Modeling and Multivariate Methods - SAS

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390 Performing Categorical Response Analysis Chapter 15 Failure Rate Examples To generate the appropriate analysis, select failureS and click Multiple Delimited. The X, Grouping Category columns are clean and date. The ID column is ID. When you click OK, you get the same output as in Figure 15.3. Note: If more than one delimited column is specified, separate analyses are produced for each column. Multiple Response By ID Suppose each failure type is a separate record, with an ID column that can be used to link together different defect types for each unit, as in Failure3ID.jmp, in the Quality Control folder. Launch the Categorical platform. Specify failure as Multiple Response by ID, N as Freq, SampleSize as Sample Size, and clean and date as X, Grouping variables. Results are identical to those in Figure 15.3.
Chapter 15 Performing Categorical Response Analysis 391 Categorical Reports Multiple Response Suppose that the defects for each unit are entered via a web page, but since each unit rarely has more than three defect types, the form has three fields to enter any of the defect types for a unit, as in Failure3MultipleField.jmp, in the Quality Control folder. Select the three columns containing defect types (Failure1, Failure2, and Failure3), and click the Multiple Response button. After specifying clean and date as X, Grouping variables, click OK to see the analysis results. Figure 15.4 lists failure types and counts for each failure type from the Multiple Response analysis. Figure 15.4 Multiple Response Failures from Failure3MultipleField.jmp Categorical Reports Report Content The Categorical platform produces a report with several tables and charts. Frequency, Share of Responses, Rate Per Case tables, and a Share Chart are included by default. You can also select a Frequency Chart. A Crosstab Format, instead of a Table Format can also be selected. The defect data for Failure3Freq.jmp is illustrated below. The topmost table is a Frequency count table, showing the frequency counts for each category with the total frequency (Responses) and total units (Cases) on the right. In this example, we have total defects for each defect type by the 7 batches. The last two columns show the total number of defects (Responses) and the total number of units (Cases). The Share of Responses table is built by dividing each count by the total number of responses.
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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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30 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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Contents Example of a Multiple Resp
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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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Page 340 and 341: 340 Recursively Partitioning Data C
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Page 354 and 355: 354 Performing Time Series Analysis
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Page 386 and 387: 386 Performing Categorical Response
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440 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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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 Overview of the Partial Le
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508 Fitting Partial Least Squares M
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Contents Introduction to Item Respo
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526 Scoring Tests Using Item Respon
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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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546 Plotting Surfaces Chapter 23 Th
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548 Plotting Surfaces Chapter 23 Pl
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550 Plotting Surfaces Chapter 23 Pl
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Contents Introduction to Profiling
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556 Visualizing, Optimizing, and Si
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630 Comparing Model Performance Cha
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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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