MODELING BRONCHO-PNEUMONIA STATUS IN INFANTS USING DISCRIMINANT AND LOGISIC REGRESSION ANALYSES
MODELING BRONCHO-PNEUMONIA STATUS IN INFANTS USING DISCRIMINANT AND LOGISIC REGRESSION ANALYSES
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Date
2017-04
Authors
SHEHU , Sule Ahmed
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Abstract
This work applies Discriminant Analysis and Logistic Regression models to predict the
prevalence of Broncho-Pneumonia status (BPn) in infants. The data used in this study
were collected from two tertiary health institutions in North Central Zone; University
Teaching Hospital (UTH), Abuja and Federal Medical Centre (FMC), Keffi, Nassarawa
State. Five predictors which are well-recognized for characterizing broncho-pneumonia
in infants (baby‟s weight at birth, baby‟s weight 4week after, sex, mother‟s age and
mother‟s occupation) were considered. One hundred and eighty (180) and two hundred
and fifty three (253) infants with Low Birth Weight (LBW) were randomly sampled
using simple random sampling technique from UTH, Abuja and FMC, Keffi respectively
to build up the models. Both Linear Discriminant and Logistic Regression Models were
fitted to the data for the two groups, and the best model was identified. Ten different
samples of size 10 each were randomly taken from the dataset using SPSS package. The
new datasets were used to validate the two models. It was observed that Discriminant
Model is better used in the zone than Logistic Regression Model. We also find out that
baby‟s weight at birth is best at discriminating between the two groups, since it has the least value
of Wilk‟s Lambda compare to other predictor variables.
Description
A THESIS SUBMITTED TO THE SCHOOL OF POSTGRADUATE
STUDIES,
AHMADU BELLO UNIVERSITY, ZARIA
IN PARTIAL FULFILLMENT OF THE REQUIREENTS FOR THE AWARD OF
DEGREE OF MASTER OF SCIENCE IN STATISTICS
DEPARTMENT OF STATISTICS,
FACULTY OF PHYSICAL SCIENCES
AHMADU BELLO UNIVERSITY,
ZARIA, NIGERIA
Keywords
MODELING BRONCHO-PNEUMONIA STATUS,, INFANTS USING DISCRIMINANT,, LOGISIC REGRESSION ANALYSES