Generally, you can include an interaction (or moderator) term in a linear
model, like
y = b0 + b1 * x1 + b2 * x2 + b3 * x1*x2,
and the model still is linear. If you decide not to include x1 and x2, like
y = b0 + b1 * x1*x2,
you still have a linear model.
BUT: I don't understand the purpose and technique of the aggregation of
values of different persons. What do you want do do? Predict Y with X1 and
X2?
Best wishes,
Johannes Hartig
Wen-Feng Hsiao schrieb:
> Dear all,
>
> Suppose I have an aggregation model which is in the following form:
> Y = X11 * X12 + X21 * X22.
>
> This model could be thought as an aggregation of two knowledge, namely
> X1. and X2.. Each knowledge contains two pieces of information
> (attributes). For example, X1 contains X11 ans X12. Now if X.1 is the
> height, and X.2 is the weight of a person. Then, the aggregation of any
> two persons, say, Student1(height=170cm, weight=60kg),
> Student2(height=180cm, weight=68kg) can be represented by
>
> Y = 170*60+180*68=22440.
>
> My question: a model as the above form is linear or interactive? I doubt
> it is not a linear model. Since it is not in this form: Y= c1 X1 + c2 X2,
> where c1 and c2 are constant. I doubt it is not a pure interactive form,
> since X.1 and X.2 are dependent. Sorry for this stupid question.
>
> Wen-Feng
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