Research ArticleOpen AccessGoogle Scholar indexed
Using Baseball Data as a Gentle Introduction to Teaching Linear Regression
School of Business, Wake Forest University, Winston-Salem, NC, USA
- 1 School of Business, Wake Forest University, Winston-Salem, NC, USA
Creative Education·Volume 06 (2015)·Pages 1477–1483·Published 7 August 2015·DOI10.4236/ce.2015.614148
Copy link · social · email
Abstract
This effort describes a successful classroom exercise to introduce simple and multiple linear regression to working professional MBA students. The exercise starts by exploring the relationship between a baseball team’s payroll with its winning percentage. The exercise then continues with the introduction of additional predictor variables so that the students are able to build a strong predictive model for winning percentage. Student feedback consistently praises the exercise as an effective way to learn about linear regression.
KeywordsTeachingLinear RegressionStatisticsSurveyBaseball
- Albright, S. C., Winston, W. L., & Zappe, C. J. (2011) Data Analysis and Decision Making (4th ed.). Mason, Ohio: Southwestern/ Cengage Learning.
- Hoaglin, D., & Velleman, P (1995). A Critical Look at Some Analyses of Major League and Baseball Salaries. The American Statistician, 49, 277-285.
- USA Today. http://www.usatoday.com/sports/mlb/salaries/2013/team/all/
- Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. New York: W. W. Norton & Company.
- Watnik, M. R. (1988). Pay for Play: Are Baseball Salaries Based on Performance? Journal of Statistics Education, 6.