independent variable

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variable

 [var´e-ah-b'l]
something that changes; an attribute or property of a person, event, or object that is known to vary in a given study.
dependent variable in a mathematical equation or relationship between two or more variables, a variable whose value depends on those of others; it represents a response, behavior, or outcome that the researcher wishes to predict or explain.
extraneous variable a factor that is not itself under study but affects the measurement of the study variables or the examination of their relationships.
independent variable in a mathematical equation or relationship between two or more variables, any variable whose value determines that of others; it represents the treatment or experimental variable that is manipulated by the researcher to create an effect on the dependent variable.

in·de·pen·dent var·i·a·ble

a characteristic being measured or observed that is hypothesized to influence another event or manifestation (the dependent variable) within a defined area of relationships under study; that is, the independent variable is not influenced by the event or manifestation, but may cause it or contribute to its variation. See: dependent variable.

in·de·pen·dent var·i·a·ble

(in'dĕ-pen'dĕnt var'ē-ă-bĕl)
statistics A variable that is manipulated by the researcher and measured by the effect it has on the dependent variable or variables.

in·de·pen·dent var·i·a·ble

(in'dĕ-pen'dĕnt var'ē-ă-bĕl)
Characteristic being measured or observed that is hypothesized to influence another event or manifestation within a defined area of relationships under study.
References in periodicals archive ?
In order to test the hypothesis of this study uses a statistical software Eviews that have test the mathematical equation and will check the dependency of dependent variables over independent variables. Regression analysis will be used to ascertain the 18 association among the defined variables and simultaneously correlation analysis have identified the significance relationship between variables.
In training set, the most correlated independent variables [X.sub.1] and [X.sub.2] were added to a layer by creating a Sugeno fuzzy model (SFM) in both training and test sets, then intermediate output [U.sub.1] was obtained.
The other limitation of the study is that R square of this study is 0.689; therefore 68.9% variance in intention to leave is described by three independent variables. Though, there are still 31.1% in intention to leave variance unexplained in the present study.
Where, R2j is the coefficient of determination between the jth independent variable and all other independent variables.
Caption: Figure 10: Simulation results for a branch pathway system under different experiment environments (independent variables [x.sub.5] = 0.6, 0.3 and 0.9) with various initial conditions: the initial conditions are set at [1.6 1.6 0.4 1.6] (black solid lines), [2.2 2 1.6 2.3] (red dashed lines), and [2 1.2 2.3 0.8] (blue dashed lines) for the case x5 = 0.6, at [1.6 1.6 0.4 1.6] (red solid lines) and [2.7 1.6 2 0.4] (blue dashed lines) for the case x5 = 0.3, and at [2.7 0.4 0.4 1.6] (black solid lines), [1.6 1.6 0.4 1.6] (red dashed lines), and [0.4 2.7 0.4 2.7] (blue dashed lines) for the case x5 = 0.9.
Results for this data show that all independent variables are independent from each other by a comfortable margin.
3) The P-Value indicates the predictive power for each of the independent variables. The smaller the P-Value, the more significant; a P-value less than or equal to .05 is considered significant for predicting the dependent variable.
The comparisons of the initial BCVAs between the cases with and without these five significant independent variables are presented in Table 5.
In our current study, we found that a quick way is to generate the inputs according to the trained weights of each independent variables: the independent variable with a higher numerical weight of the model will be assigned more possible values as the input of ANN during prediction.
GEM automatically calculates the cycle average BSFC or fuel mass values for each of the three cycles, based on numerical algorithm and two independent variables from the particular vehicle configuration that was entered.
In case of direct causal effect of leadership styles on the dependent variable, job satisfaction, the significant path coefficients for H6, H7, H8 and H9 affirmed that all four independent variables have direct effect on the level of job satisfaction.
It is widely used to predict the probability of the presence or absence of a disease, success or failure, or an outcome generally based on discrete, continuous, or categorical independent variables. General form of Logistic regression is as follow Equations, where are estimated parameters which are estimates with maximum likelihood method.

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