independent variable

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Independent variable

Term used in regression analysis to represent the element or condition that is expected to influence another (so-called dependent) variable.
Copyright © 2012, Campbell R. Harvey. All Rights Reserved.

Independent Variable

In technical analysis, a variable whose value is not determined by the value of other variable(s), but rather determines 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, supply and demand are independent variables because together they determine the product's price. See also: Dependent variable.
Farlex Financial Dictionary. © 2012 Farlex, Inc. All Rights Reserved

independent variable

A variable that is not affected by any other variables with which it is compared. For example, in comparing the price of an electric utility stock with interest rates, the interest rates are an independent variable because they are not affected by utility stock prices. Compare dependent variable.
Wall Street Words: An A to Z Guide to Investment Terms for Today's Investor by David L. Scott. Copyright © 2003 by Houghton Mifflin Company. Published by Houghton Mifflin Company. All rights reserved. All rights reserved.

independent variable

a variable that affects some other variable in a model. For example, the price of a product (the independent variable) will influence the demand for it (the DEPENDENT VARIABLE). It is conventional to place the independent variable on the right-hand side of an EQUATION. See DEMAND FUNCTION, SUPPLY FUNCTION.
Collins Dictionary of Economics, 4th ed. © C. Pass, B. Lowes, L. Davies 2005
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).
Simulation with Three Independent Variables Independent variables were derived from normal distributions as being [X.sub.1]~N(200,45), [X.sub.2]~N(130,30), [X.sub.3]~N(60,14) and correlated to one another ([r.sub.12]=0.704, [r.sub.13] =0.553, [r.sub.23]=0.372).
The multiple regression results indicate different values for each of the three independent variables, which help in accepting and rejecting the hypotheses.
The correlation coefficient between the independent variables shows that there is a multicollinearity problem if its absolute value is close to 1 (Albayrak, 2005).
In the future we shall further discuss the dynamic response to various perturbations (initial states, initial time, independent variables, and dependent variables) and the second order sensitivity (showing the parameter sloppiness) at any time instant or around the steady state.
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.
The performance of a PPV, aphakia after the initial trauma, the loss of iris tissue, a penetrating injury, and the cutting of any prolapsed vitreous in the primary surgery are the independent variables with significant effects on the final visual outcome.
To acquire a well-trained machine learning model, in addition to the regular training and testing processes as shown in Sections 2.2 and 2.3, another key step is to define the independent variables for training.
A cycle average map in terms of dependent variables, which could be BSFC or fuel mass as function of two independent variables for each certification cycle, will be generated.
In case of direct causal effect of leadership styles on the dependent variable, job satisfaction, the significant path coefficients for H12, H13, H14, and H15 affirmed that all four independent variables have direct effect on the level of job satisfaction.
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.

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