It’s not only about interactions: the prediction variance is minimal with an orthogonal array of n runs. Now, you can go for near-orthogonal experiments using a custom doe algorithm if the number of run doesn’t suit you. OFAT is not optimal, you will loose information with OFAT.

]]>I used plackett Burman for media optimization, and upon the analysis of data with minitab 16, i found out that partial confounding is present. What do i conclude with that ? Please help. ]]>

The whole point of a DOE is to study interactions, which is why I prefer resolution V DOEs. If interactions are unimportant, there is no need for a DOE.

If PB can actually be done in 8 runs, as mentioned below by Charlie, that would beat my 10 run OFAT. I might be interested in that.

]]>do you have any example of Plackett-Burman Design in SAS.

I want tr analyze the dfata with SAS commands.

thanks ]]>

If I do have interactions I can run double the 8 runsand they (Main Effects and 2 factor interactions) are pretty free and clear of confounding.

I am still struggling with the point of using a PB design.

]]>As Mike stated well, I’d take a resolution V design with 16 experimental runs and get an idea about main effects separately from the 2-way interactions which a 12 experimental PB design can’t do.

]]>As far as the value of the Y it is really irrelevant. Most screening designs are followed by a full factorial and that still only gives you the largest value inside your experimental space. If you are looking for an optimized value for the Y then there are techniques for that.

I am still trying to get to the specific advantage of the Plackett-Burman design.

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