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Use Create 2-Level Factorial Design (Specify Generators) to create a designed experiment with different design generators than those Minitab uses by default. A common reason to specify a non-default design generator is because you need to change the terms that are aliased. For more information, go to What are confounding and alias structure?, What is a defining relation in a two-level factorial design?, and What is a design generator?.
With a 2-level factorial design, you can identify important factors to focus on with further experimentation. When you create a design, Minitab stores the design information in the worksheet, which shows the order in which data should be collected. Once you have collected your data, use Analyze Factorial Design to analyze the data.
For example, a quality engineer wants to conduct a 9-factor experiment using the 1/16th fraction of the design. The engineer needs the two-factor interactions that include factors A or B to be free from aliasing with other 2-factor interactions. However, the default generators in Minitab alias the 2-factor interactions involving factors A or B with other 2-factor interactions. Therefore, the engineer specifies different generators by creating a 5-factor design and specifying generators to add 4 more factors.
This Minitab worksheet shows a portion of a 2-level factorial design with blocks and center points. The analysts perform the experiment by collecting data using the order in the RunOrder column, which contains the randomized order of the runs.
| C1 | C2 | C3 | C4 | C5 | C6 | C7 | C8 | C9 | C10 | C11 |
|---|---|---|---|---|---|---|---|---|---|---|
| StdOrder | RunOrder | CenterPt | Blocks | A | B | C | D | E | F | |
| 4 | 1 | 1 | 1 | 1 | –1 | 1 | –1 | 1 | –1 | |
| 6 | 2 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
| 3 | 3 | 1 | 1 | –1 | –1 | 1 | 1 | –1 | –1 | |
| 5 | 4 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | |
| 1 | 5 | 1 | 1 | –1 | 1 | –1 | –1 | 1 | –1 | |
| 2 | 6 | 1 | 1 | 1 | 1 | –1 | 1 | –1 | –1 | |
| 8 | 7 | 1 | 2 | 1 | –1 | –1 | –1 | –1 | 1 |
After collecting the data, an analyst enters the response data in an empty column in the worksheet and then analyzes the design.
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.
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