Prediction of Motion Acceleration under Newton's Second Law: A Multivariate Linear Regression Approach

Thawatchai Promrat; Dusadee Khamboonrueang; Jantakarn Sangnoi

doi:10.53848/ssstj.v13i2.1216

Authors

Thawatchai Promrat Phranakhon Si Ayutthaya Rajabhat University
Dusadee Khamboonrueang Nakhon Sawan Rajabhat University https://orcid.org/0009-0001-2694-5876
Jantakarn Sangnoi Nakhon Sawan Rajabhat University

DOI:

https://doi.org/10.53848/ssstj.v13i2.1216

Keywords:

Movement, Multivariate linear regression algorithm, Machine learning

Abstract

One of the most significant lessons in basic physics is Newton’s Second Law of Motion, which teaches the relationship between the acceleration of an object, force, and mass. However, we found that the problem is that some schools do not have enough experimental equipment for students, causing teaching to focus only on theory and the memorization of content. This research aims to predict the relationship between acceleration caused by force and mass using machine learning with a multivariate linear regression algorithm. We have prepared three datasets from laboratory rooms, from image analyses obtained from laboratory rooms with the Tracker program, and from all of those. After that, we will train and compare models for each dataset using linear regression algorithms. The experimental R-squared values are 99% for dataset one, 99% for dataset two, and 97% for dataset three. Therefore, the model provides relatively stable generalization under each individual experimental condition. It can be used as a tool to help predict physics results in situations where real experiments are not feasible due to time, budget, or equipment constraints.

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