If you want to improve your product or service by using simulated data, you can insert and run a Monte Carlo Simulation. Monte Carlo simulation uses repeated random sampling to simulate data for a given mathematical model and evaluate and optimize the outcome.
After you run a Monte Carlo simulation, Workspace displays the results, how your results compare to generally accepted values, and guidance for next steps.
Parameter optimization identifies optimal settings for the inputs that you can control. Workspace searches a range of values for each input to find settings that meet the defined objective and lead to better performance of the system.
Sensitivity analysis identifies inputs that have little effect on the variation of the output, or inputs that reduce the variation of the output. Workspace displays a graph that shows the effect of changing the input standard deviation on the percent of output that is out of spec.
After you analyze the results, you can change inputs or outputs, and then rerun the analysis to evaluate a number of "what if" scenarios.
For videos, how-to's, and glossary terms, go to Minitab Workspace.