Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/2293
Title: LONGITUDINAL ANALYSIS OF WEIGHTS OF BABIES IN BOLGATANGA MUNICIPALITY
Authors: Akansuke, M. A.
Issue Date: 2015
Abstract: Weights and ages of babies play vital role in the administering of a particular quantity of drugs which could either constitute overweight or underweight. Research works have been done using various techniques to model weights of babies and to find out causes of low birth weights as well as high birth weights per World Health Organization standards(i.e. normal birth weight of a baby = 2.5kg) of children in Ghana and Africa as a whole but little work is done on the issue of developing a model to predict the weights of infants as well as determining the biological and maternal demographic factors influencing the weights of infants as well as adults. This research work is geared towards determining the pattern in change of weights of babies as well as any pattern differential among some of the groups, identify the factors that significantly affect the change in weights of babies and to develop Gornpertz and Mixed models for prediction of weights of babies in the municipality. The dependent variable is the weights of babies whilst the independent variables are gender, breast feeding type, marital status, age of mother, parity, type of health facility, mother's religious denomination, educational status of mother, occupation of mother and last type of family planning mother ended with before becoming pregnant. Maternal factors such as family planning type, parity group and religion significantly determined birth weight whilst biological determinants such as gender, breast feeding type as well as maternal factors which included marital status, age of mother, and mother's educational and occupational status variables in the equation. were statistically not significant. Following the analysis using Stata, it is found out that there is significant difference in mean weights of babies among the maternal factors. The models that fit the data for this research work are the Gornpertz and the reduced models.
Description: MASTER OF SCIENCE IN BIOMETRY
URI: http://hdl.handle.net/123456789/2293
Appears in Collections:Faculty of Mathematical Sciences

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