Re: stepwise regression by GENSTAT

On 17 aug, 01:47, RichUlrich <rich.ulr...@xxxxxxxxxxx> wrote:
On Sat, 16 Aug 2008 07:40:57 -0700 (PDT), Old Mac User

<chendrixst...@xxxxxxxxx> wrote:
On Aug 16, 3:20 am, hubert.ruyp...@xxxxxxxxx wrote:
I am using GENSTAT for performing a multiple linear regression. To
select the most suitable explanatory variables this programm provides
a "stepwise regression method". However I do not understand how this
method leads to a result.

According to my handbook it drops the variable which gives the lowest
mean square (Residual) in an ANOVA set-up.

What it drops is the variable that makes the least improvement
in the mean square (Residual).   That is the variable that has the
least effect, at that point.  If your handbook says otherwise, you
need a new handbook.


Is "the least improvement in the mean square (Residual)" not the same
as "the variable that results in the highest value of the mean square
(Residual)?

If your handbook makes a general recommendation, that stepwise
methods are desirable, you need a new handbook -- there was a
spell in the 1970s, when computers were first available to them,
that some practitioners generated a fad for Stepwise; but wiser
statisticians (and their own experiences) shortly convinced them
that there was no magic in it.  You can Google groups
< stepwise group:sci.stat.*  author:ulrich > for comments and
threads on the subject.

My handbook considers only stepwise regression as a method to select
the explanatory variables (Mc Conway, Jones and Taylor, Statistical
Modelling using Genstat").




And here I am losing the logic. I was convinced that a lower mean
square (Residual) resulted in a higher variance ratio (which is the
m.s.(Regression) divided by the m.s.(Residual) and thus in a lower F-
probability.

Can some-one help me out ?

Hubert

I respectfully submit that if you do not understand multiple
regression
and it's related offshoots, you should get some assistance from a
practicing statistician.  Building models from data has many facets,
it can be tricky, and the risk of getting silly results is high.  OMU

Right.

Who am I to contest this. However some knowledge of the statistical
possibility's (and limitations) is never wrong (I think)


--
Rich Ulrich

.

Relevant Pages

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