TechDiva001/ghana-road-accident-stats

A data science project analyzing road accidents in Ghana using statistical methods, visualizations, and trend analysis to uncover insights # Road Accident Analysis in Ghana ## 📌 Project Overview This project analyzes road accident data in Ghana using statistical and mathematical foundations, rather than machine learning models. The goal is to understand patterns, variability, and relationships in road accidents and fatalities across time, regions, and urban–rural classifications, and to communicate these insights clearly through statistics and visualizations. This project is intentionally statistics-driven and written to be human-readable, so that both technical and non-technical readers can follow the reasoning step by step. ## 🎯 Problem Context Road accidents remain a major public safety concern in Ghana, affecting lives, infrastructure, and economic productivity. Before building predictive or machine learning systems, it is critical to first answer foundational questions: 1. How do accident counts behave statistically? 2. Are observed differences real or just random variation? 3. Which regions or settings show significantly higher risk? 4. Can we trust the averages we compute from real-world data? *This project focuses on answering these questions using statistics.* ## 🔍 Research Questions The analysis is structured around the following questions: 1. How have road accident cases and fatalities changed over time? 2. How do accident levels differ across regions in Ghana? 3. Are urban areas statistically more dangerous than rural areas? 4. How variable are accident outcomes, and are there extreme patterns? 5. Which statistical measures best summarize road accident data? ## 📊 Mathematical & Statistical Frameworks Used This is a statistics-first project, built on the following foundations: 1. Descriptive statistics (mean, spread, skewness) 2. Probability distributions (Poisson behavior of accident counts) 3. Central Limit Theorem 4. Inferential statistics (T-tests) 5. ANOVA (comparison across multiple regions) 6. Correlation and covariance analysis 7. Rate normalization (per 100,000 popula …