Fundamentals of Statistics contains material of various lectures and courses of H. Lohninger on statistics, data analysis and chemometrics......click here for more. 
Home Multivariate Data Modeling Introduction  
See also: classification vs. calibration, modeling  example  
ModelingIn many cases, we often suspect some relationships among the data when acquiring the data. However, in order to make more precise statements, draw conclusions, or predict from the measured data, we have to set up a model which represents the nature of the underlying relationship. Models can either be based on some theoretical laws or principles (such as the relationship between a measured spectrum and the concentration of the analyte) or can be empirical without any explicitly known relationship (such as the toxicity of some chemical substances in relation to their chemical structure). The variables which form the basis (input) of the model are called predictor variables, the variable which is to be estimated by the model is called the response variable. Another aspect to discriminate between models is their (non)linearity. Depending on the circumstances we may either try to linearize nonlinear models, or apply nonlinear models. Using nonlinear models generally requires much more caution than linear models, since nonlinear models are much more likely to adapt to noise in the data than linear models. A third aspect is the type of the dependent (response) variable, which may be either qualitative or quantitative. Qualitative variables will result in classification models, quantitative variables will result in calibration models.
In general, there are several terms which have been developed historically
to describe some aspects of a model:
Methods for modeling cover a wide range. The following is a short list of the more important ones:


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