Regression analysis depends on several assumptions that have to be satisfied. A major assumption that is
never satisfied when variables are from contiguous observations is the independence of error terms. Spatial
analysis treated the violation of that assumption by two derived models that put contiguity of observations
into consideration. Data used are from Egypt's 2006 latest census, for 93 counties in middle delta seven
adjacent Governorates. The dependent variable used is the percent of individuals classified as poor (those
who make less than 1$ daily). Predictors are some demographic indicators. Explanatory Spatial Data
Analysis (ESDA) is performed to examine the existence of spatial clustering and spatial autocorrelation
between neighboring counties. The ESDA revealed spatial clusters and spatial correlation between
locations. Three statistical models are applied to the data, the Ordinary Least Square regression model
(OLS), the Spatial Error Model (SEM) and the Spatial Lag Model (SLM).The Likelihood Ratio test and
some information criterions are used to compare SLM and SEM to OLS. The SEM model proved to be
better than the SLM model. Recommendations are drawn regarding the two spatial models used.