Editing Sensitivity analysis (section)

=== Regression analysis ===
[[Regression analysis]], in the context of sensitivity analysis, involves fitting a [[linear regression]] to the model response and using [[Standardized coefficient|standardized regression coefficients]] as direct measures of sensitivity. The regression is required to be linear with respect to the data (i.e. a hyperplane, hence with no quadratic terms, etc., as regressors) because otherwise it is difficult to interpret the standardised coefficients. This method is therefore most suitable when the model response is in fact linear;  linearity can be confirmed, for instance, if the [[coefficient of determination]] is large. The advantages of regression analysis are that it is simple and has a low computational cost.