Estadistica 2 _ Migración

This project explores computational linear algebra in statistics, beginning with matrix structures such as data, deviation, sums of squares, and variance-covariance matrices. It introduces the SWEEP operator for efficient inversion, determinant calculation, and correlation matrices, as well as Cholesky decomposition. These methods are applied to multiple regression to derive coefficients, variance-covariance estimates, leverage, residuals, and the hat matrix, and then extended to ANOVA to analyze variation, degrees of freedom, mean squares, the F-statistic, and model fit. Together, these techniques show how linear algebra supports estimation, diagnostics, and model evaluation.