A statistical interaction means that the effect of one predictor depends on another on the chosen response scale. In a linear regression, an interaction term changes the slope in to . Whether interaction is present depends on the comparison scale; additive effects and multiplicative effects use different null contrasts. Comparing subgroup effects requires an interaction contrast, rather than contrasting their separate significance labels.
An interaction term lets the effect of one predictor depend on another. Inthe slope with respect to is .
A coefficient multiplying an interaction term controls its strength relative to the chosen field normalization. Its engineering dimension determines whether it is dimensionless, relevant or irrelevant. A renormalized coupling constant constant is specified at a renormalization scale; its change with that scale is described by the renormalization-group beta function.
A hierarchical statistical model that contains an interaction term retains its associated lower-order effects. For two categorical predictors this means keeping both main effects when keeping their interaction. A nonsignificant averaged main effect may coexist with large opposite effects in different groups. Dropping its lower-order term while retaining the interaction can make the model depend on arbitrary factor coding.
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In statistics, "interaction" refers to a situation in which the effect of one independent variable on a dependent variable differs depending on the level of another independent variable. In other words, the impact of one factor is not consistent across all levels of another factor; instead, the relationship is influenced or modified by the presence of the second factor. Interactions are commonly examined in the context of factorial experiments or regression models.