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, Elizabethen_ZA
dc.contributor.authorMasekoameng, John Lehlakaen_ZA
dc.date.accessioned2025-04-10T13:39:23Z
dc.date.available2025-04-10T13:39:23Z
dc.date.issued2024en_ZA
dc.descriptionDissertation (M.Sc.(Applied Statistics))--University of the Free State, 2024en_ZA
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.en_ZA
dc.identifier.urihttp://hdl.handle.net/11660/13008
dc.language.isoen
dc.publisherUniversity of the Free Stateen_ZA
dc.rights.holderUniversity of the Free Stateen_ZA
dc.subjectMultiple Regression Analysis (MRA)en_ZA
dc.subjectLog transformationen_ZA
dc.subjectAir Traffic Movements (ATM)en_ZA
dc.subjectCOVID-19en_ZA
dc.subjectAviation forecastingen_ZA
dc.subjectModel fit assessmenten_ZA
dc.subjectR-squareden_ZA
dc.subjectAdjusted R-squareden_ZA
dc.subjectResidual analysisen_ZA
dc.subjectStatistical modelingen_ZA
dc.subjectAir Traffic Navigation Services (ATNS)en_ZA
dc.subjectNormality testsen_ZA
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 pandemicen_ZA
dc.typeDissertation
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