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The development and use of synthetic regression models has proven to assist statisticians in better understanding bias in data, as well as how to best interpret various statistics associated with a modeling situation. In this article, I present code that can be easily amended for the creation of synthetic binomial, count, and categorical response models. Parameters may be assigned to any number of predictors (which are shown as continuous, binary, or categorical), negative binomial heterogeneity parameters may be assigned, and the number of levels or cut points and values may be specified for ordered and unordered categorical response models. I also demonstrate how to introduce an offset into synthetic data and how to test synthetic models using Monte Carlo simulation. Finally, I introduce code for constructing a synthetic NB2-logit hurdle model.


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