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All Journal Parameter: Journal of Statistics
Ihlasia, Nurmasyita
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Computer Science & IT Decision Sciences, Operations Research & Management Mathematics

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Analysis of The Effect of Life Expectancy (AHH) and Per Capita Expenditure on The Human Development Index (HDI) in Central Sulawesi Province in 2019 Sakinah, Nur; Ihlasia, Nurmasyita; Nurfitra; Sagap, Marni; Rachman, Rohis; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 3 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i3.15373

Abstract

A measurement of a nation's human resource condition is the human development index (HDI). The three components of the human development index are living standards, often known as economics, and health. In Central Sulawesi Province in 2019, this study seeks to ascertain the impact of life expectancy (AHH) and per capita spending on the human development index (HDI). Secondary data from the Central Statistics Agency (BPS) of Central Sulawesi Province, corroborated by additional sources, was used in this study. The multiple linear regression analysis methods were the analysis technique used in this study.The findings demonstrated a positive and significant impact of partially variable Life Expectancy (AHH) and per capita spending variables on the Human Development Index (HDI). The Human Development Index (HDI) in Central Sulawesi Province is thereafter significantly impacted by the combination of the two independent factors in 2019.
Modeling of Poverty Level in Central Sulawesi Using Nonparametric Kernel Regression Analysis Approach Sakinah, Nur; Nurfitra; Ihlasia, Nurmasyita; Handayani, Lilies
Parameter: Journal of Statistics Vol. 2 No. 3 (2022)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Tadulako

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22487/27765660.2022.v2.i3.15743

Abstract

Poverty is defined as a person's inability to meet their basic needs. The level of poverty that exists can be used to assess the good or bad of a country's economy. The kernel regression method is used in this study to model the poverty rate in Central Sulawesi in 2020. According to the findings of this study, comparing poverty rate predictions for the Gaussian Kernel function and the Epanechnikov Kernel function with optimal bandwidth can be said to use different kernel functions with optimal bandwidth for each - each of these kernel functions will produce the same curve estimate. So, in kernel regression, the selection of the optimal bandwidth value is more important than the selection of the kernel function. Because of the use of various kernels functions with optimal bandwidth values results in almost the same curve estimation.
Co-Authors Lilies Handayani Nur Sakinah Nurfitra Rachman, Rohis Sagap, Marni