the response variable, brand, with values m and x
an effect called softness (water softness) with values soft, medium, and hard
an effect called previous use with values yes and no
an effect called temperature with values high and low
a count variable, count, which gives the frequency counts for each combination of effect categories
Select Help > Sample Data Library and open
Select Analyze > Fit Model.
Select brand from the Select Columns list and click Y.
Because brand is a Nominal column with only two levels, the Target Level option appears. This option enables you to specify the response level whose probability you want to model.
Select count and click Freq.
Select softness through temperature and click Macros > Full Factorial.
Click Run.
Nominal Logistic Fit for Three-Factor Factorial Model
The Effect Likelihood Ratio Tests report shows that the effects that include softness do not contribute significantly to the model fit. This leads you to consider removing softness from the model. You can do this from the Effect Summary report.
In the Effect Summary report, select softness*previous use through softness under Source and click Remove.
The report updates to show the two-factor factorial model (Nominal Logistic Fit for Two-Factor Factorial Model). The Whole Model Test report shows that the two-factor model is also significant as a whole.
Nominal Logistic Fit for Two-Factor Factorial Model
From the report shown in Nominal Logistic Fit for Two-Factor Factorial Model, you conclude that previous use of a detergent brand and water temperature have an effect on detergent preference. You also note that the interaction between temperature and previous use is not statistically significant, so you conclude that the effect of temperature does not depend on previous use.

Help created on 9/19/2017