This document presents an applied statistical modeling lab using data from the 2023 Behavioral Risk Factor Surveillance System (BRFSS), a nationally representative U.S. health survey. The analysis demonstrates key regression modeling concepts in epidemiology including simple and multiple logistic regression, dummy variable coding, interaction testing, model diagnostics, and model comparison, all applied to predicting hypertension among U.S. adults. Key topics covered include building and interpreting logistic regression models, controlling for confounding, testing for effect modification using likelihood ratio tests, checking model assumptions with variance inflation factors and Cook's Distance, and selecting the best fitting model using AIC and BIC. Results show that age and obesity are the strongest independent predictors of hypertension in this sample, with no significant Age × BMI interaction detected. This lab was completed as part of EPID 553 Statistical Inference and is intended as a practical introduction to regression modeling for public health and epidemiology students.

This analysis uses NHANES data to examine bone mineral density (BMD) through one-way ANOVA and correlation methods. Part 1 tests whether BMD differs across five ethnic groups, including assumption checks, effect size calculation, and post-hoc comparisons. Results show significant ethnic differences (p < 0.001), with Non-Hispanic Black individuals having higher BMD. Part 2 explores correlations between BMD and potential predictors (age, BMI, calcium, vitamin D, physical activity) using transformed variables. BMI shows the strongest association with BMD (r = 0.425), while age shows a negative relationship (r = -0.232). Part 3 reflects on methodological choices, assumption challenges, and R programming skills developed during the analysis. Methods: R with tidyverse, ggplot2, broom, and car packages.

This analysis examined the linear association between height and weight among U.S. adults using Pearson correlation. Results indicated a moderate positive relationship (r = 0.451), suggesting that taller individuals tend to weigh more. The association was statistically significant (t = 42.618, p < 0.001), and the 95% confidence interval (0.432, 0.469) excluded zero, providing strong evidence against the null hypothesis of no correlation. The coefficient of determination (r² = 0.203) indicates that approximately 20.3% of the variability in one measure is explained by the other, reflecting a meaningful but not complete linear relationship.

A one-way analysis of variance (ANOVA) was used to compare mean days of poor mental health across three physical-activity groups defined from survey responses: individuals reporting no regular activity, those engaging in moderate-intensity activity, and those engaging in vigorous-intensity activity. The analysis tested whether between-group differences in means exceeded within-group variability using the F-statistic, under assumptions of independent observations, approximately normally distributed residuals, and homogeneity of variances.

I conducted an exploratory data analysis using NHANES data to examine how the prevalence of hypertension varies across education levels. I cleaned and grouped the data in R, generated summary statistics for systolic blood pressure and hypertension prevalence and interpreted the observed social gradient in cardiovascular risk. The report highlights how education, a key social determinant of health, is associated with meaningful differences in population health outcomes and discusses implications for public health practice and policy.

This document presents introductory R programming exercises for EPI 553 (Statistical Inference), including data manipulation, basic visualization, and reproducible reporting using R Markdown. It demonstrates reproducible analysis practices, including data import, summary statistics, graphical exploration, and rendered HTML output for coursework submission.