Practical Monte Carlo Simulation with Excel - Part 1 of 2: Basics and Standard Procedures

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About Practical Monte Carlo Simulation With Excel - Part

There is a fair number of stand alone applications as well as add on’s to Microsoft Excel in the market used to run Monte Carlo Simulation (MCS) models. However, out of the box, Excel has all the functions you need to develop such models. What is needed are robust modeling procedures, techniques and analytic formulations. Initially, I started with one book. This grew out of proportion as more and more applications and models were identified. Some of these had not been modeled with MCS before. I had to break the book into two parts.Part 1 presents the basics of modeling always providing methods and typical models as applications of simulation. Part 1 also spends time on clarifying different ways of analyzing the simulation output using a variety of statistical functions and procedures all found within Excel. The eBook clarifies a variety of Excel facilities needed in different parts of simulation: sensitivity analysis, linear regression and the Analysis Toolpack. Finally, Part 1 presents a few standard modeling techniques that can be used in a variety of models, specifically in Part 2.Part 2 concentrates on applications such as project management, acceptance sampling, sales and budget forecasting, queuing models, reliability engineering and more. Since these operations behave according to specific statistical distributions, time is spent on clarifying a variety of these functions. When one or two are not available in Excel, alternative methods of computation are presented. A special chapter addresses Markov Processes and shows how simulation can be coupled to such an analysis.The uses and applications of statistical distributions in these operations are addressed in depth. Having covered Uniform, Normal and Discrete Distributions in Part 1, Part 2 proceeds to present and give applications for the following distributions: binomial, negative binomial, geometric, hypergeometric, triangular (not commonly used but is the basis as to why betaPERT is prefe