Regression analysis is a fundamental statistical technique used for modelling relationships between multiple variables, playing a significant role in predictive analytics and artificial intelligence. It helps in evaluating dependencies between a dependent variable and one or more independent variables, making it essential for forecasting outcomes. This study focuses on the application of regression models to analyse vehicle dynamics. By utilising data such as traffic velocity, road gradient, and actual speed, we aim to predict a vehicle’s velocity profile. Various regression techniques—including linear regression, multivariate linear regression, and nonlinear regression—are examined to determine their effectiveness in data processing. The core objective of this research is to develop reusable functions for each model, eliminating dependence on predefined programming functions, while enabling effective visualisation of data for optimal model selection. One advantage of a regression model over factor or cluster analysis is that the regression model can be used to obtain an estimate of the actual amount of change in a dependent variable that occurs as a result of a change in an independent variable. This study only focuses on simple linear regression; future work should focus on the use of multiple regression to capture more complex relationships.
Author (s) Details
Sandhya Tatekalva
Department of Computer Science, S.V. University, Tirupati, India.
Please see the book here:- https://doi.org/10.9734/bpi/erpra/v8/5675
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