The majority of signals that you will model as random have multiple values, that is, they are multivariate. Examples include multiple samples of a time signal and signals measured at different sensors. In this lesson you will learn the tools used to jointly characterize the random behavior of multiple signal components. You will learn how to interpret the covariance matrix, which is the most widely used characterization for multivariate random signals. This foundation will position you to understand and properly use many signal processing techniques.