Distinction on Carbon Dioxide Emissions
Date of Award
Spring 6-14-2025
Document Type
Undergraduate Honors Thesis
Degree Name
B.S. in Business Administration, Business Information and Analytics
Organizational Unit
Daniels College of Business, Business Information and Analytics
First Advisor
Anthony Hayter
Copyright Statement / License for Reuse
All Rights Reserved.
Keywords
Carbon dioxide emissions, Automobiles
Abstract
The purpose of this paper is to find variables to predict carbon dioxide emissions on a state level using simple and multiple linear regression models. After thorough analysis of models, a simple linear regression model was the best model to predict carbon dioxide emissions. This model used total energy consumed as an independent variable. Simple linear models using population and total number of cars were also used. The first multiple linear regression was statistically insignificant and while the second was significant, it did not make logical sense.
Copyright Date
4-11-2025
Publication Statement
Copyright is held by the author. Permanently suppressed.
Rights Holder
Kati Cooper
Provenance
Received from author
File Format
application/pdf
Language
English (eng)
Extent
20 pgs
File Size
961 KB
Recommended Citation
Cooper, Kati, "Distinction on Carbon Dioxide Emissions" (2025). Business Information and Analytics: Undergraduate Distinction Theses. 1.
https://digitalcommons.du.edu/business_info_udtheses/1