IA idea · Probability & chance
How many extra tickets should an airline sell?
Research question
For a 180-seat flight with a given no-show rate, how many tickets maximise the airline's expected revenue once compensation for bumped passengers is included?
Adapt it: change the place, the data or the comparison until the question is yours.
Why it makes a good exploration
Airlines deliberately sell more seats than they have. The binomial distribution and expected value give a clear optimum — and the ethics of bumping passengers gives you something real to reflect on.
The mathematics you'll need
- Binomial distribution of passengers who show up
- Expected revenue as a function of tickets sold
- Maximising a discrete function
- Normal approximation (optional)
Course labels show where a technique sits; using maths from outside your course is fine if you explain it clearly and say it is new to you.
Where the data comes from
Use a published no-show rate range and published compensation rules (e.g., EU Regulation 261/2004 amounts) with citations.
- Desmos graphing calculator — Free graphing and regression (y₁ ~ ax₁ + b) — fit models to your data and show residuals.
Cite every source in a footnote where you use it and in your bibliography. Check the licence of any dataset you download.
A possible outline
- Set up the revenue and compensation model.
- Compute expected profit for each number of tickets sold.
- Find the optimum and test sensitivity to the no-show rate.
- Compare risk (probability of bumping anyone).
- Reflect on independence (families and groups travel together).
Pitfalls that cost marks
- Treating passengers as independent without comment.
- Using a no-show rate with no source.
- Ignoring the reputational cost.
Showing personal engagement
- Use a route you have flown.
- Include groups travelling together and see how the answer changes.
- Discuss fairness from the passenger's point of view.
See Criterion C: personal engagement for what examiners look for.
Taking it further
Model group bookings with a compound distribution via simulation.
Turn this idea into your IA
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