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