2,947
Views
3
CrossRef citations to date
0
Altmetric
Articles

Bringing Visual Inference to the Classroom

ORCID Icon
Pages 171-182 | Published online: 28 May 2021

References

  • Agresti, A., Franklin, C. A., and Klingenberg, B. (2017), Statistics: The Art and Science of Learning from Data (4th ed.), Boston, MA: Prentice-Hall.
  • Bray, A., Ismay, C., Chasnovski, E., Baumer, B. and Cetinkaya-Rundel, M. (2019), infer: Tidy Statistical Inference. R package version 0.5.1. Available at https://CRAN.R-project.org/package=infer
  • Buja, A., Cook, D., Hofmann, H., Lawrence, M., Lee, E. K., Swayne, D. F. and Wickham, H. (2009), “Statistical Inference for Exploratory Data Analysis and Model Diagnostics,” Philosophical Transactions of the Royal Society, Series A, 367, 4361–4383. DOI: 10.1098/rsta.2009.0120.
  • Cannon, A., Cobb, G. W., Hartlaub, B. A., Legler, J. M., Lock, R. H., Moore, T. L., Rossman, A. J. and Witmer, J. A. (2018), STAT2: Modeling with Regression and ANOVA (2nd ed.), Macmillan.
  • Chang, W., Cheng, J., Allaire, J., Xie, Y. and McPherson, J. (2019), Shiny: Web Application Framework for R. R package version 1.4.0. Available at https://CRAN.R-project.org/package=shiny
  • Chihara, L., and Hesterberg, T. (2011), Mathematical Statistics with Resampling and R, Wiley.
  • Cobb, G. W. (2007), “The Introductory Statistics Course: A Ptolemaic Curriculum?,” Technology Innovations in Statistics Education, 1.
  • Cobb, G. W. (2011), “Teaching Statistics: Some Important Tensions,” Chilean Journal of Statistics, 2, 31–62.
  • De Veaux, R., Velleman, P., and Bock, D. (2018), Intro Stats (5th ed.), Boston, MA: Pearson.
  • GAISE College Report ASA Revision Committee (2016), “Guidelines for Assessment and Instruction in Statistics Education College Report 2016’. Available at http://www.amstat.org/education/gaise
  • Garfield, J. (1993), “Teaching Statistics Using Small-Group Cooperative Learning,” Journal of Statistics Education: An International Journal on the Teaching and Learning of Statistics, 1. Available at DOI: 10.1080/10691898.1993.11910455.
  • Gelman, A., and Hill, J. (2007), Data Analysis Using Regression and Multilevel/Hierarchical Models, New York: Cambridge University Press.
  • Hildreth, L. A., Robison-Cox, J., and Schmidt, J. (2018), “Comparing Student Success and Understanding in Introductory Statistics Under Consensus and Simulation-Based Curricula,” Statistics Education Research Journal, 17, 103–120.
  • Kaplan, D., and Pruim, R. (2019), ggformula: Formula Interface to the Grammar of Graphics. R package version 0.9.1. Available at https://CRAN.R-project.org/package=ggformula
  • Lock, R., Frazer Lock, P., Lock Morgan, K., Lock, E., and Lock, D. (2017), Statistics: Unlocking the Power of Data (2nd ed.), Hoboken, NJ: John Wiley & Sons.
  • Loy, A., Hofmann, H., and Cook, D. (2017), “Model Choice and Diagnostics for Linear Mixed-Effects Models Using Statistics on Street Corners,” Journal of Computational and Graphical Statistics, 26, 478–492. Available at DOI: 10.1080/10618600.2017.1330207.
  • Maurer, K. and Lock, D. (2016), “Comparison of Learning Outcomes for Simulation-Based and Traditional Inference Curricula in a Designed Educational Experiment,” Technology Innovations in Statistics Education, 9(1), 1–21.
  • Pruim, R., Kaplan, D. T. and Horton, N. J. (2017), “The Mosaic Package: Helping Students to ‘Think With Data’ Using R,’’ R Journal, 9, 77–102. Available at https://journal.r-project.org/archive/2017/RJ-2017-024/RJ-2017-024.pdf
  • R Core Team. (2019), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org/
  • Ramsey, F., and Schafer, D. (2013), The Statistical Sleuth: A Course in Methods of Data Analysis (3rd ed.), Boston, MA: Cengage Learning.
  • Roseth, C. J., Garfield, J. B., and Ben-Zvi, D. (2008), “Collaboration in Learning and Teaching Statistics,” Journal of Statistics Education: An International Journal on the Teaching and Learning of Statistics, 16. Available at DOI: 10.1080/10691898.2008.11889557.
  • Tintle, N., Chance, B., Cobb, G., Rossman, A., Roy, S., Swanson, T., and VanderStoep, J. (2015), Introduction to Statistical Investigations, Wiley.
  • Tintle, N., Chance, B., Cobb, G., Roy, S., Swanson, T. and VanderStoep, J. (2015), ‘Combating Anti-Statistical Thinking Using Simulation-Based Methods Throughout the Undergraduate Curriculum,” The American Statistician, 69, 362–370. DOI: 10.1080/00031305.2015.1081619.
  • Tintle, N. L., Topliff, K., and VanderStoep, J. (2012), “Retention of Statistical Concepts in a Preliminary Randomization-based Introductory Statistics Curriculum,” Statistics Education Research Journal, 11, 21–40.
  • Tintle, N., Rogers, A., Chance, B., Cobb, G., Rossman, A., Roy, S., Swanson, T. and VanderStoep, J. (2014), “Quantitative Evidence for the Use of Simulation and Randomization in the Introductory Statistics Course,” in Sustainability in Statistics Education. Proceedings of the Ninth International Conference on Teaching Statistics (ICOTS9, July, 2014), eds. K. Makar, B. de Sousa and R. Gould, Flagstaff, AZ.
  • Tintle, N., VanderStoep, J., and Holmes, V. L. (2011), “Development and Assessment of a Preliminary Randomization-based Introductory Statistics Curriculum,” Journal of Statistics Education, 19. DOI: 10.1080/10691898.2011.11889599.
  • Wasserstein, R. L. and Lazar, N. A. (2016), “The ASA Statement on p-values: Context, Process, and Purpose,” The American Statistician, 70, 129–133. Available at DOI: 10.1080/00031305.2016.1154108.
  • Wickham, H. (2016), ggplot2: Elegant Graphics for Data Analysis, New York: Springer-Verlag. Available at https://ggplot2.tidyverse.org
  • Wickham, H., Chowdhury, N. R. and Cook, D. (2014), nullabor: Tools for Graphical Inference. R package version 0.3.1. Available at http://CRAN.R-project.org/package=nullabor
  • Wild, C. J., Pfannkuch, M., Regan, M., and Parsonage, R. (2017), “Accessible Conceptions of Statistical Inference: Pulling Ourselves Up by the Bootstraps,” International Statistical Review, 85, 84–107. Available at https://onlinelibrary.wiley.com/doi/full/10.1111/insr.12117 DOI: 10.1111/insr.12117.
  • Witmer, J. (2019), “Editorial,” Journal of Statistics Education, 27, 136–137. Available at DOI: 10.1080/10691898.2019.1702415.