Counting the number of poor have often been modeled as a function of a global regression, which meant that the regression coefficient value applied to all geographic regions. Though this assumption was not always valid because of the differences in geographic locations most likely causing the spatial heterogeneity. In case of spatial heterogeneity, the regression parameters would vary spatially, so if the global regression model was applied, would produce an average value of those regression parameters which vary spatially. This study uses the method Geographically Weighted Regression (GWR) to analyze data that contains spatial heterogeneity. In GWR model estimation, the model parameters are obtained by using the Weighted Least Square (WLS) which gives a different weighting in each location. This study discusses the factors that influence the level of poverty in the province of West Sumatra. Suitability test of the model results shows that there is no influence of spatial factors on the level of poverty in the province of West Sumatra. The results shows that there are four variables that are assumed to affect the level of poverty in the province of West Sumatra, they are the variable of floor space, the facility to defecate, ability to pay the cost of health center / clinic and education levels of household head. The four variables have a similar effect in every city and county.
Regression analysis is an analysis used to model the relationship between the dependent variable (Y) and the independent variable (X). If the dependent variable is a discrete random variable, it is developed using the Poisson regression model. Poisson regression models require non-over-dispersion model assumptions. To deal with over-dispersion, a Generalized Poisson regression model was developed. Generalized Poisson regression (GPR) model is an extension of the Poisson regression model. In this study a GPR model is applied to model the number of dengue hemorrhagic fever (DHF) sufferers in East Nusa Tenggara Province in 2018. The independent variables used include percentage of poor population (X1), population density (X2), percentage of proper sanitation (X3), percentage of decent homes (X4), number of doctors (X5), percentage of access to improved drinking water (X6), average length of schooling (X7), human development index (X8). In the resulting model, Poisson regression experiences multicollinearity and overdisception occurs. To overcome multicollinearity, variable selection is performed. Based on the measurement of the goodness of the model using AIC, the GPR model provides better accuracy than Poisson regression to model DHF in East Nusa Tenggara which is 218.5.
Dengue Hemorrhagic Fever is one of the major public health problems in Indonesia. From year to year, DHF causes Extraordinary Event in most parts of Indonesia, especially Central Java. Central Java consists of 35 districts or cities where each region is close to each other. Spatial regression is an analysis that suspects the influence of independent variables on the dependent variables with the influences of the region inside. In spatial regression modeling, there are spatial autoregressive model (SAR), spatial error model (SEM) and spatial autoregressive moving average (SARMA). Spatial durbin model is the development of SAR where the dependent and independent variable have spatial influence. In this research dependent variable used is number of DHF sufferers. The independent variables observed are population density, number of hospitals, residents and health centers, and mean years of schooling. From the multiple regression model test, the variables that significantly affect the spread of DHF disease are the population and mean years of schooling. Moran’s I test results stated that there are spatial dependencies between dependent and independent variables. The best model produced is the SAR model because it has the smallest AIC value of 49.61
The development of a growing population, causing many problems within national development, so the program necessary to reduce the population of family planning program, one of the programs is Contraceptive Services. A variety of contraceptive choices provided by the government especially for women, including: pill, injection, IUD, implant, tissue KB, tubectomy, cream, jelly, and foam. The selection of contraceptives for women have to weigh various factors. So we want to know the factors which influence women in choosing a particular contraceptive. By testing the significance of the multinomial logistic regression model through the G test statistic can be shown there are four factors that influence contraceptive use, namely maternal age, number of living children, age of last child, and pregnancy plans.
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