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The selection of statistical analysis techniques to be used must be adjusted to the data. Data in the form of time cycle or point position to the angle of possibility is no longer suitable to be analyzed using classical linear statistic method because the direction and the angle influence the position between one data with other data. This paper aims to examine the comparison of Linear Regression Analysis with Circular Regression Analysis. The writing method used is literature review using simulation data. Simulation of data and analysis is done with the help of R program. The results showed that circular data is better in Circular Regression Analysis than Classical Linear Regression Analysis, so the conclusion is Selection of statistical analysis techniques to be used must be adjusted with the data that already held. Data in the form of time cycles or position of the point to the angle of probability is no longer suitable to be analyzed using classical linear statistic method because the direction and the angle influence the position between one data with other data.

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This research discussed about the case of diabetes, overweight, and obesity which aimed to determine the factors that most affect the number of adult people with Diabetes from Obesity and Overweight in the world and looking for the best spatial model to make predictions in the next period. This research based on data WHO in 2015 from The 2016 Global Nutrition Report. At 5% level of significance for 2015, factor that influence diabetes is obesity and the most excellent spatial model used in the analysis is Spatial Error Model (SEM) that use Weight Level Order 1 and has R2 value 81.82%.