Calculating Joint Distribution

In summary, the given joint distribution table represents the probabilities of X and Y taking on certain values. Part 'a' asks for the probability of X and Y being equal, which would be the sum of the probabilities of all the pairs (X,Y) where X and Y have the same value, resulting in 16/136 + 10/136 + 7/136 + 1/136. Part 'b' asks for the probability of X and Y adding up to 5, which can be calculated by summing the probabilities of all the pairs (X,Y) where X+Y=5, resulting in 13/136 + 4/136. Part 'c' asks for the probability of both X and Y
  • #1
BlueScreenOD
14
0

Homework Statement


Let X and Y be two random variables, with joint distribution given by the following table:
Code:
                a
b       1       2       3       4
--------------------------------------
1       16/136  3/136   2/136   13/136
2       5/136   10/136  11/136  8/136
3       9/136   6/136   7/136   12/136
4       4/136   15/136  14/136  1/136


What is:

a.) P(X = Y)
b.) P(X + Y = 5)
c.) P(1 < X <= 3, 1 < Y <= 3)
d.) p((X,Y) [tex]\in[/tex] {1,4} x {1,4})


Homework Equations





The Attempt at a Solution



To be honest, I'm not exactly sure what exactly these kinds of operators mean (e.g. what does it mean to say P(X = Y). But here's what I have so far:

P(a = 1) = P(a = 2) = P(a = 3) = P(a = 4) = P(b = 1) = P(b = 2) = P(b = 3) = P(b = 4) = 34/136

a.) P(X = Y) is [P(a = 1, b = 1) + ... + P(a = 4, b =4)] = 16/136 + 10/136 + 7/136 + 1/136

b.) P(X + Y = 5) = [P(a = 1, b = 4) + P(a = 4, b = 1)] = 13/136 + 4/136

c.) P(1 < X <= 3, 1 < Y <= 3) = c.) P(1 < a <= 3, 1 < b <= 3) =
10/136 + 11/136 + 6/136 + 7/136

d.) p((X,Y) [tex]\in[/tex] {1,4} x {1,4}) = ? I have no idea what this syntax means
 
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  • #2
BlueScreenOD said:

Homework Statement


Let X and Y be two random variables, with joint distribution given by the following table:
Code:
                a
b       1       2       3       4
--------------------------------------
1       16/136  3/136   2/136   13/136
2       5/136   10/136  11/136  8/136
3       9/136   6/136   7/136   12/136
4       4/136   15/136  14/136  1/136


What is:

a.) P(X = Y)
b.) P(X + Y = 5)
c.) P(1 < X <= 3, 1 < Y <= 3)
d.) p((X,Y) [tex]\in[/tex] {1,4} x {1,4})


Homework Equations





The Attempt at a Solution



To be honest, I'm not exactly sure what exactly these kinds of operators mean (e.g. what does it mean to say P(X = Y). But here's what I have so far:

P(a = 1) = P(a = 2) = P(a = 3) = P(a = 4) = P(b = 1) = P(b = 2) = P(b = 3) = P(b = 4) = 34/136
I don't know what you mean by the equations above. The probabilities in the table are joint probabilities.
BlueScreenOD said:
a.) P(X = Y) is [P(a = 1, b = 1) + ... + P(a = 4, b =4)] = 16/136 + 10/136 + 7/136 + 1/136
Looks good (above).
BlueScreenOD said:
b.) P(X + Y = 5) = [P(a = 1, b = 4) + P(a = 4, b = 1)] = 13/136 + 4/136
What other ways can X + Y add to 5? You're showing just two of them. (above).
BlueScreenOD said:
c.) P(1 < X <= 3, 1 < Y <= 3) = c.) P(1 < a <= 3, 1 < b <= 3) =
10/136 + 11/136 + 6/136 + 7/136
Looks good (above).
BlueScreenOD said:
d.) p((X,Y) [tex]\in[/tex] {1,4} x {1,4}) = ? I have no idea what this syntax means
It's asking for P((X, Y)) where X can be 1, 2, 3, or 4 and Y can be 1, 2, 3, 4.
 
  • #3
Part 'd' means the probability that the pair X,Y will be in the Cartesian product of {1,4} x {1,4}. Which means, P(X=1, Y=1) or P(X=2, Y=1) or ... and so on, for all pairs in that product.

Seeing your table, it looks like that would cover all the possible values in that table, so it would be 1.
 

Related to Calculating Joint Distribution

1. What is the purpose of calculating joint distribution?

Joint distribution is used to determine the probability of two or more events occurring simultaneously. It allows scientists to analyze the relationship between multiple variables and make predictions about the likelihood of certain outcomes.

2. How is joint distribution calculated?

Joint distribution is calculated by multiplying the individual probabilities of each event. For example, if event A has a probability of 0.5 and event B has a probability of 0.3, the joint probability of both events occurring is 0.5 x 0.3 = 0.15.

3. What is the difference between joint distribution and marginal distribution?

Joint distribution looks at the probability of multiple events occurring together, while marginal distribution looks at the probability of a single event occurring. Marginal distribution is calculated by summing the probabilities of all possible outcomes for a specific event.

4. How is joint distribution used in statistical analysis?

Joint distribution is used in statistical analysis to determine the relationship between two or more variables. It can help identify patterns and correlations between variables, and can also be used to make predictions about future outcomes.

5. Can joint distribution be used for more than two variables?

Yes, joint distribution can be used for any number of variables. In this case, it is referred to as multivariate joint distribution. The calculation process is the same, but it involves multiplying the individual probabilities of all the events involved.

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