References
- Adams, B., Baller, D., Jonas, B., Joseph, A.-C., and Cummiskey, K. (2021), “Computational Skills for Multivariable Thinking in Introductory Statistics,” Journal of Statistics and Data Science Education, 29, S123–S131. DOI: 10.1080/10691898.2020.1852139.
- Adrian, D., Reischman, D., Anderson, K., Richardson, M., and Stephenson, P. (2020), “Helping Introductory Statistics Students Find Their Way Using Maps,” Journal of Statistics Education, 28, 56–74. DOI: 10.1080/10691898.2020.1721035.
- ASA Undergraduate Guidelines Workgroup. (2014), “Curriculum Guidelines for Undergraduate Programs in Statistical Science,” https://www.amstat.org/docs/default-source/amstat-documents/guidelines2014-11-15.pdf
- Baumer, B. (2015), “A Data Science Course for Undergraduates: Thinking with Data,” The American Statistician, 69, 334–342. DOI: 10.1080/00031305.2015.10811.
- Beckman, M. D., Çetinkaya-Rundel, M., Horton, N. J., Rundel, C. W., Sullivan, A. J., and Tackett, M. (2021), “Implementing Version Control with Git and GitHub as a Learning Objective in Statistics and Data Science Courses,” Journal of Statistics and Data Science Education, 29, S132–S144. DOI: 10.1080/10691898.2020.1848485.
- Bergmann, J., and Sams, A. (2012), Flip Your Classroom: Reach Every Student in Every Class Every Day, Washington, DC: International Society for Technology in Education.
- Blades, N. J., Schaalje, G. B., and Christensen, W. F. (2015), “The Second Course in Statistics: Design and Analysis of Experiments,” The American Statistician, 69, 326–333. DOI: 10.1080/00031305.2015.1086437.
- Bryan, J. (2018), Happy Git and GitHub for the User, GitHub. Available at https://happygitwithr.com
- Cannon, A., Cobb, G., Hartlaub, B., Legler, J., Lock, R., Moore, T., Rossman, A., and Witmer, J. (2018), STAT2: Modeling with Regression and ANOVA, New York City: W. H. Freeman.
- Carver, R., Everson, M., Gabrosek, J., Horton, N., Lock, R., Mocko, M., Rossman, A., Roswell, G. H., Velleman, P., Witmer, J., Wood, B. (2016), “Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report 2016,” Available at https://www.amstat.org/docs/default-source/amstat-documents/gaisecollege/_full.pdf
- Çetinkaya Rundel, M., and Hardin, J. (2021), Introduction to Modern Statistics, OpenIntro, Inc.
- Çetinkaya-Rundel, M., and Ellison, V. (2021), “A Fresh Look at Introductory Data Science,” Journal of Statistics and Data Science Education, 29, S16–S26. DOI: 10.1080/10691898.2020.1804497.
- Çetinkaya-Rundel, M., and Rundel, C. (2018), “Infrastructure and Tools for Teaching Computing Throughout the Statistical Curriculum,” The American Statistician, 72, 58–65. DOI: 10.1080/00031305.2017.1397549.
- Chatterjee, S., and Simonoff, J. S. (2013), Handbook of Regression Analysis, Hoboken, NJ: Wiley.
- Cline, K. S., (2008), “A Writing-Intensive Statistics Course,” Primus, 18, 399–410. DOI: 10.1080/10511970701203239.
- Farmus, L., Cribbie, R. A., and Rotondi, M. A. (2020), “The Flipped Classroom in Introductory Statistics: Early Evidence from a Systematic Review and Meta-Analysis,” Journal of Statistics Education, 28, 316–325. DOI: 10.1080/10691898.2020.1834475.
- Gradescope. (2020). Accessed: 2022–10-20. https://www.gradescope.com
- Hardin, J., Hoerl, R., Horton, N. J., Nolan, D., Baumer, B., Hall-Holt, O., Murrell, P., Peng, R., Roback, P., Temple Lang, D. et al. (2015), “Data Science in Statistics Curricula: Preparing Students to “Think with Data”,” The American Statistician, 69, 343–353. DOI: 10.1080/00031305.2015.
- Hiebert, J., and Wearne, D. (2003), “Developing Understanding Through Problem Solving,” Teaching Mathematics Through Problem Solving: Grades, 6, 3–14.
- Kaggle. (2018), “House Sales in King County, USA,” Accessed: 2022-05-01. Available at https://www.kaggle.com/harlfoxem/housesalesprediction/home
- Love, T. E. (1998), “A Project-driven Second Course,” Journal of Statistics Education, 6. DOI: 10.1080/10691898.1998.11910605.
- Lynch, S. D., Hunt, J. H., and Lewis, K. E. (2018), “Productive Struggle for All: Differentiated Instruction,” Mathematics Teaching in the Middle School, 23, 194–201. https://www.jstor.org/stable/10.5951/mathteacmiddscho.23.4.0194 DOI: 10.5951/mathteacmiddscho.23.4.0194.
- National Academies of Sciences, Engineering, and Medicine and others. (2018), Data Science for Undergraduates: Opportunities and Options, Washington DC: National Academies Press. Available at DOI: 10.17226/25104.
- Nolan, D., and Stoudt, S. (2021), Communicating with Data: The Art of Writing for Data Science, Oxford: Oxford University Press.
- Nolan, D., and Temple Lang, D. (2010), “Computing in the Statistics Curricula,” The American Statistician, 64, 97–107. DOI: 10.1198/tast.2010.09132.
- Peterson, A. D., and Ziegler, L. (2021), “Building a Multiple Linear Regression Model with LEGO Brick Data,” Journal of Statistics and Data Science Education, 29, 297–303. DOI: 10.1080/26939169.2021.1946450.
- Ramsey, F., and Schafer, D. (2012), The Statistical Sleuth: a Course in Methods of Data Analysis, Boston: Cengage Learning.
- Roback, P. J. (2003), “Teaching an Advanced Methods Course to a Mixed Audience,” Journal of Statistics Education, 11. DOI: 10.1080/10691898.2003.11910706.
- Rundel, C., and Cetinkaya-Rundel, M. (2022), ghclass: Tools for Managing Classes on GitHub. R package version 0.2.1. Available at https://cran.r-project.org/web/packages/ghclass
- Staff Editor - Statistical Modeling. (2020). Accessed: 2020-01-08. Available at https://nytimes.wd5.myworkdayjobs.com/en-US/News/job/New-York-NY/Staff-Editor–-Statistical-Modeling/_REQ-006725
- Student Learning Outcomes. (2022). Accessed: 2022-05-01. Available at https://stat.duke.edu/undergraduate/current-students/student/_learning/_outcomes
- Vance, E. A. (2021), “Using Team-Based Learning to Teach Data Science,” Journal of Statistics and Data Science Education, 29, 277–296. DOI: 10.1080/26939169.2021.1971587.
- Woodard, V., Lee, H., and Woodard, R. (2020), “Writing Assignments to Assess Statistical Thinking,” Journal of Statistics Education, 28, 32–44. DOI: 10.1080/10691898.2019.1696257.