Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2008

Abstract

The pervasiveness of spreadsheets 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, data collation, and distribution function fitting. In fact, the latter can be overwhelming for undergraduate students to learn and 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 inverse distribution function inversion method and can better focus on learning business modeling and analysis.

Keywords

Resampling, Monte-Carlo Simulation, Spreadsheet

Discipline

Business | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Optimization

Publication

Spreadsheets in Education

Volume

3

Issue

1

First Page

70

Last Page

78

ISSN

1448-6156

Publisher

Bond University

Additional URL

https://epublications.bond.edu.au/ejsie/vol3/iss1/6/

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