To specify the design, select the number of center points and replicates. You can use Power and Sample Size for Plackett-Burman Design to help you to determine an appropriate number of center points and replicates.

Select the number of runs for the base design. The total number of runs can also change based on other design elements. For example, the inclusion of center points can increase the number of runs.

If you want to include center points in your design, select the appropriate number of points. You can use center points to detect curvature in the response. You can also use center points to estimate variability without having to replicate all the corner points.

Center points are runs where numeric factors are set midway between their low and high levels. For example, if a numeric factor has levels 100 and 200, the center point is set at 150. If you have text factors, then Minitab adds a center point at each level of the text factor and the midway level of the numeric factors. For example, your design includes a text factor with the levels A and B and a numeric factor with the levels 100 and 200. If you add 1 center point to the base design, Minitab adds 1 center point at levels A and 150 and 1 center point at levels B and 150. Thus, Minitab adds 2 center points for each center point that you specify.

Each replicate doubles the number of center points. For example, if you specify 3 center points per replicate and 2 replicates, then the design includes 6 center points. The number of center points is the same whether the replicates are in one block or separate blocks.

For more information, go to How Minitab adds center points to a two-level factorial design.

Select the number of replicates for the corner points. Replicates are multiple experimental runs with the same factor settings (levels). One replicate is equivalent to the base design, where you conduct each run once. With two replicates, you perform each run twice (in random order), and so on.

Select whether to put each replicate in its own block. Blocks account for the differences that might occur between runs that are performed under different conditions. For example, an engineer designs an experiment to study welding and cannot collect all of the data on the same day. Weld quality is affected by several variables that change from day-to-day that the engineer cannot control, such as relative humidity. To account for these uncontrollable variables, the engineer groups the runs performed each day into separate blocks. The blocks account for the variation from the uncontrollable variables so that these effects are not confused with the effects of the factors the engineer wants to study. For more information on how Minitab assigns runs to blocks, go to What is a block?.