Modeling and Multivariate Methods - SAS

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608 Visualizing, Optimizing, and Simulating Response Surfaces Chapter 24 The Simulator Figure 24.61 Yield Maximum The maximum Yield is approximately 0.62 at a Time of 0.116 hours and Temperature of 539.92 degrees Kelvin, or hot and fast. [Your results may differ slightly due to random starting values in the optimization process. Optimization settings can be modified (made more stringent) by selecting Maximization Options from the Prediction Profiler pop-up menu. Decreasing the Covergence Tolerance will enable the solution to be reproducible.] In a production environment, process inputs can’t always be controlled exactly. What happens to Yield if the inputs (Time and Temperature) have random variation? Furthermore, if Yield has a spec limit, what percent of batches will be out of spec and need to be discarded? The Simulator can help us investigate the variation and defect rate for Yield, given variation in Time and Temperature. Select Simulator from the Prediction Profiler pop-up menu. As shown in Figure 24.62, fill in the factor parameters so that Temperature is Normal weighted with standard deviation of 1, and Time is Normal weighted with standard deviation of 0.03. The Mean parameters default to the current factor values. Change the number of runs to 15,000. Yield has a lower spec limit of 0.55, set as a column property, and shows on the chart as a red line. If it doesn’t show by default, enter it by selecting Spec Limits on the Simulator pop-up menu.
Chapter 24 Visualizing, Optimizing, and Simulating Response Surfaces 609 The Simulator Figure 24.62 Initial Simulator Settings LSL = 0.55 Mean = factor value number of runs = 15,000 With the random variation set for the input factors, you are ready to run a simulation to study the resulting variation and defect rate for Yield. Click the Simulate button.
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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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Contents Example Using Standard Lea
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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 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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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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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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Modeling and Multivariate Methods - SAS

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