[Chapter 7] #2 Covariance

[@hoowoo4832]

Thank you so much! My Professor made every single statement super complicated. This made me understand things better.

[@sanelisiwesithole1463]

Thank you for this but we need examples. I watched your video but I still don’t know how to find Cov(XY) for discrete random variables

[@username32689]

A random variable itself is a set of values. These values can be interpreted as data points. E[X] is the weighted average (= the outcome that we most likely expect). Variance Var[X] is the degree of spread in the set of data points around E[X]. It shows the amount of variance among the data points. The larger the variance, the „fatter“ the distribution (= the graph is spread further from the middle point E[X]). Covariance Cov(X,Y) inspects how the values of two random variables X,Y correspond to each other. If Cov(X,Y) is large, then X and Y have correlating high values (= the values are high at the same points). For example: Studying more correlates to higher grades. But if Cov(X,Y) is negative, it shows that the high values of X correspond with the low values Y. For example: If it rains a lot, less people go outside.