Gaussian Conditional Model and Properties

This lecture covers the Gaussian conditional model for linear regression, the properties of the model if the data is Gaussian, and the paradox of Simpson illustrated with the example of kidney stone treatment comparison. It also discusses the normal equations, linear vs affine regression, and the maximum likelihood estimation principle.