Calculating the covariance of two discrete random variables

In summary: To summarize, the problem can be interpreted as assuming that the five given points are equally probable and exhaustive, and from there one can find the covariance of T and U using the given equation.
  • #1
FissionChips
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Homework Statement


If the random variables T and U have the same joint probability function at the following five pairs of outcomes: (0, 0), (0, 2), (-1, 0), (1, 1), and (-1, 2). What is the covariance of T and U?

Homework Equations


σxy = E(XY) - μx⋅μy

The Attempt at a Solution


My issue with this problem is interpreting what is meant by each of the points having the same joint probability function. The only way I am able to proceed is by considering that the joint probability function (whatever that may be for the two variables) evaluated at each of the five outcomes returns the same value. In other words, each of the five outcomes listed above have an equal probability of occurring. I am able to find the covariance if this is the case.

Is this interpretation of the problem statement correct? If not, what is the proper interpretation? I have no difficulty with the mathematical operations associated with this problem, I'm just not sure if I'm understanding the problem statement.

Any help is appreciated.
 
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  • #2
FissionChips said:

Homework Statement


If the random variables T and U have the same joint probability function at the following five pairs of outcomes: (0, 0), (0, 2), (-1, 0), (1, 1), and (-1, 2). What is the covariance of T and U?

Homework Equations


σxy = E(XY) - μx⋅μy

The Attempt at a Solution


My issue with this problem is interpreting what is meant by each of the points having the same joint probability function. The only way I am able to proceed is by considering that the joint probability function (whatever that may be for the two variables) evaluated at each of the five outcomes returns the same value. In other words, each of the five outcomes listed above have an equal probability of occurring. I am able to find the covariance if this is the case.

Is this interpretation of the problem statement correct? If not, what is the proper interpretation? I have no difficulty with the mathematical operations associated with this problem, I'm just not sure if I'm understanding the problem statement.

Any help is appreciated.

I think your "equi-probable" assumption is the only reasonable interpretation of this poorly-worded problem. If I were doing it I would assume each of the five points has probability 1/5, and I would also add the statement that I was assuming those five points are exhaustive (i.e., that all other points have zero probabilities --- not stated in the problem as you wrote it).
 
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Likes Orodruin
  • #3
Thank you for chiming in; it's good to know I'm not the only one who thought the wording was a bit murky.
 

1. What is the main purpose of calculating the covariance of two discrete random variables?

The main purpose of calculating the covariance of two discrete random variables is to measure the linear relationship between the two variables. It helps to determine how much the variables vary together in a systematic way.

2. How is the covariance of two discrete random variables calculated?

The covariance of two discrete random variables is calculated by multiplying the differences of each value from their respective means and then taking the average of these products. This can be represented by the formula: Cov(X,Y) = E[(X-μx)(Y-μy)].

3. What is the range of possible values for the covariance of two discrete random variables?

The range of possible values for the covariance of two discrete random variables is from negative infinity to positive infinity. A positive covariance indicates a positive relationship between the variables, while a negative covariance indicates a negative relationship. A covariance of zero indicates no linear relationship between the variables.

4. Is the covariance affected by changes in the scale or units of measurement of the variables?

Yes, the covariance is affected by changes in the scale or units of measurement of the variables. This is because the differences between the values and their means will also change, resulting in a different covariance. Therefore, it is important to use standardized variables when comparing covariances.

5. Can the covariance be used to determine causation between two discrete random variables?

No, the covariance cannot be used to determine causation between two discrete random variables. It only measures the strength and direction of the linear relationship between the variables, but does not indicate a cause-and-effect relationship. Other methods, such as experiments or observational studies, are needed to establish causation.

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