This analysis examines the individual, behavioral, and socioeconomic factors associated with mentally unhealthy days among 5,000 U.S. adults drawn from the Behavioral Risk Factor Surveillance System (BRFSS) 2020 survey. Using multiple linear regression, we model self-reported mentally unhealthy days as a function of sleep hours, physical health days, age, sex, household income, and exercise status, following a sequential model-building approach to illustrate how confounding adjustment influences coefficient estimates and model fit. Results indicate that poor physical health, shorter sleep duration, female sex, and lower income were each independently associated with more mentally unhealthy days, while older age was associated with fewer. Residual diagnostics, ANOVA decomposition, and marginal effects plots are presented throughout. This report was produced as part of EPI 553: Statistical Inference at the University at Albany, School of Public Health, Spring 2026.

Comparing how much a car weighs to how good its gas mileage is.

Unidad 2

Asunto: Solicitud de contrato adicional N° 1 para la inclusión de nuevos ítems y ampliación del plazo al contrato MA-0023038.

Asunto: Retraso en la Ejecución de trabajos.