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Modelling of Data Blood Pressure by Using Robust Regression<br />

Shuwatie Binti Maidin<br />

Supervisor: Puan Azlida Binti Aleng@Mohamad<br />

Bachelor of Science (Financial Mathematics)<br />

School of Informatics and Applied Mathematics<br />

The presence of outliers can influence the significant error rates. It can contribute to the<br />

incorrect of the analysis results. Generally, ordinary least square estimation (OLS) is most<br />

frequently used for estimation of the parameters in the model. Unfortunately, OLS cannot<br />

produce the best result when the data are contaminated with outliers. To remedy this<br />

problem, robust regression is an alternative to OLS when data are contaminated with<br />

outliers. The purpose of this study is to detect the presence of outliers in blood pressure<br />

data. The blood pressure data will be modeling by using the robust regression model.<br />

Least Trimmed Square (LTS) and MM-estimation were used to develop the best fit model<br />

by using Statistical Analysis System (SAS) 9.4.<br />

884 | UMT UNDERGRADUATE RESEARCH DAY 2018

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