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http://dx.doi.org/10.5351/KJAS.2019.32.4.485

Theoretical statistics education using mathematical softwares  

Lee, Geung-Hee (Department of Data Science and Statistics, Korea National Open University)
Publication Information
The Korean Journal of Applied Statistics / v.32, no.4, 2019 , pp. 485-502 More about this Journal
Abstract
Theoretical statistics is a calculus based course. However, there are limitations to learn theoretical statistics when students do not know enough calculus techniques. Mathematical softwares (computer algebra systems) that enable calculus manipulations help students understand statistical concepts, by avoiding the difficulties of calculus. In this paper, we introduce mathematical software such as Maxima and Wolfram Alpha. To foster statistical concepts in theoretical statistics education, we present three examples that consist of mathematical derivations using wxMaxima and statistical simulations using R.
Keywords
mathematical statistics; statistical inference; CAS; Maxima; Wolfram Alpha; R; wxMaxima;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Baglivo, J. (1995). Computer Algebra Systems: maple and mathematica, The American Statistician, 49, 235-249.
2 Berger, R. L. (1998). Using computer algebra systems to teach graduate mathematical statistics: Potential and pitfalls, Statistical Education - Expanding the Network: Proceedings of the Fifth International Conference on Teaching of Statistics (eds. Pereira-Mendoza, L., et al.). International Statistical Institute, Voorburg, Netherlands. Volume 1, 189-195.
3 Carver R., Everson, M., Gabrosek, et al. (2016). Guidelines for assessment and instruction in statistics education: College report, ASA GAISE College working group.
4 Doi, J., Potter, G., Wong, J., Alcaraz, I., and Chi, P. (2016). Web Application Teaching Tools for Statistics Using R and Shiny, Technology Innovations in Statistics Education, 9. Available from: https://escholarship.org/uc/item/00d4q8cp
5 Efron, B. and Hastie, T. (2016). Computer Age Statistical Inference Algorithms, Evidence, and Data Science, Cambridge University Press.
6 Jang, D. H. (2009). Application of R for inferential statistics in the elementary Statistics Course, Journal of Applied Statistics, 22, 893-910.
7 Green, J. L. and Blankenship, E. E. (2015). Fostering conceptual understanding in mathematical statistics, The American Statistician, 69, 315-325.   DOI
8 Horton, N. J., Brown, E. R., and Qian, L. (2004). Use of R as a toolbox for mathematical statistics exploration, The American Statistician, 58, 343-357.   DOI
9 Hunter, D. R. (2005). Teaching computing in statistical theory courses, The American Statistician, 59, 327-333.   DOI
10 Kang, K. and Park, J. (2015). Mathematical Statistics-Practice with R, Freedom Academy, Seoul.
11 Kim, W. C. (2011). Mathematical Statistics, Yougjisa, Seoul.
12 Kumar, A. and Kumaresan, S. (2008). Use of Mathematical software for teaching and learning mathematics, Proceedings of 11th International Congress on Mathematics Education, Mexico.
13 Lee, G. H., Kim, H., Kim, J., Park, J., and Lee, J. (2019). Concepts and Controversies of Statistics, KNOU Press, Seoul.
14 Lee, S. G. and Park, K. E. (2015). Improving computational thinking abilities Through the teaching of mathematics with Sage, Communications of Mathematical Education, 29, 19-33.   DOI
15 Leydold, J. and Petry, M. (2011). Introduction to Maxima for Economics, Institute for Statistics and Mathematics, WUWien.
16 Moore, D. S. (1997). New pedagogy and new content: the case of statistics, International Statistical Review, 65, 123-165.   DOI
17 Rose, C. and Smith, M. D. (2000). Symbolic maximum likelihood estimation with Mathematica, Journal of the Royal Statistical Society. Series D (The Statistician), 49, 229-240.   DOI
18 Rossman, A. and Chance, B. (1999). Teaching the reasoning of statistical inference: A "Top Ten" List, The College Mathematics Journal, 30, 297-305.   DOI