Spreadsheet Data Resampling for Monte-Carlo Simulation
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.
Resampling, Monte-Carlo Simulation, Spreadsheet
Business | Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics
Spreadsheets in Education
LEONG, Thin Yin and LEE, Wee Leong.
Spreadsheet Data Resampling for Monte-Carlo Simulation. (2008). Spreadsheets in Education. 3, (1), 70-78. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/796