Unemployments Influence on Traffic, Diesel and Housing Burden

Using CalEnviroScreen 4.0 data

Presenter: Madison Rhinehart

Co-Presenter(s):
n/a

Presenter Status: Undergraduate student

Academic Year: 22-23

Semester: Spring

Faculty Mentor: Omayra Ortega

Department: Mathematics

Funding Source/Sponsor: Class Project

President's Strategic Plan Goal: Sustainability and Environmental Inquiry

Abstract:
This is an analysis of the CalEnviroScreen data also known as the CES 4.0 data using the variable Unemployment rate and looking at its influence if any on variables such as Traffic, Diesel PM, and Housing burden. The importance of comparing these variables is to see if higher unemployment rates will lead to less pollution and less housing burden. Using tests such as correlation, linear regression, and anova. Our data ultimately finds no correlation between most of the variables and an interesting opposite correlation that was unexpected for the Unemployment Rate and housing burden.