independent variable


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independent variable

n.
1. Mathematics A variable whose value determines the value of other variables.
2. Statistics A manipulated variable in an experiment or study whose presence or degree determines the change in the dependent variable.

independent variable

n
1. (Mathematics) Also called: argument a variable in a mathematical equation or statement whose value determines that of the dependent variable: in y = f(x), x is the independent variable
2. (Statistics) statistics Also called: predictor the variable which an experimenter deliberately manipulates in order to observe its relationship with some other quantity, or which defines the distinct conditions in an experiment. See also experimental condition

in′depend′ent var′iable


n.
a variable in a functional relation whose value determines the value or values of other variables, as x in the relation y= 3x2.
[1850–55]

in·de·pen·dent variable

(ĭn′dĭ-pĕn′dənt)
Mathematics
A variable whose value determines the value of other variables. For example, in the function y = x - 4, x is the independent variable because its value determines the value of y.
ThesaurusAntonymsRelated WordsSynonymsLegend:
Noun1.independent variable - (statistics) a variable whose values are independent of changes in the values of other variables
variable quantity, variable - a quantity that can assume any of a set of values
factor - an independent variable in statistics
statistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population parameters
References in periodicals archive ?
Demonstration of a functional relationship is compromised when (a) there is a long latency between manipulation of the independent variable and change in the dependent variable, (b) mean changes across conditions are small and/or similar to changes within conditions, and (c) trends do not conform to those predicted following introduction or manipulation of the independent variable.
where Y is the computed value of the dependent variable obtained from a specific value of the independent variable; a is the constant, or intercept, where the regression line crosses the vertical axis (the value of Y when X = 0); b is the slope of the regression line, describing the change in the value of Y for each unit change in the value of X; and X is the value of the independent variable.
The multiple baseline across participants addresses the impact of the treatment of the independent variable on the dependent variable, the same behavior, for different participants.
The logistic regression model predicts logit values for each case as linear combinations of the independent variable values.
A simple regression has one independent variable paired with a dependent variable, while multiple regression can have two or more independent variables with a dependent variable.
Regression coefficients indicate the amount of change in the dependent variable for the one-unit change in the independent variable.
Although the interest rate was initially included among the independent variables, its exclusion improved model fit substantially.
Another downside is the big volume of data and the costs for data collection in order to establish the initial regression equation and for the analysis of its validity in time (if a modification in the causal relation between an independent variable and the dependent one appears, the collection of new data is necessary, along with the redefinition of the regression equation).
With regarding to 1-4chart, because significant level of independent variable is less than 0.
It is simply the variance in the dependent variable that is explained by the independent variable, which is 67% in this case showing that the model is explaining the findings to approximately 67% which is a good range.
Their independent variable was EPS/P, and the results showed that in longer time periods, correlation between two variables increases, and profit justifies a much more amount of return.

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