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N-Level Factorial How-To¶
This walkthrough builds a mixed-level factorial, where the factors do not all have the same number of levels. It is the design to reach for when one factor is a three-way choice and another is on or off.
Nothing here needs data. A design is built before the experiment is run, so every number below is determined by the design itself and you can check each one against your own sheet.
Steps¶
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QXL DOE New > Create Design, choose N Level Factorial, press Next.
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On the factor page set Select number of factors: to 2. Set the first factor's # Levels to 3 and the second's to 2. Name them Supplier and Coating, tick Categorical on Supplier, and give it three level names.
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Name one output and press Next, then leave replicates and blocking alone and press Finish.
What you get¶
Six runs, which is 3 times 2, with the first factor changing fastest:
Press Copy for Excel and paste the block into an empty part of the worksheet to compare it against your sheet.
| Run | Supplier | Coating |
|---|---|---|
| 1 | level 1 | level 1 |
| 2 | level 2 | level 1 |
| 3 | level 3 | level 1 |
| 4 | level 1 | level 2 |
| 5 | level 2 | level 2 |
| 6 | level 3 | level 2 |
The run count is the product of the level counts, so this design's size follows directly from what you asked for: 3 x 2 is 6, and a 4 x 3 x 2 design is 24.
A categorical factor's levels are names, not numbers. The model fits one term per level beyond the first, so a three-level categorical factor spends two degrees of freedom rather than one.
Things to try next¶
- Set both factors to 2 levels. You get the four run design a two-level factorial would build, which shows this design type as the general case of that one.
- Add a 4-level factor. 24 runs, and the arithmetic is still just the product.
- Untick Categorical on Supplier. Its levels become quantitative settings and the model can fit a trend across them rather than a separate term for each.
See Also¶
- N-Level Factorial Design
- Options, every control on every page of the builder
- Math Details
- Two-Level Factorial Designs