Overview of the Nominal and Ordinal Logistic Personalities
Logistic regression models the probabilities of the levels of a categorical Y response variable as a function of one or more X effects. The Fit Model platform provides two personalities for fitting logistic regression models. The personality that you use depends on the modeling type (Nominal or Ordinal) of your response column.
For more information about fitting logistic regression models, see Walker and Duncan (1967), Nelson (1976), Harrell (1986), and McCullagh and Nelder (1989).
For more information about the parameterization of the logistic regression model, see Statistical Details for the Logistic Regression Model.
About Nominal Logistic Regression
When the response variable has a nominal modeling type, the platform fits a linear model to a multilevel logistic response function using maximum likelihood. Therefore, all but one response level is modeled by a logistic curve that represents the probability of the response level given the value of the X effects. The probability of the final response level is 1 minus the sum of the other fitted probabilities. As a result, at all values of the X effects, the fitted probabilities for the response levels sum to 1.
If the response variable is binary, you can set the Target Level in the Fit Model window to specify the level whose probability you want to model. By default, the model estimates the probability of the higher level of the response variable.
For more information about fitting models for nominal response variables, see “Nominal Responses”.
About Ordinal Logistic Regression
When the response variable has an ordinal modeling type, the platform fits the cumulative response probabilities to the logistic function of a linear model using maximum likelihood. Therefore, the cumulative probability of being at or below each response level is modeled by a curve. The curves are the same for each level except that they are shifted to the right or left.
Tip: If there are many response levels, the ordinal model is much faster to fit and uses less memory than the nominal model.
For more information about fitting models with ordinal response variables, see “Ordinal Responses”.
Other JMP Platforms That Fit Logistic Regression Models
There are many other places in JMP where you can fit logistic regression models:
• To fit logistic regression models with a single continuous main effect, you can use the Fit Y by X platform to see a cumulative logistic probability plot for each effect. See “Logistic Analysis” in Basic Analysis.
• To perform variable selection in logistic regression models, you can use the Stepwise personality of the Fit Model platform. See “Stepwise Regression Models”.
• To fit logistic regression models that use a link function other than the Logit link, you can use the Generalized Linear Model personality of the Fit Model platform. See “Generalized Linear Models”.
• 
To perform variable selection in logistic regression models and fit penalized logistic regression models, you can use the Generalized Regression personality of the Fit Model platform. See “Generalized Regression Models”.