Lecture
This lecture covers the concept of Principal Component Analysis (PCA) as a tool to reduce the dimensionality of data by creating linear combinations of variables. It explains how PCA aims to interpret the variance-covariance structure and how to determine the number of principal components to retain. The lecture also delves into the caution needed when interpreting PCA results, methods for assessing data quality using PCA, and the importance of controlling error rates in hypothesis testing.