A market research study was undertaken to evaluate preference for a brand of detergent. See Ries and Smith (1963). The results of the study are in the Detergent.jmp sample data table. The model is defined by the following:
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the response variable, brand, with values m and x
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an effect called softness (water softness) with values soft, medium, and hard
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an effect called previous use with values yes and no
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an effect called temperature with values high and low
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a count variable, count, which gives the frequency counts for each combination of effect categories
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Select Analyze > Fit Model.
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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.
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Click Run.
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The Whole Model Test report shows that the three-factor full factorial model as a whole is significant (Prob>ChiSq = 0.0006).
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.
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In the Effect Summary report, select softness*previous use through softness under Source and click Remove.
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The report updates to show the two-factor factorial model (Figure 10.10). The Whole Model Test report shows that the two-factor model is also significant as a whole.
Figure 10.10 Nominal Logistic Fit for Two-Factor Factorial Model
From the report shown in Figure 10.10, 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.