Example of creating a Plackett-Burman design with center points
main topic     interpreting results     session command     see also 

Suppose you want to study the effects of 9 factors using only 12 runs, with 3 center points. In this 12 run design, each main effect is partially confounded with more than one 2-way interaction.

1    Choose Stat > DOE > Factorial > Create Factorial Design.

2    Choose Plackett-Burman design.

3    From Number of factors, choose 9.

4    Click Designs.

5    From Number of runs, choose 12.

6    In Number of center points per replicate, enter 3.

7    Click Results. Choose Summary table and design table. Click OK in each dialog box.

Session window output

Plackett-Burman Design

 

 

Factors:       9     Replicates:     1

Base runs:    15     Total runs:    15

Base blocks:   1     Total blocks:   1

 

Center points: 3

 

 

Design Table (randomized)

 

Run  Blk  A  B  C  D  E  F  G  H  J

  1    1  -  -  -  +  +  +  -  +  +

  2    1  +  +  +  -  +  +  -  +  -

  3    1  +  -  +  -  -  -  +  +  +

  4    1  +  -  +  +  -  +  -  -  -

  5    1  -  +  +  -  +  -  -  -  +

  6    1  +  +  -  +  -  -  -  +  +

  7    1  0  0  0  0  0  0  0  0  0

  8    1  -  -  -  -  -  -  -  -  -

  9    1  +  -  -  -  +  +  +  -  +

 10    1  0  0  0  0  0  0  0  0  0

 11    1  -  +  -  -  -  +  +  +  -

 12    1  -  -  +  +  +  -  +  +  -

 13    1  -  +  +  +  -  +  +  -  +

 14    1  0  0  0  0  0  0  0  0  0

 15    1  +  +  -  +  +  -  +  -  -

Interpreting the results

In the first table, Total runs shows the total number of runs including any runs created by replicates and center points. For this example, you specified 12 runs and added 3 runs for center points, for a total of 15.

Minitab does not display an alias tables for this 12 run design because each main effect is partially confounded with more than one 2-way interaction.

Minitab shows the experimental conditions or settings for each of the factors for the design points. When you perform the experiment, use the order that is shown to determine the conditions for each run. For example, in the first run of your experiment, you would set Factor A low, Factor B low, Factor C low, Factor D high, Factor E high, Factor F high, Factor G low, Factor H high, and Factor J high.

Minitab randomizes the design by default, so if you try to replicate this example your runs may not match the order shown.