# A Simulation of Sample Variance Calculation in the Teaching of Business Statistics to English Majors

Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

## Abstract

Variance is important for statistical description of a data set. Yet, the denominator of (n–1) in sample variance calculation confuses many Business English learners of statistics. In order to give learners an impressive instruction, a statistical simulation of population and sample variance calculation is designed with self-code Python program. The experimental simulation shows that the sample variances calculated with divisor of (n–1) are averagely closer to population variance than with n. The latter underestimates the population variance. The simulation offers an important explanation for statistics learners and can help them learn business statistics better.

## Notes

### Acknowledgments

This research was supported by Graduate Education Innovation Plan of Guangdong Province (2015JGXM-MS22) and the Science and Technology Innovation Project of Guangdong Province (2013KJCX0070).

## References

1. 1.
Ge, S., Fang, F., & Chen, X. (2015). The simulation of sampling distribution in the teaching of business statistics to English majors. Journal of Simulation, 3(2), 43–46.Google Scholar
2. 2.
Selvanathan, E. A., Selvanathan, S., & Keller, G. (2014). Business statistics: Australia and New Zealand (6th ed.). Sydney: Cengage Learning Australia Pty Limited.Google Scholar
3. 3.
Zhao, J., & Guo, L. (2016). An analysis on the definition of sample variance. Journal of Science of Teachers’ College and University, 36(7), 61–62+.Google Scholar
4. 4.
Groebner, D. F., Shannon, P. W., Fry, P. C., & Smith, K. D. (2011). Business statistics: A decision-making approach (8th ed.). Upper Saddle River: Pearson Education, Inc.Google Scholar
5. 5.
Anderson, D. R., Sweeney, D. J., & Williams, T. A. (2011). Statistics for business and economics (11th ed.). Mason: South-Western Cengage Learning.Google Scholar
6. 6.
Mendenhall, W., Beaver, R. J., & Beaver, B. M. (2013). Introduction to probability and statistics (14th ed.). Boston: Brooks/Cole, Cengage Learning.Google Scholar
7. 7.
Walpole, R. E., Myers, R. H., Myers, S. L., & Ye, K. (2012). Probability & statistics for engineers & scientists (9th ed.). Boston: Pearson Education.Google Scholar
8. 8.
Ross, S. M. (2013). Simulation. San Diego: Elsevier.Google Scholar