# Nonlinear Regression

Nonlinear regression is a method of finding a nonlinear model of the relationship between the dependent variable and a set of independent variables. Unlike traditional linear regression, which is restricted to estimating linear models, nonlinear regression can estimate models with arbitrary relationships between independent and dependent variables. This is accomplished using iterative estimation algorithms. Note that this procedure is not necessary for simple polynomial models of the form Y = A + BX**2. By defining W = X**2, we get a simple linear model, Y = A + BW, which can be estimated using traditional methods such as the Linear Regression procedure.

Example. Can population be predicted based on time? A scatterplot shows that there seems to be a strong relationship between population and time, but the relationship is nonlinear, so it requires the special estimation methods of the Nonlinear Regression procedure. By setting up an appropriate equation, such as a logistic population growth model, we can get a good estimate of the model, allowing us to make predictions about population for times that were not actually measured.

Statistics. For each iteration: parameter estimates and residual sum of squares. For each model: sum of squares for regression, residual, uncorrected total and corrected total, parameter estimates, asymptotic standard errors, and asymptotic correlation matrix of parameter estimates.

## Nonlinear Regression data considerations

Data. The dependent and independent variables should be quantitative. Categorical variables, such as religion, major, or region of residence, need to be recoded to binary (dummy) variables or other types of contrast variables.

Assumptions. Results are valid only if you have specified a function that accurately describes the relationship between dependent and independent variables. Additionally, the choice of good starting values is very important. Even if you've specified the correct functional form of the model, if you use poor starting values, your model may fail to converge or you may get a locally optimal solution rather than one that is globally optimal.

Related procedures. Many models that appear nonlinear at first can be transformed to a linear model, which can be analyzed using the Linear Regression procedure. If you are uncertain what the proper model should be, the Curve Estimation procedure can help to identify useful functional relations in your data.

## Obtaining a Nonlinear Regression Analysis

This feature requires Custom Tables and Advanced Statistics.