References
- Abu-Jamous, B., Fa, R., and Nandi, A. K. (2015), Integrative Cluster Analysis in Bioinformatics, Chichester: Wiley.
- Armbruster, P., Patel, M., Johnson, E., and Weiss, M. (2009), “Active Learning and Student-Centered Pedagogy Improve Student Attitudes and Performance in Introductory Biology,” CBE—Life Sciences Education, 8, 203–213. DOI: 10.1187/cbe.09-03-0025.
- Bien, J., and Tibshirani, R. (2011), “Hierarchical Clustering With Prototypes via Minimax Linkage,” Journal of the American Statistical Association, 106, 1075–1084. DOI: 10.1198/jasa.2011.tm10183.
- Becker, R. A., Chambers, J. M., and Wilks, A. R. (1988), The New S Language, Monterey, CA: Wadsworth & Brooks/Cole.
- Chatfied, C., and Collins, A. J. (1980), Introduction to Multivariate Analysis, Boca Raton, FL: Chapman & Hall/CRC.
- Cudney, E. A., and Ezzell, J. M. (2017), “Evaluating the Impact of Teaching Methods on Student Motivation,” Journal of STEM Education, 18, 32–49.
- De Veaux, R., Agarwal, M., Averett, M., Baumer, B. S., Bray, A., Bressoud, T. C., Bryant, L., Cheng, L. Z., Francis, A., Gould, R., Kim, A. Y., Kretchmar, M., Lu, Q., Moskol, A., Nolan, D., Pelayo, R., Raleigh, S., Sethi, R. J., Sondjaja, M., Tiruviluamala, N., Uhlig, P. X., Washington, T. M., Wesley, C. L., White, D., and Ye, P. (2017), “Curriculum Guidelines for Undergraduate Programs in Data Science,” Annual Review of Statistics and Its Applications, 4, 15–30. DOI: 10.1146/annurev-statistics-060116-053930.
- Eadie, G., Huppenkothen, D., Springford, A., and McCormick, T. (2019), “Introducing Bayesian Analysis With m&m’s[textregistered]: An Active-Learning Exercise for Undergraduates,” Journal of Statistics Education, 27, 60–67.
- Everitt, B., and Dunn, G. (2001), Applied Multivariate Data Analysis (2nd ed.), Chichester: Wiley
- Everitt, B., and Hothorn, T. (2011), An Introduction to Applied Multivariate Analysis With R, New York: Springer-Verlag.
- Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., and Wenderoth, M. P. (2014), “Active Learning Boosts Performance in STEM Courses,” Proceedings of the National Academy of Sciences of the United States of America, 111, 8410–8415. DOI: 10.1073/pnas.1319030111.
- Garfield, J. (1993), “Teaching Statistics Using Small-Group Cooperative Learning,” Journal of Statistics Education, 1, 1–9. DOI: 10.1080/10691898.1993.11910455.
- Gnanadesikan, M., Scheaffer, R. L., Watkins, A. E., and Witmer, J. A. (1997), “An Activity-Based Statistics Course,” Journal of Statistics Education 5, 1–16. DOI: 10.1080/10691898.1997.11910531.
- Green, J. J., and Blackenship, E. E. (2015), “Fostering Conceptual Understanding in Mathematical Statistics,” The American Statistician, 69, 315–325. DOI: 10.1080/00031305.2015.1069759.
- Grus, J. (2015), Data Science from Scratch, Sebastopol, CA: O’Reilly Media.
- Hastie, T., Tibshirani, R., and Friedman, J. (2016), The Elements of Statistical Learning, New York: Springer.
- Hu, G., Kaur, M., Hewage, K., and Sadiq, R. (2019), “Fuzzy Clustering Analysis of Hydraulic Fracturing Additives for Environmental and Human Health Risk Mitigation,” Clean Technologies and Environmental Policy, 21, 39–53. DOI: 10.1007/s10098-018-1614-3.
- Irizarry, R. (2000), Introduction to Data Science. Data Analysis and Prediction Algorithms With R (online textbook), available at https://rafalab.github.io/dsbook/\#.
- James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013), An Introduction to Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.), New York: Springer.
- Johnson, R. A., and Wichern, D. W. (2008), Applied Multivariate Statistical Analysis, Upper Saddle River, NJ: Pearson Prentice Hall.
- Kotu, V., and Deshpande, B. (2018), Data Science: Concepts and Practice (2nd ed.), Cambridge, MA: Morgan Kaufmann.
- Kvam, P. H. (2012), “The Effect of Active Learning Methods on Student Retention in Engineering Statistics,” The American Statistician, 54, 136–140. DOI: 10.2307/2686032.
- Misseyanni, A., Lytras, M., Papadopoulou, P., and Marouli, C., eds. (2018), Active Learning Strategies in Higher Education: Teaching for Leadership, Innovation, and Creativity (1st ed.), Bingley, UK: Emerald.
- Jiménez-Murcia, S., Granero, R., Fernández-Aranda, F., Stinchfield, R., Tremblay, J., Steward, T., Mestre-Bach, G., Lozano-Madrid, M., Mena-Moreno, T., Mallorquí-Bagú, N., Perales, J. C., Navas, J. F., Soriano-Mas, C., Aymamí, N., Gómez-Peña, M., Agüera, Z., del Pino-Gutiérrez, A., Martín-Romera, V., and Menchón, J. M. (2019), “Phenotypes in Gambling Disorder Using Sociodemographic and Clinical Clustering Analysis: An Unidentified New Subtype?,” Front Psychiatry, 10, 173.
- Preszler, R. W., Dawe, A., Shuster, C. B., and Shuster, M. (2007), “Assessment of the Effects of Student Response Systems on Student Learning and Attitudes Over a Broad Range of Biology Courses,” CBE—Life Sciences Education, 6, 29–41. DOI: 10.1187/cbe.06-09-0190.
- Prince, M. (2004), “Does Active Learning Work? A Review of the Research,” Journal of engineering education, 93.3, 223–231. DOI: 10.1002/j.2168-9830.2004.tb00809.x.
- Schwartz, T. A. (2013), “Teaching Principles of One-Way Analysis of Variance Using M&M’s Candy,” Journal of Statistics Education, 21, 1–13.
- Sokal, R., and Mitchener, C. A. (1958), “Statistical Method for Evaluating Systematic Relationships,” The University of Kansas Science Bulletin, 38, 1409–1438.
- Thorndike, R. L. (1953), “Who Belongs in the Family?,” Psychometrika, 18, 267–276. DOI: 10.1007/BF02289263.
- Wagaman, A. (2016), “Meeting Student Needs for Multivariate Data Analysis: A Case Study in Teaching an Undergraduate Multivariate Data Analysis Course,” The American Statistician, 70, 405–412. DOI: 10.1080/00031305.2016.1201005.
- Wedel, M., and Kamakura, W. A. (1999), Market Segmentation: Conceptual and Methodological Foundations (2nd ed.), New York: Springer Science & Business Media.
- Zelterman, D. (2015), Applied Multivariate Statistics With R, Cham: Springer.