Lecture
This lecture covers the concept of conditional distributions and multiple correlations in multivariate statistics. It explains partial variance, covariance, and correlation, as well as the properties of these conditional measures. The lecture also delves into the bivariate case, discussing examples and the implications of conditional mean adjustments. Furthermore, it explores the generalization of these concepts to non-multivariate normal distributions, highlighting the regression of one variable on another. The lecture concludes with an extension to Week 3, focusing on the multivariate normal distribution, spherical and elliptical distributions, and their applications in modeling risk-factor changes.