Is the Calculation of Cov(X1 + X2, X2 + X3) Correct?

In summary, covariance of sums is a statistical measure that quantifies the relationship between two variables by measuring how much they vary together. It is important in statistics because it helps us understand the relationship between variables and make predictions about a population. It is calculated by taking the sum of products of the deviations of each data point from the mean of each variable, and can indicate a positive or negative relationship between the variables. It is different from correlation in that it measures the strength and direction of the relationship, while correlation is a standardized measure. Covariance of sums can be used to interpret data by providing information about the relationship between variables, identifying outliers, and making predictions, but does not imply causation.
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Homework Statement



Suppose X1 , X2 , X3 , and X4 are independent with a common mean 1 and common variance 2. Compute Cov( X1 + X2 , X2 + X3).

Homework Equations



Cov (X,Y) = E[(X-u)(Y-v)] = E[XY] - uv, where u and v are the means of X and Y
E[XY] = E[X]E[Y]
E[X+Y] = E[X] + E[Y]

The Attempt at a Solution



Since there is a common mean=1, Cov(X,Y) = E[XY] -1 = E[X]E[Y] - 1
Substituting for E[XY]= E[X1+X2]E[X2+X3]
Substituting for E[X+Y]= (E[X1]+E[X2])(E[X2]+E[X3])
Expanding (E[X1]+E[X2])(E[X2]+E[X3]) = E[X1]E[X2] + E[X1]E[X3] + E[X2]^2+ E[X2]E[X3]
Substituting for mean=1: (1)(1) + (1)(1) + 1^2 +(1)(1) = 4

Cov (X,Y) = E[XY] - 1 = 4 -1 = 3

Can someone check this for me? I think it is wrong. If the variables are independent with identical means and variances, shouldn't the covariance equal the variance?
 
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Your solution is correct. The covariance between two variables is equal to the product of their variances when they are independent. In this case, the covariance between X1+X2 and X2+X3 is equal to the product of their variances, which is 2*2 = 4. Therefore, the covariance is equal to 4-1 = 3.
 

Related to Is the Calculation of Cov(X1 + X2, X2 + X3) Correct?

What is covariance of sums and why is it important in statistics?

Covariance of sums is a statistical measure that quantifies the relationship between two variables. Specifically, it measures how much two variables vary together. It is important in statistics because it helps us understand the relationship between variables and can be used to make predictions and draw conclusions about a population.

How is covariance of sums calculated?

Covariance of sums is calculated by taking the sum of the products of the deviations of each data point from the mean of each variable. It is then divided by the total number of data points in the sample.

What does a positive or negative covariance of sums indicate?

A positive covariance of sums indicates that the two variables are positively related, meaning that as one variable increases, the other tends to increase as well. A negative covariance of sums indicates a negative relationship, where as one variable increases, the other tends to decrease.

How is covariance of sums different from correlation?

Covariance of sums and correlation are similar in that they both measure the relationship between two variables. However, covariance of sums is a measure of the strength and direction of the relationship, while correlation is a standardized measure that ranges from -1 to 1, making it easier to compare relationships between different variables.

How can covariance of sums be used to interpret data?

Covariance of sums can be used to interpret data by providing information about the strength and direction of the relationship between two variables. It can also be used to identify outliers, understand patterns and trends in the data, and make predictions about the population. However, it is important to note that covariance of sums does not necessarily imply causation between the variables.

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