Many of the choices you make when you create a design depend on your overall experimental plan. For more information, go to Phases of a designed experiment.
Select the number of hard-to-change factors in your design. A hard-to-change factor is a factor that is difficult to randomize completely because of time or cost constraints. For example, temperature is a common hard-to-change factor because temperature often requires a significant amount of time to stabilize after adjustments are made. For more information, go to What is a hard-to-change factor?.
Select the number of whole plots, subplots, runs, and the design resolution.
For some numbers of easy-to-change and hard-to-change factors, more than one design exists with the same number of runs. The list of designs is ordered by number of whole plots, and within a fixed number of whole plots in increasing order of subplots, or equivalently runs. So, for a fixed number of whole plots, the resolution of the design increases as you go down the list.
For example, a quality engineer wants to study the factors that affect the texture of frozen yogurt. The engineer can quickly change the amount of air added to the yogurt mix and the mixing speed by adjusting settings on each machine. However, to change the temperature on a machine, the engineer has to change the temperature setting, then wait for the temperature of the yogurt mix to stabilize. Temperature is a hard-to-change factor that defines the whole plots.
The engineer runs all 4 combinations of settings for air and mixing speed at the high level of temperature, which makes one whole plot. The engineer changes the temperature of the machine, then runs the 4 combinations of the other factors again. These second 4 runs form a second whole plot. To complete the design, the engineer replicates the first two whole plots. Overall, this full factorial design includes 4 whole plots, 8 subplots, and 16 total runs.
Select the number of times to replicate the base design. Replicates are multiple experimental runs with the same factor settings (levels). For some designs, if you select 1, then the statistical significance of the hard-to-change factor is impossible to calculate. For these designs, Minitab sets the number of whole-plot replicates to 2 by default. You can add replicates after you store the design in the worksheet with .
Select whether to put each replicate of the base design in a separate 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?.
Select how many times to repeat the runs with the same settings for the easy-to-change factors within each whole plot. For example, if the base design has 2 runs per whole plot and you select 2 subplot replicates, then each whole plot has 4 runs.