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Survival Analysis of Airlines
- Given the high failure rate (53%), you could conduct a Kaplan-Meier survival analysis to examine the lifespan of airlines in Nigeria.
- Variables needed: Year of establishment, year of closure (if applicable), and operational status (active/inactive).
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Regression Analysis on Airfare Reduction & Passenger Growth
- A linear regression could assess the relationship between airfare reductions (independent variable) and passenger traffic growth (dependent variable).
- Hypothesis: Lower fares significantly increase passenger numbers.
- Data needed: Yearly airfare prices and corresponding passenger traffic.
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ANOVA for Flight Delays & Economic Losses
- One-way ANOVA could determine if there are significant differences in flight delay times across different airlines.
- Dependent variable: Average flight delay time per airline.
- Independent variable: Airline group (e.g., domestic vs. international, low-cost vs. full-service).
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Correlation Between Airline Failures & Economic Factors
- A Pearson correlation could test whether factors like fuel costs, GDP growth, or currency exchange rates are linked to airline failures.
- Data needed: Historical airline failures, aviation fuel prices, and macroeconomic indicators.
Month: March 2025
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