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:
You need to specify that the drug
and disease
variables 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 drug
and disease
variables 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 Figure 24.1.
from the pop-up menu, as shown in
Clear the selected variables by clicking the blank cell in the upper left corner of the data table.