本章概览 (Guide)：

• 两个变量能够建立完美的线性关系的情况在心理学的研究情境中是极其罕见的。只能通过统计方法，建立与数据最佳拟合的直线，达到描述和预测的目的。在本章中，我们将利用最小平方法求得最佳拟合直线，并建立起回归方程。
  The situation where two variables can establish a perfect linear relationship is extremely rare in the research context of psychology. Only through statistical methods can we establish a straight line that best fits the data to achieve the purpose of description and prediction. In this chapter, we will use the least squares method to obtain the best fitting straight line and establish a regression equation.

学习要点 (Learning Points)：

1. 了解什么是回归
   Understand what regression is
2. 学会建立回归方程的计算方法
   Learn the calculation method for establishing regression equations
3. 学习考查回归方程准确性的方法
   Methods for learning and testing the accuracy of regression equations
4. 了解如何解释回归方程
   Understand how to interpret regression equations
5. 了解一元线性回归的统计前提
   Understanding the statistical prerequisites of univariate linear regression

目录

• 回归方程 (regression equation)
• 回归的矩阵表达形式 (Regression's Matrix version)
• 最小平方法求回归系数 (Finding regression coefficients using the least squares method)
• 回归估计的标准误 (Standard error of regression estimation)
• 一元线性回归的假设检验 (Hypothesis testing of univariate linear regression)
• 一元线性回归的效应量 (The effect size of univariate linear regression)
• 一元线性回归的统计前提 (Statistical prerequisites for univariate linear regression)

作业

• 2023作业 (Homework2023)
• 2024作业 (Homework2024)