Final answer:
Sensitivity analysis is used to determine the impact of changes in input variables on the output of a mathematical model. It helps decision-makers assess the importance of different factors in a model and can be crucial in fields like engineering and business for evaluating the cost-effectiveness and performance of different system options.
Step-by-step explanation:
Sensitivity analysis determines the impact of changes in input variables on the output of a mathematical model. It is a method used to predict the outcome of a decision when there is uncertainty in the forecast or estimation of the performance of a system. By altering one variable at a time, sensitivity analysis can show how different values of an independent variable can affect a dependent variable within a given model.
Mathematical models are used to describe scientific phenomena and can evaluate cost. For instance, engineers use mathematical and science principles to build models that help them understand and solve complex problems. The analysis allows decision-makers to put a value on inputs and outputs of a decision that aren't generally quantifiable. This kind of analysis is crucial in fields like engineering where the choice of a system is an important analytical step both in solving problems and in thoroughly understanding the dynamics involved.
In business, mathematical models are often developed to estimate costs for various approaches during the design process and can help analyze the relationship between the science of a technique and its cost-effectiveness. Sensitivity analysis plays a key role in understanding how changes in these models can affect outputs, such as costs or performance metrics.