Figure 6.10 shows the multiple-table launch window, with the Profile Data outline populated using Potato Chip Profile.jmp.
Identifier for each row of choice combinations. If the Profile ID column does not uniquely identify each row in the profile data table, you need to add Grouping columns. Add Grouping columns until the combination of Grouping and Profile ID columns uniquely identifies the row, or profile.
A column which, when used with the Choice Set ID column, uniquely designates each choice set. For example, if Profile ID = 1 for Survey = A, and a different Profile ID = 1 for Survey = B, then Survey would be used as a Grouping column.
For information about the Construct Profile Effects panel, see Construct Model Effects in the Fitting Linear Models book.
Computes bias-corrected MLEs that produce better estimates and tests than MLEs without bias correction. These estimates also improve separation problems that tend to occur in logistic-type models. Refer to Heinze and Schemper (2002) for a discussion of the separation problem in logistic regression.
Figure 6.11 shows the Response Data outline populated using Potato Chip Responses.jmp.
Figure 6.11 Response Data Outline
The columns that contain the Profile IDs of the set of possible choices for each choice set.
A column which, when used with the Profile ID Chosen column, uniquely designates each choice set.
A column containing frequencies. If n is the value of the Freq variable for a given row, then that row is used in computations n times. If it is less than 1 or missing, then JMP does not use it to calculate any analyses.
Figure 6.12 shows the Subject Data outline populated using Potato Chip Subjects.jmp.
Figure 6.12 Subject Data Outline
For information about the Construct Model Effects panel, see Construct Model Effects in the Fitting Linear Models book.