The pervasiveness of spreadsheet software resulted in its increased application as a simulation tool for business analysis. Random values generation supporting such evaluations using spreadsheets are simple and yet powerful. However, the typical approach to Monte-Carlo simulations, which is what simulations with stochasticity are called, requires significant amount of time to be spent on data collection and distribution function fitting. In fact, the latter can be overwhelming for undergraduate students to do properly in a short time. Resampling eliminates both the need to fit distributions to the sample data, and to perform the ensuing tests of goodness of fit, where sufficiently large data sets are necessary to achieve satisfactory levels of statistical confidence. In contrast, resampling methods can be used even with small data sets. This not only saves class time required to teach statistical data fitting; by generating random values, students also need not learn to use the more complex distribution function inversion method and can better focus on learning business modeling and analysis.
Leong, Thin Yin Dr and Lee, Wee Leong Dr
"Spreadsheet Data Resampling for Monte-Carlo Simulation,"
Spreadsheets in Education (eJSiE):
1, Article 6.
Available at: http://epublications.bond.edu.au/ejsie/vol3/iss1/6