The least squares method is a mathematical technique used to find the best-fitting curve or line to a set of data points by minimizing the sum of the squares of the differences between the observed values and the values predicted by the model. This approach helps to reduce the impact of errors or deviations in the data, providing an optimal solution that balances all points collectively rather than fitting perfectly to any single point. Widely used in regression analysis, data fitting, and statistical modeling, the least squares method enables accurate predictions and insights from noisy or imperfect data by finding the most reliable approximation.
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