Modeling and Multivariate Methods - SAS

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612 Visualizing, Optimizing, and Simulating Response Surfaces Chapter 24 The Simulator Figure 24.66 Results of Gaussian Process Model Fit The Gaussian Process platform automatically starts the Prediction Profiler. The desirability function is already set up to minimize the defect rate. To find the settings of Temperature and Time that minimizes the defect rate, select Maximize Desirability from the Prediction Profiler pop-up menu.
Chapter 24 Visualizing, Optimizing, and Simulating Response Surfaces 613 The Simulator Figure 24.67 Settings for Minimum Defect Rate The settings that minimize the defect rate are approximately Temperature = 527 and Time = 0.27. Select Factor Settings > Copy Settings Script from the Prediction Profiler pop-up menu. Return to the original Profiler report window and select Factor Settings > Paste Settings Script. This sets Temperature and Time to those settings that minimize the defect rate. Use Remember Settings as before to save these new settings. Figure 24.68 Minimum Defect Settings With the new settings in place, click the Simulate button to estimate the defect rate at the new settings.
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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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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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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 Introduction to Choice Mod
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404 Performing Choice Modeling Chap
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Contents Launch the Multivariate Pl
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Contents Introduction to Clustering
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462 Clustering Data Chapter 18 The
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464 Clustering Data Chapter 18 Hier
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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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556 Visualizing, Optimizing, and Si
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Page 640 and 641: 640 References Becker, R.A. and Cle
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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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