Undergraduate Honors Thesis Projects

Date of Award

Spring 2023

Document Type

Honors Paper

Degree Name

Business Analytics-BS

Department

Business, Accounting, & Economics

Advisor

Dr. Michael Levin

First Committee Member

Dr. Kyriacos Aristotelous

Second Committee Member

Dr. Michelle Acker

Keywords

K-means Cluster, Discriminant Analysis, Mass Transportation, Analysis of Variance, Ordinary Least Squares (OLS) Regression, Federal Funding, Sustainability

Subject Categories

Business Analytics | Environmental Policy | Marketing | Public Policy | Transportation

Abstract

This paper explores the role of efficiency, and effectiveness, measured in relation to the differentiation of public transportation agencies operating in the United States with a bus mode. Data is gathered from the National Transit Database (NTD) from 2015-2019, at the agency level. A k-means clustering approach is used to determine how these ratios segment agencies in the market. After agencies are classified into three groups, a discriminant analysis is done to affirm the distinctiveness of the solution. Next, an Ordinary Least Squares (OLS) regression model is run each year to investigate whether or not the ratios used to form a cluster solution predict federal funding amounts received by each agency. I then present the results of my analysis before concluding with suggestions for public policy makers and provide directions for future research. Hence, the overarching purpose of this research project is to develop a cluster solution that reflects a firm’s efficiency and effectiveness and then modeling its positional advantage to determine if the firm is achieving superior financial performance as measured by federal funds.

Licensing Permission

Copyright, all rights reserved. Fair Use

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Acknowledgement 2

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