Geographically weighted regression analysis for human development index

2016 ◽  
Author(s):  
Syarifah Diana Permai ◽  
Heruna Tanty ◽  
Anita Rahayu
Keyword(s):  
Regression Analysis ◽  
Human Development ◽  
Geographically Weighted Regression ◽  
Human Development Index ◽  
Weighted Regression ◽  
Development Index ◽  
Weighted Regression Analysis

scholarly journals Pemodelan Indeks Pembangunan Manusia (IPM) Metode Baru Menurut Provinsi Tahun 2015 Menggunakan Geographically Weighted Regression (GWR)

2019 ◽  
Vol 2 (1) ◽  
pp. 21
Author(s):  
Akbar Maulana ◽  
Renny Meilawati ◽  
Vita Widiastuti
Keyword(s):  
Regression Analysis ◽  
Human Development ◽  
Geographically Weighted Regression ◽  
Human Development Index ◽  
Spatial Regression ◽  
Least Square ◽  
Gaussian Kernel ◽  
Weighted Regression ◽  
Development Index ◽  
Spatial Regression Analysis

<p>The Human Development Index (HDI) is a parameter of quality of life for an area. The HDI explains how residents can access the results of development in obtaining income, health and education. One method that can be used to find out the factors that influence the human development index in modeling is regression analysis of ordinary least square (OLS). In the Human Development Index data, there is a dependency between measuring data and the location of a region. Therefore, spatial regression analysis can be used in this study. The local form of spatial regression analysis is <em>geographically weighted regression</em> (GWR). GWR shows the existence of spatial heterogeneity (location). This study compares between OLS regression and GWR in the new human development index method by province in 2015. In the GWR model we use fixed Gaussian kernel and kernel fixed bisquare as weighted function. The optimal bandwidth value is obtained by minimizing the cross validation (CV) and Akaike information criterion (AIC) coefficients. The results showed that the GWR model with Gaussian kernel function is better than GWR with bisquare kernel function and OLS model.</p><p><strong>Keywords</strong><strong>: </strong>human development index, ordinary least square,<strong> </strong>geographically weighted regression, kernel fixed Gaussian,  kernel fixed bisquare</p>

scholarly journals Application of geographically weighted regression analysis to assess predictors of short birth interval hot spots in Ethiopia

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0233790
Author(s):  
Desalegn Markos Shifti ◽  
Catherine Chojenta ◽  
Elizabeth G. Holliday ◽  
Deborah Loxton
Keyword(s):  
Regression Analysis ◽  
Geographically Weighted Regression ◽  
Birth Interval ◽  
Hot Spots ◽  
Weighted Regression ◽  
Short Birth Interval ◽  
Weighted Regression Analysis

ANALISIS PENGARUH INDEKS PEMBANGUNAN MANUSIA DAN TINGKAT PENGANGGURAN TERBUKA TERHADAP KEMISKINAN DI INDONESIA

2019 ◽  
Vol 2 (2) ◽  
pp. 77-89
Author(s):  
Saparuddin Mukhtar ◽  
Ari Saptono ◽  
As’ad Samsul Arifin
Keyword(s):  
Regression Analysis ◽  
Data Analysis ◽  
Human Development ◽  
Unemployment Rate ◽  
Human Development Index ◽  
Secondary Data ◽  
Lagrange Multiplier Test ◽  
Development Index ◽  
Analysis Techniques ◽  
Negative Effect

Abstract - This study aims to determine the effect of Human Development Index and Open Unemployment to poverty in Indonesia. The data in this study are secondary data about the human development index, the opened unemployment rate, and the percentage of poverty. The data is obtained from panel data of 33 provinces in Indonesia for 4 years from 2011 to 2014. The data analysis techniques uses regression analysis by using Random Effects based on the results of the Lagrange Multiplier test. The results showes that the Human Development Index hasa significant negative effect to poverty. Meanwhile, the level of opened unemployment has no significant effect to poverty in Indonesia. Keywords: Human Development Index, Opened Unemployment Rate, Poverty

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