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

References

• Aliaga, M., Cobb, G., Cuff, C., Garfield, J. (Chair), Gould, R., Lock, R., Moore, T., Rossman, A., Stephenson, B., Utts, J., Velleman, P., and Witmer, J. (2005), GAISE (Guidelines for Assessment and Instruction in Statistics Education) College Report, Alexandria, VA: American Statistical Association.
   Google Scholar
• Bernstein, P.L. (1992), Capital Ideas: The Improbable Origins of Modern Wall Street, New York: Free Press.
   Google Scholar
• Carver, R., Everson, M. (Co-chair), Gabrosek, J., Holmes Rowell, G., Horton, N., Lock, R., Mocko, M. (Co-chair), Rossman, A., Velleman, P., Witmer, J., and Wood, B. (2016), Guidelines for Assessment and Instruction in Statistics Education (GAISE) College (DRAFT) Report, Alexandria, VA: American Statistical Association.
   Google Scholar
• Chang, G. E., Kerns, G. J., Lee, D. J., and Stanek, G. L. (2009), “Calibration Experiments for a Computer Vision Oyster Volume Estimation System,” Journal of Statistics Education, 17.
   PubMedGoogle Scholar
• Coakley, C. W. (1996), “Suggestions for Your Nonparametric Statistics Course,” Journal of Statistics in Education, 4.
   Google Scholar
• Cobb, G. (2007), “The Introductory Statistics Course: A Ptolemaic Curriculum?,” Technology Innovations in Statistics Education, 1, 16. Available at http://repositories.cdlib.org/uclastat/cts/tise/vol1/iss1/art1/.
   Google Scholar
• ——— (2013), “What Might a Twenty-Year Old Conference Tell Us About the Future of Our Profession?,” Journal of Statistics Education, 21, 17.
   Google Scholar
• del Mas, R. C., Garfield, J., and Chance, B. L. (1999), “A Model of Classroom Research in Action: Developing Simulation Activities to Improve Students' Statistical Reasoning,” Journal of Statistics in Education, 7.
   Google Scholar
• eCOTS16: Electronic Conference on the Teaching of Statistics (2016), Penn State University. Available at https://www.causeweb.org/cause/ecots/ecots16.
   Google Scholar
• Ernst, M. D. (2004), “Permutation Methods: A Basis for Exact Inference,” Statistical Science, 19, 676–685.
   Web of Science ®Google Scholar
• ——— (2009), “Teaching Inference for Randomized Experiments,” Journal of Statistics Education, 17.
   PubMedGoogle Scholar
• Eudey, T. L., Kerr, J. D., and Trumbo, B. E. (2010), “Using R to Simulate Permutation Distributions for Some Elementary Experimental Designs,” Journal of Statistics Education, 18, 30.
   PubMedGoogle Scholar
• Everson, M., and Mocko, M. (2015a), Updating the Guidelines for Assessment and Instruction in Statistics Education (GAISE). Available at www.causeweb.org/webinar/teaching/2015-07/index.php.
   Google Scholar
• ——— (2015b), “GAISE into the Future: Updating a Landmark Report for an Increasingly Data-Centric World,” in annual Joint Statistics Meetings (JSM), Seattle, WA.
   Google Scholar
• Forbes, S. (2014), “Using Action Research to Develop a Course in Statistical Inference for Workplace-Based Adults,” Journal of Statistics Education, 22.
   Google Scholar
• Garrett, L., and Nash, J. C. (2001), “Issues in Teaching the Comparison of Variability to Non-Statistics Students,” Journal of Statistics Education, 9.
   Google Scholar
• Graham, J., and Harvey, C. (2002), “How Do CFOs Make Capital Budgeting and Capital structure Decisions,” Journal of Applied Corporate Finance, 15, 8–23.
   Google Scholar
• Hesterberg, T., Monaghan, S., Moore, D. S., Clipson, A., and Epstein, R. (2003), “Bootstrap Methods and Permutation Tests,” Companion Chapter 18 in The Practice of Business Statistics, eds. Moore, D. S., McCabe, G. P., Duckworth, W. M., and Sclove, S. L., New York: W. H. Freeman and Company.
   Google Scholar
• Jaggia, S., and Kelly, A. (2013), Business Statistics: Communicating with Numbers. New York: McGraw-Hill.
