Statistical Analysis of Log Transformation Effectiveness in Air Traffic Movement Forecasting During COVID-19 in South Africa.
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| Title: | Statistical Analysis of Log Transformation Effectiveness in Air Traffic Movement Forecasting During COVID-19 in South Africa. |
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| Authors: | Masekoameng, John Lehlaka1 |
| Source: | Journal of Aviation Technology & Engineering. 2026, Vol. 15 Issue 1, p101-118. 18p. |
| Subjects: | Multiple regression analysis, Logarithms, Traffic estimation, Economic statistics, South Africans, COVID-19 pandemic |
| Geographic Terms: | South Africa |
| Abstract: | This study evaluates the effectiveness of log transformation in enhancing multiple regression models used to forecast air traffic movements (ATMs) in South Africa during the COVID-19 pandemic. Using 60 monthly observations from October 2016 to September 2021, the analysis incorporates variables such as revenue, lockdown levels, COVID-19 metrics, exchange rates, gross domestic product, and population. Two models are compared: one using raw ATMs and another with log-transformed ATMs as the dependent variable. While the untransformed model shows stronger explanatory power (R² = 0.904, adjusted R² = 0.891) compared to the log-transformed model (R² = 0.772, adjusted R² = 0.741), the latter demonstrates improved residual normality (Shapiro-Wilk p < 0.001) and homoscedasticity (Breusch-Pagan p = 0.852). Both models suffer from high multicollinearity with maximum variance inflation factors of 57.3, necessitating cautious interpretation of coefficients. The Durbin-Watson statistic indicates potential positive serial correlation in the untransformed model (1.235), while the log-transformed model shows a more acceptable value (1.842). Given the atypical and volatile nature of the pandemic data, along with limited sample size, results are restricted to in-sample fit. The study finds that log transformation can improve adherence to statistical assumptions but does not universally enhance model fit or forecasting accuracy in complex economic contexts. The study also highlights the trade-offs between interpretability, explanatory power, and statistical rigor. Recommendations for future work include exploring advanced time series and nonlinear modeling techniques. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Aviation Technology & Engineering is the property of Purdue University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Statistical Analysis of Log Transformation Effectiveness in Air Traffic Movement Forecasting During COVID-19 in South Africa. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Masekoameng%2C+John+Lehlaka%22">Masekoameng, John Lehlaka</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Aviation+Technology+%26+Engineering%22">Journal of Aviation Technology & Engineering</searchLink>. 2026, Vol. 15 Issue 1, p101-118. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multiple+regression+analysis%22">Multiple regression analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Logarithms%22">Logarithms</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+estimation%22">Traffic estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Economic+statistics%22">Economic statistics</searchLink><br /><searchLink fieldCode="DE" term="%22South+Africans%22">South Africans</searchLink><br /><searchLink fieldCode="DE" term="%22COVID-19+pandemic%22">COVID-19 pandemic</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22South+Africa%22">South Africa</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study evaluates the effectiveness of log transformation in enhancing multiple regression models used to forecast air traffic movements (ATMs) in South Africa during the COVID-19 pandemic. Using 60 monthly observations from October 2016 to September 2021, the analysis incorporates variables such as revenue, lockdown levels, COVID-19 metrics, exchange rates, gross domestic product, and population. Two models are compared: one using raw ATMs and another with log-transformed ATMs as the dependent variable. While the untransformed model shows stronger explanatory power (R² = 0.904, adjusted R² = 0.891) compared to the log-transformed model (R² = 0.772, adjusted R² = 0.741), the latter demonstrates improved residual normality (Shapiro-Wilk p < 0.001) and homoscedasticity (Breusch-Pagan p = 0.852). Both models suffer from high multicollinearity with maximum variance inflation factors of 57.3, necessitating cautious interpretation of coefficients. The Durbin-Watson statistic indicates potential positive serial correlation in the untransformed model (1.235), while the log-transformed model shows a more acceptable value (1.842). Given the atypical and volatile nature of the pandemic data, along with limited sample size, results are restricted to in-sample fit. The study finds that log transformation can improve adherence to statistical assumptions but does not universally enhance model fit or forecasting accuracy in complex economic contexts. The study also highlights the trade-offs between interpretability, explanatory power, and statistical rigor. Recommendations for future work include exploring advanced time series and nonlinear modeling techniques. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Aviation Technology & Engineering is the property of Purdue University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.7771/2159-6670.1356 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 101 Subjects: – SubjectFull: Multiple regression analysis Type: general – SubjectFull: Logarithms Type: general – SubjectFull: Traffic estimation Type: general – SubjectFull: Economic statistics Type: general – SubjectFull: South Africans Type: general – SubjectFull: COVID-19 pandemic Type: general – SubjectFull: South Africa Type: general Titles: – TitleFull: Statistical Analysis of Log Transformation Effectiveness in Air Traffic Movement Forecasting During COVID-19 in South Africa. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Masekoameng, John Lehlaka IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 21596670 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: Journal of Aviation Technology & Engineering Type: main |
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