Department of Statistics
http://ugspace.ug.edu.gh/handle/123456789/4862
2018-12-14T04:18:01ZFactors that Influence Promotion of University of Ghana Lecturers : A Survival Analysis Approach
http://ugspace.ug.edu.gh/handle/123456789/26348
Factors that Influence Promotion of University of Ghana Lecturers : A Survival Analysis Approach
Yeboah, E.K.
Survival analysis has been invaluable in studies involving time to an event. The methods encompassing the idea of Survival Analysis appears to be commended by most researchers particularly in the medicinal, engineering, agricultural and actuarial fields.
This paper applied survival methods to promotion data of lecturers in the University of Ghana. The main objective was to identify the factors prominent in facilitating an early promotion of University of Ghana lecturers. The proportionality assumptions were assessed and some model diagnostics were made with some graphical presentations. The proportionality assumption was satisfied and the models were compared by their AIC values. The Weibull Proportional Hazard model recorded the lowest AIC value making it a better model to fit the data set.
The average survival time for promotion is 78.847 months with a standard deviation of 3.72 and a 95% CI [71.497, 86.198]. This means that it take an average of 6.57 years for a lecturer to earn their first promotion.
This study considers the variables age, number of UG committee membership, number of international conferences attended, number of technical reports written, number of working papers, number of books published, qualification before entry and origin of Master’s certificate as significant at 5% significance level, in influencing the time until a lecturer gets promoted at the University of Ghana.
The variables, number of children, marital status, gender, national committee membership, and college of affiliation were not significant at 5% significance level.
Evidently, young lecturers are more likely to be promoted as compared to aged lecturers. But the higher the number of UG committee membership, International conferences attended, books published and technical reports written, the more likely it is to get a promotion.
MPhil.
2017-07-01T00:00:00ZStatistical Analysis of Factors Affecting Maternal Mortality in Ghana
http://ugspace.ug.edu.gh/handle/123456789/26024
Statistical Analysis of Factors Affecting Maternal Mortality in Ghana
Acquah-Mantey , J.
October 25, 2018
This study evaluated the relationship between possible medical and social
factors affecting maternal deaths. A conceptual framework was developed to
help identify, classify, model and resolve these factors (by implementing the
recommendations). A generalized linear model was fixed using the ‘R’ statistical
analysis software. Three data sets (2016 and 2013 data sets on maternal mortality,
and a data set on the causes of maternal deaths) were used in the analysis,
which showed wide regional variations in maternal mortality ratios and maternal
mortality rates. For the data set on causes of maternal deaths, Hypertensive
disorder was the leading determining factor of maternal deaths, followed by
Hemorrhage. Eastern Region was leading in Maternal Mortality Ratio (461
maternal deaths per 100000 live births) in 2016, followed by the Volta region
with Maternal Mortality Ratio (MMR) of approximately 445 per 100000 live
births. The Upper West Region had the least MMR (179 per 100000 live births)
in 2016. Antenatal care and Postnatal care attendance have been found to be
significant contributing factors of maternal mortality. Conclusively, the Eastern Region showed the highest risk of death per woman in 2016. The National
Maternal Mortality Ratio for 2016 was approximately 306 per 100000 live births,
which was an improvement on 2013 MMR, approximately 379 maternal deaths
per 100000 live births. It is therefore recommended that particular attention be
given to Antenatal care and Postnatal care in the quest to attain the Sustainable
Development Goal target of 70 maternal deaths per 100000 live births by 2030.
MPhil.
2018-10-01T00:00:00ZStatistical Assessment of Imputation Algorithms for Estimation of Missing
http://ugspace.ug.edu.gh/handle/123456789/25984
Statistical Assessment of Imputation Algorithms for Estimation of Missing
Gyimah, O.
The validity and quality of data analysis relies largely on the data accuracy
and completeness of the data matrix. Missing values are unavoidable statistical
research problems in almost every research study and if not handled properly,
may provide negative and bias conclusion. This study purposely sought to
investigate the efficacy and accuracy of the convergence of five imputation
algorithms: expectation maximization (EM), multiple imputation by chained
equation (MICE), k nearest neighbor (KNN), mean substitution (MS) and
regression substitution (RS) in estimating and replacing missing values in crosssectional
world population data sheet using MCAR and MAR assumptions. This
thesis used Little’s Test to verify whether a given data matrix with missing values
is MCAR or MAR. Multiple linear regression analysis model was used to run the
complete data of the world population data sheet, and thereafter, missing values
in the complete data sets were artificially introduced at 5%, 10%, 20%, 30%
and 40% under two missing data mechanisms (MCAR & MAR). The imputation
algorithms used for evaluating missing data problems were assessed and compared
using average coefficient difference (ACD) of multiple linear regression (MLR)
model, mean absolute difference (MAD) and the coefficient of determination (R2).
The study suggested that, when data on cross-sectional World Population Data
Sheet is missing completely at random (MCAR) and normally distributed, the
regression substitution is the best approach. The MICE algorithm was found to be
comparatively the best method for replacing missingness under MAR assumption.
Since this thesis is mainly concentrated on missing data imputation in a crosssectional
dataset, it is recommended that in future categorical and longitudinal
studies should be considered.
MPhil.
2018-10-01T00:00:00ZPredictive Models for Identifying Critical Units for Inspection in a Regulatory Body
http://ugspace.ug.edu.gh/handle/123456789/25889
Predictive Models for Identifying Critical Units for Inspection in a Regulatory Body
Djokoto, F.D.
Routine inspections are conducted at various food establishments that yield large
data sets, which capture attributes useful for data mining algorithms to predict
critical violations. Critical violations related to food establishments cause serious
public health problems, which may happen as result of unhygienic environment,
leading to food contamination. This study presents predictive models to detect
critical violations in food establishments by employing Logistic Regression (LR),
Support Vector Machine (SVM) and K-Nearest Neighbour (KNN). A database
from the City of Chicago data portal that contained food inspections from 2011
to 2014 was used. In the preliminary analysis, Principal Component Analysis
was utilised and ten (10) relatively relevant variables, that are independent of
each other, were selected from twenty-eight (28) to be used as inputs in the
models. In the family of the SVM, several kernels were used and the optimal
model selected was based on the performance measures Receiver Operating
Characteristic (ROC), sensitivity and specificity. The optimal model of the KNN was also selected based on the same performance measures. The out of sample
classification accuracies for the LR, SVM and KNN classifiers were 92:7872%,
92:7873% and 92:6650% respectively. The performances of the models showed no
large marginal differences in classification accuracies; however, the SVM model
appears to provide a better discrimination ability as compared to the LR and
KNN.
MPhil.
2018-11-01T00:00:00Z