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"Least Squares" as a book category would collect works about the art and science of fitting imperfect data to models — from rigorous textbooks on linear and nonlinear regression, optimization and numerical linear algebra (QR, SVD, regularization) to applied guides showing how to implement and interpret models in fields like economics, engineering, astronomy, and machine learning. It would also include case-study collections and software tutorials (R/Python, MATLAB), historical and philosophical treatments of measurement, error and approximation (Gauss, Legendre, inverse problems), and even literary or popular-science books that use "best fit" and approximation as a metaphor for decision-making and reconciling conflicting evidence. Targeted at students, researchers and practitioners, the category emphasizes practical model-building, uncertainty quantification, and the conceptual idea of finding the "best possible" explanation when data are noisy.

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