This project focuses on automating the analysis and reporting of bibliometric data, specifically targeting the annual production of academic articles. The primary goal is to understand trends, anomalies, and patterns in bibliometric data through a combination of statistical modeling and exploratory data analysis.

Developed a linear regression model to forecast case shipments considering various predictor variables such as time trends (month), seasonality (seasonal index), and promotions. Conducted Durbin Watson test and generated a forecast and prediction interval.