Least squares problems involve finding the best-fitting curve or line through 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 method is widely used in regression analysis, making it essential for statistical modeling and data analysis. It provides a way to handle overdetermined systems of equations, where there are more equations than unknowns, ensuring that the solution is as close as possible to the actual data.
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