In this study we compared the performance of Ordinary Least Squares Regression (OLSR) and the Artificial Neural Network (ANN) in the presence of multicollinearity using two datasets – a real life insurance data and a simulated data – to know which of the methods, models a highly correlated dataset better using the Root Mean Square Error (RMSE) as the performance measure. The ANN performed better than the OLSR model for all the different ANN models except the models with nine and ten nodes in the hidden layer for the real life data. The network with four hidden nodes was the best model. For the simulated data, the ANN model with two hidden nodes gave us the least RMSE when compared to the OLSR model and the other ANN models in the testing set. The network with two hidden nodes modelled the data very well. In the presence of multicollinearity, ANN model achieves a better fit and forecast than the OLSR.

Having lectured in some universities and polytechnics in Nigeria, the researchers observed problems in course allocations. There are no lay-down techniques on how courses should be allocated with respect to the minimum and maximum credit a lecturer should carry in a semester. Many lecturers were overloaded while others were under-loaded. For this reason, dynamic programming model was developed for allocating courses among lecturers in the Nigerian universities using the Department of Statistics, Federal University of Technology Owerri, as a case study. From our analysis, we observed that among all the optimal allocations discovered in the study, the best optimal allocation policy was achieved at the point (1, 2, 1, 2). Allocation of courses in this order will yield an optimal credit hour of 12 per lecturer per semester.

This study investigated the Effect of Levels of Education on the Choice of Medical Treatment Options for three illnesses (Malaria, Mental Disorder and HIV/AIDS) in Nigeria. The study was carried out in ten randomly selected Local Government Areas (L. G. As) in Imo State using a stratified random sample of 500 individuals selected from a population of 194,932 and the data was collected using questionnaires. The Multinomial Logistic Regression Model was adopted in the analysis of the data. The result of the analysis showed that there was a significant association between Educational Level and choice of treatment of Malaria, Mental Disorder and HIV/AIDS. It was further discovered that it is only the “WAEC/GCE” level of education that is significant in the Choice of Treatment of Mental Disorder. It is therefore recommended that government should beam its searchlight on this educational level to find out the cause(s) of their Mental Disorder.

Water scarcity is the major problem confronting both urban and rural dwellers in Enugu State. This scarcity emanated from indiscriminate pipe failure, lack of adequate maintenance, uncertainty on the time of repair or replacement of pipes etc. There is no systematic approach to determining replacement or repair time of the pipes. Hence, the rule of thumb is used in making such a vital decision. The population is increasing, houses are built but the network is not expanded and the existing ones that were installed for no less than two to three decades ago are not maintained. These compounded the problem of scarcity of water in the state. Replacement or repair of water pipes when they are seen spilling water cannot solve this lingering problem. The solution can be achieved by developing an adequate predictive model for water pipe replacement. Hence, this research is aimed at providing a solution to this problem of water scarcity by suggesting a policy that will be used for better planning. The interests in this paper were to obtain a water pipe failure model, the intensity function λ(t) [failure rate], the reliability R(t) and the optimal time of replacement and they were achieved. It was observed that the failure rate of the pipes increases with time while their reliability deteriorates with time. Hence, the Optimal replacement policy is that each pipe should be replaced after 4 th break when the reliability = 0.0011.

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