In analysis of variance, there is usually little attention for interpretingthe terms of the effects themselves, especially for interactioneffects. One of the reasons is that the number of interaction-effectterms increases rapidly with the number of predictor variables andthe number of categories. In this paper, we propose a new model,called the interaction decomposition model, that allows to visualizethe interactions. We argue that with the help of the visualization, theinteraction-effect terms are much easier to interpret. We apply ourmethod to predict holiday spending1 using seven categorical predictorvariables.