Linear Models: Introduction

This lecture introduces linear models, focusing on regression models and the statistical problem of estimating the relationship between random variable Y and non-random variable x. It covers the general formulation of Y ~ Distribution{g(x)}, the concept of data compression, and examples like Honolulu tide and gas mileage. The lecture also discusses normal linear regression, the importance of Gaussian distribution and linearity, and the gradual generalization of the normal linear model to linear Gaussian regression, nonlinear Gaussian regression, and nonparametric Gaussian regression.