This lecture covers the theory and applications of Principal Component Analysis (PCA) in multivariate statistics. It explains the definition and properties of principal components, including population and sample principal components. The lecture discusses the correlation structure, choice of components, and graphical representations. Additionally, it explores sampling properties, hypothesis testing, and the use of PCA in regression analysis.