Dependent and independent variables

[a] In this sense, some common independent variables are time, space, density, mass, fluid flow rate,[1][2] and previous values of some observed value of interest (e.g. human population size) to predict future values (the dependent variable).

[3] Of the two, it is always the dependent variable whose variation is being studied, by altering inputs, also known as regressors in a statistical context.

Sometimes, even if their influence is not of direct interest, independent variables may be included for other things, such as to account for their potential confounding effect.

[5] A symbol that stands for an arbitrary input is called an independent variable, while a symbol that stands for an arbitrary output is called a dependent variable.

[citation needed] In the simple stochastic linear model yi = a + bxi + ei the term yi is the ith value of the dependent variable and xi is the ith value of the independent variable.

[22] An example is provided by the analysis of trend in sea level by Woodworth (1987).

Use was made of a covariate consisting of yearly values of annual mean atmospheric pressure at sea level.

So that the variable will be kept constant or monitored to try to minimize its effect on the experiment.

Extraneous variables, if included in a regression analysis as independent variables, may aid a researcher with accurate response parameter estimation, prediction, and goodness of fit, but are not of substantive interest to the hypothesis under examination.

For example, in a study examining the effect of post-secondary education on lifetime earnings, some extraneous variables might be gender, ethnicity, social class, genetics, intelligence, age, and so forth.

If it is excluded from the regression and if it has a non-zero covariance with one or more of the independent variables of interest, its omission will bias the regression's result for the effect of that independent variable of interest.