N Zare, M Sayadi, E Rezaeyan Fard, H Ghaem,
Volume 6, Issue 1 (20 2010)
Abstract
Background & objectives: statistical modeling explicates the observed changes in data by means of mathematics equations. In cases that dependent variable is count, Poisson model is applied. If Poisson model is not applicable in a specific situation, it is better to apply the generalized Poisson model. So, our emphasis in this study is to notice the data structure, introducing the generalized Poisson regression model and its application in estimates of effective factors coefficients on the number of children and comparing it with Poisson regression model results.
Methods: Besides introducing Poisson regression model, we introduced its application in fertility data analysis. A sample of 1019 women in rural areas of Fars was selected by cross sectional and stratified sampling methods. The number of children of family was determined as a count response variable for model validation.
Results: The sample mean and sample variance of the response variable Y, the number of children, are respectively 4.3 and 8.3 (over-dispersion). Log-likelihood was -1950.93 for Poisson regression and -1946.93 for generalized Poisson regression model.
Conclusions: The results revealed that this data have over-dispersion. According to selection criteria, the suitable model for this data analysis was generalized Poisson regression model. It can estimate effective factors coefficients on the number of children exactly.
Ha Nikbakht, H Ghaem, Hr Tabatabaee, A Mirahmadizadeh, S Hassanipour, S Zahmatkesh, A Hemmati, F Moradi, A Abbasi,
Volume 15, Issue 3 (Vol.15, No.3 2019)
Abstract
Background and Objectives: Anthropometric indices, especially weight, provide useful information for the care and treatment of newborn infants and can be used to identify infants at risk. Therefore, this study was conducted to examine the mean weight, height and head circumference measurements of infants and some related factors.
Methods: This cross-sectional study was performed to investigate the anthropometric indices (weight, height and head circumference), demographic characteristics, and delivery data of 1484 newborns in 2016 using multi-stage sampling. Moreover, the predictors of these indices were analyzed using a linear regression model.
Results: The mean weight, height and head circumference of the newborn infants was 3185 ± 465 g, 49.92 ± 2.92 cm, and 34.58 ± 2.29 cm respectively, and 7% of newborns were low birth weight. The male newborns weighed 57.29 g more than females on average at birth (p <0.05). Besides, the height and head circumference of the male newborns were 0.15 and 0.10 cm larger than the female newborns respectively but the difference was not statistically significant. In addition to gender, gestational age at birth (week) and type of delivery correlated with all three anthropometric indices in multivariate analysis.
Conclusion: Identifying and controlling largely adjustable risk factors can make it possible to prevent low anthropometric parameters, particularly low birth weight.
