Able Construction: A Spreadsheet Activity for Teaching Bayes’ Theorem

David Wheatley (), Tiffany Bayley and Mojtaba Araghi
Additional contact information
David Wheatley: Wilfrid Laurier University
Tiffany Bayley: Western University
Mojtaba Araghi: Wilfrid Laurier University

SN Operations Research Forum, 2022, vol. 3, issue 1, 1-18

Abstract: Abstract Using classroom activities to motivate the teaching and learning of Bayes’ theorem is not new. However, many of the textbook exercises and published simulations gloss over how the requisite probabilities are determined. In our case study, Able Construction is a fictional company hoping to exploit historical bidding data to inform its own bidding strategy on a municipal construction project. Unlike most other classroom activities, we challenge students to calculate the necessary probabilities directly from a given dataset. In our experience with implementing this case in introductory business analytics courses at the undergraduate- and graduate-level, we find that this spreadsheet activity gives students the opportunity to exercise their own judgement regarding data manipulation and definition of states of nature. This autonomy in analysis develops in students a deeper appreciation for practical skills required for possible analytics careers after graduation, and leads to engaging discussions of the applicability of Bayes’ theorem in practice.

Keywords: Bayesian; Excel; Decision trees; Analytics; Case study (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s43069-021-00119-3 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:snopef:v:3:y:2022:i:1:d:10.1007_s43069-021-00119-3

Ordering information: This journal article can be ordered from
https://www.springer.com/journal/43069

DOI: 10.1007/s43069-021-00119-3

Access Statistics for this article

SN Operations Research Forum is currently edited by Marco Lübbecke

More articles in SN Operations Research Forum from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().