Thanks very much for your helpful response.
1) My factors are continous. I have multiple responses. Some are
continous and some are categorical. I need to optimize my resonses.
The main region that they are interested is for A between-35 and 95
and for B between 900 and 1750.
In addition they want to run couple of points outside of this region,
as there is reason to believe that it will optimize the response.
These are B=2000 and A is any pt between 65 and 95, say 80.
Also, they want to run the combination A=35 and B=1650.
Also, would like to include A=90 and B=1650.
Also, would like to include A=105 and B close to 1325.
these points are not totally fixed. If I can get close to it that will
work.
Everything else looks like flexible. I'll be able to run the experiment
21 times. I can include replications. Will replication on some runs and
not on other destroy orthogonality?
I'm not sure how to set this up.
I appreciate your help very much.
SH. Lee
In article <[EMAIL PROTECTED]>,
[EMAIL PROTECTED] wrote:
> Flash response:
>
> 1) Are the levels fixed by some characteristic of the process?
they
> look continuous, and you could do much better if they were, and you
> could select different intermediate levels.
>
> 2) the number of levels can be what you want of it. Some good
> response surface designs use 5 levels. some use more.
>
> 3) Factor B levels are equally spaced, which is good. Factor A
> levels are not evenly spaced. A full factorial will not give you a
> 'clean' design - Without doing the math, I don't believe it will be
> orthogonal, even if you did do all the combinations.
>
> 4) what are you going to do with the results of this experiment?
If
> you wish to build a model of the system behavior, then a full
factorial
> type approach is a waste of your effort, time, and experimental runs.
>
> 5) Suggest you look at a Response model, with maybe 3-5 levels in
> both factors, but using a proper RSM type design. If you do it
> properly, you can avoid a single 'corner' point and recover it
> mathematically.
>
> 6) I'd also ask if you have hard reason to believe that a RSM type
> model, which will get you quadratic terms in a model, is in fact
worth
> doing (financial/your time costs) the first time out? If little
prior
> information is available, it would probably be better to do a
simpler,
> 2-level factorial first, if at all possible. Doing this will teach
you
> a great deal [that you probably don't already know]. Your choice
here,
> but remember - most people overestimate their knowledge level :)
>
> 7) You haven't discussed the response yet. Please spend some time
> thinking about that, too.
>
> More later, if this helps at all. Let me know.
>
> Jay
>
> [EMAIL PROTECTED] wrote:
>
> > Hi,
> >
> > I have two factors A and B and I want to run a DOE to study my
response.
> > My factor B is at 3 levels; (900, 1450 and 2000) , my factor A is
at 4
> > levels 35, 65, 80 and 105.
> > First of all is it right to have one factor at 4 levels. I have
> > encountered situations where the factors are either at 2 levels or 3
> > levels.?
> > This will require me to have 12 runs for a full factorial, right?
> > Also, I do not want to run only the level 35 of factor A with the
level
> > 900 of factor B. If I remove the combination 35, 1450 and 35, 2000;
> > I'll have only 10 runs and the resulting design space will not be
> > orthogonal. How do I tackle this problem?
> > Is there a different design that you would suggest.
> > Thanks for your help.
> > SH Lee
> >
> >
> > Sent via Deja.com
> > http://www.deja.com/
> >
> >
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>
> --
> Jay Warner
> Principal Scientist
> Warner Consulting, Inc.
> 4444 North Green Bay Road
> Racine, WI 53404-1216
> USA
>
> Ph: (262) 634-9100
> FAX: (262) 681-1133
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>
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