Wikipedia: In probability theory and statistics, covariance is a measure of the joint variability of two random variables. Try the math of a simple 2x2 times the transpose of the 2x2. So now, if we transpose the matrix and multiply it by the original matrix, look at how those equations in the matrix are being multiplied with all the other variables (and itself). Now you can use a matrix to show the relationships between all these measurements and state variables.
What if you are looking for 4 state variable that each are composed of 2 other state variables that those in itself take 2 or 3 or 4 or however many measurements to calculate. You will have an equation with 2 variables to show the relationship between the measurements and the state variable. Now imagine a more complicated scenario where you need more than 1 measurement to get the desired state variable that you are looking for.
Now this is a very simple example, but it shows the relationship between this measurement (and its error) and all the calculates values (state variables). If you continue to calculate distance, than that acceleration error is going to also continue to propagate into the distance calculation. If you want to calculate velocity from that acceleration, then that acceleration error is going to propagate into the velocity calculations. This error propagates as you continue your calculations. Each time you measure acceleration 'a', you have an error of +- 0.1*a. But your measurement of acceleration always has an error. Let's say you are measuring something like acceleration. (bare with me and please tell me where I am wrong). But I'll attempt to explain it in a simpler example than what Brethlosze said. John is right, in your example, it doesn't make sense. I've been trying to figure this out my myself recently, and I think I understand it. Because the numbers are less, I will assume that we want to see the daily sales in numbers of all companies. Lets say there are $4$ companies $A$, $B$, $C$ and $D$ and all of them sell three fruits Apples, Oranges and Pears. I would like to visualize just this particular problem. There are great answers by fellow members.
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