Are These Random Variables Independent or Identically Distributed?

In summary, the 3 random variables X, Y, and Z with the given probabilities and values have the same distribution. The probability distributions of X+Y, Y+Z, and X+Z are 1/3 for values 2, 4, and 6, and 0 for all other values. X and Y are not independent as P{X=1 & Y=1} does not equal P{X=1}*P{Y=1}. The only random variable W that can make X and W independent is a degenerate random variable where W = k for all three points w_i.
  • #1
squenshl
479
4

Homework Statement


Let [tex]\Omega[/tex] = {w1, w2, w3}, P(w1) = 1/3, P(w2) = 1/3, P(w3) = 1/3, and define X, Y, Z as follows:
X(w1) = 1, X(w2) = 2, X(w3) = 3
Y(w1) = 2, Y(w2) = 3, Y(w3) = 1
Z(w1) = 3, Z(w2) = 1, Z(w3) = 2

(a) Show that these 3 random variables have the same distribution.
(b) Find the probaility distribution of X+Y, Y+Z and X+Z.
(c) Show that X and Y are not independent by verifying that Bienaymé's formula doesn't hold.
(d) Find a random variable W such that X and W are independent.

Homework Equations


The Attempt at a Solution


For (a) do we just add the values of X(w1), X(w2) and X(w3) with the values of Y(w1), Y(w2) and Y(w3) and so on and so forth. Find out they add to the same values so they must have the same prob distn.
 
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  • #2
Or is it for:
X: 1 x 1/3 + 2 x 1/3 + 3 x 1/3 = 2
Y: 2 x 1/3 + 3 x 1/3 + 1 x 1/3 = 2
Z: 3 x 1/3 + 1 x 1/3 + 2 x 1/3 = 2

Since the 3 random variables have the same expectation, they have the same probability distribution.
 
  • #3
I have done (a), (b), (c) but how do I do (d). I was told to show that P(X [tex]\cap[/tex] W) = P(X)P(W) but how do I do that?
 
  • #4
squenshl said:
Since the 3 random variables have the same expectation, they have the same probability distribution.

3 random variables with the same expectation do not necessarily have the same distribution. The expectation is just the mean, you can find a counter example to that easily. MGFs can show that distributions are the same?
 
  • #5
squenshl said:

Homework Statement


Let [tex]\Omega[/tex] = {w1, w2, w3}, P(w1) = 1/3, P(w2) = 1/3, P(w3) = 1/3, and define X, Y, Z as follows:
X(w1) = 1, X(w2) = 2, X(w3) = 3
Y(w1) = 2, Y(w2) = 3, Y(w3) = 1
Z(w1) = 3, Z(w2) = 1, Z(w3) = 2

(a) Show that these 3 random variables have the same distribution.
(b) Find the probaility distribution of X+Y, Y+Z and X+Z.
(c) Show that X and Y are not independent by verifying that Bienaymé's formula doesn't hold.
(d) Find a random variable W such that X and W are independent.

Homework Equations





The Attempt at a Solution


For (a) do we just add the values of X(w1), X(w2) and X(w3) with the values of Y(w1), Y(w2) and Y(w3) and so on and so forth. Find out they add to the same values so they must have the same prob distn.

(a) P{X=1} = P(w_1) = 1/3, P{Y=1} = P(w_3) = 1/3, etc. So, they all have the same distribution.

(b) P{X+Y=2} = P{X=1 &Y=1} = P{w_1 & w_2} = 0, P{X+Y=3} + P{X=1 & Y=2} + P{X=2 & Y=1} = P{w_1 & w_1} + P{w_2 & w_3} = P{w_1} = 1/3. P{X+Y=4} = P{w_2 & w_2} = 1/3, and P{X+Y = 6} = P{w_3 & w_3} = 1/3, so the probabilities P{X+Y= = i} for i=2,3,4,5,6 are 1/3,0,1/3,0,1/3. You can do Y+Z and X+Z similarly.

(c) P{X=1 & Y=1} = 0, while P{X=1}*P{Y=1} = (1/3)^2 = 1/9.

(d) An obvious solution W = k (k = an arbitrary constant) for all three points w_i . This is a degenerate random variable. With more work (eg., using moment-generating functions) we can show that this is the _only_ solution.

R.G. Vickson
 

FAQ: Are These Random Variables Independent or Identically Distributed?

What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of each possible outcome of an event. It assigns a probability to each possible outcome, and the sum of all probabilities must equal 1.

What are the types of probability distributions?

There are many types of probability distributions, but some of the most commonly used ones include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. Each distribution has its own unique characteristics and is used to model different types of events.

How do you calculate the mean and standard deviation of a probability distribution?

The mean of a probability distribution is calculated by multiplying each outcome by its respective probability and then adding all of the products together. The standard deviation is calculated by taking the square root of the variance, which is the sum of the squared differences between each outcome and the mean, multiplied by its respective probability.

What is the central limit theorem?

The central limit theorem states that when independent random variables are added, their sum tends to follow a normal distribution. This theorem is important because it allows us to make inferences about a population based on a sample, even if the population is not normally distributed.

How are probability distributions used in real life?

Probability distributions are used in a variety of fields, such as finance, physics, and biology. In finance, they are used to model stock prices and predict market trends. In physics, they are used to describe the behavior of particles and the likelihood of different outcomes in experiments. In biology, they are used to model genetic traits and the likelihood of certain diseases occurring in a population.

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