ANOVA: Model Coefficients and Sequential Sum of Squares

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.