Image by Anh Vy. In this article we will cover Generalised Additive Models (GAMs). We’ll cover the base case by modelling a response variable with a univariate smooth function. We’ll then build on this by incorporating multiple exogenous variables to create additive models. After then, the GAM can be covered. For more details about this methods please read the book by Simon N. Wood about Generalised Additive Models. GAMs GAMS: GAMs are a form of generalised linear models with linear response variables that depend on unknown smooth functions of some exogenous variables. These forms of models were initially developed to blend the properties of Generalised Linear Models (GLMs) with additive models.