Abstract
Linear regression is one of the most widely used predictive models in statistics and machine learning. This paper aims to comprehensively discuss the theoretical basis, mathematical principle and application of linear regression algorithm in various fields. Firstly, this paper introduces the research background and significance of linear regression, and summarizes its important role in modern data analysis. Then, the paper elaborates the basic theory of linear regression, including its definition, assumptions, parameter estimation methods and model diagnosis and selection. In addition, different types of linear regression are classified and discussed, such as simple linear regression, multiple linear regression and logistic regression, and the specific application scenarios of each type are analyzed.