This project presents a multiple linear regression analysis of mentally unhealthy days among U.S. adults using the 2023 Behavioral Risk Factor Surveillance System (BRFSS) data (n = 8,000). The analysis examines how physical health, BMI, sex, exercise, age, income, education, and smoking status predict self-reported mentally unhealthy days in the past 30 days. Key components include full model estimation with coefficient interpretation, Type III partial F-tests, chunk tests for income and education, and an interaction analysis exploring whether the association between current smoking and mental health differs by sex. Findings are visualized through adjusted predicted marginal means with 95% confidence intervals. All data processing, recoding, and modeling were conducted in R using tidyverse, broom, gtsummary, car, and ggeffects.

This project consists of using the New York Times Article Search API to examine how the volume and framing of soccer coverage in the New York Times has evolved since the modern Major League Soccer (MLS) expansion era began in 2005.In fact d,API provides rich article-level metadata such as headline, publication date, section name, news desk, word count, and multimedia flags since 1851, making it well suited for detecting decade-long window in editorial attention and story framing by querying the keyword “soccer”