Technically speaking, Nonlinear Estimation is a general fitting procedure that will estimate any kind of relationship between a dependent (or response variable), and a list of independent variables. In general, all regression models can be stated as:

y = F(x1, x2, ... , xn)

In most general terms, we are interested in whether and how a dependent variable is related to a list of independent variables; the term F(x...) in the expression above means that y, the dependent or response variable, is a function of the x's, that is, the independent variables.

An example of this type of model would be the linear multiple regression model as described in Multiple Regression. For this model, we assume the dependent variable to be a linear function of the independent variables, that is:

y = a + b1*x1+ b2*x2+ ... + bn*xn

If you are not familiar with multiple linear regression, you may want to read the Multiple Regression Overviews at this point (however, it is not necessary to understand all of the nuances of multiple linear regression techniques in order to understand the methods discussed here).

Use Nonlinear Estimation to specify
essentially any type of continuous or discontinuous regression model.
Some of the most common nonlinear models (such as probit,
logit,
exponential growth, and
breakpoint regression)
are pre-defined in Nonlinear Estimation and can simply be requested as
dialog options; however, note that the Generalized
Linear/Nonlinear Models (GLZ) module includes more efficient
algorithms for fitting general probit and logit regression models, and
STATISTICA only includes these
models here for compatibility purposes. You can also type in any type
of regression equation, which STATISTICA
will then fit to your data (see User-Specified
Regression, Least Squares and User-Specified
Regression, Custom Loss). Moreover, you can specify either standard
least squares estimation, maximum likelihood estimation (where appropriate),
or, again, define your own "loss function" (see below) by typing
in the respective equation.

In general, whenever the simple linear regression model does not appear to adequately represent the relationships between variables, then the nonlinear regression model approach is appropriate. See the following topics for overviews of the common nonlinear regression models, nonlinear estimation procedures, and evaluation of the fit of the data to the nonlinear model:

Common Nonlinear Regression Models