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A statistical approach on demographic dynamics and population projection in Tanzania
Journal of the Korean Data & Information Science Society 2018;29:567-76
Published online March 31, 2018
© 2018 Korean Data and Information Science Society.

Peter A. Mwandri1 ∙ Kee-Won Lee2 ∙ Songyong Sim3

123Department of Statistics, Hallym University
Correspondence to: Professor, Department of Statistics, Hallym University, Chuncheon 24252, Korea. E-mail: sysim@hallym.ac.kr
Received December 28, 2017; Revised February 20, 2018; Accepted February 22, 2018.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Population projection provides profound information to policy makers of formulation of policies and decision making. It also provides information about evaluation of the progress towards achieving Millennium Development Goals in Tanzania. We use five different models to project Tanzanian population: linear model, exponential model, modified exponential model, logistic model and cohort component model. Among these models, logistic growth model provides the lowest projection followed by modified exponential model. On the other hand, the exponential growth model gives the highest projection followed by cohort component model. Cohort component model seems to be the best projection model among the above mentioned models since it incorporates birth, death and migration information in projection process.
Keywords : Concept of cohort, demography, fertility, population projection, survivorship function.