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Select Analyze > Fit Model.

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Click Run.

In this example, Drug has three levels, a, d, and f. The standard least squares fitting method translates this specification into a linear model as follows: The nominal variables define a sequence of indicator variables, which only assume the values 1, 0, and –1. The linear model is written as follows:
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β0, β1, and β2 are parameters for the intercept, the first indicator variable, and the second indicator variable, respectively

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ει are the independent and normally distributed error terms

The first indicator variable, , is defined as follows. Note that Drug=a contributes a value 1, Drug=d contributes a value 0, and Drug=f contributes a value –1 to the indicator variable:
Therefore, if regressor variables are coded as indicators for each level minus the indicator for the last level, then the parameter for a level is interpreted as the difference between that level’s response and the average response across all levels. See the appendix Statistical Details for additional information about the interpretation of the parameters for nominal factors.
Leverage Plot and LS Means Table for Drug shows the Leverage Plot and the LS Means Table for the Drug effect. Parameter Estimates and Effect Tests for Drug.jmp shows the Parameter Estimates and the Effect Tests reports for the oneway analysis of the drug data.
The Drug effect can be studied in more detail by using a contrast of the least squares means, as follows:
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Click the + boxes for drugs a and d, and the  box for drug f to define the contrast that compares the average of drugs a and d to f (shown in Contrast Example for the Drug Experiment).

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Click Done.

The Contrast report shows that the LSMean for drug f is significantly different from the average of the LSMeans of the other two drugs.