This document discusses linear dependence and covariance. It defines linear dependence as the relationship between analyzed variables, and covariance as a measure of how two random variables vary together compared to their means. Covariance measures the strength and direction of the linear relationship between variables. A positive covariance means the variables increase together, while a negative covariance means one increases as the other decreases. The document also introduces the Pearson correlation coefficient, which is a dimensionless measure of the linear correlation between two variables that ranges from -1 to 1. It provides an example calculation of covariance and correlation coefficient for a given data set.