ABSTRACT
The multiple regression model described in Chapter 8 and the generalised linear model featured in Chapter 10 can accommodate nonlinear functions of the explanatory variables-for example, quadratic or cubic terms-if these are thought to be necessary to provide an adequate fit. In this chapter, however, we consider some alternative and generally more flexible statistical methods for modelling nonlinear relationships between a response variable and one or more explanatory variables. The main component of these methods, known as generalised additive models (GAMs), is the fitting of a ‘smooth’ relationship between the response and each explanatory variable by means of a scatter plot smoother (see Chapter 7 and Section 11.2). GAMs are useful when
r The relationship between the variables is expected to be of complex form not easily fitted by standard linear or nonlinear models.
