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.
Select Help > Sample Data Library and open Car Physical Data.jmp.
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.
6.
7.
Select Analyze > Fit Y by X.
8.
Select Type and click Y, Response.
9.
Select Weight and click X, Factor.
10.
Figure 7.74 Example of Type by Weight Logistic Plot
In Figure 7.74, note the following observations:
Markers for the data are drawn at their x-coordinate, with the y position jittered randomly within the range corresponding to the response category for that row.
If the x -variable has no effect on the response, then the fitted lines are horizontal and the probabilities are constant for each response across the continuous factor range. Figure 7.75 shows a logistic plot where Weight is not useful for predicting Type.
Figure 7.75 Examples of Sample Data Table and Logistic Plot Showing No y by x Relationship
Note: To re-create the plots in Figure 7.75 and Figure 7.76, you must first create the data tables shown here, and then perform steps 7-10 at the beginning of this section.
Figure 7.76 Examples of Sample Data Table and Logistic Plot Showing an Almost Perfect y by x Relationship
In this case, the parameter estimates become very large and are labeled unstable in the regression report. In these cases, you might consider using the Generalized Linear Model personality with Firth bias-adjusted estimates. See Launch the Generalized Linear Model Personality in the Fitting Linear Models book.

Help created on 10/11/2018