This study examined the contribution of the various sectors to the Gross Domestic Product (GDP) of Nigeria and also developed a model for forecasting the Gross Domestic Product of Nigeria within a time frame of 33 years. Data emanating from Central Bank of Nigeria was used and analyzed using regression analysis and time series analysis. The regression results shows that the three sectors; Agricultural sector, Industrial sector and Service sector has a positive relationship and only the Industrial sector and the Service sector contribute significantly with a coefficient of 0.286, 0.631 while the contribution of the Agricultural sector is not significant with a coefficient of -0.039 implying that service sector contributes the most with 631 million naira followed by the industrial sector with 286 million naira while the agricultural sector does not contribute significantly since it decreases by 39 million naira. The contribution of the agricultural sector is not significant. A time domain model (fundamental approach) which makes use of Box Jenkins approach was applied to a developing country like Nigeria to forecast Gross domestic Product for the period 1987 to 2019 using ARIMA model. The result reveals that there is an upward trend and the First difference of the series was stationary, meaning that the series was I (1). Using expert modeler, the best model that explains the series was found to be ARIMA (1, 1, 0). The diagnosis on such model was confirmed, the error was white noise, presence of no serial correlation and a forecast for period of 10 years terms was made which indicates that GDP will continue to appreciate with these forecasted time period.

Road traffic crashes in Edo State Nigeria was considered in this paper, secondary data were mainly used, and was sourced from the office of the Federal Road Safety Corps; Policy, Research and Statistics Department RSHQ Abuja. In order to determine how well a set of independent variables (Mechanical Fault, Reckless Driving and Over-Loading) can predict road accidents in the state of Edo, Regression Analysis was used, indicating the best road accident predictor in the state. The results show that the assumptions of Normality, Homoscedasticity, Freedom, Linearity, Multicollinearity and Outliers are not violated. The three predictors significantly predicted road accident {F(3,9) = 14.132, p-value =0.001 < 0.005}, R2adjusted= 0.767; 76.7% , of the total variance in road accident cases was explained by the model, Mechanical Fault made the strongest unique significant contribution to explaining road accident cases when the variance explained by all other variables in the model is controlled for (βeta value = 0.841, p-value = 0.001), Reckless driving made less of a contribution (βeta value =0.591, p-value = 0.004), while overloading did not make a significant contribution to the prediction of road accident when the variance explained by other variables in the model is controlled for (βeta value = 0.173, p-value = 0.228). The developed prediction model is; Number of Road Accident = 6.407 + 1.300Reckless Driving + 1.959Mechanical Fault + 0.733Overloading.

This paper attempts to model the exchange rate between Naira and U.S Dollar using Autoregressive Moving Average Models (ARIMA). From the examined models, ARIMA (1, 0, 1) out-performed other models based on the lowest AIC, BIC and HQC values. The forecasted result suggests that Naira to Dollar exchange rate would slightly be on the increase in upcoming weeks.