Linear Regression: Least Squares Method

Related lectures (29)

Page 3 of 3

Delves into regression analysis, emphasizing distributional checks, weighted least squares, and hypothesis testing.

Explores Singular Value Decomposition theory, linear system solutions, least squares, and data fitting concepts.

Introduces linear regression concepts, from X-bands creation to slope estimator properties and tests.

Covers least squares, QR factorization, linear models, and regression analysis with applications to experimental data.

Explores orthogonal projections and the Gram-Schmidt method for constructing bases.

Covers auto-encoders, linear systems modeling, system identification, and recursive least squares.

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Explores linear regression in high dimensions and practical house price prediction from a dataset.

Introduces the Gram-Schmidt algorithm, QR factorization, and the method of least squares.