Fitting Linear Models • Logistic Regression Models

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Logistic Regression Models
Fit Regression Models for Nominal or Ordinal Responses
When your response variable has discrete values, you can use the Fit Model platform to fit a logistic regression model. 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 nominal response variables, the Nominal Logistic personality fits a linear model to a multi-level logistic response function.
For ordinal response variables, the Ordinal Logistic personality fits the cumulative response probabilities to the logistic distribution function of a linear model.
Both personalities provide likelihood ratio tests for the model, a confusion matrix, and ROC and lift curves. When the response is binary, the Nominal Logistic personality also provides odds ratios (with corresponding confidence intervals).
Figure 11.1 Logistic Plot for a Nominal Logistic Regression Model
Contents 
Overview of the Nominal and Ordinal Logistic Personalities
Nominal Logistic Regression
Ordinal Logistic Regression
Other JMP Platforms That Fit Logistic Regression Models
Examples of Logistic Regression
Example of Nominal Logistic Regression
Example of Ordinal Logistic Regression
Launch the Nominal and Ordinal Logistic Personalities
Validation
The Logistic Fit Report
Whole Model Test
Fit Details
Lack of Fit Test
Logistic Fit Platform Options
Options for Nominal and Ordinal Fits
Options for Nominal Fits
Options for Ordinal Fits
Additional Examples of Logistic Regression
Example of Inverse Prediction
Example of Using Effect Summary for a Nominal Logistic Model
Example of a Quadratic Ordinal Logistic Model
Example of Stacking Counts in Multiple Columns
Statistical Details for the Nominal and Ordinal Logistic Personalities
Logistic Regression Model
Odds Ratios
Relationship of Statistical Tests

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Help created on 3/19/2020