Investigating the Relationship between Fuel Consumption and Fuel Properties: A Regression Analysis

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© 2024 by IJCTT Journal
Volume-72 Issue-11
Year of Publication : 2024
Authors : Asish Pradhan
DOI :  10.14445/22312803/IJCTT-V72I11P106

How to Cite?

Asish Pradhan , "Investigating the Relationship between Fuel Consumption and Fuel Properties: A Regression Analysis," International Journal of Computer Trends and Technology, vol. 72, no. 11, pp. 39-62, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I11P106

Abstract
This study investigates the relationship between fuel consumption and fuel properties, including vehicle type, cetane number, density, viscosity, initial boiling point, final boiling point, and flash point. A linear regression analysis was conducted to identify the most significant predictors of fuel consumption. The results show that vehicle type, cetane number, and initial boiling point are the most significant predictors of fuel consumption. The study also found that the interaction between vehicle type and initial boiling point significantly impacts fuel consumption. This study's findings can inform the development of more efficient and environmentally friendly vehicles.

Keywords
Fuel consumption, Exploratory data analysis, Cross Validation, Linear regression, Multivariate Analysis, Regression Analysis.

Reference

[1] Roger Westerholm, and Hang Li, “A Multivariate Statistical Analysis of Fuel-Related Polycyclic Aromatic Hydrocarbon Emissions from Heavy-Duty Diesel Vehicles.” Environmental Science & Technology, vol. 28, no. 5, pp. 965-972, 1994.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Saurabh Kumar, and Avinash Sinha, “Predicting Used Car Prices with Regression Techniques,” International Journal of Computer Trends and Technology, vol. 72, no. 6, pp. 132-141, 2024.
[CrossRef] [Publisher Link]
[3] Trishit Banerjee, “Forecasting Apple Inc. Stock Prices Using S&P500– An OLS Regression Approach with Structural Break,” 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), Kolkata, India, pp. 306-310, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Yanming Yang, “Prediction and Analysis of Aero-Material Consumption based on Multivariate Linear Regression Model,” 2018 IEEE 3rd International Conference on Clouds Computing and Big Data Analysis (ICCCBDA), Chengdu, China, pp. 628-632, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Renyan Jiang, Pei Li, and Kunpeng Zhang, “Quantile-Quantile Plot of Folded-Normal Distribution and its Applications in Reliability and Quality Modeling,” 2024 10th International Symposium on System Security, Safety, and Reliability (ISSSR), Xiamen, China, pp. 44 50, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Douglas C. Montgomery, Elizabeth A. Peck, and G. Geoffrey Vining, Introduction to Linear Regression Analysis, John Wiley & Sons, United States, pp. 1-672, 2015.
[Google Scholar] [Publisher Link]
[7] Michael H. Kutner, Applied Linear Statistical Models, 5th ed., McGraw-Hill, pp.1-396, 2005.
[Google Scholar] [Publisher Link]
[8] Ethm Alpaydin, Introduction to Machine Learning, 4th ed., MIT Press, pp. 1-712, 2020.
[Google Scholar] [Publisher Link]
[9] Eric Matthes, Python Crash Course, A Hands-On, Project-Based Introduction to Programming, 2nd ed., No Starch Press, pp. 1-544, 2019.
[Google Scholar] [Publisher Link]
[10] Lynette Cheah et al., Factor of Two: Halving the Fuel Consumption of New U.S. Automobiles by 2035, Reducing Climate Impacts in the Transportation Sector, pp. 49-71, 2008.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Jim Frost, Model Specification: Choosing the Best Regression Model, Statistics by Jim. [Online]. Available: https://statisticsbyjim.com/regression/model-specification-variable-selection/
[12] Sara Stoudt, The Origins of Ordinary Least Squares Assumptions, Feature Column, 2022. [Online]. Available: https://mathvoices.ams.org/featurecolumn/2022/03/01/ordinary-least-squares/