REGRESSION ANALYSIS

This research investigates the impact of smoking, level of exercise, and weight on health outcomes. The research employs a cross-sectional research design to investigate the associations between smoking, level of exercise, weight, and health outcomes among adults residing in Uyo Local Government Area, Akwa Ibom State, Nigeria. The study population comprises 360 adults, with a sample size of 124 determined using Taro Yamane’s sample size determination formula. Data collection involves a self-administered questionnaire covering demographic information, smoking habits, physical activity levels, weight, height, and health status. Convenience sampling is utilized at various locations within each of the four clans in Uyo LGA, facilitated by trained research assistants. Statistical Package for Social Sciences (SPSS) is employed for data analysis, including regression analysis to determine the relationships between independent variables (smoking, lack of regular exercise, weight) and the dependent variable (health outcome).The analysis reveals significant associations between these lifestyle factors and health outcomes, highlighting the need for targeted interventions to mitigate their adverse effects. Findings indicate a moderate positive correlation between smoking and health outcomes, emphasizing the importance of smoking cessation interventions. Similarly, a significant positive relationship is observed between lack of regular exercise and health outcomes, underscoring the importance of promoting physical activity. Additionally, the analysis reveals a moderate positive correlation between weight and health-related quality of life, emphasizing the need for interventions to promote healthy weight management.

This study investigates the environmental impact of the Ekosodin dumpsite in Benin City, Edo State, on surrounding groundwater quality, specifically addressing the risks of leachate infiltration. The research aim was to evaluate twenty-two physicochemical and microbial parameters across eight sampling locations to determine the spatial extent of contamination and assess the suitability of local water resources for domestic use. By benchmarking these parameters against World Health Organization (WHO) and Nigerian Industrial Standards (NIS), the study provides a comprehensive overview of how inadequate waste management practices threaten the availability of safe potable water for the community. The methodology integrated systematic laboratory analysis with advanced geospatial modeling using ArcGIS 10.8. Groundwater samples were collected from eight borehole locations and analyzed for various physical, chemical, and biological properties, including heavy metals like Lead (Pb) and Cadmium (Cd). A Water Quality Index (WQI) was calculated for each site to classify water quality, while Inverse Distance Weighting (IDW) interpolation was applied to map the spatial distribution of pollutants. Furthermore, a Multiple Linear Regression (MLR) model was developed to quantify the relationship between five key parameters—including Electrical Conductivity (EC) and Iron (Fe)—and the calculated WQI, achieving a high predictive accuracy with an R 2 value of 0.9983. Results revealed a significant degradation gradient, with WQI values ranging from 27.20% to 130.05% (and up to 945.24% in specific computations), indicating that boreholes closest to the dumpsite possess very poor water quality unsuitable for drinking. Spatial analysis confirmed the dumpsite as the primary source of elevated heavy metals and organic contaminants, though quality generally improves as the distance from the waste source increases. The study concludes that leachate from the Ekosodin dumpsite severely impairs groundwater safety, leading to the recommendation that future boreholes be sited at least 400 meters away from disposal areas. These findings emphasize the urgent need for modernized waste management strategies and continuous groundwater monitoring to protect public health and ensure a sustainable water supply.

This study focuses on evaluating the relationship between destructive and nondestructive testing methods for predicting the in-situ compressive strength of concrete. The main aim is to develop a reliable regression-based model capable of estimating concrete strength using non-destructive approaches. The study specifically examined Grade 20 (C20) and Grade 25 (C25) concrete to determine the correlation between rebound hammer results, and conventional compressive strength tests. The experimental procedure involved casting and curing concrete cubes and beams in the laboratory under controlled conditions. Both destructive tests (compressive and flexural strength) and non-destructive tests (rebound hammer) were carried out at curing ages of 7, 14, and 28 days, following BS EN 12390-3:2019, ASTM C39, and BS EN 12504- 2:2012 standards. Rebound hammer readings were taken before compression tests on each specimen to establish a correlation between rebound number and actual strength. The data obtained were analyzed statistically using regression techniques to develop predictive models capable of estimating compressive strength from non-destructive test results. The findings revealed that compressive and flexural strengths increased consistently with curing age for both concrete grades. At 28 days, C20 achieved an average compressive strength of 20.16 N/mm², while C25 reached 25.15 N/mm², aligning with their design targets. Rebound hammer values showed a strong positive correlation with destructive test results, with a prediction accuracy of about ±5%. The study concludes that properly calibrated non-destructive methods, particularly the rebound hammer tests, can effectively predict the in-situ compressive strength of concrete. This approach provides a cost-effective, rapid, and non-invasive means for quality control and structural assessment in modern construction practice.

Background to the Study
National income measures the total value of all goods and services produced in a country in a year. It is the main way to know if an economy is growing or not (World Bank, 2020). For a country like Nigeria, understanding what makes national income rise or fall is very important for planning and improving the lives of its people.