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EPI 553: Lab 03 CORRELATION – Frimpong
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.
EPI 553: Lab 02 ANOVA – Frimpong
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.
EPI 553: Lab 01 NHANES – Frimpong
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.
EPI 553: Week 1 Setup Checklist - Frimpong
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.