Building a Model

Create a Main Effect

Create Crossed Effects (Interactions)

Create a Nested Effect

Create a Full Factorial Model

Create an N-Way Factorial

Create Polynomial Effects of the Nth Order

Setting the Model Options

Create a Main Effect

Select the variable name in the Variables box.
Click Add to add the variable to the list of model effects.

Create Crossed Effects (Interactions)

Select two or more variables in the Variables box. To select more than one variable, press Ctrl.
Click Cross.

Create a Nested Effect

Nested effects are specified by following a main effect or crossed effect with a classification variable or list of classification variables enclosed in parentheses. Here are examples of nested effects: B(A), C(B*A), D*E(C*B*A). In this example, B(A) is read "B within A."

Select the classification variable in the Model Effects Builder.
Click Nest. The Nested window appears.
Select the variable to use in the nested effect. Click Outer or Nested within Outer to specify how to create the nested effect.

Note: The Nested within Outer button is available only when a classification variable is selected.
Select the effect that you want to nest.
Click Add.

Create a Full Factorial Model

Select two or more variables in the Variables box.
Click Full Factorial.

For example, if you select the Height, Weight, and Age variables and then click Full Factorial, these model effects are created: Age, Height, Weight, Age*Height, Age*Weight, Height*Weight, and Age*Height*Weight.

Create an N-Way Factorial

Select two or more variables in the Variables box.
Click N-way Factorial and specify the value of N.

For example, if you select the Height, Weight, and Age variables, click N-way Factorial, and then specify the value of N as 2, these model effects are created: Age, Height, Weight, Age*Height, Age*Weight, and Height*Weight. If N is set to a value greater than the number of variables in the model, N is effectively set to the number of variables.

Create Polynomial Effects of the Nth Order

Select one or more continuous variables in the Variables box.
In the list of standard models, click Polynomial Order=N.
Specify higher-degree crossings by adjusting the number in the N field.

For example, if you select the Age and Height variables, click Polynomial Order=N, and specify 3 as the value of N, these model effects are created: Age, Age*Age, Age*Age*Age, Height, Height*Height, and Height*Height*Height.

Setting the Model Options

Option	Description
Model
Include an intercept in the model	specifies whether to include the intercept in the model.
Offset variable	specifies a variable to be used as an offset to the linear predictor. An offset plays the role of an effect whose coefficient is known to be 1. Observations that have missing values for the offset variable are excluded from the analysis.