#23: Understanding Income Inequality Through Statistical Modeling — Presentation Detail — Celebration of Scholars

Instructions

Student presentations must have a faculty sponsor.

Abstracts must include a title and a description of the research, scholarship, or creative work. The description should be 150-225 words in length and constructed in a format or style appropriate for the presenter’s discipline.

The following points should be addressed within the selected format or style for the abstract:

A clear statement of the problem or question you pursued, or the scholarly goal or creative theme achieved in your work.
A brief comment about the significance or uniqueness of the work.
A clear description of the methods used to achieve the purpose or goals for the work.
A statement of the conclusions, results, outcomes, or recommendations, or if the work is still in progress, the results you expect to report at the event.

Presenter photographs should be head and shoulder shots comparable to passport photos.

Additional Information

More information is available at carthage.edu/celebration-scholars/. The following are members of the Research, Scholarship, and Creativity Committee who are eager to listen to ideas and answer questions:

Jun Wang
Kim Instenes
John Kirk
Nora Nickels
Andrew Pustina
James Ripley

#23: Understanding Income Inequality Through Statistical Modeling

Name: Elizabeth Rahmel
Major: Mathematics
Hometown: Hartland, Wisconsin
Faculty Sponsor:
Other Sponsors: Kateryna Sylaska, Jeffrey Thomas
Type of research: Senior thesis

Abstract

In this thesis, we use statistical modeling to help us understand and analyze data related to income inequality. Data can act as one of the most powerful tools not only for teaching important theoretical math concepts but also for highlighting inequity and injustice. For this project, we curated data sets of pedagogical merit, focusing specifically on U.S. Census data related to income inequality and various demographic variables like race, ethnicity, gender, and age. We gathered and cleaned local, regional, and national data and formatted it for clarity and ease of analysis. We investigated patterns, trends, and relationships in this data using techniques like: regression analysis, chi-square goodness-of-fit, unequal variance t-test, and time series analysis. The results of these statistical tests help us to understand the implications of the data and how it relates to issues of diversity, equity, and inclusion, especially when we consider our nation’s current income inequality crisis. Thinking expansively, our research team sees the value of implementing this data into an undergraduate mathematics course. Furthermore, using this data in a general interdisciplinary context could be beneficial for all undergraduate students, no matter their degree-seeking focus. Exposing Carthage students to explicit examples of social injustice highlights the need for change within the Carthage community, nationally, and globally.

Poster file