Teaching Practice & Strategies Topic
Topic: Assumptions & diagnostics in linear regression
Course level: This should fall in an introduction to the topic of regression course for first-year graduate students (Masters level) in the Master of Data Science program. You may assume that students are familiar with introductory statistical inference and probability, programming in R, and calculus or linear algebra (not necessarily both) at the level of an introductory undergraduate course.
TEACHING PRACTICE [about 30 minutes]
Simulate an in-class experience that you would use as part of how you would teach the topic chosen from the list above. This simulated in-class experience could be set in a lecture or lab setting. We welcome any pedagogical choices that you think best suits the topic, and you will be able to justify your choices in the teaching strategies section. At the end of this section (teaching practice demonstration), present the assignment question in the context of the teaching practice. This is a simulated in-class learning experience where the people in the room will pretend to be students in the course. You will carry this out at a pace that you would use in the course with “real” students. Before you begin the simulation, please be sure to clarify:
- Prerequisite knowledge. Clarify the prerequisite knowledge that you expect the students to know before they engage with the topic.
- Learning outcomes. Describe some learning outcomes you would want your students to be able to attain on the topic by the end of the course. Please highlight which learning outcomes your simulation and assignment question will touch on.
TEACHING STRATEGY [about 20 minutes]
You will describe to us how you would holistically teach the chosen topic (with the in-class simulation presented during the teaching practice being one part of this). Please include the following:
- Student engagement and learning. Describe how you would engage the students with the topic. How would you assist them in attaining your learning outcomes? Your discussion can involve activities both in and out of scheduled class time.
- Assessment. How would you assess students’ attainment of your learning outcomes for this topic? Describe examples of both formative and summative assessment you may use.
- Technology. What, if any, learning technologies can assist in the learning of the topic?
- Difficulties students might have learning the topic. What difficulties would you expect students to have when they engage with this topic?
Tell us how you would do things differently in an in-person context.
VISION [about 15 minutes]
What is your vision for Data Science education in regards to degree programs (professional masters, minor and/or major)? What should be taught? And how should it be taught?
Links used toprepare the presentation:
- http://www.sthda.com/english/articles/39-regression-model-diagnostics/161-linear-regression-assumptions-and-diagnostics-in-r-essentials/
- https://online.stat.psu.edu/stat501/lesson/4
- https://www.rpubs.com/stevenlsenior/normal_residuals_with_code
- http://www.pmean.com/09/NormalPlot.html
- https://view.officeapps.live.com/op/view.aspx?src=https%3A%2F%2Fqualityandinnovation.files.wordpress.com%2F2012%2F09%2Fqq-plot-75-925.doc&wdOrigin=BROWSELINK
- https://cran.r-project.org/web/packages/GlmSimulatoR/vignettes/dealing_with_right_skewed_data.html
- https://www.ucd.ie/ecomodel/Resources/QQplots_WebVersion.html
- https://whitlockschluter3e.zoology.ubc.ca/Tutorials%20using%20R/R_tutorial_Normal_and_sample_means.html#activity_1_give_the_ta_the_finger(s)
- https://clas.ucdenver.edu/marcelo-perraillon/sites/default/files/attached-files/week_7_diagnostics_0.pdf
- https://online.stat.psu.edu/stat500/lesson/9/9.2/9.2.3
- https://rstudio-pubs-static.s3.amazonaws.com/525414_3c5d6bb14df24e81af20c2dc98085f73.html