| Title: |
Introducing Bayesian Analysis with m&m's®: An Active-Learning Exercise for Undergraduates |
| Language: |
English |
| Authors: |
Eadie, Gwendolyn; Huppenkothen, Daniela; Springford, Aaron; McCormick, Tyler |
| Source: |
Journal of Statistics Education. 2019 27(2):60-67. |
| Availability: |
Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals |
| Peer Reviewed: |
Y |
| Page Count: |
8 |
| Publication Date: |
2019 |
| Document Type: |
Journal Articles; Reports - Descriptive |
| Education Level: |
Higher Education; Postsecondary Education |
| Descriptors: |
Undergraduate Students; Bayesian Statistics; Active Learning; Learning Activities; Advanced Courses; Statistical Distributions; Student Educational Objectives; Teaching Methods; Lesson Plans; Instructional Materials; Open Source Technology |
| DOI: |
10.1080/10691898.2019.1604106 |
| ISSN: |
1069-1898 |
| Abstract: |
We present an active-learning strategy for undergraduates that applies Bayesian analysis to candy-covered chocolate m&m's®. The exercise is best suited for small class sizes and tutorial settings, after students have been introduced to the concepts of Bayesian statistics. The exercise takes advantage of the nonuniform distribution of m&m's® colors, and the difference in distributions made at two different factories. In this paper, we provide the intended learning outcomes, lesson plan and step-by-step guide for instruction, and open-source teaching materials. We also suggest an extension to the exercise for the graduate level, which incorporates hierarchical Bayesian analysis. |
| Abstractor: |
As Provided |
| Entry Date: |
2019 |
| Accession Number: |
EJ1225203 |
| Database: |
ERIC |