The MLR Model & Matrix Formulation

Express multiple regression through design matrices, normal equations and projections. Prerequisites: vectors, matrix products, rank and elementary least squares. Practice dimensions, fitted values, residual geometry, covariance and degrees of freedom, then examine redundant predictors, stable numerical solutions and equivalent parameterizations. Worked reasoning connects the algebra to regression interpretation without requiring executable programming.

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