomplished
> using the hat matrix.  So, it might be worth while to incorporate
> caching of the hat matrix in any implementation.
+1
 
Phil

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