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.
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.
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