The expected value should be regarded as the average value. When X is a discrete random variable, then the expected value of X is precisely the mean of the corresponding data.
The variance should be regarded as (something like) the average of the difference of the actual values from the average. Variance is the expectation of the squared deviation of a random variable from its population mean or sample mean. Variance is a measure of dispersion, meaning it is a measure of how far a set of numbers is spread out from their average value. Variance is squared STD.
https://math.berkeley.edu/~scanlon/m16bs04/ln/16b2lec30.pdf
Covariance is correlation times STD of data point set 1 times STD of data point set 2. Both data sets are to be compared for their relationship. Covariance measures the joint variability of two random variables, indicating the direction of their linear relationship. A positive value means variables tend to move in the same direction, while a negative value indicates they move in opposite directions. Covariance measures the direction of the relationship between two variables. So covariance is hard to interpret directly. Correlation is the standardized version of covariance. It measures both direction and strength of a linear relationship.
