Covariance/ Correlation Calculation

In summary, to find the correlation and covariance of N and T, we can use the formulas COV(X,Y) = E[XY]-E[X]E[Y] and ρ_{X,Y} = (E[XY]-E[X]E[Y])/(σ_{X}σ_{Y}). To find the expected value from the joint PDF, we can use the formula E[x]=\int x f(x,y) dx dy, keeping in mind that the joint PDF in this case is integrated for t.
  • #1
ewoeckel
2
0
1. Let N and T be the number of users logged on and the time until the next log-off. The joint probability of N and T is given by P(N=η, X≤t) = (1-ρ)ρ^{η-1}(1-e^{-ηλt}) for η=1,2,...;t>0.) Find the correlation and covariance of N and T.

2. COV(X,Y) = E[XY]-E[X]E[Y]
ρ_{X,Y} = (E[XY]-E[X]E[Y])/(σ_{X}σ_{Y})
E[XY] = ∫∫ xy f_{XY}(x,y) dx dy


3. I do not know how to find the expected value from the joint PDF and was hoping for some guidance.
 
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  • #2
Same as with a single variable PDF. Assume f(x,y) is the normalized PDF

E[x]=\int x f(x,y) dx dy, etc.

But remember that "your PDF" is *not* a true PDF, it is integrated for t! (X<=t)!
 

1. What is the difference between covariance and correlation?

Covariance and correlation are both measures of the relationship between two variables. However, covariance measures the direction and strength of the linear relationship between two variables, while correlation measures the strength and direction of the linear relationship as well as the degree to which the variables are related to each other.

2. How do you calculate covariance?

Covariance is calculated by taking the product of the differences between each data point and its respective mean for two variables, and then dividing that sum by the total number of data points.

3. What does a positive/negative covariance value indicate?

A positive covariance value indicates that the two variables have a positive linear relationship, meaning that as one variable increases, the other variable also tends to increase. A negative covariance value indicates a negative linear relationship, where as one variable increases, the other variable tends to decrease.

4. How is correlation different from regression?

Correlation measures the strength and direction of the linear relationship between two variables without assuming a causal relationship. Regression, on the other hand, uses the relationship between two variables to make predictions or determine the effect of one variable on the other.

5. Can correlation be used to determine causation?

No, correlation does not imply causation. Just because two variables are highly correlated does not necessarily mean that one variable causes the other. It is important to consider other factors and conduct further research to determine causation.

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