↓Skip to main content

Mini Project 1: Average Income vs Air Quality

Table of Contents

Introduction #

The purpose of this study is to evaluate the correlation between air quality and average income across the majority of United States counties. The parameter of concern is all such counties in the United States. Our null hypothesis states that there is no correlation between air quality of an area and that area’s average income.

Data #

We collected data on median AQI (Air Quality Index) as an indicator of air pollution exposure by county and 2023 average income per county in the United States.

Methodology #

A simple linear regression analysis was performed using:

  • Independent variable: 2023 average income per county
  • Dependent variable: Median AQI

We used this regression model to assess:

  1. Correlation: The direction (positive or negative slope) and strength (R² value) of the relationship
  2. Statistical significance: p-value

Scatterplot of Median AQI vs. Average Income per County
The relationship between median AQI and average income for each United States county, with the regression line overlaid.

Discussion #

The regression results showed that R² = 0.007, indicating that less than 1% of the variation in median AQI is explained by income. Additionally, we obtained a p-value of 0.030, suggesting that the relationship is statistically significant at the 5% level, despite the very weak explanatory power.

Thus, we are able to reject the null hypothesis that there is no correlation between the two variables and accept the alternative hypothesis that there is a correlation between income and the air quality of a particular region.

The regression line has a slightly positive slope, meaning that there is a very weak positive correlation between income and AQI. In other words, counties with higher average incomes tended to have slightly higher AQI values in this dataset compared to counties with lower average incomes.

This result contradicts general environmental justice findings, which often observe that lower-income areas have worse air quality. However, the extremely low R² suggests the correlation is not practically significant, and other confounding factors such as urban density, traffic, and industrial zoning likely drive air quality outcomes more than income alone.