Using Financial Investment Measures to Proactively Engage Students in the Introductory Business Statistics Course

Mark L. Berenson, Nicole Koppel, Richard Lord, Laura L. Chapdelaine

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Typically, the core-required undergraduate business statistics course covers a broad spectrum of topics with applications pertaining to all functional areas of business. The recently updated American Statistical Association's GAISE (Guidelines for Assessment and Instruction in Statistics Education) College Report once again stresses the pedagogical importance of topic and application relevancy in an increasingly data-centered world. To this end, only two introductory textbooks have incorporated some financial investment measures (Sharpe ratio and beta coefficient) in the teaching of numerical descriptive measures and simple linear regression analysis, respectively, while a few others include them as real-data application exercises at the end of their respective chapters. Although this latter coverage is in compliance with GAISE College Report recommendations on the importance of using relevant real data applications in the teaching of introductory business statistics, it forgoes an opportunity to provide more detailed discussion within the text itself and add value to a business student's learning. Given that all business students will have an opportunity to learn about financial investment, regardless of their functional area major, this paper offers a more proactive use of these and other financial investment measures as part of the current, traditional course or as part of a suggested dedicated introductory business statistics course for finance majors.

Original languageEnglish
Pages (from-to)17-30
Number of pages14
JournalJournal of Statistics Education
Volume26
Issue number1
DOIs
StatePublished - 2 Jan 2018

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financial investment
education statistics
statistics
Statistics
Statistics Education
instruction
student
Teaching
textbook
regression analysis
finance
coverage
Simple Linear Regression
Student Learning
Finance
Regression Analysis
Compliance
Exercise
learning
Business

Keywords

  • Alpha and beta coefficients
  • Bootstrap percentile confidence interval
  • Capital asset pricing model
  • GAISE
  • Permutation test
  • Resampling method
  • Sharpe ratio
  • Simulation-based inference
  • Treynor ratio

Cite this

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abstract = "Typically, the core-required undergraduate business statistics course covers a broad spectrum of topics with applications pertaining to all functional areas of business. The recently updated American Statistical Association's GAISE (Guidelines for Assessment and Instruction in Statistics Education) College Report once again stresses the pedagogical importance of topic and application relevancy in an increasingly data-centered world. To this end, only two introductory textbooks have incorporated some financial investment measures (Sharpe ratio and beta coefficient) in the teaching of numerical descriptive measures and simple linear regression analysis, respectively, while a few others include them as real-data application exercises at the end of their respective chapters. Although this latter coverage is in compliance with GAISE College Report recommendations on the importance of using relevant real data applications in the teaching of introductory business statistics, it forgoes an opportunity to provide more detailed discussion within the text itself and add value to a business student's learning. Given that all business students will have an opportunity to learn about financial investment, regardless of their functional area major, this paper offers a more proactive use of these and other financial investment measures as part of the current, traditional course or as part of a suggested dedicated introductory business statistics course for finance majors.",
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Using Financial Investment Measures to Proactively Engage Students in the Introductory Business Statistics Course. / Berenson, Mark L.; Koppel, Nicole; Lord, Richard; Chapdelaine, Laura L.

In: Journal of Statistics Education, Vol. 26, No. 1, 02.01.2018, p. 17-30.

Research output: Contribution to journalArticle

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