Modeling and Multivariate Methods - SAS

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414 Performing Choice Modeling Chapter 16 Platform Options Platform Options The Choice Modeling platform has many available options. To access these options, click on the platform drop-down menu. Likelihood Ratio Tests tests the significance of each effect in the model. These are done by default if the estimate of cpu time is less than five seconds. Joint Factor Tests tests each factor in the model by constructing a likelihood ratio test for all the effects involving that factor. Confidence Intervals produces a 95% confidence interval for each parameter (by default), using the profile-likelihood method. Shift-click on the platform drop-down menu and select Confidence Intervals to input alpha values other than 0.05. Correlation of Estimates shows the correlations of the parameter estimates. Effect Marginals shows the fitted utility values for different levels in the effects, with neutral values used for unrelated factors. Profiler time. produces a response surface viewer that takes vertical cross-sections across each factor, one at a Save Utility Formula makes a new column with a formula for the utility, or linear model, that is estimated. This is in the profile data table, except if there are subject effects. In that case, it makes a new data table for the formula. This formula can be used with various profilers with subsequent analyses. Save Gradients by Subject constructs a new table that has a row for each subject containing the average (Hessian-scaled-gradient) steps on each parameter. This corresponds to using a Lagrangian multiplier test for separating that subject from the remaining subjects. These values can later be clustered, using the built-in-script, to indicate unique market segments represented in the data. Model Dialog shows the Choice dialog box, which can be used to modify and re-fit the model. You can specify new data sets, new IDs, and new model effects. Example: Valuing Trade-offs The Choice Modeling platform is also useful for determining the relative importance of product attributes. Even if the attributes of a particular product that are important to the consumer are known, information about preference trade-offs with regard to these attributes might be unknown. By gaining such information, a market researcher or product designer is able to incorporate product features that represent the optimal trade-off from the perspective of the consumer. The advantages of this approach to product design can be found in the following example. It is already known that four attributes are important for laptop design--hard-disk size, processor speed, battery life, and selling price. The data gathered for this study are used to determine which of four laptop attributes (Hard Disk, Speed, Battery Life, and Price) are most important. It also assesses whether or not there are Gender or Job differences seen with these attributes.
Chapter 16 Performing Choice Modeling 415 Example: Valuing Trade-offs • Select Analyze > Modeling > Choice to open the launch dialog box. • Open Laptop Profile.jmp from the sample data directory and Select Data Table under Profile Data. Select Laptop Profile.jmp. A partial listing of the Profile Data table is shown in Figure 16.14. The complete data set consists of 24 rows, 12 for Survey 1 and 12 for Survey 2. Survey and Choice Set define the grouping columns and Choice ID represents the four attributes of laptops: Hard Disk, Speed, Battery Life, and Price. Figure 16.14 Profile Data Set for the Laptop Example • Select Choice ID for Profile ID, and ADD Hard Disk, Speed, Battery Life, and Price for the model effects. • Select Survey and Choice Set as the Grouping columns. The Profile Data dialog box is shown in Figure 16.15. Figure 16.15 Profile Data Dialog Box for Laptop Study
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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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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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Contents Launch the Fit Model Platf
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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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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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306 Fitting Dispersion Effects with
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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 Principal Components. . .
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484 Analyzing Principal Components
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Contents Introduction . . . . . . .
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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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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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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