An interaction term in a multiple regression model may be used when

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BlackTom AI · Accepted Answer

In a multiple regression framework, an interaction term is included to capture a situation where the effect of one predictor on the outcome depends on the level of another predictor. Specifically, an interaction between X1 and X2 allows the slope of Y with respect to X1 to change as X2 changes, meaning the relationship between X1 and Y is not constant across all values of X2. This is appropriate when theory or prior data suggest that the impact of X1 on Y is moderated by X2.

Because the provided options are missing, we cannot enumerate all candidate statements and evaluate them individually. Nevertheless, the core idea remains: the interaction term is used to model a conditional effect, where the X1→Y relationship is conditional on X2. If an option stated that the effect of X1 on Y changes for different values of X2, that would accurately describe the purpose of an interaction term. Any option claiming that the effect is constant across all levels of X2, or that interactions are unnecessary unless both variables are categorical, would be incorrect.

In summary, the interaction term is appropriate when the influence of X1 on Y varies with X2, i.e., the X1 coefficient is not the same across levels of X2, indicating a moderation effect.

33:136:385:6&12&15 STAT METH BUSINESS Sample questions for final exam

An interaction term in a multiple regression model may be used when

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