Re: interaction terms in regression model

Old Mac User wrote:
Bruce W.:

You wrote...

OMU, I just want to make sure I understand your terminology. Where you
say 2-level factorial design, I think you mean that even though X1 and
X2 are continuous variables, only two fixed levels of each are used in
the study. Is that correct?

That's the way I described the situation because that's an easy version
to describe. If X1 and X2 can only be set to discrete choices such as
"yes vs. no" or "Supplier 1 vs. Supplier 2) then there are no values
between these
(no interpolation) hence contour maps would not be meaningful.

But even if the continuous variables are not set to precisely "low" and
"high" values... if there's a standalone interaction... then a contour
map derived from the model will be saddle-points. OMU


I was with you until that last paragraph. Let me try again.

Imagine an experiment in which for each trial, the values of X1 and X2 are random numbers within some ranges I choose--so there are observations of X1 and X2 across the whole range, and combinations of X1 and X2 across the whole range. Would you still be concerned about a standalone interaction? Or is your concern limited to the case where there are only 4 possible combinations of X1 and X2?

--
Bruce Weaver
bweaver@xxxxxxxxxxxx
www.angelfire.com/wv/bwhomedir
.

Relevant Pages

  • Re: how to analyze interaction between tow risk factors?
    ... Continuous variables, not ... I'd be even more concerned about a standalone interaction when the data ... may, in fact, have the first clear-cut example of a pure standalone ... definitely thinking of them as nominal variables. ...
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  • Re: interaction terms in regression model
    ... hence contour maps would not be meaningful. ... But even if the continuous variables are not set to precisely "low" and ... Bruce Weaver wrote: ... 2-level factorial design here... ...
    (sci.stat.edu)