Multiple Linear Regression Model of Rice Production using Conjugate Gradient Methods

Authors

  • Nur Idalisa Norddin Jabatan Matematik dan Statistik, Fakulti Sains Komputer dan Matematik Univesiti Teknologi Mara (UiTM) Cawangan Terengganu Kampus Kuala Terengganu, Malaysia
  • Mohd Rivaie Mohd Ali Jabatan Matematik dan Statistik, Fakulti Sains Komputer dan Matematik Univesiti Teknologi Mara (UiTM) Cawangan Terengganu Kampus Kuala Terengganu, Malaysia
  • Nurul Hafawati Fadhilah Jabatan Matematik dan Statistik, Fakulti Sains Komputer dan Matematik Univesiti Teknologi Mara (UiTM) Cawangan Terengganu Kampus Kuala Terengganu, Malaysia
  • Nur Atikah Jabatan Matematik dan Statistik, Fakulti Sains Komputer dan Matematik Univesiti Teknologi Mara (UiTM) Cawangan Terengganu Kampus Kuala Terengganu, Malaysia
  • Anis Shahida Jabatan Matematik dan Statistik, Fakulti Sains Komputer dan Matematik Univesiti Teknologi Mara (UiTM) Cawangan Terengganu Kampus Kuala Terengganu, Malaysia
  • Nur Hidayah Nohd Noh Jabatan Matematik dan Statistik, Fakulti Sains Komputer dan Matematik Univesiti Teknologi Mara (UiTM) Cawangan Terengganu Kampus Kuala Terengganu, Malaysia

DOI:

https://doi.org/10.11113/matematika.v35.n2.1180

Abstract

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.

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Published

2019-07-31

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Section

Articles