Syllabus (public companion)
Orientation and course shape — Blackboard is authoritative for graded specifics
This public page is an orientation companion to the syllabus, not the operational syllabus itself. It describes the shape of the course in qualitative terms. It carries no numeric grade weights, no due dates, and no submission rules. For everything that counts toward your grade — exact weights, deadlines, policies, and submissions — Blackboard (the LMS) is authoritative. If this page and Blackboard ever disagree, follow Blackboard.
Course description
Statistical Modeling treats modeling as a way to make careful claims from data. We develop regression as a language for relating variables, comparing groups, quantifying uncertainty, and criticizing our own models. Beginning with statistical questions, data structure, and visualization, the course builds through simple and multiple regression, adjustment and confounding, categorical predictors, interactions, diagnostics, prediction and validation, logistic regression, model comparison, and the link between ANOVA and regression — closing with reproducible reporting and synthesis. The emphasis throughout is interpretation, communication, and model criticism, with R and Quarto used to fit, check, and communicate models.
Who it is for
This course assumes a prior introductory statistics course or comparable background: you should be comfortable with variables, graphs, numerical summaries, sampling variability, confidence intervals, hypothesis tests, and basic regression ideas. Prior R experience is helpful but not required — the modeling workflow is scaffolded for students learning R as part of the modeling process. The main expectation is willingness to work carefully through examples, revise code, and explain model output in words.
Learning outcomes
By the end of the course, a successful student will be able to:
- Identify the statistical question, unit of observation, response, explanatory variables, and the comparison a model makes.
- Use graphs and summaries to prepare for modeling rather than treating a model as a first step.
- Fit and interpret simple linear regression — slope, intercept, fitted values, residuals, uncertainty, and fit.
- Fit and interpret multiple regression with numerical and categorical predictors, and explain adjustment and confounding.
- Use residual plots and other diagnostics to evaluate model adequacy for a purpose.
- Interpret interaction terms and describe effect modification.
- Distinguish explanatory from predictive modeling, and use validation to evaluate prediction and detect overfitting.
- Fit and interpret logistic regression for binary outcomes and read odds ratios.
- Compare reasonable models without treating automatic selection as a substitute for statistical judgment.
- Produce a reproducible statistical modeling report and communicate model-based conclusions carefully, including limitations, uncertainty, and possible bias.
Weekly rhythm
This is a lecture/lab course meeting three days a week, and each day has a settled role:
- Monday — model concept + interpretation checkpoint. We introduce a modeling idea, work examples, and close with a short checkpoint focused on interpretation or model reasoning.
- Wednesday — modeling lab. We use R and Quarto to fit models, produce graphics, inspect output, and write short conclusions. These days are hands-on.
- Friday — quiz / model critique / workshop. A short quiz on interpretation or code reading, then a model critique, a comparison of approaches, or project-related work.
This rhythm is the plan; the authoritative weekly schedule, including any shifts, lives in Blackboard.
Assessment shape (indicative — not a contract)
The table below conveys the relative emphasis of each graded category in qualitative terms only. It is not a grading contract: it carries no percentages and no point values, and the actual weights live in Blackboard.
| Category | Rough emphasis |
|---|---|
| Modeling checkpoints | small |
| Modeling labs | moderate |
| Weekly quizzes | small |
| Homework / modeling memos | the largest single category |
| Midterm | moderate |
| Project | moderate |
| Final | small |
Read this as a picture of where the weight sits: regular homework and modeling memos are the backbone of the grade, the modeling labs and the two exams carry moderate weight, and the smaller pieces — checkpoints and quizzes — add up around them. For the exact weighting, consult Blackboard.
Software and reproducibility
We use R (through RStudio or Posit Cloud) and Quarto to fit, check, and report models. Computation supports the modeling rather than replacing it: you read, edit, and run code; interpret output; and write short written conclusions. Every analysis is written so it can be reproduced — a single Quarto file, a fixed random seed, and recorded session information — so the same code yields the same result. Setup instructions are on the R · Quarto setup page.
AI use (summary)
Generative AI tools may be used as a study and workflow aid — to explain a concept a second way, help debug your own R code, identify possible coding errors, or help you read unfamiliar output. They may not produce work you submit as your own, write your interpretations, choose your model, or fabricate results, and they are prohibited on quizzes and exams unless explicitly allowed.
Because this is a modeling course, AI use in code is allowed only when documented and verified. Whenever you use AI on a graded written assignment, lab, modeling memo, or project component, include a brief AI Use Note with three labeled lines:
| Field | What to record |
|---|---|
| Tool | which assistant you used (with approximate date or version) |
| Purpose | what you used it for |
| Verification | how you checked, tested, revised, or validated the output |
Verification is the load-bearing line: rerun the code, check the documentation, compare to class examples, inspect the residual plots yourself, or rewrite the explanation in your own words after checking it. You are responsible for every line of code and every sentence of interpretation you submit. The full policy lives in Blackboard.
Materials
You will need:
- Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, 2nd ed. (Ismay, Kim & Valdivia) — the free primary text, CC BY-NC-SA 4.0, at moderndive.com/v2.
- Beyond Multiple Linear Regression (Roback & Legler) — a free optional supplement, CC BY-NC-SA 4.0, at bookdown.org/roback/bookdown-BeyondMLR, used selectively for logistic and generalized models.
- R, RStudio or Posit Cloud, and Quarto — for the modeling labs and reproducible reports.
- Blackboard (the LMS) — for all graded work, dates, and announcements.
- A scientific calculator for in-class quizzes; most computation is done in R.
Not used in this course: Cengage, WebAssign, MyLab, or any paid homework platform.
Where things live
Keep the two homes of the course straight:
- Blackboard (the LMS) is the operational home: graded modeling checkpoints, labs, quizzes, homework and modeling memos, the midterm, the project, the final, all due dates, all submissions, and all grades. It is authoritative.
- This public site is the public notes home: weekly notes, labs as study material, resources, and orientation pages. It is ungraded.
This companion exists to orient you. Whenever a graded specific is at stake — a weight, a deadline, a policy — go to Blackboard, which is authoritative.