Sums and products of random variables

In summary, the conversation discusses the dependence or independence of random variables X and Y and their correlation in three different scenarios. The first scenario involves X and Y being uniformly distributed on a circle, resulting in them being independent and uncorrelated. The second scenario involves X and Y being uniformly distributed on a parallelogram, resulting in them also being independent and uncorrelated. The third scenario involves X and Y being uniformly distributed on a diamond, but the outcome is unclear due to uncertainty about the shape and limits of integration. The conversation also mentions the use of calculus, specifically multiple integration, to determine the independence and correlation of the variables. The background of the person asking the question is also briefly mentioned.
  • #1
dizzle1518
17
0
Can anyone help me with the below question?

for each of the following pairs of random variables X,Y, indicate
a. whether X and Y are dependent or independent
b. whether X and Y are positively correlated, negatively correlate or uncorrelated

i. X and Y are uniformly distributed on the disk {(x,y) in R^2: 0<=x^2+y^2<=1}
since this is a circle with R=1 the area is pi. Since x and y are uniform then fxy(xy) is 1/area = 1/pi. In order to see if x and y are independent i need to computer marginal densities and multiply to see if i get fxy(xy). This is where I am stuck. I know that to get fx i need to integrate fxy(xy) with respect to dy and for fy integrate with respect to dx. But what am i integrating? if fx = integral from -1 to 1 of 1/pi dy? and fy = to integral from -1 to 1 1/pi dx? For correlation i first need COV(XY) which equals = E(XY)-E(X)E(Y). To get E(XY) i integrate xy*fxy(xy) so the double integral from -1 to 1 of xy/pi? Is this correct? Now, since x and y are uniform then E(X) and E(Y) are just the interval over two so since each is between -1 and 1 we get 2/2=1=E(X)=E(Y). The VAR(X) and VAR(Y) is 2^/12, the radical of which gives standard deviation and from these value we can compute rho.

ii. X and Y are uniformly distributed on the parallelogram {(x,y) in R^2: x-1<=y<=x+1, -1<=x<=1}. the area of the parallelogram is 4, so fxy(xy) is 1/4. to get fx i integrate 1/4 from x-1 to x+1 with respect to dy which yields 1/2. To get fy i integrate 1/4 from -1 to 1 with respect to dx which also yields 1/2. since fx*fy=1/4=fxy(xy) then x and y are independent and thus uncorrelated. correct?

X and Y are uniformly distributed on the diamond {(x,y) in R^2: |x|+|y|<=1, |x|<=1}. i am not sure about this one. what will the graph of this one look like? is it a diamond on -1<=x<=1 and -1<=y<=1?
 
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  • #2
dizzle1518 said:
. But what am i integrating? if fx = integral from -1 to 1 of 1/pi dy? and fy = to integral from -1 to 1 1/pi dx?

What is your background in calculus? Have you done multiple integration problems where the limits of integration on one variable were stated as a function of the other variable?
 

Related to Sums and products of random variables

1. What is the difference between a sum and a product of random variables?

A sum of random variables is calculated by adding the values of the variables together. A product of random variables is calculated by multiplying the values of the variables together. In other words, a sum represents the combined effect of the variables, while a product represents the joint effect of the variables.

2. How are sums and products of random variables used in statistics?

Sums and products of random variables are commonly used in statistics to calculate probabilities and distributions. For example, the sum of two dice rolls can be used to determine the likelihood of rolling a certain number. The product of two independent events can be used to calculate the overall probability of both events occurring.

3. What is the central limit theorem and how does it relate to sums of random variables?

The central limit theorem states that the sum of a large number of independent and identically distributed random variables will tend to follow a normal distribution. This is useful in statistics because many real-world phenomena can be approximated by a normal distribution, making it easier to analyze and make predictions based on sums of random variables.

4. Can sums and products of dependent random variables be calculated?

Yes, sums and products of dependent random variables can be calculated, but the equations and methods used will differ from those used for independent variables. For example, if two variables are positively correlated, the product of their values will tend to be higher than if they were independent. This relationship can be expressed mathematically using covariance and correlation coefficients.

5. How does the law of large numbers affect sums and products of random variables?

The law of large numbers states that as the number of trials or samples increases, the average or mean of those trials will tend to approach the expected value. This means that as more random variables are added together, the sum or product will tend to approach its theoretical value. In other words, the more trials there are, the more accurate the calculated sum or product will be.

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