Notes

The weekly modeling spine

The notes are the backbone of the course. There is one note per week, and reading the current week’s note before class — and again after — is the single most reliable way to keep up. Every note follows the same shape, so once you learn to read one, you can read them all.

How to read a week

Each weekly note is built from the same parts, in the same order. Knowing the anatomy lets you skim for what you need and study with intent:

  • The week question. A single modeling question that the week exists to answer. Hold it in mind as you read; everything else is in service of it.
  • Concept development. The core ideas, built up in a few short sections from intuition toward a precise model. This is the part to read slowly.
  • Worked examples. Each idea is worked on the recurring studyhabits teaching dataset (a synthetic campus learning-analytics study) — the model is fit, the output is read, and the result is interpreted in sentences — plus one transfer example in a fresh context, so you see the idea move.
  • A common mistake. The modeling error students most often make on this topic, named plainly so you can watch for it in your own work.
  • Ungraded self-checks. A few low-stakes practice prompts to test yourself. These are self-check only — no points, no submission.
  • Reading pointer. Where to read more: the relevant ModernDive chapter (and, for logistic and generalized models, Beyond Multiple Linear Regression), with the reminder that these notes are the course’s own synthesis.
  • Looking ahead. A sentence or two connecting this week to the next, so the modeling arc stays visible.

Keep the notation glossary and the modeling reference open alongside the notes.

The five parts

The fifteen weeks fall into five parts. Each part has a job, and the weeks within it build on one another.

Part I — Framing & data. What a statistical question is, what a model is for, and how to look at data before fitting.

  1. Week 1 — Models, data & statistical questions
  2. Week 2 — Visualization & the modeling workflow

Part II — Simple regression. Fitting, reading, and criticizing a one-predictor regression line.

  1. Week 3 — Simple linear regression
  2. Week 4 — Interpreting regression output
  3. Week 5 — Diagnostics & model adequacy

Part III — Multiple regression & explanation. Adding predictors, confronting confounding, comparing groups, and letting associations differ across groups.

  1. Week 6 — Multiple regression & adjustment
  2. Week 7 — Confounding & explanation (midterm)
  3. Week 8 — Categorical predictors & group comparisons
  4. Week 9 — Interactions & effect modification

Part IV — Prediction & broader models. From explanation to prediction, binary outcomes, model comparison, and ANOVA as regression.

  1. Week 10 — Prediction & validation
  2. Week 11 — Logistic regression
  3. Week 12 — Model comparison & selection
  4. Week 13 — ANOVA, regression & broader modeling ideas

Part V — Reporting & synthesis. Making the work reproducible and communicable, then pulling the arc together.

  1. Week 14 — Reproducible modeling reports
  2. Week 15 — Final review & synthesis

Public vs. graded

These notes and the practice in them are public and ungraded — study material only. No graded prompts, answer keys, rubrics, point values, or due dates appear on this site. Graded modeling checkpoints, labs, quizzes, homework and modeling memos, the midterm, the project, and the final live in Blackboard (the LMS), which is authoritative for due dates, submissions, and grades. If this page and Blackboard ever disagree, follow Blackboard.