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This paper is focusing on the application of robust method in multiple linear regression (MLR) model towards diabetes data. The objectives of this study are to identify the significant variables that affect diabetes by using MLR model and using MLR model with robust method, and to measure the performance of MLR model with/without robust method. Robust method is used in order to overcome the outlier problem of the data. There are three robust methods used in this study which are least quartile difference (LQD), median absolute deviation (MAD) and least-trimmed squares (LTS) estimator. The result shows that multiple linear regression with application of LTS estimator is the best model since it has the lowest value of mean square error (MSE) and mean absolute error (MAE). In conclusion, plasma glucose concentration in an oral glucose tolerance test is positively affected by body mass index, diastolic blood pressure , triceps skin fold thickness, diabetes pedigree function, age and yes/no for diabetes according to WHO criteria while negatively affected by the number of pregnancies. This finding can be used as a guideline for medical doctors as an early prevention of stage 2 of diabetes.

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Forecasting crude palm oil price is important, particularly when the investors encounter with the increasing risks and uncertainties in the future. Therefore, the aim of this study is to forecast the price of palm oil in Malaysia for the next years based on price for the period of 31 years. The objective of the research is to propose an appropriate model to forecast the CPO price. Thus, this study proposes three types of models, which are namely: Autoregressive Integrated Moving Average (ARIMA), Autoregressive Conditional Heteroskedasticity (ARCH) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH). Akaike Information Criterion (AIC) and Hannan-Quinn Criterion (H-Q) statistic were used to obtain the best model. It was found that ARIMA (2, 1, 5) performed better compared to ARCH and GARCH models. It is concluded that ARIMA (2, 1, 5) can be used as an alternative model to forecast the CPO price.