A quality engineer wants to study the influence that six input variables (factors) have on the shrinkage of a plastic fastener of a toy. The engineer plans a pilot study to screen these six factors to determine which factors have the greatest influence on the response. The engineer is primarily interested in main effects and some 2-way interactions, so a resolution IV factorial design is appropriate. The engineer decides to generate a 16-run factorial design from Minitab's catalog of design experiments.
The first table provides a summary of the design. With 6 factors, a full factorial design has 64 runs. Because of limited resources, the engineer selected the ¼ fraction with 16 runs. The ¼ fraction is a resolution IV design. The alias table shows that main effects are confounded with 3-way interactions, but not with any 2-way interaction or other main effects. Because 2-way interactions are confounded with each other, any significant interactions will need to be evaluated further to define their nature.
The Minitab worksheet below shows the settings for each factor for only the first 6 of the 16 experimental runs. The design was created using the default settings of −1 for low and 1 for high, although it is recommended that you enter actual settings for each level. The engineer uses the order that is shown in the RunOrder column to determine the settings for each run. For example, in the first run of the experiment, the settings are as follows: Factor A is high, Factor B is low, Factor C is low, Factor D is low, Factor E is high, and Factor F is low.
Minitab randomizes the design by default, so when you create this design, the run order will not match the order in the worksheet shown below.