Theorem of least squares

The Least Squares Theorem is a mathematical method used to find the best-fitting line or curve for a set of data points by minimizing the total of the squared differences between the observed values and the values predicted by the model. In essence, it finds the line that most closely approximates the data, reducing overall errors. This approach ensures the differences are balanced, so the fit isn’t skewed by a few large discrepancies. It’s widely used in statistics and data analysis to derive meaningful trends and relationships from noisy or imperfect data.