This Quarto-based R project conducts an in-depth exploratory data analysis (EDA) of the 2021 household census dataset, focusing on relationships between income, education, ethnicity, gender, household size, and marital status. Through data cleaning, variable derivation (e.g., Household_Size and Income_Category), and visualizations like line charts, heatmaps, boxplots, and bar plots using ggplot2, the analysis reveals key disparities—such as higher incomes in smaller White households and education barriers in larger minority families. Includes policy recommendations like targeted scholarships and tax credits. Ideal for data science students and policymakers interested in reproducible socioeconomic research.

This project presents a comprehensive exploratory data analysis (EDA) and statistical investigation of a malignant melanoma dataset using R. Key focuses include examining relationships between patient gender, tumor thickness, age, and survival time through visualizations (histograms, boxplots, Q-Q plots), summary statistics, and hypothesis testing (t-tests). The analysis highlights significant gender-based differences in tumor characteristics and survival patterns, with discussions on normality assumptions, limitations, and recommendations for advanced survival modeling techniques like Kaplan-Meier and Cox regression. Built with Quarto/R Markdown for reproducible research—ideal for students and researchers in biostatistics or medical data science.