Solve Covariance Question: X,Y Means, Variances, Correlation

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In summary, the joint p.m.f. for random variables X and Y is (x+y)/32 for x=1,2 and y=1,2,3,4. To find the means, \mux= 25/16 and \muy= 45/16. The variances are \sigmax= 63/256 and \sigmay= 295/256. To calculate the correlation coefficient, \rho, the formula \rho=(COV(X,Y))/\sigmax\sigmay is used. To find the covariance, 2x4 terms need to be calculated, one for each x and y outcome.
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ank0006
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Homework Statement


Let the random variables X and Y have the joint p.m.f.:

f(x,y) = (x+y)/32 x=1,2, y=1,2,3,4.

find the means [tex]\mu[/tex]x and [tex]\mu[/tex]y, the variances [tex]\sigma[/tex]2x and [tex]\sigma[/tex]2y, and the correlation coefficient [tex]\rho[/tex].

Homework Equations


[tex]\rho[/tex]=(COV(X,Y))/[tex]\sigma[/tex]x[tex]\sigma[/tex]y


The Attempt at a Solution


I was able to find both [tex]\mu[/tex]'s:
[tex]\mu[/tex]x= (25/16)
[tex]\mu[/tex]y= (45/16)

and both variances:
[tex]\sigma[/tex]x=(63/256)
[tex]\sigma[/tex]y=(295/256)

But I can't seem to find how to get the covariance...I tried just using the 1 and 2 values for x and y, but it hasn't worked. I think I'm getting confused because there are more y values than x values. Any help would be much appreciated!
 
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  • #2
do you knw the formula for covariance?

you will need to calculate 2x4 terms, one for each x & y outcome
 

Related to Solve Covariance Question: X,Y Means, Variances, Correlation

1. What is covariance and why is it important?

Covariance is a measure of how two variables change together. It is important because it helps us understand the relationship between two variables and can be used to predict future values.

2. How do you calculate covariance?

Covariance is calculated by taking the sum of the product of the differences between each variable and their respective means, and dividing by the total number of observations.

3. How does covariance differ from correlation?

Covariance measures the direction and strength of the relationship between two variables, while correlation measures the strength of the linear relationship between two variables. Correlation takes into account the scale of the variables, while covariance does not.

4. Can covariance be negative?

Yes, covariance can be negative if there is a negative relationship between the two variables. This means that when one variable increases, the other variable decreases.

5. How can covariance be used in data analysis?

Covariance can be used to identify patterns and relationships between variables in a dataset. It can also be used to determine which variables have the strongest influence on each other and to make predictions about future values.

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