Using Baseball Data as a Gentle Introduction to Teaching Linear Regression

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.

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McMullen, P. (2015) Using Baseball Data as a Gentle Introduction to Teaching Linear Regression. Creative Education, 6, 1477-1483. doi: 10.4236/ce.2015.614148.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Albright, S. C., Winston, W. L., & Zappe, C. J. (2011) Data Analysis and Decision Making (4th ed.). Mason, Ohio: Southwestern/ Cengage Learning.
[2] Hoaglin, D., & Velleman, P (1995). A Critical Look at Some Analyses of Major League and Baseball Salaries. The American Statistician, 49, 277-285.
[3] USA Today.
http://www.usatoday.com/sports/mlb/salaries/2013/team/all/
[4] Lewis, M. (2003). Moneyball: The Art of Winning an Unfair Game. New York: W. W. Norton & Company.
[5] Watnik, M. R. (1988). Pay for Play: Are Baseball Salaries Based on Performance? Journal of Statistics Education, 6.

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