Multiple Linear Regression Model of Rice Production using Conjugate Gradient Methods

Nur Idalisa Norddin, Mohd Rivaie Mohd Ali, Nurul Hafawati Fadhilah, Nur Atikah, Anis Shahida, Nur Hidayah Nohd Noh


Regression is one of the basic relationship models in statistics. This paper focuses on the formation of regression models for the rice production in Malaysia by analysing the effects of paddy population, planted area, human population and domestic consumption. In this study, the data were collected from the year 1980 until 2014 from the website of the Department of Statistics Malaysia and Index Mundi. It is well known that the regression model can be solved using the least square method. Since least square problem is an unconstrained optimisation, the Conjugate Gradient (CG) was chosen to generate a solution for regression model and hence to obtain the coefficient value of independent variables.  Results show that the CG methods could produce a good regression equation with acceptable Root Mean-Square Error (RMSE) value.

Full Text:




  • There are currently no refbacks.

UTM Logo

Copyright © 2016 Penerbit UTM Press, Universiti Teknologi Malaysia

Disclaimer: This website has been updated to the best of our knowledge to be accurate. However, Universiti Teknologi Malaysia shall not be liable for any loss or damage caused by the usage of any information obtained from this web site.

Best viewed: Mozilla Firefox 4.0 & Google Chrome at 1024 × 768 resolution.