In statistics, the dependent variable is the event studied and expected to change whenever the independent variable is altered. The dependent variables represent the output or outcome whose variation is being studied and the independent variables represent inputs or causes for variation. Models explain the effects that the independent variables have on the dependent variables. Independent variables may be included for other reasons, such as for their potential confounding effect. In simulation, the dependent variable is changed in response to changes in the independent variables. In data mining, the depending variable is assigned a role as target variable while a dependent variable may be assigned a role as regular variable.
In quasi-experiment, differentiating between dependent variable may be downplayed in favour of differentiating between those variables that can be altered by the researcher and those that cannot be altered by the researcher. In mathematical modelling, there are dependent variables and independent variables. The dependent variables represent the output whose variation is being studied.
Analysis of Multiple Dependent Variables.
- By Patrick Dattalo.
Analysis of Panels and Limited Dependent Variable Models. Cheng Hsiao, M. Hashem Pesaran, Kajal Lahiri.
Regression Models for Categorical and
Limited Dependent Variables
J. Scott Long, John Scott Long. A unified treatment of the most useful models for categorical and limited dependent variables.
Investigating Welfare State Change: The 'dependent Variable Problem'
Jochen Clasen, Nico A. Siegel. By discussing the most salient aspects of the 'dependent variable problem', this work offers suggestions as to how the problem might be tackled within empirical cross-national analyses of modern welfare states.
Econometrics of Qualitative Dependent Variables. - By Christian Gourieroux. Professor Gourieroux also looks at Tobit models, in which the exogenous variable is sometimes qualitative and sometimes quantitative, and changing-regime models.