This project analyzes lung cancer risk factors using R programming. It explores the relationships between psychosocial, clinical, demographic, and environmental variables through data visualization, correlation analysis, and logistic regression modeling to identify key predictors of lung cancer.

This project presents a comprehensive time series analysis of monthly measles cases reported in Jere Local Government Area, Borno State, Nigeria, from 2016 to 2022. The analysis involves data preprocessing, visualization, stationarity testing using the Augmented Dickey–Fuller (ADF) test, and model identification through ACF and PACF plots. An ARIMA model was fitted to the transformed (square-rooted) series to forecast measles incidence for future periods. The results include decomposition of the series into trend, seasonal, and random components, along with forecast plots and confidence intervals (80% and 95%). The study offers valuable insights into the temporal patterns of measles transmission, supporting evidence-based public health planning for disease control and prevention in the region.

Time Series Analysis of Measles in Jere LGA, Borno State, Nigeria Measles remains a major public health concern in many parts of Nigeria, especially in conflict-affected areas. Jere Local Government Area (LGA) in Borno State has experienced recurrent outbreaks, often linked to low vaccination coverage, population displacement, and challenges in health service delivery.