Forecast the road accidents in Malaysia using exponential smoothing and multiple linear regression modelling / Nor Salam Abdul Manaf
In Malaysia, traffic accidents are a significant public health issue, and the government is continuously seeking for measures to prevent them. Creating precise forecasting algorithms that can anticipate future traffic accidents is one method to do this. In this study, multiple linear regression and...
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my-uitm-ir.971612024-06-24T16:47:05Z Forecast the road accidents in Malaysia using exponential smoothing and multiple linear regression modelling / Nor Salam Abdul Manaf 2023 Abdul Manaf, Nor Salam Analytical methods used in the solution of physical problems In Malaysia, traffic accidents are a significant public health issue, and the government is continuously seeking for measures to prevent them. Creating precise forecasting algorithms that can anticipate future traffic accidents is one method to do this. In this study, multiple linear regression and exponential smoothing as two forecasting models examined. A straightforward forecasting methodology called exponential smoothing uses historical data to forecast future values. The concept is predicated on the idea that recent data points are more significant than historical data points. Multiple independent variables are used in a more intricate forecasting model called multiple linear regression to predict a dependent variable. 2023 Thesis https://ir.uitm.edu.my/id/eprint/97161/ https://ir.uitm.edu.my/id/eprint/97161/1/97161.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Mathematics Mat Ripin, Rohayati |
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Universiti Teknologi MARA |
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UiTM Institutional Repository |
language |
English |
advisor |
Mat Ripin, Rohayati |
topic |
Analytical methods used in the solution of physical problems |
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Analytical methods used in the solution of physical problems Abdul Manaf, Nor Salam Forecast the road accidents in Malaysia using exponential smoothing and multiple linear regression modelling / Nor Salam Abdul Manaf |
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In Malaysia, traffic accidents are a significant public health issue, and the government is continuously seeking for measures to prevent them. Creating precise forecasting algorithms that can anticipate future traffic accidents is one method to do this. In this study, multiple linear regression and exponential smoothing as two forecasting models examined. A straightforward forecasting methodology called exponential smoothing uses historical data to forecast future values. The concept is predicated on the idea that recent data points are more significant than historical data points. Multiple independent variables are used in a more intricate forecasting model called multiple linear regression to predict a dependent variable. |
format |
Thesis |
qualification_level |
Bachelor degree |
author |
Abdul Manaf, Nor Salam |
author_facet |
Abdul Manaf, Nor Salam |
author_sort |
Abdul Manaf, Nor Salam |
title |
Forecast the road accidents in Malaysia using exponential smoothing and multiple linear regression modelling / Nor Salam Abdul Manaf |
title_short |
Forecast the road accidents in Malaysia using exponential smoothing and multiple linear regression modelling / Nor Salam Abdul Manaf |
title_full |
Forecast the road accidents in Malaysia using exponential smoothing and multiple linear regression modelling / Nor Salam Abdul Manaf |
title_fullStr |
Forecast the road accidents in Malaysia using exponential smoothing and multiple linear regression modelling / Nor Salam Abdul Manaf |
title_full_unstemmed |
Forecast the road accidents in Malaysia using exponential smoothing and multiple linear regression modelling / Nor Salam Abdul Manaf |
title_sort |
forecast the road accidents in malaysia using exponential smoothing and multiple linear regression modelling / nor salam abdul manaf |
granting_institution |
Universiti Teknologi MARA, Terengganu |
granting_department |
College of Computing, Informatics and Mathematics |
publishDate |
2023 |
url |
https://ir.uitm.edu.my/id/eprint/97161/1/97161.pdf |
_version_ |
1804890016541835264 |