This is a function to simulate a black box process for teaching the use of designed experiments. The optimal factor settings can be found using a sequential assembly strategy i.e. apply a 2^k factorial design first, calculate the path of the steepest ascent, again apply a 2^k factorial design and augment a star portion to find the optimal factor settings. Of course other strategies are possible.

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`x1` |
numeric vector containing the values for factor 1. |

`x2` |
numeric vector containing the values for factor 2. |

`x3` |
numeric vector containing the values for factor 3. |

`noise` |
logical value deciding whether noise should be added or not. Default setting is ‘FALSE’. |

factor 1 is best within [40, 250]; factor 2 within [90, 240]

`simProc`

returns a numeric value within the range [0,1].

For an example in context which shows the usage of the function `simProc()`

please read the vignette for the package `qualityTools`

at http://www.r-qualitytools.org/html/Improve.html

Thomas Roth thomas.roth@tu-berlin.de

`facDesign`

for 2^k factorial designs

`rsmDesign`

for response surface designs

`fracDesign`

for fractional factorial design

http://www.r-qualitytools.org/html/Improve.html

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