Schedule

Course calendar

The calendar below represents a broad overview of our course schedule. This will be updated on a week-by-week basis. To incorporate this calendar into your personal Macalester Google Calendar, press the +Google Calendar at the bottom.

NOTE: Religious holidays are not marked on the schedule. However, the instructor supports your personal observances. Please reach out to discuss accommodations around holidays.





Day-by-day schedule

The following schedule outlines a more detailed weekly plan to prepare, practice, and explore. Please note:

  • Homework, checkpoints, etc are listed on the day that they’re due though you should certainly start these assignments earlier. To this end, be sure to use the calendar above for longer term planning.
  • The videos are accessible on YouTube.
  • Rmd templates for the activities are linked here and posted on Moodle.


Before class: prepare During & after class: practice & explore
Week 1 (1/20 - 1/21)
Thurs Take it easy 1: Welcome to class!
Week 2 (1/24 - 1/28)
Tues Complete the ‘Get up and running’ activity.
Checkpoint 1
2: RStudio workshop
Thurs Video: Univariate viz (slides)
Checkpoint 2
3: Exploring univariate patterns
Open this Rmd
Week 3 (1/31 - 2/4)
Tues Video: Intro to modeling bivariate trends (slides)
Checkpoint 3
4: Bivariate relationships – Part 1
Thurs Video: Categorical predictors (slides)
Checkpoint 4
5: Bivariate relationships – Part 2
Week 4 (2/7 - 2/11)
Tues Homework 1
Video 1: Is the model wrong? (slides)
Video 2: Is the model fair? (slides)
Checkpoint 5
6: Model evaluation – Part 1 (Rmd)
Thurs Video: Is the model strong? (slides)
Checkpoint 6
7: Model evaluation – Part 2 (Rmd)
Week 5 (2/14 - 2/18)
Tues Homework 2
Video: Multivariate modeling principles (slides)
Checkpoint 7
8: Multivariate modeling principles (Rmd)
Thurs 2 videos on multivariate intepretations: Part 1 and Part 2 (slides)
Checkpoint 8
9: Covariates & multicollinearity (Rmd)
Week 6 (2/21 - 2/25)
Tues Homework 3
  1. Model building (Rmd)
    11. Simpson’s Paradox – OPTIONAL (Rmd)
Thurs (Nothing is due) Quiz 1
Week 7 (2/28 - 3/4)
Tues Video: Interaction (slides)
Checkpoint 9
  1. Interaction (Rmd)
Thurs (Nothing is due) No regular class! Attend & summarize 2 MSCS Capstone talks (~ 1 hour total).
Week 8 (3/7 - 3/11)
Tues Homework 4
Video 1: Measuring uncertainty (slides)
Video 2: Logistic regression foundations (slides)
Checkpoint 10
  1. Logistic Regression I (Rmd)
Thurs Video: Interpreting logistic regression models (slides)
Checkpoint 11
  1. Logistic Regression II (Rmd)
Week 9 (3/21 - 3/25)
Tues Nothing is due
  1. Logistic regression review (Rmd)
    Project introduction
Thurs Homework 5
Video 1: exploration vs inference (slides)
Video 2: Normal probability model (slides)
Checkpoint 12
  1. Normal model / random samples (Rmd)
Week 10 (3/28 - 4/1)
Tues Video 1: Sampling distributions (slides)
Video 2: Central Limit Theorem (slides)
Checkpoint 13
  1. Sampling distributions + Central Limit Theorem (Rmd)
Thurs Nothing is due today. Quiz 2
Week 11 (4/4 - 4/8)
Tues Video: Confidence intervals (slides)
Checkpoint 14
  1. Confidence intervals (Rmd)
Thurs Homework 6
Video: Confidence intervals recap (slides)
Video: Using CIs to test hypotheses (slides)
Checkpoint 15
  1. Using confidence intervals for inference (Rmd)
Week 12 (4/11 - 4/15)
Tues Mini-project Phase 1
Video: Hypothesis testing concepts (17+ minutes) (slides)
Checkpoint 16
NOTE: This checkpoint is very important!
  1. Hypothesis testing (Rmd)
Thurs Video: Errors in hypothesis testing (slides)
Checkpoint 17
  1. Cautions in hypothesis testing – 1 (Rmd)
Week 13 (4/18 - 4/22)
Tues Nothing!
  1. Cautions in hypothesis testing – 2 (Rmd)
Thurs Mini-project Phase 2
Homework 7 (last one!)
Work time!
Week 14 (4/25 - 4/29)
Tues nothing is due (quiz day) Quiz 3
Thurs nothing is due (project day) Work time!



More OH opportunities: MAX Center support

The MAX Center also has student staff members that can answer questions on STAT 155 content (though are not affiliated with this course). You can find their hours on the website here.