When simulating a model, some random variable inputs may be correlated. One way to introduce this correlation into the simulation is by rank order correlation. In this method a correlation matrix is used to define the correlation structure. The correlation matrix definiteness is important. A correlation matrix must be either positive definite (PD) or positive …
Presenting the results of a Monte Carlo simulation can be challenging when your audience isn’t inclined to receive probabilistic information. Decision makers often want a single number for net present value, project completion date, or profit. How do we as the modeler/analyst present this information so it is understood and appreciated for the extra information …
A box plot, aka box and whisker chart, is a simple way to show the results of a Monte Carlo simulation. It’s also a useful way to visualize the differences between several simulations in one plot. Typically the results of a single simulation is shown as a histogram. This a good tool for visualizing outcomes, …
An easy to make mistake when modeling identically distributed random variables is to use a single variable to model several identically distributed values. We could easily assume that several random variables that have the same probability distribution could be modeled by sampling a single variable and multiplying by the total number of variables. This is …
Building a spreadsheet model for a business case, product profitability projection, etc. often entails estimating some input factors. The estimates are often a most likely value, an estimated average, or median. These are single-point estimates since you’re only using one number (a single data point) for each input. Finally, you get an output number from …
When data is available, an empirical distribution can be used in a Monte Carlo simulation. In this article we’ll cover the basics of the empirical distribution and how to use it in your models. Empirical vs. Parametric Distributions Before we get into details, let’s look at the big picture for a minute. A parametric probability …
Performance and cost trade-offs are a constant in engineering design. Optimizing a design to balance these competing forces can be challenging to quantify and we often have to resort to experience or intuition. One of the traditional cost drivers is specifying tolerances. Part dimensions are naturally random variables since each part manufactured will not be …
Monte Carlo simulation for business purposes is a great tool to have in your toolbox. If you’re a business owner or a product line owner in a larger enterprise, chances are you’ve created a spreadsheet to model a business case or something similar. Anytime you have a spreadsheet that contains estimates that are inputs to …
In this article, we will discuss input correlation methods that can be used with Simulation Master. In a previous article, we discussed why it is important to account for correlation among input random variables when performing Monte Carlo simulation. There are two input correlation methods available in Simulation Master: Rank order correlation Bivariate copulas Rank …
This is a Monte Carlo simulation checklist to ensure proper model set-up when working with Simulation Master. Unless stated otherwise, each item refers to both the standard and premium editions. Monte Carlo Simulation Checklist If you want to record random variable values in the simulation data sheet, the random variables must be in the same …