Identifying Factors that Influence Life Expectancy in Central Java Using Spatial Regression Models
DOI:
https://doi.org/10.36456/jstat.vol16.no2.a8375Keywords:
Life Expectancy, Spatial Regression, Central Java, Lagrange Multiplier Log (SAR)Abstract
Life Expectancy is an average calculated over several years, assuming that mortality remains constant as age increases. It serves as a metric to gauge the success of population health development at the urban level and overall well-being, particularly in terms of health. Various indicators, including socioeconomic conditions, environmental factors, and health indicators, influence the highs and lows of life expectancy. This study in Central Java Province's 35 districts and cities aims to identify crucial components impacting life expectancy through a process-oriented spatial regression analysis. Additionally, the research endeavors to determine the optimal spatial regression equation for modeling life expectancy in the province. Spatial regression, a linear regression development method falling under the point element model, is employed. Utilizing two independent variables selected from seven, the study explores spatial regression equations using SAR, SEM, and SARMA area approaches. Data sourced from BPS in 2020 reveals that the SAR model, with a p-value of 0.02183, is apt for identifying spatial effects on Central Java's life expectancy. The Open Unemployment Rate (X4) and the Percentage of Poor Population (X6) emerge as significant spatial factors influencing life expectancy in Central Java.
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