利用MINITAB做蒙特卡洛模拟.doc

Doing Monte Carlo Simulation in Minitab Statistical Software Doing Monte Carlo simulations in Minitab Statistical Software is very easy. This article illustrates how to use Minitab for Monte Carlo simulations using both a known engineering formula and a DOE equation. by Paul Sheehy and Eston Martz Monte Carlo simulation uses repeated random sampling to simulate data for a given mathematical model and evaluate the outcome. This method was initially applied back in the 1940s, when scientists working on the atomic bomb used it to calculate the probabilities of one fissioning uranium atom causing a fission reaction in another. With uranium in short supply, there was little room for experimental trial and error. The scientists discovered that as long as they created enough simulated data, they could compute reliable probabilities—and reduce the amount of uranium needed for testing. Today, simulated data is routinely used in situations where resources are limited or gathering real data would be too expensive or impractical. By using Minitab’s ability to easily create random data, you can use Monte Carlo simulation to: Simulate the range of possible outcomes to aid in decision-making Forecast financial results or estimate project timelines Understand the variability in a process or system Find problems within a process or system Manage risk by understanding cost/benefit relationships Steps in the Monte Carlo Approach Depending on the number of factors involved, simulations can be very complex. But at a basic level, all Monte Carlo simulations have four simple steps: 1. Identify the Transfer Equation To do a Monte Carlo simulation, you need a quantitative model of the business activity, plan, or process you wish to explore. The mathematical expression of your process is called the “transfer equation.” This may be a known engineering or business formula, or it may be based on a model created from a designed experiment (DOE) or regression analysis. 2. Define the Input Paramet