Modeling and Multivariate Methods - SAS

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610 Visualizing, Optimizing, and Simulating Response Surfaces Chapter 24 The Simulator Figure 24.63 Simulation Results mean defective batches below specification defect rate = 5.5% Mean and standard deviation The predicted Yield is 0.62, but if the factors have the given variation, the average Yield is 0.60 with a standard deviation of 0.03. The defect rate is about 5.5%, meaning that about 5.5% of batches are discarded. A defect rate this high is not acceptable. What is the defect rate for other settings of Temperature and Time? Suppose you change the Temperature to 535, then set Time to the value that maximizes Yield? Enter settings as shown in Figure 24.64 then click Simulate.
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.
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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10 8 Analyzing Screening Designs Us
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12 Examining the Residuals . . . .
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14 Response Frequencies . . . . . .
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16 19 Analyzing Principal Component
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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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56 Fitting Standard Least Squares M
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158 Fitting Multiple Response Model
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Contents Overview of Generalized Li
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180 Fitting Generalized Linear Mode
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200 Performing Logistic Regression
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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Contents Overview of the Loglinear
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306 Fitting Dispersion Effects with
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Contents Introduction to Partitioni
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318 Recursively Partitioning Data C
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Contents Launch the Platform . . .
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354 Performing Time Series Analysis
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Contents The Categorical Platform .
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386 Performing Categorical Response
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Contents Launch the Multivariate Pl
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Contents Introduction to Clustering
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470 Clustering Data Chapter 18 K-Me
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472 Clustering Data Chapter 18 K-Me
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474 Clustering Data Chapter 18 Norm
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476 Clustering Data Chapter 18 Norm
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478 Clustering Data Chapter 18 Self
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480 Clustering Data Chapter 18 Self
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Contents Principal Components. . .
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484 Analyzing Principal Components
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Contents Introduction . . . . . . .
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494 Performing Discriminant Analysi
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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Contents Introduction to Profiling
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556 Visualizing, Optimizing, and Si
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558 Visualizing, Optimizing, and Si
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700 Index Stable 363 Stable Inverti
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702 Index
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Modeling and Multivariate Methods - SAS

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