Abstract
This chapter provides an introduction to nonlinear modeling of data. It aims to describe general methods to analyze data and extract relevant information. These methods involve analyzing the data by using appropriate models. The models should be accurate mathematical descriptions of the response measured in the experiment that include all relevant contributions to the resulting signal. The goal of the computer data analysis is to reveal the relevant information in the raw experimental data in a form that is useful to the experimenter. Computer modeling of experimental data involves using a computer program that can fine tune the details of the model so that it agrees with the data to the best of its ability to describe the experiment. Procedures in this computer program are designed to provide the best values of numerical parameters in the model. The computer programs utilized for modeling data should provide statistical and graphical measures of how well the model fits the data. This goodness of fit criteria can be used to distinguish between several possible models for a given set of data. Goodness of fit parameters and graphic representations of deviations from models can be used as the basis for an expert system; that is, a computer program that finds the best model for sets of data on its own.
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