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In general terms, sensitivity analysis describes how susceptible a dependent variable is (i.e., observation of this variable depends on the presence of another variable) when a change occurs in a given independent variable (i.e., a variable is present without the necessity of another variable being present). It can be computed in a number of different ways, and these statistical tests determine what the actual observation of these new values will be, if the predicted value of the dependent variable has changed. Sensitivity analysis is most commonly used with mathematical models of prediction, in which the independent variable is usually termed input and the dependent variable is known as the output. In modeling, researchers build testable equations that represent observable phenomena in order to predict values ...

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