Analyzing CDC data to model relationships between health factors proved an engaging project. Performing linear regression with variables like obesity, inactivity, and diabetes rates across counties allowed for quantifying predictive correlations. It was fascinating to work with a real-world public health dataset, rather than an abstract simulation. Seeing first-hand how increases in obesity and sedentary lifestyles related to rises in diabetes prevalence brought the statistics to life. The ability to tangibly demonstrate how targeting issues like obesity can influence conditions like diabetes made the exercise impactful. Working hands-on with the rich, real-world data source of CDC records fundamentally enhanced the interest and meaningfulness of applying linear regression techniques. Overall, the project provided a practical yet stimulating opportunity to synthesize statistical methodology to address today’s public health challenges.Project1_CDC