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Sensitivity analysis is a technique used to determine how changes in input variables affect the output of a mathematical model.

In mathematical modelling, it is important to understand how changes in input variables affect the output of the model. Sensitivity analysis is a technique used to determine the degree to which changes in input variables affect the output of a model. It involves varying one or more input variables while keeping all other variables constant, and observing the resulting changes in the output variable(s).

There are different methods of conducting sensitivity analysis, including one-way sensitivity analysis, which involves varying one input variable at a time, and multi-way sensitivity analysis, which involves varying multiple input variables simultaneously. Sensitivity analysis can be used to identify which input variables have the greatest impact on the output of the model, and to determine the range of values for each input variable that produces a desired output.

For example, consider a mathematical model that predicts the sales of a product based on its price, advertising budget, and the level of competition. Sensitivity analysis can be used to determine how changes in each of these input variables affect the sales of the product. This information can be used to make informed decisions about pricing, advertising, and other factors that affect sales.

Overall, sensitivity analysis is a valuable tool for understanding the behaviour of mathematical models and making informed decisions based on their outputs.

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