Multiple Logistic Regression
Use to model the relationship two or more continuous or categorical explanatory variables has with a categorical outcome variable. Useful for estimating the probability of the occurrence of an event for different values of the explanatory variables.
Multiple Logistic Regression Using Fit Model
- From an open JMP® data table, select Analyze > Fit Model.
- Click on a categorical variable from Select Columns, and click Y (nominal variables have red bars, ordinal variables have green bars).
- Choose explanatory variables from Select Columns,and click Add.
- Click Run Model. By default, JMP will provide the following results:
- The Iterations history (not shown).
- The Whole Model Test.
- Lack of Fit (not shown).
- Parameter Estimates for the model.
- Effect Likelihood Ratio Tests (not shown).
Tips:
- When the response is ordinal, an ordinal logistic model will be fit. When the response is nominal, as in this example, a nominal logistic model will be fit.
- To save the predicted probabilities to the data table, click on the top red triangle, select Save Probability Formula.
- To fit a model for grouped or summarized data, use Freq in the Fit Model Specification window - specify the variable that contains the frequency (count) for each level of the response.
- To view the effect of an explanatory variable on the predicted probabilities, click on the top red triangle and select Profiler. In the Prediction Profiler, click and drag the vertical red line for a variable to change the level or value. The predicted probabilities are displayed.
Visit Fitting Linear Models > Logistic Regression Models in JMP Help to learn more.