Assessing Multiple Regression Analysis (MRA) model fit for forecasting air traffic movements using log transformation: a case study on ATNS air traffic movement dataset during COVID-19 pandemic

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dc.contributor.advisorGirmay, Elizabeth
dc.contributor.authorMasekoameng, John Lehlaka
dc.date.accessioned2025-06-04T22:26:29Z
dc.date.available2025-06-04T22:26:29Z
dc.date.issued2024
dc.description.abstractThe COVID-19 pandemic introduced unprecedented challenges to the aviation industry, significantly impacting air traffic movements (ATM). This study investigates the effectiveness of log transformation in evaluating the goodness of fit of multiple regression models in predicting ATM within the South African aviation sector. Specifically, it compares the performance of a standard Multiple Regression Analysis (MRA) model with a log-transformed MRA model to determine whether log transformation enhances model accuracy and reliability. The research explores traditional model fit assessment techniques, including R-squared (R²), Adjusted R-squared (R²adj), p-values, F-tests, residual analysis, Mean Squared Error (MSE), and normality tests such as the Shapiro-Wilk Test. Using data from Air Traffic and Navigation Services (ATNS), the study applies MRA to assess the impact of key predictors such as revenue, lockdown levels, confirmed COVID-19 cases, COVID-19-related deaths, exchange rates, GDP, and population on ATM. Findings indicate that the standard MRA model outperforms the log-transformed model in terms of explained variance, predictive accuracy, and coefficient significance. While the log-transformed model offers slight improvements in residual normality and insights into non-linear relationships, it does not surpass the standard model in overall predictive power. As a result, the study concludes that, for practical forecasting and decision-making in air traffic management, the standard MRA model is preferable. However, future research exploring non-linear relationships may benefit more from advanced modeling techniques, such as polynomial regression or machine learning, rather than a simple log transformation.
dc.identifier.otherDissertation (M.Sc.(Applied Statistics))--University of the Free State, 2024
dc.identifier.urihttp://hdl.handle.net/11660/13080
dc.language.isoen
dc.publisherUniversity of the Free State
dc.rights.holderUniversity of the Free State
dc.subjectMultiple Regression Analysis (MRA)
dc.subjectLog transformation
dc.subjectAir Traffic Movements (ATM)
dc.subjectCOVID-19
dc.subjectAviation forecasting
dc.subjectModel fit assessment
dc.subjectR-squared
dc.subjectAdjusted R-squared
dc.subjectResidual analysis
dc.subjectStatistical modeling
dc.subjectAir Traffic Navigation Services (ATNS)
dc.subjectNormality tests
dc.titleAssessing Multiple Regression Analysis (MRA) model fit for forecasting air traffic movements using log transformation: a case study on ATNS air traffic movement dataset during COVID-19 pandemic
dc.typeDissertation
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