All data analysis performed on SPSS, a joint venture partner with LWI.
Tests of Between-Subjects Effects
Dependent Variable: Experimental Output
| Source | Type III Sum of Squares | df | Mean Square | F | Sig. | Eta Squared |
| Corrected Model | 104.725a | 3 | 34.908 | 9.773 | .000 | .449 |
| Intercept | 9620.777 | 1 | 9620.777 | 2693.542 | .000 | .987 |
| Setting A | 40.802 | 1 | 40.802 | 11.424 | .002 | .241 |
| Setting B | 1.238 | 1 | 1.238 | .347 | .560 | .010 |
| Setting A * Setting B | 62.684 | 1 | 62.684 | 17.550 | .000 | .328 |
| Error | 128.585 | 36 | 3.572 | |||
| Total | 9854.086 | 40 | ||||
| Corrected Total | 233.309 | 39 |
a R Squared = .449 (Adjusted R Squared = .403)
To minimize Output, run Setting A at 20 and Setting B at 20. You would want to ask yourself if you were able to go beyond the current setting range on Setting A, (about 5-25) to further minimize the Output. In some cases, this will not be possible for considerations outside of the experiment, e.g., safety, cost, productivity, etc. You will find in most mature industries that if there is a problem that cannot be solved it is likely due to an interaction. It is easy to pick up a direct effect, such as Output that goes down as you turn up Setting B, so you will likely already know these effects. But the human brain is not set up to recognize even a two-way interaction (as above) much less more. The only way to catch these effects and discriminate them from random effects is through statistical analyses.