Kernel Nonparametric Regression Modeling with the Nadaraya-Watson Estimator (Case Study: Fertility in the Southern Sumatra Region)

Muhammad Arib Alwansyah Arib

doi:10.36456/jstat.vol16.no2.a8248

Authors

Muhammad Arib Alwansyah Arib Program Studi Statistika, Fakultas Matematika dan Ilmu Pengetahuan Alam, Universitas Bengkulu

DOI:

https://doi.org/10.36456/jstat.vol16.no2.a8248

Keywords:

Fertility, Kernel Nonparametric Regression, MSE, Nadaraya-Watson

Abstract

Fertility is a live birth, namely the release of a baby from a woman's womb with signs of life such as screaming, breathing, a throbbing heart, and so on. The source of this research data comes from the publication of the official website of the Central Statistics Agency (BPS). This study aims to model and predict fertility data in 2020 with kernel nonparametric regression using the Nadaraya-Watson estimator. The nonparametric kernel model shows the relationship between fertility (Y) and the percentage of underage women at first marriage , the percentage of women 15-49 years who do not use traditional KB or conventional methods , the number of active family planning participants , the number of couples of childbearing age , the percentage of the average length of schooling , and the total expenditure per capita based on Gaussian kernel function and bandwidth values. Based on the results of the analysis, the independent variables that have a significant effect are , , , on the dependent variable with the optimum bandwidth value of 0.490 and the value of R² of 99.6%, and the MSE value of 0.332. Modeling fertility is important as it helps understand and predict population trends. It provides insights into the potential number of births in a population in the future. This information can be used for policy planning, including health, educations, and social policies.

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