Scholarlink

Linear regression is the workhorse of statistical model development due to its simplicity, intuitive appeal, and ubiquitous availability in numerical libraries and toolkits. The model defines a linear parametric relationship between an endogenous (dependent) variable and a group of exogenous (independent) variables, including an optional constant. Parameters defining this relationship are predicted by fitting the data using the linear model. In addition to predicting the parameters, the method also furnishes confidence intervals for those estimates. The model can be written as shown in Equation 2-1. \(\epsilon \) represents the error between actual output, y, and predicted output, \(\hat {y}\) , as shown in Equation 2-2.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Linear Regression

  • 章节

  • 发表日期：

    2025年

查看全文
  • Samit Ahlawat

摘要

Linear regression is the workhorse of statistical model development due to its simplicity, intuitive appeal, and ubiquitous availability in numerical libraries and toolkits. The model defines a linear parametric relationship between an endogenous (dependent) variable and a group of exogenous (independent) variables, including an optional constant. Parameters defining this relationship are predicted by fitting the data using the linear model. In addition to predicting the parameters, the method also furnishes confidence intervals for those estimates. The model can be written as shown in Equation 2-1. \(\epsilon \) represents the error between actual output, y, and predicted output, \(\hat {y}\) , as shown in Equation 2-2.