American Journal of Theoretical and Applied Statistics 2017; 6(4): 209-213 http://www.sciencepublishinggroup.com/j/ajtas doi: 10.11648/j.ajtas.20170604.17 ISSN: 2326-8999 (Print); ISSN: 2326-9006 (Online) Geostatistics Analysis of Infant Mortality Rate in Ethiopia Montasir Ahmed Osman Mohamed Scientific Research and Development Center, Nawroz University, Kurdistan Region, Iraq Email address: a.montasir@yahoo.com To cite this article: Montasir Ahmed Osman Mohamed. Geostatistics Analysis of Infant Mortality Rate in Ethiopia. American Journal of Theoretical and Applied Statistics. Vol. 6, No. 4, 2017, pp. 209-213. doi: 10.11648/j.ajtas.20170604.17 Received: May 17, 2017; Accepted: May 24, 2017; Published: July 10, 2017 Abstract: In this paper, spatial statistical analysis of infant mortality rate in Ethiopia is addressed. The analysis investigated of a significance spatial autocorrelation attendance as well as an adapting of a generalized linear mixed model with spatial covariance structure. The results showed the distribution is much spatially associated. Some geographical, economical and healthy variables are used to estimate the model. Several examined variables have a significant effect in the model contrast to other have an insignificant impact. The results highlight the role of improving education to decline the risk of infant mortality rate. Male and children with extra weight are higher exposed and the risk is highly different from one zone to another. Keywords: Spatial Statistics, Infant Mortality, Generalized Models, Mixed Models, Moran’s I 1. Introduction Infant mortality is the death of a child before completing the first year of age. The infant deaths number from every 1000 live births called infant mortality rate. This rate can be taken as an indicator to measure the health care and well- being of the society [3]. The most causes of infant mortality are birth defects, preterm, low birth weight, maternal complications of pregnancy and injuries such as suffocation [3, 6]. The infant mortality causes are significantly associated to structural factors like economic development, general living conditions, social wellbeing and the environment quality [4]. In 2005 the United Nations stated in the human development report the most powerful indicator to capture the divergence in the human development is child mortality [5]. The recent World Health Organization (WHO) reports showed 75% of under-five mortality happens within the first year of age. The risk of infant mortality in African countries is 55 per 1000 live births, and this is more than five times higher compared to European countries which the rate is 10 per 1000 live births [1]. Longitudinally, rates of infant mortality have clearly declined. In 1960, the estimated rate was 122 deaths per 1000 live births, while is 32 deaths per 1000 live births in 2015 [1, 2, 7]. When the analyzing data is collected in a geographical dimension, it is important to test for spatial dependence. If a spatial autocorrelation is detected, the locational attributes of units contain information about the variables. If this association is ignored, this might lead to biased estimators and false conclusion from the study. So, spatial statistical methods are necessary to be used to improve the precision of the results [8]. Nowadays, it is not uncommon for spatial statistical modelling to be used in medicine, biology, demography, environment and other fields because of the need for describing spatial variability in the data. The mixed generalized linear model is a useful tool to analyze spatial data [9, 10]. In this paper, spatial autocorrelation of the infant mortality rate in Ethiopia will be investigated. Moran’s I and other related tests are the tools to examine the significance of spatial autocorrelation of infant mortality rate among the Ethiopian regions. Depending on the results of spatial autocorrelation, a generalized linear mixed model with spatial covariance structure will be adapted. Fifteen independent variables covered geographic, demographic and social domains are used in the model. For analyzing purpose, ArcGis, GeoDa and SAS software are used. 2. Spatial Autocorrelation Spatial autocorrelation investigates in the term of what happens in a location is related or is not related to what happens in the neighboring locations, in addition to measure