线性拟合 英文介绍

Linear regression, also known as linear fitting or linear interpolation, is a statistical method used to model the relationship between two variables by fitting a linear equation to the observed data. The goal of linear regression is to find the best-fitting line through the data, which can be used to predict future values or to understand the relationship between the variables.

Linear regression involves minimizing the sum of squared differences between the observed values and the predicted values, which is known as the residual sum of squares (RSS). The line that minimizes the RSS is called the least squares regression line, and it represents the best linear approximation of the relationship between the variables.

Linear regression can be used for both simple and multiple regression problems. In simple linear regression, there is only one independent variable, while in multiple regression, there are multiple independent variables that are used to predict the dependent variable.

Linear regression is widely used in various fields such as economics, engineering, and social sciences. It is a powerful tool for understanding the relationships between variables and making predictions about future values.