CCD facial Design with blocked factors

Question

Hi,

I have trouble to replicate a design a customer had made with Statistica. My two goals are to once replicate and second to find a probably better or similar good design with less runs.

The setup is the following:

* They do a CCD facial design with one replicate, the 35th run is one setting they want to test in each lot, as this is an extreme result they had got in pre-experimentation and is only in the first two lots
* Over the run of the experiments they will have three starting materials (Lots):* the first lot has 10 Liter aka max of 10 runs,
* the second lot has a 10 Liter aka 10 runs max
* the third can have more (in their design 15 runs) 
* they cannot be randomly changed they are one after each other
* they want to understand the lot by lot variation, therefore they have it as blocking factor in their design

* They want to do one complete replicate for the design* so two Operators do the same experiments 
* want to randomly vary them within the Lot block
* currently they just alternate the runs between operators as they cannot do this in their tool

* Three continuous factors
* (want to relate the factors to several responses)

Attached a JMP table of an imported Excel file. It is anomized due to confidentiallity.

What I have done so far:

* I tried the custom designer, but failed with restriction of different block sizes for first two lots 
* I tried to do a CCD orthogonal blocks design, on face, with 3CP and one replicate aka 34 runs at all (see CCD 3CP 1repl.jmp), however I could not include the Operator (needs to be manually added).

==> Do you have any idea on how to use custom designer to generate a similar design?

==> Do you know of any other design approach what would be helpful in such a requirement?

Thanks,

Martin

CCD 3CP 1repl.jmp

Auswertemaster_DB_Anonymized.jmp

Phil_Kay · Accepted Answer

Hi Martin,

Does it help if you start with a table for the 35 runs by Lot factor? (10 rows Lot = 1, 10 rows Lot = 2, 15 rows Lot =3)

Then use this a covariate factor in Custom Design.

Regards,

Phil

DonMcCormack · Accepted Answer

If you do this, I'd remove Lot from any term in the model when you build the design. Once the design is created, change lot to Nominal and add it as a Random effect to the Model dialog.

DonMcCormack · Accepted Answer

* If by replication you mean the entire design (as opposed to a single run or set of runs), then yes.
* I thought this would be the case, too, but the designs I've created keep Lots in the same order as the covariate table. Not sure if this is relatively new (works this way in 14, too) or something about Lot.
* Why not add Operator as a two level Categorical Factor? For your first design, don't include the Lot*X2, Lot*X3 and Lot*X4 cross terms. You want to treat Lot as a (random) block to estimate the variability due to the Lot to Lot differences. You will also need to alter the model that gets saved to the design table (see below).
* Are you treating Operator as an additional blocking factor or trying to estimate the differences between the operators? If you want two blocking factors, it gets a bit more complicated. Blocking factors are design components where the interest is in estimating variability not mean differences. This is done by treating the factor as a random effect. In your attached design, you need to apply the Random attribute to Lot (and remove any cross terms it's involved with). With a bit of care, setting up a two blocking factor design isn't difficult, but requires you specify the correct model for both design and analysis.

DonMcCormack · Accepted Answer

Martin,

If Operator is another blocking factor, it would not be crossed with any of the other experimental factors, but may possibly be crossed with Lot. It would also be random. The model would look something like this (order is not important):

A, B, C, A*B, A*C, B*C, A*A, B*B, C*C, Lot&Random, Operator&Random, Lot*Operator&Random

The Lot*Operator interaction is quantifying the variability due to different operators using different lots. It's not something I'd expect to contribute much to variability (if anything). If it were something like Tool*Operator then, it might be worth looking at (e.g., different operators my be better at using particular tools, so you might expect more variability).

Don