dependent variable

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Dependent variable

Term used in regression analysis to represent the element or condition that is dependent on values of one or more other independent variables.

Dependent Variable

In technical analysis, a variable whose value is determined by the value of other variable(s), but plays no part in determining the value of those other variable(s). For example, if a product's price is determined by some equation involving the product's supply and its demand, the price is the dependent variable because the price does not affect the supply or demand.

dependent variable

A variable affected by another variable or by a certain event. For example, because a stock's price is affected by dividend payments, earnings projections, interest rates, and many other things, stock price is a dependent variable. Compare independent variable.

dependent variable

a variable that is affected by some other variable in a model. For example, the demand for a product (the dependent variable) will be influenced by its price (the INDEPENDENT VARIABLE). It is conventional to place the dependent variable on the left-hand side of an EQUATION. See DEMAND FUNCTION, SUPPLY FUNCTION.
References in periodicals archive ?
This allowed us to not only analyze all of the dependent variables in our sample, but also to specifically focus on performance as a dependent variable of interest.
The third criterion used to identify the final models is whether or not the efficient models for each dependent variable meet four common linear regression assumptions.
All of the independent variables were entered into stepwise regression models for each of the dependent variables examined in this research.
Trend references the rate of increase or decrease of the best-fit straight line for the dependent variable within a condition (i.
Many human aggregates in all parts of the world have evolved culture patterns--time-binding structural relationships--that so little disturbed the equilibrium developed among the dependent variables of the physical environment in which they live that, measured by the biological criterion of survival, they have been extremely successful.
Sweeny and Ulveling (1972) suggested a process, referred to as Shifting Process II, for shifting the interpretative framework of the coefficients to an "average," where the "average" is indeed the overall mean of the dependent variable.
Unfortunately, Campbell rejects all means of controlling for those ups and downs that do not involve the use of a dependent variable lagged one or two quarters--a flawed practice for two reasons.
Results revealed that UN, CSH, TOEPH, ARH(1), ANTE(1) and UNR heterogeneous variance- covariance models were the best model group in modeling the variance- covariance matrix structure regarding the dependent variable and CS, TOEP and AR(1) homogeneous variance- covariance models were the worst model group.
The logarithmic gains simply represent the percentage change in the logarithmic value of the steady state of a dependent variable and net flux or local flux as a result of an infinitesimal change in the logarithmic value of an independent variable [15], which are computed as follows:
3 provides a variety of measures assessing the success of the model in predicting the dependent variable.
Likewise, a negative correlation exists when a decrease in the value of x results in an increase in the value of y Thus, a negative correlation occurs when the value of the dependent variable moves in a direction opposite to that of the independent variable.
The dependent variable in the first three columns of Table 3 is a fatal or incapacitating injury.