Least squares criterion
Definition
The least squares criterion is used to find an equation (usually linear) that minimizes the sum of squared differences between observed and predicted values.
Analogy
Imagine you're playing darts and trying to hit a target with multiple rings. The least squares criterion would be like finding an equation for throwing darts that minimizes the total distance between where your darts land and the center of each ring. You want to get as close as possible to the bullseye.
Related terms
Residuals: Residuals are the differences between observed values and predicted values in a regression analysis.
Regression Line: The regression line is the line that best fits a set of data points using the least squares criterion.
Coefficient of Determination (R-squared): R-squared measures how well a regression model fits the data, ranging from 0 to 1.
"Least squares criterion" appears in:
Practice Questions (2)
- Why do we use the least squares criterion in linear regression?
- Why are the residuals squared in the least squares criterion?
Resources
© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.