Statistical models normally specify how one set of variables, called dependent variables, functionally depend on another set of variables, called independent variables.
Statistical models normally specify how one set of variables, called dependent variables, functionally depend on another set of variables, called independent variables. While analysts typically specify variables in a model to reflect their understanding or theory of "what causes what," setting up a model in this way, and validating it through various metrics, does not, by itself, confirm causality. The term "(in)dependent" reflects only the functional relationship between variables within a model. Several models based on the same set of variables may differ by how the variables are subdivided into dependent and independent variables.
Alternative names for independent variables (especially in data mining and predictive modeling) are input variables, predictors or features. Dependent variables are also called response variables, outcome variables, target variables or output variables.
The terms "dependent" and "independent" here have no direct relation to the concept of statistical dependence or independence of events.
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