Modeling and Multivariate Methods - SAS

Chapter 24 Visualizing, Optimizing, and Simulating Response Surfaces 611
The Simulator
Figure 24.64 Defect Rate for Temperature of 535
The defect rate decreases to about 1.8%, which is much better than 5.5%. So, what you see is that the fixed
(no variability) settings that maximize Yield are not the same settings that minimize the defect rate in the
presence of factor variation.
By running a Simulation Experiment you can find the settings of Temperature and Time that minimize the
defect rate. To do this you simulate the defect rate at each point of a Temperature and Time design, then fit
a predictive model for the defect rate and minimize it.
Before running the Simulation Experiment, save the factor settings that maximize Yield so you can reference
them later. To do this, re-enter the factor settings (Mean and SD) from Figure 24.62 and select Factor
Settings > Remember Settings from Prediction Profiler pop-up menu. A dialog prompts you to name
the settings then click OK. The settings are appended to the report window.
Figure 24.65 Remembered Settings
Select Simulation Experiment from the Simulator pop-up menu. Enter 80 runs, and 1 to use the whole
factor space in the experiment. A Latin Hypercube design with 80 design points is chosen within the
specified factor space, and N Runs random draws are taken at each of the design points. The design point
are the center of the random draws, and the shape and variance of the random draws coming from the factor
distributions.
A table is created with the results of the experiment. The Overall Defect Rate is given at each design point.
You can now fit a model that predicts the defect rate as a function of Temperature and Time. To do this,
run the attached Guassian Process script and wait for the results. The results are shown below. Your results
will be slightly different due to the random draws in the simulation.