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Is there some basic examples to show that there are difference meaning between X and E(X) in the covariance formula?

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Suppose $X$ and $Y$ are random variables which can take the following pairs of values with the following probabilities

X   Y    Probability
10  20      0.6 
30  50      0.3 
40  10      0.1

Then $E[X]$, the expected value of $X$, is $0.6 \times 10 + 0.3 \times 30 +0.1 \times 40 = 19$.

Similarly $E[Y]$, the expected value of $X$, is $0.6 \times 20 + 0.3 \times 50 +0.1 \times 10 = 28$.

So the covariance $E\left[(X-E[X])(Y-E[Y])\right]$, the expected value of the products of the differences between the random variables and their expected values, is $$0.6 \times (10-19)\times (20-28) + 0.3 \times (30-19)\times ( 50-28)+0.1 \times (40 -19) \times (10-28) = 78.$$

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The value $X$ represents a randomly-varying quantity. The value $E(X)$, if known, represents its average. For example, toss a fair coin 100 times and let $X$ denote the number of heads you see. In this case, if the coin is fair, you would reasonably expect $E(X) = 50$. The value $X$ will vary around 50 as you repeat the experiment.

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When you say "average", there is a difference between the population average or expectation and the sample average –  Henry Sep 3 '12 at 23:36
    
Yes. $E(X)$ refers to a population average. A sample average is still a random quantity. Repeated trials will, in general, yield varying results. –  ncmathsadist Sep 3 '12 at 23:38
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