A comparison between continuous exponential, discrete logistics and continuous logistics growth models in forecasting birth rate of newborn in Malaysia

  • Authors

    • Ahmad Nazim National Population and Family Development Board Malaysia
    2014-07-04
    https://doi.org/10.14419/gjma.v2i3.2950
  • This research develops techniques which are helpful in forecasting birth rate of male and female newborn in Malaysia. The techniques used in this study are Continuous Exponential, Discrete Logistics and Continuous Logistics Growth models. For the purpose of this study, secondary data of Total Birth Rate in Malaysia obtained from National Population and Family Development Board (NPFDB) Malaysia covering the period 1995 up to 2009 was used. From the result, it was found that Continuous Logistics model is the best model to forecast the birth rate of newborn in Malaysia since it has the lowest SSE values which are 598.462 for male and 392.8738 for female.

    Keywords: Mathematical Modeling, Exponential Growth, Logistics Growth, Logistics Continuous Growth, Birth Rate, Malaysia.

  • References

      1. Hamid Arshat and Tey Nai Peng ,1988, An Overview of the Population Dynamics in Malaysia , Malaysian Journal of Reproductive Health, Vol. 6, No. 1, pp. 23- 46. (SCOPUS-Cited Publication)
      2. The Office of Chief Statistician Malaysia, 2014, Population And Housing Census Of Malaysia, Department of Statistics Malaysia.
      3. Alexei Sharov, 1997, Exponential Model. URL https://home.comcast.net/~sharov/PopEcol/lec5/exp.html
      4. Lewis D. Ludwig, n.d.,Discrete VS Continuous Population Models. Department of Mathematics and Computer Science, Denison University.
      5. Amrita Vishwa Vidyapeetham University, n.d., Logistic Population Growth: Continuous and Discrete. http://amrita.vlab.co.in/?sub=3&brch=65&sim=1110&cnt=1
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  • How to Cite

    Nazim, A. (2014). A comparison between continuous exponential, discrete logistics and continuous logistics growth models in forecasting birth rate of newborn in Malaysia. Global Journal of Mathematical Analysis, 2(3), 111-114. https://doi.org/10.14419/gjma.v2i3.2950