Estimation of the general spatial regression model (SAC) by the maximum likelihood method

That there are indicators or statistical transactions that have appeared in a large way in recent times to describe, summarize and analyse spatial data, when a study is done of many phenomena or a disease is studied, whether it is on humans or animals, we need to analyze the spatial data resulting from those phenomena, as it includes observations of the spatial units. For example, countries or provinces ... etc., all of these are linked to certain points or locations. The study uses the maximum likelihood method to estimate the parameters of the General Spatial Model by employing the model to study cancer which shows the relationship between the dependent variable Y represented by the number of patients and the explanatory variables represented (average age, tumor size, treame, hormone, immunity) in light of the effect of spatial juxtaposition and using Rook neigh boring criteria. One of the most important conclusions reached is the emergence of significant effects of some explanatory variables on the dependent variable Y, and the estimated values of the dependent variable Y are close to the real values of the same variable.