+Evaluating whether interaction terms are important or improve the fit of your model is a topic best left out of this discussion for now. That said, if you need to include an interaction term, you now have a basic idea of how to do it. Interpreting interaction terms is generally best done using model-predicted values. For example, in this case where `x` is continuous and `j` is dichotomous, you might generate a "hypothetical" dataset incorporating the range of observed values for `x` at each of the two values of `j` (that would yield a "fake" data frame with $n \times 2$ rows). You can then plot the predicted values of `y` for each of the `j` categories over the `x` distribution (imagine: a plot with `x` on the x-axis, `y` on y-axis, and lines of different colors corresponding to the different levels of `j`). [^1]
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+[^1]: An okay example of this appears in [this paper](https://doi.org/10.1093/joc/jqx003) that I worked on a few years ago.
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+Needless to say, there's a lot more to be said about interactions. You can read more [in this econometrics textbook](https://www.econometrics-with-r.org/8-3-interactions-between-independent-variables.html), which seems to have pretty thorough coverage of the fundamentals as well as example R code.
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