Flight Delays: Quantification, Modeling, and Testing a Mitigation Strategy

Scenario

You are a data scientist investigating flight delays using historical flight records (scheduled vs. actual departure/arrival timestamps) and are asked to evaluate a new operational mitigation strategy intended to reduce delays.

Assumed Data (minimal)

• Flight-level: flight_id, date/time, origin, destination, carrier, flight number, tail number.
• Times: scheduled/actual departure and arrival timestamps; taxi-out/in times; cancellation/diversion flags.
• Context: weather (origin/destination), airport congestion, holiday/seasonality indicators, day-of-week, time-of-day, prior leg arrival time for the same tail (propagated delay).

Task

1. Quantify and model flight delays given historical data. Describe metrics, distributional assumptions, and modeling approaches (regression and/or time series).
2. Specify statistical tests and confidence intervals to determine whether the new mitigation strategy significantly reduced delays. Address design options (randomized vs. observational), appropriate tests, and confidence levels.

Hints

• Consider skewed/heavy-tailed delay distributions and on-time thresholds.
• Use robust statistics (medians/percentiles) alongside means.
• Choose regression or time-series/segmented models with seasonality and exogenous factors.
• Use hypothesis testing with appropriate confidence intervals and standard errors (e.g., cluster-robust, HAC).