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## Quick reference

### Fractional Factorial Design of Experiments

Fractional Factorial DOE is a statistical test methodology that uses a selected set of test samples with a precise configuration of factor settings to determine the impact of the factors on the system response throughout the design space represented by the factors.

### When to use

Fractional Factorial DOE is normally used when determining the simultaneous impact of many factors on system response. It does require the ability to control the test sample configuration with respect to the factors being analyzed.

### Instructions

The Fractional Factorial DOE is similar in many respects to the Full Factorial DOE. A set of factors is selected and a high and low level is established for each factor. However, now the differences start. Rather than testing all combinations of highs and lows, the Fractional Factorial DOE only tests a fraction of those combinations. Which configurations will be carefully selected so that the results can still be statistically analyzed.

Often this methodology will have several phases of testing. The first is the fraction of tests just mentioned. After this test, if there are factors that are significant and whose effect is likely to be non-linear, they are further analyzed with a second set of tests that are only using those factors. If necessary, a final confirming test is done using the optimal settings.

This approach will provide an analysis and equation that define the full design space based upon the factors represented. However, this analysis may not be able to fully analyze all interaction effects. Even with the two phases, this approach normally will not conduct nearly as many tests as the Full Factorial DOE.

### Hints & tips

- The factor levels must be controllable in order to establish the correct test sample configuration of high and low values.
- All the tests must be completed in order to do the statistical analysis. This is not like the OFAAT method where performance continues to improve as more tests are done.
- The two-level factors will assume the factor effects on system response are linear. If the effects are non-linear, multi-level factors must be used.

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