An analysis of global wellbeing data from 153 countries, exploring the relationships between economy, health, freedom, job satisfaction, and corruption on national happiness scores.

This report presents a guided walkthrough and in-class lab activity from EPI 553 (Statistical Inference), exploring the use of dummy variables in multiple linear regression using the Behavioral Risk Factor Surveillance System (BRFSS) 2020 dataset (n = 5,000). The analysis investigates how general health status and educational attainment are associated with the number of mentally unhealthy days in the past 30 days, after adjusting for age, sex, physical health days, and sleep hours. Topics covered include the failure of naive numeric coding for categorical predictors, construction and interpretation of dummy variables, reference group selection and re-leveling, the dummy variable trap, partial F-tests for overall categorical variable significance, Type III ANOVA, and alternative coding schemes including effect coding and orthogonal polynomial contrasts. All analyses were conducted in R using tidyverse, broom, gtsummary, car, and ggeffects.

A case study to determine if the 2024 U.S. Olympic Men's basketball team was the best players based on the 2023-24 NBA stats.