Covariance of Discrete Random Variables

In summary, for the given joint distribution of random variables X and Y, the expected value of their product (E[XY]) can be calculated by taking the sum of the product of each value of X and Y multiplied by their corresponding probabilities. Once E[XY] is found, the covariance and correlation can be easily calculated. However, since X and Y are not independent, the limits of integration for the double integral may not be obvious and an alternative approach may be needed.
  • #1
Shackman
22
2

Homework Statement


Find E(XY), Cov(X,Y) and correlation(X,Y) for the random variables X, Y whose joint distribution is given by the following table.

X
1 2 3
Y -1| 0 .1 .1

0| 0 .5 .6

1| .2 0 0

The Attempt at a Solution



The covariance and correlation fall into place quite easily once I have found E(XY). I have found E(X), E(Y), Var(X) and Var(Y) but none of these values help as the variables are not independent. So in trying to find E(XY), I am trying to set up the double integral, but am confused by the fact that the variables are discrete. The limits of integration are not obvious and it is not obvious how to integrate a discrete function either. Is there another way I can look at this?
 
Last edited:
Physics news on Phys.org
  • #2
[tex]E[XY]=\sum\sum xyf(x,y)[/tex]
 
  • #3
That is so much better. Thanks. The organization of my book is pretty terrible, I'm just finding that equation now.
 

What is covariance?

Covariance is a measure of how two random variables change together. It is used to determine the relationship between two variables and whether they tend to increase or decrease together.

How is covariance calculated?

Covariance is calculated by multiplying the difference between each pair of values of the two variables, and then finding the average of all these products. A positive covariance value means the variables have a positive relationship, while a negative value indicates a negative relationship. A covariance of zero means there is no relationship between the variables.

What does a high covariance value indicate?

A high covariance value indicates a strong relationship between the two variables. This means that when one variable increases or decreases, the other tends to do the same.

What is the difference between covariance and correlation?

Covariance and correlation are both measures of the relationship between two variables, but they differ in their scale. Covariance is measured in the units of the variables being compared, while correlation is a standardized measure that ranges from -1 to 1. Correlation is a more commonly used measure as it allows for easier comparison across different datasets.

How is covariance used in statistical analysis?

Covariance is used in statistical analysis to determine the strength and direction of the relationship between two variables. It can also be used to identify patterns and make predictions about future values of the variables. Additionally, covariance is used in the calculation of other statistical measures such as regression coefficients and variance.

Similar threads

  • Calculus and Beyond Homework Help
Replies
2
Views
986
  • Calculus and Beyond Homework Help
Replies
8
Views
753
  • Calculus and Beyond Homework Help
Replies
8
Views
1K
  • Calculus and Beyond Homework Help
Replies
1
Views
483
  • Calculus and Beyond Homework Help
Replies
9
Views
2K
  • Calculus and Beyond Homework Help
Replies
4
Views
837
  • Calculus and Beyond Homework Help
Replies
2
Views
1K
  • Calculus and Beyond Homework Help
Replies
5
Views
1K
  • Calculus and Beyond Homework Help
Replies
1
Views
1K
  • Calculus and Beyond Homework Help
Replies
1
Views
650
Back
Top