Least-Squares Regression Lines

Definition

Least-squares regression lines are used to model the relationship between two variables by minimizing the sum of the squared differences between observed data points and predicted values. They provide an equation that represents the best-fit line through the data.

Analogy

Imagine you have a scatterplot of your test scores and study hours. The least-squares regression line is like drawing a line through the middle of all your points, trying to get as close as possible to each point without going too far away. It represents the overall trend in how study hours relate to test scores.

Related terms

Correlation Coefficient: A statistical measure that quantifies the strength and direction of the linear relationship between two variables.

Residuals: The differences between observed data points and predicted values on a regression line.

Extrapolation: Estimating or predicting values outside the range of observed data based on a regression model.