In statistics, an additive model (AM) is a nonparametric regression method.

Like other regression methods, the goal is to estimate a response (dependent variable) based on one or more predictors (independent variables).

The conventional approach to such questions is to use ordinary least squares regression methods, which are often unsatisfactory.

Like all regression methods, the computer fits weights to best predict data.

The coefficients of the scoring function are usually fit using multiple linear regression methods.

I will omit the "hedonic regression method" for estimating the value of quality change as somewhat too sensational.

Many different semiparametric regression methods have been proposed and developed.

Both Bienaymè and Cauchy published regression methods at about the same time.

Dummy variables may be incorporated in traditional regression methods or newly developed modeling paradigms.

However, in many applications, especially with small effects or questions of causality based on observational data, regression methods can give misleading results.