Schedule (topic flow)
A 15-week pacing plan — Blackboard is authoritative for dates and deadlines
This is a pacing plan, not a calendar. It shows the order in which modeling topics unfold and roughly when, so you can read ahead and see where you are headed. It is not the authoritative calendar: exact dates, deadlines, and any mid-term adjustments live in Blackboard (the LMS), which is authoritative. A few fixed semester dates are noted for orientation only — confirm everything against Blackboard.
The course runs fifteen weeks in five parts. Each part below lists its weeks, the theme for each week, and the throughline — the modeling question that carries the week. The theme links to that week’s note.
Part I — Framing & data (Weeks 1–2)
We set up the modeling frame: what a statistical question is, what a model is for, and how to look at data before fitting anything.
| Week | Theme | The throughline |
|---|---|---|
| 1 | Models, data & statistical questions | What question is a model of final ~ study actually trying to answer? |
| 2 | Visualization & the modeling workflow | What does a scatterplot of final score against study hours tell us before we model? |
Part II — Simple regression (Weeks 3–5)
We fit, read, and criticize a one-predictor regression line.
| Week | Theme | The throughline |
|---|---|---|
| 3 | Simple linear regression | What line best summarizes how final changes with study, and what does “best” mean? |
| 4 | Interpreting regression output | What do the slope, intercept, \(R^2\), residual error, and a confidence interval actually say? |
| 5 | Diagnostics & model adequacy | Is this line good enough for its purpose — what do the residuals reveal? |
Note: Labor Day (Mon, Sep 7) falls in Week 3 — no class that Monday, so the week runs Wednesday and Friday.
Part III — Multiple regression & explanation (Weeks 6–9)
We add predictors, confront confounding, compare groups, and let associations differ across groups.
| Week | Theme | The throughline |
|---|---|---|
| 6 | Multiple regression & adjustment | What happens to the study effect once we adjust for incoming GPA? |
| 7 | Confounding & explanation (midterm) | When is a crude comparison misleading, and what does “explain” mean here? |
| 8 | Categorical predictors & group comparisons | How does regression compare the in-person, hybrid, and online sections? |
| 9 | Interactions & effect modification | Does studying help working students as much as it helps everyone else? |
Note: the midterm is Friday, Oct 9 (in class), during Week 7. No graded content appears on this public site — see Blackboard.
Part IV — Prediction & broader models (Weeks 10–13)
We turn from explanation to prediction, model binary outcomes, compare models, and connect ANOVA to regression.
| Week | Theme | The throughline |
|---|---|---|
| 10 | Prediction & validation | Why can a model that fits the data better predict worse on new students? |
| 11 | Logistic regression | How do we model the probability of passing, and what is an odds ratio? |
| 12 | Model comparison & selection | Which of several reasonable models should we trust, and why not just the biggest? |
| 13 | ANOVA, regression & broader modeling ideas | Why is a one-way ANOVA just a regression with a categorical predictor? |
Note: fall break is Nov 22–28 (no classes).
Part V — Reporting & synthesis (Weeks 14–15)
We make the work reproducible and communicable, then pull the whole arc together.
| Week | Theme | The throughline |
|---|---|---|
| 14 | Reproducible modeling reports | How do you turn a fitted model into a report someone else can trust and re-run? |
| 15 | Final review & synthesis | How do questions, fitting, criticism, explanation, and prediction fit as one practice? |
Note: the last class is Mon, Dec 7, the consultation day is Dec 8, and the final-exam window is Dec 9–15 (exact block via Blackboard).
Again: this page is a pacing plan to help you read ahead. For the real calendar — every date, every deadline, and any adjustment during the term — Blackboard (the LMS) is authoritative.