2^ ?3^ 2 : 2n^The adequacy of the GPR model over the PR model
As the title= fpsyg.2015.00360 dependent variable is more appropriate for the GPR model because of its underdispersion property, we Ineen B, Bourne RR, Shah SP, Khan MA, Johnson GJ, Gilbert applied this model to estimate the regression parameters () Eded to provide further conclusions around the clinical significance of those including `p' values based on Wald Chi-square values. The statistical software packages SAS 9 and SPSS 11.5 are used to extract the information from BDHS 2007, to recode the variables, and to perform univariate and bivariate analyses. Finally we used R 2.14.1 to estimate parameters of the GPR model.Results The estimated mean ?0:626?and variance (s2 = 0.369) y of the outcome variable reveal the under-dispersionTable 2 Bivariate associations between the number of under-five malnourished children in a family and different predictors in Bangladesh,Characteristics Place of residence Mother's education Father's education Father's occupation Wealth index Sources of drinking water Toilet facility Religion Access to media Total number of children ever-born to a woman 2 55.36* 252.70* 241.*P title= fnins.2015.00094 maximum likelihood approach. The loglikelihood functions of the GPR model isLog ? ; y ?Xn i ? i ?1?log? ?yi ? i log i? 1 ?iSimple summary statistics (either as percentage for the categorical variables or mean for the continuous variables) are shown for selected socioeconomic predictors (Table 1). At the outset of analyses, sample mean and sample variance of the dependent variable are calculated in order to check whether it follows the standard Poisson regression model or GPR model. Then the Z test is performed to check whether the dispersion parameter significantly deviates from zero. Here the null hypothesis (H0 : = 0) states that the value of dispersion parameter is zero. In contrast, a two-sided alternative hypothesis (H1) is used which indicates that the value of the dispersion parameter is unequal to zero. Bivariate analyses (based on Pearson Chisquare test) are performed to examine association between dependent variable and each of the selected predictors (Table 2). All significant predictors are then finally included into the GPR model. As the title= fpsyg.2015.00360 dependent variable is more appropriate for the GPR model because of its underdispersion property, we applied this model to estimate the regression parameters () including `p' values based on Wald Chi-square values.