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Abstract
The monsoon is affected by factors like wind speed, humidity, and temperature. Outliers can significantly skew the data, and this study presents an outlier detection method using the COVRATIO statistic, derived from the covariance matrix within a simultaneous Linear Functional Relationship Model (LFRM) for linear variables. The cut-off point for the 5% upper percentiles of the maximum value of the COVRATIO statistic is established through a Monte Carlo simulation study. The findings indicate that outliers are detected when the COVRATIO statistic surpasses these cut-off points. The effectiveness of the simultaneous LFRM is demonstrated using Butterworth environmental data, with variables including wind speed, humidity, and temperature. The data s normality is confirmed by the Kolmogorov-Smirnov test. This research supports the National Policy on Climate Change by contributing to knowledge-based decision-making in climate-related studies, particularly in the domains of environmental monitoring, renewable energy planning, and data analysis.
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Details
1 Mathematical Sciences Studies, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM) , Cawangan Johor Kampus Segamat, 85000 Segamat, Johor, Malaysia
2 School of Mathematical Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA , 40450 Shah Alam, Selangor, Malaysia
3 Institute for Advanced Studies, Universiti Malaya , 50603 Kuala Lumpur, Malaysia