Lecture
This lecture delves into non-linear regression models, extending the general regression framework to include non-linear relationships between variables. The instructor discusses logistic growth models, likelihood estimation, model fitting using Taylor expansions, and the geometry of non-linear least squares. The lecture also covers the Newton-Raphson algorithm, the geometry of linear approximation, choosing initial values for model fitting, and approximate confidence intervals for model parameters.