Session 2: Practice

Which summary of your key variable would you defend in print?

Thierry Warin, PhD

HEC Montréal

Outline · The workshop

  1. Ninety minutes
  2. Before you start
  3. Did not get the data? Two minutes, now
  4. Reproduce — 20 min
  5. Apply to your own angle — 35 min
  6. Break one assumption — 15 min
  7. Write it down — 20 min
  8. Submitting
  9. What loses marks
  10. Two-minute report
  11. After class, if you want it

What session 2 is for

The mathematics

  • Distinguish mean, median and trimmed mean, and say which question each answers
  • Explain why the sample variance divides by \(n-1\)
  • Test skewness and excess kurtosis against \(2\sqrt{6/n}\) and \(4\sqrt{6/n}\)

The Python

  • Compute the moments directly, and check them against a library
  • Load the course panel with pd.read_parquet and .dropna, and say how many rows went
  • Take any quantile — median, quartile, percentile — with .quantile

The international business

  • Read a mean–median gap as evidence about a distribution’s shape
  • Choose the summary a manager should see when one country dominates the total
  • Say what heavy tails cost a plan built on an average

Your Python so far

Everything you need to type is on one sheet — how to start Python in VS Codium on your platform, how to make a file and run it, the DataFrame basics, and every name from sessions 1 to 2, each with a line showing it in use.

Python Cheatsheet · Session 02 (PDF) · in the repository at 02-…/PYTHON-CHEATSHEET.pdf

One sheet per package, functions explained with pictures: pandas · NumPy · SciPy · statsmodels

Print it, or keep it open beside the editor. Nothing on it is examinable; all of it is assumed.

Practice

Which summary of your key variable would you defend in print?

Ninety minutes

00–20 Reproduce one result from the paper you read
20–55 Apply the method, to your own angle
55–70 Break one assumption, deliberately
70–90 Write, and report 250 words, then two minutes to the room

The presenter is drawn at random when your group is called. All three of you prepare.

Before you start

Everyone pushes at least once. Replace XX with your group number — group 07 uses group-07.

git checkout -b group-XX
mkdir -p groups/A2026/group-XX/session-02      # macOS and Linux
git checkout -b group-XX
mkdir groups/A2026/group-XX/session-02         # Windows — no -p

The git log is the participation record. It is not a formality.

Did not get the data? Two minutes, now

The pre-session asked you to download it. If you did not, do it before the room starts — everything after this slide needs it.

mkdir -p 02-exploratory-data-analysis/data/replication      # macOS and Linux
cd 02-exploratory-data-analysis/data/replication
curl -L -o replication.zip \
  "https://dataverse.harvard.edu/api/access/dataset/:persistentId/?persistentId=doi:10.7910/DVN/QNKFJF"
unzip -q replication.zip && rm replication.zip && cd -
mkdir 02-exploratory-data-analysis/data/replication         # Windows
cd 02-exploratory-data-analysis/data/replication
curl.exe -L -o replication.zip `
  "https://dataverse.harvard.edu/api/access/dataset/:persistentId/?persistentId=doi:10.7910/DVN/QNKFJF"
Expand-Archive replication.zip .; Remove-Item replication.zip; cd -
ls 02-exploratory-data-analysis/data/replication/public   # code  data  results

33 MB. Windows PowerShell: curl.exe, and Expand-Archive replication.zip .

1 · Reproduce — 20 min

Take the result the paper rests on, and reproduce it.

import pandas as pd, qmib

REP = "02-exploratory-data-analysis/data/replication/public"

# The authors ship their outputs as CSV beside the figures they became, which
# is what makes a reproduction checkable: your number against their number.
fig2a = pd.read_csv(f"{REP}/results/figure_2a.csv")
print(fig2a.shape)
print(fig2a.head())

core = qmib.load("core")     # the session's own data, for comparison

Their code/ is Stata. You are not running it — you are reproducing one number in Python and saying whether you got it.

Paper: Fraiberger et al. (2021), Media sentiment and international asset prices · 10.7910/DVN/QNKFJF

A failed reproduction that you diagnose earns full marks. One you do not attempt earns none.

“It did not work” is not a diagnosis. Where did it stop, and what did you check?

2 · Apply to your own angle — 35 min

core = qmib.load("core")
mine = qmib.load("angle_c_country")     # your angle — see your dictionary
df   = core.merge(mine, on=["geo", "time"], how="inner")

Fit the session’s method on your data, and report what the lecture said to report — in the units of your own variables.

A coefficient without units is not a finding.

3 · Break one assumption — 15 min

Every method in this course rests on something.

Find the assumption this one leans on hardest. Break it deliberately. Record what happened to your answer.

This is the part that carries the marks. A group that shows how its result falls apart understands it; a group that only shows it holding does not yet know.

4 · Write it down — 20 min

250 words in your submissions folder:

  • What you found, in units
  • Which assumption you broke, and what it did
  • What this result does not license you to claim

And the standing rule: one LLM output you identified as wrong, and how you established that.

Submitting

git add groups/A2026/group-XX/session-02
git commit -m "Session 02 practice — group XX"
git push -u origin group-XX

Deliverable. A profile of your project’s three key variables — centre, spread and shape, thresholds tested — and the summary you would defend in print.

What loses marks

  • df.describe() pasted with no interpretation
  • A skewness reported without its threshold
  • Calling a variable normal because it is symmetric
  • Applying the empirical rule to a variable you have shown to be skewed

And everywhere in this course: an association described in causal language.

Two-minute report

One number with its units and its uncertainty
One assumption the one you broke, and what happened
One limit what you cannot claim from this

Listen for traceable evidence, not confidence of delivery.

After class, if you want it

The QMIB Lab has a knowledge check on what you have just done: warin.ca/qmib-labs

Optional, never a prerequisite for the next lecture, and never marked for correctness. A completed report counts because attempting it is the engagement being measured — it is one of the three routes to the participation mark.

What is required before the next session: the article, its Dataverse package, and the self-check. That is the pre-session deck, not this.