Line of Best Fit with Least Squares

This example demonstrates how to fit a best-fit line to scatter data using the method of least squares, visually showing that the sum of squared residuals is minimized. Core mathematical concepts include linear regression, the least-squares criterion, residuals, the Pearson correlation coefficient (PMCC), and the mean point. Construction highlights: GeoGebra commands such as OrthogonalLine create perpendicular residual segments, Intersect finds intersection points, Polygon builds squares representing squared residuals, Sum computes the total squared error, Mean calculates data averages, and PMCC gives the correlation coefficient. Objects include data points A2 through B5, line parameters m and b, residual segments, and rectangles poly1. Suitable for high school or introductory college statistics, helping students understand the derivation and optimality of the regression line.