dependent variable

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dependent variable

[di′pen·dənt ′ver·ē·ə·bəl]
(mathematics)
If y is a function of x, that is, if the function assigns a single value of y to each value of x, then y is the dependent variable.

dependent variable

the measure of the effect of the INDEPENDENT VARIABLE. Thus, in an experiment, or in data analysis, the independent variable (for example, number of years in school) is manipulated, or otherwise controlled, and the effect of this manipulation is seen by the change in the dependent variable (for example, reading proficiency). See also EXPERIMENTAL METHOD.
References in periodicals archive ?
Panel data regression is applied in this study to identify the impact of dependent variables (Total Leverage and corporate ownership) and independent variables (firm size, firm age, tax shield, growth opportunity, firm profitability, asset tangibility, assets maturity, earning volatility, corruption perceived index, dividend payout firm level investment and long term leverage).
With reference to any dependent variable three ways of return or income are possible: 1) increasing return, 2) constant return and 3) decreasing return.
Values of pH and electrical conductivity and concentrations of aluminum (Al), calcium (Ca), nickel (Ni), iron (Fe) and zinc (Zn) metals and sulfate (SO4 2-), nitrate (NO3-) and chloride (Cl-) ions in the particulate matter were taken as dependent variables, while the values of pH and electrical conductivity and concentrations of metals and ions mentioned above in the inert matter/waste were taken as independent variables.
Our dependent variable (i.e., net outlays) is taken from the General Fund financial statements.
Our dependent variable includes data for 2000 to allow for a lagged dependent variable throughout the sample.
Practically when we deal regression analysis and our dependent variable is categorical then we are not able to use simple linear or multiple linear regression, especially when dependent variable is binary (dichotomous) then we can use Logistic Regression and the independent variables are of any type like categorical or continuous.
The purpose of multiple regression (a term used by Pearson, 1908) is to highlight the relation between a dependent variable (explained, endogenous or resultant variables) and a lot of independent variables (explanatory, factor, exogenous, predictor ones).
For example, a study would be considered to have neutral or mixed effects if a functional relationship were established between the independent and dependent variables with positive effects for two of four participants in an ABAB study, but no effects or negative effects were observed for the remaining participants.
When the results were averaged for the 58 patients (aggregated over the five APs and the covariates), the PFT tube had lower (better) mean responses on each of the dependent variables. Likewise, for all three dependent variables, the adjusted means resulting from the model described above were lower for the PFT.
The responses were later tabulated and analyzed using regression analysis to understand the effect of the independent variables on the dependent variables. Freedman (2005) also mentioned that it is a common statistical tool used in researches where there is a need to identify the effect of one or more variables on other variables.
"Why Lagged Dependent Variables Can Suppress the Explanatory Power of Other Independent Variables." Paper prepared for the Annual Meeting of the Political Methodology Section of the American Political Science Association.
Multiple Regression methodology is used to analyze several variables and to establish a relationship between dependent variable and independent variables.