Basic Analysis and Graphing - SAS

228 Performing Simple Logistic Regression Chapter 7
Additional Examples of Logistic Regression
Where there are many points, the curves are pushed apart. Where there are few to no points, the curves are
close together. The data pushes the curves in that way because the criterion that is maximized is the product
of the probabilities fitted by the model. The fit tries to avoid points attributed to have a small probability,
which are points crowded by the curves of fit. See the Modeling and Multivariate Methods book for more
information about computational details.
For details about the Whole Model Test report and the Parameter Estimates report, see “The Logistic
Report” on page 219. In the Parameter Estimates report, an intercept parameter is estimated for every
response level except the last, but there is only one slope parameter. The intercept parameters show the
spacing of the response levels. They always increase monotonically.
Additional Example of a Logistic Plot
This example uses the Car Physical Data.jmp sample data table to show an additional example of a logistic
plot. Suppose you want to use weight to predict car size (Type) for 116 cars. Car size can be one of the
following, from smallest to largest: Sporty, Small, Compact, Medium, or Large.
1. Open the Car Physical Data.jmp sample data table.
2. In the Columns panel, right-click on the icon to the left of Type, and select Ordinal.
3. Right-click on Type, and select Column Info.
4. From the Column Properties menu, select Value Ordering.
5. Move the data in the following top-down order: Sporty, Small, Compact, Medium, Large.
6. Click OK.
7. Select Analyze > Fit Y by X.
8. Select Type and click Y, Response.
9. Select Weight and click X, Factor.
10. Click OK.
The report window appears.
Figure 7.6 Example of Type by Weight Logistic Plot