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Application of Robust Regression in Air Pollution<br />

Noorazulaiza Binti Mohd Nordin<br />

Supervisor: Assoc. Prof. Dr. Norizan Binti Mohamed<br />

Bachelor of Science (Financial Mathematics)<br />

School of Informatics and Applied Mathematics<br />

Pollution can be divided into several categories and one of them is air pollution. Air<br />

pollution depends on variation nature and human factors which can contribute to the<br />

emission of differences gases. The most common pollutants produced are carbon<br />

monoxide (CO), sulfur oxide (SOx), ozone (O3) and particular matter (PM). Besides, this<br />

is not only a local problem, but it included all over the world. The objective of the research<br />

is to study the percentage of the air pollution and contributing factors for the pollution.<br />

Robust Regression is introduced to find the values that can minimize a robust estimate in<br />

the model which is highly resisted to the leverage points and robust to the outliers in the<br />

response. The LTS estimator, S estimator, M estimator and MM estimator are used in this<br />

study to compare which estimation perform the best robust estimator.<br />

840 | UMT UNDERGRADUATE RESEARCH DAY 2018

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