The art of fitting a nonlinear regression model often starts with choosing a model form. This submission is an attempt to teach the reader a simple but general paradigm for their models as a sum of fundamental shapes that are then shifted and scaled to fit the data.I've included a bestiary of fundamental forms, each of which has been plotted. Each form also has a description of some fundamental characteristics, such as limits and other special values.Who might wish to read this submission? Anyone who is interested in fitting an empirical model to their (1-d) data, although many of the ideas in here are applicable to problems in higher dimensions too.Please e-mail me of any errors I've made, as well as any interesting functional forms that I've failed to include in the bestiary.
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