dependent and independent variables

Resource allocation changes Table 2reports the mean values for the dependent and independent variables included in the multivariate regressions.

Experiments usually have dependent and independent variables.

It consists of interchanging the dependent and independent variables in the equation to achieve linearity.

The significance of this range is that cost behaviour, the relationship between the dependent and independent variables, can be established with a certain amount of accuracy.

In various fields of application, different terminologies are used in place of dependent and independent variables.

Intuitively, an inverse function is obtained from f by interchanging the roles of the dependent and independent variables.

Exotic changes, such as the mingling of dependent and independent variables in point and contact transformations, can be very complicated but allow much freedom.

The proportionality constant between dependent and independent variables is dimensionless if they are both currents (or both voltages).

In other words it enables one to modify the artificially simplistic notion of clear-cut dependent and independent variables having one-way causal links.

Cluster analysis, like factor analysis and multi-dimensional scaling, is an interdependence technique: it makes no distinction between dependent and independent variables.

This can be done by adjusting the weighting scheme to take into account errors on both the dependent and independent variables and then following the standard procedure.

Concepts explored in this lesson include dependent and independent variables, drawing graphs, calculating the slope of a line, and writing basic algebraic formulae.

A Bäcklund transformation is a transformation of the dependent and independent variables of a differential equation that transforms it to a similar equation.

Data Treatment and Statistical Analysis: Regression analysis was performed using aorta external diameter and inflation pressure as dependent and independent variables, respectively.

Internal validation, used primarily in experimental research designs, checks the relation between the dependent and independent variables (i.e. Did the experimental manipulation of the independent variable actually cause the observed results?)

In some instances of bivariate data, it is determined that one variable influences or determines the second variable, and the terms dependent and independent variables are used to distinguish between the two types of variables.

Total least squares, also known as errors in variables, rigorous least squares, or (in a special case) orthogonal regression, is a least squares data modeling technique in which observational errors on both dependent and independent variables are taken into account.

In 1880, Stokes himself inverted the dependent and independent variables, by taking the velocity potential and stream function as the independent variables, and the coordinates (x,z) as the dependent variables, with x and z being the horizontal and vertical coordinates respectively.