@article{Suzana Russo_Diego Flender_Gabriel Francisco da Silva_2021, title={Poisson Regression Models for Count Data: Use in the Number of Deaths in the Santo Angelo (Brazil)}, volume={8}, url={https://setpublisher.com/index.php/jbas/article/view/717}, DOI={10.6000/1927-5129.2012.08.02.01}, abstractNote={<p>When speaking about data, presuppose its good quality otherwise the accuracy of information would be affected, which would lead to false interpretations. In Health Statistics data is obtained through surveys presented in its simplest expression, taking advantage of existing records; making an inquiry or by means of experiments. The rational organization of the data allows characterizing the priority issues and thus establishing health programs. To analyze the mortality data it is necessary to consider the mortality rate of certain age groups, so that we can find data which shows the prevalence of major groups of deaths. The analysis of data is followed by subsequent formulation of the Poisson regression models, where each group in question by age group is represented by a number of counting time. The Poisson regression model is a specific type of Generalized Linear Models (GLM) and non-linear. As [1], its main features are: a) to provide, in general, a satisfactory description of experimental data whose variance is proportional to the mean. b) It can be deduced theoretically from the first principles with a minimum of restrictions c) If events occur independently and randomly in time with constant average rate of occurrence, the model determines the number of time specified. At the end of this study, it could be seen through the analysis of the data that the age group from 70 to 79 years old sustains the highest incidence of deaths with 21.1%. Then comes the range of 60 to 69 years old with the morality rate of 20%. This was recorded for the time worked in January 2000 to December 2004. The death rate was 52.27and variance was equal to 102.43 in the city of Santo Angelo (Brazil). It was further found that the data analyzed over dispersion variance greater than average. AS a result it was necessary to remove the over dispersion to find the appropriate template. With the pattern found, some short-term forecasts were made.</p>}, number={2}, journal={Journal of Basic & Applied Sciences}, author={Suzana Russo and Diego Flender and Gabriel Francisco da Silva}, year={2021}, month={Jul.}, pages={266–269} }