Modeling and Multivariate Methods - SAS

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312 Fitting Dispersion Effects with the Loglinear Variance Model Chapter 12 Profiling the Fitted Model 9. Click OK. Figure 12.6 Residual by Dispersion Effect In this plot it is easy to see the variance go up as the Hold Time increases. This is done by treating Hold Time as a nominal factor. Profiling the Fitted Model Use the Profiler, Contour Profiler, or Surface Profiler to gain further insight into the fitted model. To select a profiler option, click on the red triangle menu next to Loglinear Variance Fit and select one of the options under the Profilers menu. Example of Profiling the Fitted Model For example, suppose that the goal was to find the factor settings that achieved a target of 31 for the response, but at the smallest variance. Fit the models and choose Profiler from the report menu. For example, Figure 12.7 shows the Profiler set up to match a target value for a mean and to minimize variance. 1. Open the InjectionMolding.jmp sample data table. 2. Select Analyze > Fit Model. Since the variables in the data table have preselected roles assigned to them, the launch window is already filled out. 3. Click Run. 4. From the red triangle menu next to Loglinear Variance Fit, select Profilers > Profiler. 5. From the red triangle menu next to Prediction Profiler, select Prediction Intervals.
Chapter 12 Fitting Dispersion Effects with the Loglinear Variance Model 313 Profiling the Fitted Model Figure 12.7 Prediction Intervals 6. Change the following values: – MoldTemp to 0.2 – Screw Speed to 0.1 – Hold Time to -1
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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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136 Fitting Stepwise Regression Mod
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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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362 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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444 Correlations and Multivariate T
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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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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 Introduction to Item Respo
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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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640 References Becker, R.A. and Cle
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642 References Draper, N. and Smith
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644 References Hooper, J. H. and Am
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646 References Matsumoto, M. and Ni
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648 References Rawlings, J.O. (1988
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650 References Umetrics (1995), Mul
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Contents The Response Models . . .
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654 Statistical Details Appendix A
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690 Index Birth Death Subset.jmp 46
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692 Index Ellipses Transparency 451
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694 Index test for curvature 46 Kru
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696 Index Customizing 267 Nonlinear
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698 Index reliability analysis 453-
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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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