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.

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This study modeled the US Dollar and Nigerian Naira exchange rates during COVID-19 pandemic period using a classical statistical method – Autoregressive Integrated Moving Average (ARIMA) – and two machine learning methods – Artificial Neural Network (ANN) and Random Forest (RF). The data were divided into two sets namely: the training set and the test set. The training set was used to obtain the parameters of the model, and the performance of the estimated model was validated on the test set that served as new data. Though the ARIMA and random forest performed slightly better than the neural network in the training set, their performance in the test set was poor. The neural network with 5 nodes in the input layer, 5 nodes in the hidden layer and 1 node in the output layer (ANN (5,5,1)) performed better on the new data set (test set) and is chosen as the best model to forecast for future USD to NGN exchange rate. The information from the high-performance model (ANN (5, 5, 1)) for modeling the USD to NGN exchange rate will assist econometric trading of the currencies and offer both speculative and precautionary assistance to individuals, households, firms and nations who use the currencies locally and for international trade.

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.

Marriage to first birth interval is used as a measure of the fertility rate of married women and it is known to be influenced by some factors. In this paper, we studied the effect of various socio-demographic and cultural factors on the interval between marriage and first birth in Nigeria using the 2018 Nigerian Demographic and Health Survey data. The analysis was done using the accelerated failure time (AFT) model and the Kaplan-Meier plot. The result showed that on the average, the interval of marriage to first birth is 23.90 months. It means that it takes a Nigerian couple about 2 years (24 months) to give birth to their first child. The results showed that women who married early have longer first birth interval than those that married late. Muslims have longer first birth interval than Christians. Women from North East, North West and South South have longer first birth interval than women in North Central while those from South West have shorter first birth interval than those from North Central. Residence has no statistical significant effect at 0.05 alpha level on the marriage to first birth interval. Women with primary and secondary education have shorter first birth interval than women with no education while the husband's education has no statistical significant effect at 0.05 alpha level on the first birth interval. Women with early first sexual intercourse have shorter first birth interval than those with late first sexual while women that have ever used traditional and modern contraceptive method have shorter first birth interval than women that have not used any contraceptive method. There should be targeted orientation on sexual and reproductive matters to those with higher first birth interval so that desirable conditions would be yielded by controlling these groups. KeywordsAccelerated failure time model • First birth interval • Kaplan-Meier plot • Cox model • Socio-demographic and cultural factors

Early age at first sexual intercourse comes with many negative sexual outcomes namely: having unprotected sex on first sexual intercourse, condom misuse, high rate of sexually transmitted infections (STIs), teenage pregnancy, increased number of sexual partners, etc. In this paper, we considered some socio-demographic and cultural factors and their relationship with age at first sexual intercourse so as to reduce the numerous negative sexual outcomes of early age at first sexual intercourse using the 2018 Nigerian Demographic and Health Survey data. The analysis was made using the Cox proportional hazard model and the Kaplan-Meier plot. The result shows that some respondents started having their first sexual intercourse at the age of 8 years and about 54.4% of the respondents had their first sexual intercourse before age 17 years. The median age of first sexual intercourse is 16 years which implies that about 50% of the respondents had their first sexual intercourse on or before their 16 th birthday. Education, religion, region and residence significantly affects the age of first sexual intercourse while circumcision has no significant effect.

According to World Health Organization (WHO), normal weight of baby at term delivery is (2.5 – 4.2) kilograms. Every child’s birth weight below 2.5 kilograms, regardless of gestational age, is regarded as Low birth weight (LBW). WHO estimates that globally, over 20 million LBW babies are born annually and nearly 95.6% of them in developing countries. Half of all perinatal and 1/3rd of all infant deaths occur in babies with LBW. It is therefore, essential to study some of the factors that causes LBW. Hierarchical Multiple Regression analysis was used to study the effects of mother’s weight, age and height above and beyond mother’s education level in predicting the weight of the child at birth. The results showed that mother’s education level explains about 6.1% of the unexplained variations in the weight of the child at birth in block 1 and mother’s age, weight and height explained about 3.9% above and beyond mother’s education level. This implies that all the variables studied affects the baby’s weight at birth but, the mother’s educational level affects the baby’s weight much more. It was concluded that mother’s education level plays a vital role in predicting the weight of the child at birth because it has a causal effect on the use of prenatal care and improves marriage prospects.

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