Abstract
This study investigated four trend analysis models namely; linear, quadratic, semi log linear and semi log quadratic to study the pattern of live births registration in the Northern Region of Ghana. The study revealed that Semi log linear trend model is the best trend model for studying the pattern of live births registration in the Northern Region of Ghana based on AIC and BIC criteria. The study further fitted four existing fuzzy time series (FTS) models for forecasting live births registration in the Northern Region of Ghana. The Chen, Singh, Heuristic and Chen-Hsu models are the four models used to analyze the data.
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Research funding: We declare that we have not received any funding from any agency for engaging in this research.
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Conflict of interest: We hereby declare that there are no conflict of interest regarding the publication of this work.
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Articles in the same Issue
- Frontmatter
- Articles
- Did People Really “Leave It Blank”? A Tale of What Became of the Census Citizenship Question and Allocation Trends Through Time
- Estimation of the Departmental Female Employment Rate: Towards a New Strategy Based on Combining Spatial and Non-spatial Small Area Estimators
- Infrastructure and Gender Disparity in Information Communication Technology Literacy: A Cross-Country Comparative Study
- Trend and Fuzzy Time Series Analysis of Live Births Registration in Northern Ghana
- Typical Yet Unlikely and Normally Abnormal: The Intuition Behind High-Dimensional Statistics
Articles in the same Issue
- Frontmatter
- Articles
- Did People Really “Leave It Blank”? A Tale of What Became of the Census Citizenship Question and Allocation Trends Through Time
- Estimation of the Departmental Female Employment Rate: Towards a New Strategy Based on Combining Spatial and Non-spatial Small Area Estimators
- Infrastructure and Gender Disparity in Information Communication Technology Literacy: A Cross-Country Comparative Study
- Trend and Fuzzy Time Series Analysis of Live Births Registration in Northern Ghana
- Typical Yet Unlikely and Normally Abnormal: The Intuition Behind High-Dimensional Statistics