Modeling and Multivariate Methods - SAS

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226 Performing Logistic Regression on Nominal and Ordinal Responses Chapter 7 Stacking Counts in Multiple Columns Stacking Counts in Multiple Columns Data that are frequencies (counts) listed in several columns of your data table are not the form that you need for logistic regression. For example, the Ingots2.jmp data table in the data folder (see Figure 7.26) has columns Nready and Nnotready that give the number of ready and number of not ready ingots for each combination of Heat and Soak values. To do a logistic regression, you need the data organized like the table in Figure 7.27. To make a new table, suitable for logistic regression, select the Stack command from the Tables menu. Complete the Stack dialog by choosing Nready and NNotReady as the columns to stack, and then click OK in the Stack dialog. This creates the new table in Figure 7.27. If you use the default column names, Label is the response (Y) column and Data is the frequency column. The example in the section “Introduction to Logistic Models” on page 199, shows a logistic regression using a sample data table Ingots.jmp. It has a frequency column called count (equivalent to the Data column in the table below) and a response variable called Ready, with values 1 to represent ingots that are ready and 0 for not ready. Figure 7.26 Original Data Table
Chapter 7 Performing Logistic Regression on Nominal and Ordinal Responses 227 Stacking Counts in Multiple Columns Figure 7.27 Stacked Data Table
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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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276 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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318 Recursively Partitioning Data C
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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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444 Correlations and Multivariate T
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Contents Introduction to Clustering
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Contents Principal Components. . .
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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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640 References Becker, R.A. and Cle
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646 References Matsumoto, M. and Ni
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650 References Umetrics (1995), Mul
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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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