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Application of Robust Regression Model on Soybean Data
Siti Nur Aisyah Binti Ismail
Supervisor: Assoc. Prof Dr. Norizan Binti Mohamed
Bachelor of Science (Financial Mathematics)
School of Informatics and Applied Mathematics
The soybean is economically the most crucial bean in the world, providing ingredients for
hundreds of chemical products and vegetable protein for millions of people. In regression
analysis, the use of least squares method would not be appropriate in solving problem
containing outlier. So we need a parameter estimation method which is robust where the
value of the estimation is not much affected by small changes in the data and provide
results that are resistant to the outliers. The aim of this research is to construct a robust
regression model for predicting the production of soybean by using S-estimator, LTSestimator,
M-estimator, and MM-estimator. The data used are soybean data obtained
from BPS in Indonesia. Not all factors are suspected to affect soybean production
availability in Indonesia. Then, the comparison value of R 2 has been implemented
between the estimators to get the best robust estimator.
889 | UMT UNDERGRADUATE RESEARCH DAY 2018