Fitting and breaking models

Research Methods — Week 8 Application

Today’s plan

Two parts

Part	Time	What
Regression	45 min	Apply lm() to your project (if relevant)
Extrapolation	25 min	A structured exercise everyone does

• [10 min] Setup: pull latest changes. Today has both project work and a shared exercise.

Questions?

Submit questions anonymously:

PollEv.com/geol

text geol to 07480 781235

• Open the Poll Everywhere Q&A activity before the session and leave it active throughout.
• Questions appear in your Poll Everywhere presenter view; upvoted questions rise to the top.
• Address during transitions or at the end of concept blocks, not mid-explanation.
• Reset (archive) the activity between sessions so questions don’t carry over from the previous week.

Brief guided example

🎓💻 Quick demo

Regression workflow

🖥️ Switching to WebR

1. Fit lm(y ~ x, data = df)
2. summary() — coefficients, R²
3. plot(model) — diagnostics
4. Interpret: slope, R², residual patterns

• [15 min] Keep it brief. Students have seen this in the content session. Just refresh the workflow.

Group work

✏️💻 Regression on your data

Your analysis

Groups with a regression question: fit and diagnose.

Groups with t-test/ANOVA: refine existing analyses — add effect sizes, improve figures, document assumptions.

All groups: write a Methods and Results section draft.

• [30 min] Circulate. By this point groups should have substantive analysis to show. Flag any who are falling behind.

Extrapolation exercise

✏️💻 Breaking a model

UK renewable energy: 2010–2024

Everyone does this exercise, regardless of project topic.

The WebR page has scaffolded exercises:

1. Fit a linear model to the renewable trend. Plot it.
2. Extrapolate to 2040. What does it predict?
3. Fit an exponential model. Extrapolate to 2040.
4. Which is better? Is either plausible?
5. What is the model not capturing?

• [25 min] Both models “fit” the historical data well. Neither predicts the future correctly. The linear model underestimates; the exponential overestimates absurdly.
• Students should identify: planning constraints, grid limits, saturation effects, policy changes.

Discussion

💬 What did you find?

The extrapolation lesson

• Both models fit the data well in-sample
• Both produce absurd predictions out-of-sample
• In-sample quality tells you nothing about out-of-sample quality

“What is the model not capturing?”

The question that separates a good analyst from HolmesCo.

Your project

“In our project, the biggest thing our analysis might be missing is…”

• [15 min] Groups share their reflections. This builds toward the limitations section of their reports.

Wrap-up

Commit your work

Commit all analysis code and results.

Your analysis is now nearly complete.

Next two weeks: refine, write up, and have it reviewed.

• Students should be producing report-quality analysis. Check each group’s progress.