Applied Statistics: Socioeconomic & Market Analysis

Descriptive Statistics Financial Modeling Data Visualization

Overview

This comprehensive statistical analysis project bridges the gap between labor economics, public health, and financial markets. Utilizing raw datasets like the U.S. Current Population Survey (CPS) and Dow Jones Industrial Average metrics, the study extracts high-value business insights through advanced descriptive statistics, data dispersion analysis, and rigorous outlier detection.

Methodology

Socioeconomic Inequality Analysis: Analyzed hourly wage distributions for over 4,700 U.S. workers using percentiles and quintiles. Developed data visualizations to uncover income representation discrepancies across union status and demographic groups.

Financial Market Volatility: Extracted 84 months of time-series data for Dow Jones weekly returns. Executed advanced outlier detection using Interquartile Range (IQR), Left/Right Inner Fences (LIF/RIF), and Outer Fences (LOF/ROF) to isolate extreme market fluctuations.

Descriptive Statistics & Exploratory Data Analysis: Constructed presentation-ready statistical tables to summarize complex, county-level variables including vaccination rates, median income, and election outcomes. Leveraged z-scores, calculated data skewness, and designed box plots to establish foundational exploratory data analysis (EDA).