What is Sensitivity Analysis?
Sensitivity analysis is a method for exploring how the output of a system or model changes with different input values. It can also be called simulation analysis or what-if analysis. Sensitivity analysis has many applications, such as checking the reliability of a model, finding out the causes of variability, reducing the complexity of the model, enhancing communication, discovering the best solutions, and adjusting the parameters.
How does Sensitivity Analysis Work?
Sensitivity analysis is a method that examines how the input variables affect the output of a system or model. It has various applications, such as checking the reliability of the model, exploring the connections between inputs and outputs, decreasing ambiguity, making the model simpler, improving communication, discovering the best solutions, and enhancing model quality.
Sensitivity analysis has two main types: local and global. Local sensitivity analysis studies the influence of one input variable on the output variable while holding the other inputs fixed. Global sensitivity analysis assesses the possible values of all input variables and their combined effect on the output variable.
Sensitivity analysis can be used in different areas and domains, such as finance, engineering, biology, climate change, economics, and health. For instance, in finance, sensitivity analysis can help investors estimate how the stock prices of public companies or the impact of interest rates will vary under different scenarios. Sensitivity analysis can help doctors determine the most efficient antibiotic to eliminate an infecting microorganism.