Table 4.7 shows the types of effects that are available in highperformance statistical procedures; they are discussed in more detail
in the following sections. Let A
, B
, and C
represent classification variables, and let X
and Z
represent continuous variables.
Table 4.7: Available Types of Effects
Effect 
Example 
Description 

Default 
Intercept (unless NOINT) 

X Z 
Continuous variables 

X*Z 
Interaction of continuous variables 

A B 
CLASS variables 

A*B 
Crossing of CLASS variables 

A(B) 
Main effect A nested within CLASS effect B 

X*A 
Crossing of continuous and CLASS variables 

X(A) 
Continuous variable X1 nested within CLASS variable A 

X*Z*A(B) 
Combinations of different types of effects 
Table 4.8 shows some examples of MODEL statements that use various types of effects.
Table 4.8: Model Statement Effect Examples
Specification 
Type of Model 

Simple regression 


Multiple regression 

Polynomial regression 
Oneway analysis of variance (ANOVA) 


Maineffects ANOVA 

Factorial ANOVA with interaction 

Nested ANOVA 
Analysis of covariance (ANCOVA) 


Separateslopes regression 

Homogeneityofslopes regression 