PEMODELAN JUMLAH KEMATIAN BAYI DI PROVINSI MALUKU TAHUN 2010 DENGAN MENGGUNAKAN REGRESI POISSON

  • Salmon N. Aulele Jurusan Matematika FMIPA Universitas Pattimura
DOI: https://doi.org/10.30598/barekengvol6iss2pp23-27
Keywords: Infant Mortality, Poisson Distributed, Poisson Regression Model, Regression Analysis.

Abstract

Infant mortality is an experienced child death before the age of one year. Regression analysis is a statistical analysis that aims to model the relationship between response
variables (Y) with predictor variables (X). If the Poisson distributed response variables (Y), the regression model used was Poisson regression. The purpose of this research is to get a
Poisson regression model according to the significant factors that influence the infant mortality. The results shows that the significant factors are influence the infant mortality as
the presentation of non medical childbirth (X1) and quantity of medical facility (X7). The case studies are infant mortality in Provinsi Maluku in 2010.

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References

Walpole, R.E. (1982), Pengantar Statistika, edisi ketiga, Gramedia Pustaka Tama, Jakarta.
Myers, R.H. (1990), Classical and Modern Regression With Applications, PWS Kent Publishing Company, USA.
Setyorini, E. (2006), Pemodelan Regresi Poisson Pada Maternal Mortality di Jawa Timur. Tugas Akhir Jurusan Statistika FMIPA ITS, Surabaya.
Badan Pusat Statistik (2010), Angka Kematian Bayi, Data Statistik Indonesia.
Badan Pusat Statistik (2010), Maluku Dalam Angka, Badan Pusat Statistik Provinsi Maluku.
Aulele, S.N. & Purhadi. (2010), Model Geographically Weighted Poisson Regression (Studi Kasus : Jumlah Kematian Bayi di Provinsi Jawa Timur & Jawa Tengah Tahun 2007). Tesis Jurusan Statistika FMIPA ITS, Surabaya.
Published
2012-12-01
How to Cite
[1]
S. Aulele, “PEMODELAN JUMLAH KEMATIAN BAYI DI PROVINSI MALUKU TAHUN 2010 DENGAN MENGGUNAKAN REGRESI POISSON”, BAREKENG: J. Math. & App., vol. 6, no. 2, pp. 23-27, Dec. 2012.
Issue
Vol 6 No 2 (2012): BAREKENG : Jurnal Ilmu Matematika dan Terapan
Section
Articles

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