Modelling the Determinants of Babies Weight Using Quantile Regression in Bolgatanga Municipality 1. Background of the Study Traditionally, linear regression analyses have detected increasing trends in the incidence of overweight/obesity among new born babies. However, these previous regression methods were limited in their ability to capture crossdistribution variations among effects. Birth weight according to a USA National Library for Medicine is the first weight of a baby, taken just after he or she is born. How much a baby weights at birth can have a profound impact on the child's life? Babies who weigh less than 5.5 pounds at birth-the cutoff for the low birth weight-may face health challenges after birth including serious digestive issues, difficulty in breathing, and bleeding in the brain. Their risk of developing diabetes, heart diseases, high blood pressure and obesity later in life is also increased. High birth weight babies are those that weigh more than 8.8 pounds at birth and can also face an increased risk of obesity, diabetes, and high blood pressure in adulthood. Genetics certainly play a role in birth weight. Parental health therefore has a lot to do with babies' weight at birth, especially when factors such as high blood pressure, diabetes, heart diseases and the likes are not well checked. The prevalence of childhood obesity increased dramatically during the last decade in industrialized countries (Toschke et al., 2005). TV watching, formula feeding, smoking in pregnancy, maternal obesity, parental social class are well known environmental constitutional or socio-demographic risk factors (Toschke et al., 2005). However, it is uncertain as to whether these factors have effects on the entire weight distribution or just a part of it. Several data such as the birth weight data involves the analysis of highly skewed data. When a distribution of variables is highly skewed, it implies that the mean is sensitive to outliers and definitely not a good measure of central tendency. Quantile regression therefore is one method to analyze such data. Quantile regression as proposed by Koenker and Bassett (1978) has immerged as an important statistical approach for addressing the limitations of simple linear regression. Quantile regression model is a natural extension of the linear regression model estimating various conditional quantile functions. This offers a strategy for determining how the covariates influence the entire response distribution.

Statistical Analysis of the Queuing System at the OPD in Bolgatanga Regional Hospital 1. Background of the Study The total population of the Bolgatanga metropolis was 223,252 in the 2010 population and housing census representing 9 percent of the total population of the Upper East Region of Ghana. This was made up of 11,109 males and 112,143 females constituting 49.8 and 50.2 percent respectively. The metropolis has a predominantly urban population constituting 80.2 percent. Generally, the metropolis depicts a youthful population. The population aged 0-14 years is 81,156 representing 36.4 percent whiles those within 15-64 years who constitute the total labor force are 131,826 representing 59 percent. From the 2018 world population review, it is estimated that the population of the Bolgatanga municipality increased to 360,579 with a growth rate of about 3.5 percent. This rapid increase in population would have so many burdens on the social amenities in the area not excluding the health service delivery institutions. This is highlighted by the fact that the population of the area is dominated by women and children who are by far vulnerable to various chronic diseases. The sanitation situation in the metropolis is a cause for concern as the area produces a lot of waste which are not well managed. Majority of the household within the metropolis are without a toilet which force most of the residents to resort to open defecation which recently placed the Metropolis in the bottom of the open defecation league table. This practice poses serious health threat to the people. Based on the above-mentioned factors, it stands to reason that the demand for various health services in the area will increase substantially. Therefore, the health service delivery institutions in and outside the area often record high attendance because of the proportion of people demanding for health service in those institutions. Beside the various determinants of health service demand outlined above, naturally, everyone become ill at one point of time in his or her life but decision taken by people to seek for good health care is very critical since such decisions can positively or negatively affect health care delivery and by extension the socioeconomic status of the country. The health of every nation according to Ardeson (1983) is everybody's concern but individually, everyone is responsible for his or her