Least-Squares Regression Line
Definition
The least-squares regression line is a straight line that best fits the pattern of bivariate quantitative data by minimizing the sum of squared differences between the observed values and predicted values based on the line.
Analogy
Imagine you have a group of friends who want to go on a road trip but can't agree on which route to take. The least-squares regression line is like finding the route that minimizes the total distance between your friends' desired destinations and their actual locations. It's all about finding the best fit!
Related terms
Residuals: Residuals are the differences between observed values and predicted values from the least-squares regression line. They help assess how well the line fits the data.
Slope Intercept Form: The slope-intercept form is an equation used to represent a linear relationship between two variables, where 'slope' represents how much one variable changes when another variable changes by one unit.
Extrapolation: Extrapolation is the process of using the least-squares regression line to make predictions or estimate values beyond the range of the observed data.
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