Linear Regression: Model Adjustment and Parameter Estimation

This lecture covers the decomposition of the total sum of squares in linear regression, the adjustment of models for missing values, and the estimation of parameters using the maximum likelihood method. It explains how to model the relationship between variables, the importance of unbiased estimators, and the use of linear and nonlinear models for data fitting.