   Google Scholar
• Jensen, M. C. (1968), “The Performance of Mutual Funds in the Period 1945–1964,” Journal of Finance, 23, 587–636.
   Web of Science ®Google Scholar
• Jordan, B., and Miller, T. (2012), Fundamentals of Investments: Valuation and Management (6th ed.), New York: McGraw-Hill.
   Google Scholar
• Linter, J. (1965), “The Valuation of Risk Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets,” Review of Economics and Statistics, 47, 13–37.
   Web of Science ®Google Scholar
• Lock, R. (2004), “Proposed Guidelines for Teaching Statistics: K-12 and College,” Panel discussion at annual Joint Statistical Meetings (JSM), Toronto, Canada.
   Google Scholar
• Lock, R., Lock, P., Lock Morgan, K., Lock, E., and Lock, E. (2013), Statistics: Unlocking the Power of Data, New York: Wiley.
   Google Scholar
• Markowitz, H. M. (1952), “Portfolio Selection,” Journal of Finance, 7, 77–91.
   Web of Science ®Google Scholar
• ——— (1959), Portfolio Selection: Efficient Diversification of Investments, New York: Wiley.
   Google Scholar
• Mondello, J. (Nov, 2015), AACSB DataDirect ≤[email protected]≥
   Google Scholar
• Mossin, J. (1966), “Equilibrium in a Capital Asset Market,” Econometrica, 34, 768–783.
   Web of Science ®Google Scholar
• Ross, S. A., Westerfield, R. W., and Jordan, B.D. (2016), Fundamental of Corporate Finance (11th ed.), New York: McGraw Hill Education.
   Google Scholar
• Rossman, A., and Chance, B. (2008), Concepts of Statistical Inference: A Randomization-Based Curriculum. Available at http://statweb.calpoly.edu/csi.
   Google Scholar
• Rossman, A., and Chance, B. (2012), Workshop Statistics: Discovery with Data, (4th ed.), New York: Wiley.
   Google Scholar
• Rossman, A., and Cobb, G. (2015), “Interview with George Cobb,” Journal of Statistics Education, 23, 29.
   Google Scholar
• Sharpe, W. F. (1964), “Capital Asset Prices: A Theory of Market Equilibrium,” Journal of Finance, 14, 425–442.
   Google Scholar
• ——— (1966), “Mutual Fund Performance,” Journal of Business, 39, 119–138.
   Web of Science ®Google Scholar
• Simon, J. L. (1994), “What Some Puzzling Problems Teach About the Theory of Simulation and the Use of Resampling,” The American Statistician, 48, 290–293.
   Web of Science ®Google Scholar
• Simon, J. L., and Bruce, P. (1991), “Resampling: A Tool for Everyday Statistical Work,” Chance: New Directions for Statistics and Computing, 4, 22–32.
   Google Scholar
• Stephenson, W. R., Froelich, A. G., and Duckworth, W. M. (2010), “Using Resampling to Compare Two Proportions,” Teaching Statistics, 32, 66–71.
   Google Scholar
• Stine, R., and Foster, D. (2011), Statistics for Business: Decision Making and Analysis, Boston, MA: Addison-Wesley.
   Google Scholar
• The Charles Schwab Corporation. (2015), “Getting Smart on Beta,” available at https://www.schwab.com/resource-center/insights/content/getting-smart-on-beta.
   Google Scholar
• Tintle, N., VanderStoep, J., Holmes, V., Quisenberry, B., and Swanson, T. (2011), “Development and Assessment of a Preliminary Randomization-Based Introductory Statistics Curriculum,” Journal of Statistics Education, 19, 25.
   Google Scholar
• Tintle, N., Chance, B., Cobb, G., Rossman, A., Roy, S., Swanson, T., and Vanderstoep, J. (2015), Introduction to Statistical Investigations, New York: Wiley.
   Google Scholar
• Treynor, J. L. (1966), “How to Rate Management Investment Funds,” Harvard Business Review, 43, 63–75.
   Web of Science ®Google Scholar
• Wasserstein, R. L., and Lazar, N. A. (2016), “The ASA's Statement on p-Values: Context, Process and Purpose,” The American Statistician, 70, 129–133.
   Web of Science ®Google Scholar
• Wood, M. (2005), “The Role of Simulation Approaches in Statistics,” Journal of Statistics in Education, 13.
   Google Scholar