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A variable in an experiment which is held constant in order to assess the relationship between multiple variables [a], is a control variable. [2] [3] A control variable is an element that is not changed throughout an experiment because its unchanging state allows better understanding of the relationship between the other variables being tested. [4]
The simplest examples of control variables in regression analysis comes from Ordinary Least Squares (OLS) estimators. The OLS framework assumes the following: Linear relationship - OLS statistical models are linear. Hence the relationship between explanatory variables and the mean of Y must be linear.
A variable may be thought to alter the dependent or independent variables, but may not actually be the focus of the experiment. So that the variable will be kept constant or monitored to try to minimize its effect on the experiment. Such variables may be designated as either a "controlled variable", "control variable", or "fixed variable".
A scientific control is an experiment or observation designed to minimize the effects of variables other than the independent variable (i.e. confounding variables). [1] This increases the reliability of the results, often through a comparison between control measurements and the other measurements.
An optimal control is a set of differential equations describing the paths of the control variables that minimize the cost function. The optimal control can be derived using Pontryagin's maximum principle (a necessary condition also known as Pontryagin's minimum principle or simply Pontryagin's principle), [8] or by solving the Hamilton ...
The Hamiltonian of control theory describes not the dynamics of a system but conditions for extremizing some scalar function thereof (the Lagrangian) with respect to a control variable . As normally defined, it is a function of 4 variables
When the expectation of the control variable, [] =, is not known analytically, it is still possible to increase the precision in estimating (for a given fixed simulation budget), provided that the two conditions are met: 1) evaluating is significantly cheaper than computing ; 2) the magnitude of the correlation coefficient |, | is close to unity.
Departure of such a variable from its setpoint is one basis for error-controlled regulation using negative feedback for automatic control. [3] A setpoint can be any physical quantity or parameter that a control system seeks to regulate, such as temperature, pressure, flow rate, position, speed, or any other measurable attribute.