Example 1: Fit a Linear Regression Model with Classification Variables
In this example you use the Generalized Linear Models analysis to fit a linear regression model with classification variables and an interaction term. In particular, you model how two variables affect the change in blood pressure in a designed experiment.
The Drug data set contains results of an experiment that is carried out to evaluate the effect of four drugs with three experimentally
induced diseases. Each drug-by-disease combination was applied to six randomly selected dogs. The response variable, chang_bp, is the increase in systolic blood pressure due to the treatment. The variables drug and disease are classification variables: their values identify distinct levels or groups.
To fit a generalized linear model:
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You need to specify that the
druganddiseasevariables are nominal in order to model them as classification variables. The Data Table Menus section in Chapter 4 describes measure levels for variables. The following steps change the measure level of these variables from interval to nominal: -
Select the
druganddiseasevariables by holding down the CTRL key while you click the column heading for each variable. -
Right-click the column heading for either variable and select from the pop-up menu, as shown in Figure 24.1.
Figure 24.1: Changing the Measure Level for Variables
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Clear the selected variables by clicking the blank cell in the upper left corner of the data table.