- To: mathgroup at smc.vnet.net
- Subject: [mg50295] Multiple Regression
- From: "Doug" <umdougmm at hotmail.com>
- Date: Wed, 25 Aug 2004 03:36:05 -0400 (EDT)
- Organization: The University of Manitoba
- Sender: owner-wri-mathgroup at wolfram.com
This is actually meant a leaf in the thread I started circa 3 hours ago (for
some reason my posts don't post until circa 12 hrs after sending them?)
What I have found is the following: to quickly re-state my dilemma, I'm
trying to come up with a model of the following sort:
y = (B0) + (B1)*(o01) + (B2)*(o02) + (B3)*(o03) + (B4)*(o04) + (B5)*(u) +
(B6)*(v) + (B7)*(w) + (B8)*(z) + (B9)*(y) + (B10)*(x) + epsilon
and I'm of course trying to minimize epsilon. Also, the other important
point is that o01 to o04 are binary and can be 1 only exclusively (ie, if a
data row has o01=1 then o02=..=o04=0, and the same thing goes for the other
o02 to o04)
The variables which are causing me much headache are the o01..o04, because
if I include all the ~50 000 rows of data and run the Fit function as
the approximated B1=..=B4 are all equal and VERY large. So I tried to
reverse engineer this problem so as to figure out what is wrong with it.
I added 10 rows where o01=1, 10 rows where o02=1, ..., 10 rows where o04=1.
Running the exactly same fit command on this data of 40 entries returns B1,
B2, B3, B4 that are all different and acceptably small. However, once I add
more variables (~6000), the problem I describe above re-appears.
I added all the rows where o01=o02=o03=1 and no rows where o04=1. Now,
B1=..=B3=impossibly large number not within the range of y (results). B4 is
different but also very large. However, as soon as I add one row where o04
= 1 I get B1=..=B4=unacceptably/impossibly large.
Any help is really greatly appreciated. In particular if there's any
implicit assumptions which mathematica makes and I'm simply not aware of
when running Fit or Regress. I've done multiple regression with ~ 90
000 rows before and 16 variables, but when I added these four variables,
things started to go kaput like this.
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