Linear Regression Fits Straight Line to Data with Machine Learning
DOI:
https://doi.org/10.15680/IJCTECE.2020.0301004Keywords:
Linear Regression, Statistical Modeling, Predictive Analysis, Ordinary Least Squares, Model EvaluationAbstract
Linear regression is a foundational statistical method used to model the relationship between a dependent variable and one or more independent variables. By fitting a straight line to the observed data, it enables predictions and insights into the strength and nature of these relationships. This paper explores the principles of linear regression, its applications across various fields, and the methodologies employed to ensure accurate and reliable models. Through a comprehensive literature review, we examine the evolution of linear regression techniques and their practical implementations. The methodology section delves into the steps involved in performing linear regression analysis, including data preparation, model fitting, and evaluation. Finally, the paper discusses the conclusions drawn from the analysis, highlighting the significance of linear regression in statistical modeling and its continued relevance in contemporary research.
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