Joint probability distribution

In summary, a joint probability distribution is a tool used to show the likelihood of two or more events occurring simultaneously. It differs from a marginal probability distribution in that it shows the probabilities of multiple events occurring together, while a marginal probability distribution shows the probabilities of each event occurring individually. The formula for calculating joint probability is P(A∩B) = P(A) x P(B|A), where P(A∩B) represents the probability of both events A and B occurring, P(A) represents the probability of event A occurring, and P(B|A) represents the probability of event B occurring given that event A has already occurred. Joint probability is related to conditional probability, which is used to calculate the probability of an event occurring given
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toothpaste666
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Homework Statement


4. Let X and Y have the joint probability distribution

Screen Shot 2015-10-19 at 5.45.15 PM.png

(a) Find P(X +Y ≤ 4).

(b) Find the marginal probability distributions f1(x) and f2(y).

(c) Find P(X < 2|Y = 2).

(d) Are X and Y independent?

The Attempt at a Solution


a) f(1,1) + f(1,2) + f(1,3) + f(2,1) + f(2,2) + f(3,1)

b) f1(1) = .1 + .05 + .15 = .3
f1(2) = .1 + .05 + .1 = .25
f1(3) = .15 + .2 + .1 = .45

f2(1) = .35
f2(2) = .3
f2(3) = .35

c) f(1,2) = .05

d) they are independent if f(x,y) = f1(x)f2(y) for all x and y
f(1,1) = .1 f1(1)f2(1) = .3(.35) = .105
they are not independent .is this correct?
 
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  • #2
It looks right to me.
 
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thanks!
 

Related to Joint probability distribution

What is a joint probability distribution?

A joint probability distribution is a statistical tool used to describe the likelihood of two or more events occurring simultaneously. It shows the probability of each event individually and the probability of both events occurring together.

How is a joint probability distribution different from a marginal probability distribution?

A joint probability distribution gives the probabilities of multiple events occurring together, while a marginal probability distribution gives the probabilities of each event occurring individually.

What is the formula for calculating joint probability?

The formula for calculating joint probability is P(A∩B) = P(A) x P(B|A), where P(A∩B) represents the probability of both events A and B occurring, P(A) represents the probability of event A occurring, and P(B|A) represents the probability of event B occurring given that event A has already occurred.

What is the relationship between joint probability and conditional probability?

Conditional probability is used to calculate the probability of an event occurring given that another event has already occurred. Joint probability is used to calculate the probability of multiple events occurring together. Conditional probability is a component of the formula for joint probability.

How can joint probability distributions be used in data analysis?

Joint probability distributions can be used to analyze the simultaneous occurrence of multiple events in a dataset. They can help identify patterns and relationships between variables, and can also be used to make predictions based on the likelihood of certain events occurring together.

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