A detailed mathematical explanation of multiple linear regression, covering how to fit a plane to data points instead of a line. The post walks through the derivation of regression coefficients using calculus concepts like differentiation and partial derivatives, demonstrates the least squares method for minimizing residuals, and applies these formulas to a Fish Market dataset sample. It includes step-by-step derivations, explains data centering, uses Cramer's rule for solving systems of equations, and validates the mathematical results against Python's scikit-learn implementation.
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