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Regression Analysis01:11

Regression Analysis

5.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
5.6K
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

1.2K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
1.2K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Correlation and Regression00:53

Correlation and Regression

1.2K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Related Experiment Video

Updated: Jun 3, 2025

Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe
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Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe

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Application of Machine Learning to Predict CO2 Emissions in Light-Duty Vehicles.

Jeffrey Udoh1, Joan Lu1, Qiang Xu1

  • 1Department of Computer Science, School of Computing and Engineering, University of Huddersfield, Queensgate, Huddersfield HD1 3DH, UK.

Sensors (Basel, Switzerland)
|January 8, 2025
PubMed
Summary

Machine learning accurately predicts vehicle CO2 emissions using Worldwide Harmonized Light Vehicles Test Procedure data. A Decision Tree model achieved high accuracy, enabling informed decisions to reduce greenhouse gas emissions in transportation.

Keywords:
CO2 emissionmachine learningregression analysissensorstransport

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Area of Science:

  • Environmental Science
  • Transportation Engineering
  • Data Science

Background:

  • Greenhouse gas (GHG) emissions from transportation contribute significantly to climate change.
  • Vehicle emissions testing is crucial for regulatory compliance and environmental protection.
  • The Worldwide Harmonized Light Vehicles Test Procedure (WLTP) is a global standard for measuring light-duty vehicle emissions.

Purpose of the Study:

  • To develop accurate predictive models for CO2 emissions in light-duty vehicles.
  • To leverage machine learning for enhanced vehicle emissions analysis.
  • To support informed decision-making for reducing transportation-related GHG emissions.

Main Methods:

  • Utilized vehicle emissions data collected by the UK's Vehicle Certification Agency (VCA).
  • Trained and evaluated six regression machine learning models to predict CO2 emissions.
  • Implemented a Decision Tree Regression model for its superior accuracy.

Main Results:

  • The Decision Tree Regression model demonstrated the highest accuracy with a Mean Absolute Error (MAE) of 2.20 and a Mean Absolute Percentage Error (MAPE) of 1.69%.
  • A web application was developed for real-time CO2 emission estimations.
  • The model effectively predicts CO2 emissions based on WLTP data.

Conclusions:

  • Machine learning and AI approaches are effective tools for promoting sustainability in the transportation sector.
  • Accurate CO2 emission prediction facilitates targeted strategies for GHG reduction.
  • The developed application empowers users to make informed choices for environmental benefit.