Modeling and Multivariate Methods - SAS

148 Fitting Stepwise Regression Models Chapter 4
Performing Logistic Stepwise Regression
Example Using the Make Model Command for Hierarchical Terms
A simple model examines at the cost per ounce ($/oz) of hot dogs as a function of the Type of hot dog
(Meat, Beef, Poultry) and the Size of the hot dog (Jumbo, Regular, Hors d’oeuvre).
1. Open the Hot Dogs2.jmp sample data table.
2. Select Analyze > Fit Model.
3. Select $/oz and click Y.
4. Select Type and Size and click Add.
5. For Personality, select Stepwise.
6. Click Run.
7. For Stopping Rule, select P-value Threshold.
8. For Rules, select Restrict.
9. Click Go.
Figure 4.13 Current Estimates Report for a Model with Hierarchical Effects
10. Click Make Model.
The following actions occur:
• Indicator variables are created in the data table for those checked rows in the Current Estimates table
that are partial levels of a main effect. In this example, two columns are created for Type and one column
is created for Size.
• A new Fit Model launch window appears. The effects are those that were selected in the stepwise
process. Run the Fit Model window.
Performing Logistic Stepwise Regression
JMP performs logistic stepwise regression in a similar way to standard least squares logistic regression. To
run a logistic stepwise regression, simply add terms to the model as usual and choose Stepwise from the
personality drop-down menu.
The difference in the report when the response is categorical is in the Current Estimates section of the
report. Wald/Score chi-square statistics appear, and the overall fit of the model is shown as -LogLikelihood.