Marginal Probability Distribution

In summary, the joint probability density function for the useful lifetimes of two components of a laptop computer is given by f(xy)=xe^(−x(1+y)) 0 <= x <= y. The marginal probability density function of X is e^(-x), and the marginal probability density function of Y is 1/(1 + y)^2. To find the probability that the lifetime of at least one component exceeds 1 year, we can use the complement of the event "at least one component has a lifetime of >= 1 year", which is "both components have lifetimes < 1 year". This can be calculated by taking the double integral from 0 to 1 of the function with respect to x and y,
  • #1
Rifscape
41
0

Homework Statement


Two components of a laptop computer have the following joint probability density function for their useful lifetimes X and Y (in years):

f(xy)=xe^(−x(1+y)) 0 <= x <= y

0 otherwise

Find the marginal probability density function of X, fX(x). Enter a formula below. Use * for multiplication, / for division, ^ for power and exp for exponential function. For example, 3x^3*exp(-x/3) means 3x^3e^(-x/3).

I found the answer to this, it is e^(-x).

Find the marginal probability density function of Y, fY(y). Enter a formula below.

I found the answer to this one too, its 1/(1 + y)^2 .

What is the probability that the lifetime of at least one component exceeds 1 year (when the manufacturer's warranty expires)? Round your answer to 4 decimal places.

This is the part I'm having trouble on, I'm not really sure how to start or set up this question.

Thanks for the help.

Homework Equations



The marginal probability equations, I'm not sure how to write them here.

The Attempt at a Solution


I don't really know how to set up the third part.
 
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  • #2
Rifscape said:

Homework Statement


Two components of a laptop computer have the following joint probability density function for their useful lifetimes X and Y (in years):

f(xy)=xe^(−x(1+y)) 0 <= x <= y

0 otherwise

Find the marginal probability density function of X, fX(x). Enter a formula below. Use * for multiplication, / for division, ^ for power and exp for exponential function. For example, 3x^3*exp(-x/3) means 3x^3e^(-x/3).

I found the answer to this, it is e^(-x).

Find the marginal probability density function of Y, fY(y). Enter a formula below.

I found the answer to this one too, its 1/(1 + y)^2 .

What is the probability that the lifetime of at least one component exceeds 1 year (when the manufacturer's warranty expires)? Round your answer to 4 decimal places.

This is the part I'm having trouble on, I'm not really sure how to start or set up this question.

Thanks for the help.

Homework Equations



The marginal probability equations, I'm not sure how to write them here.

The Attempt at a Solution


I don't really know how to set up the third part.

The complement of the event "at least one component has a lifetime of >= 1 year" is "both components have lifetimes < 1 year".
 
  • #3
Ray Vickson said:
The complement of the event "at least one component has a lifetime of >= 1 year" is "both components have lifetimes < 1 year".
Alright yeah that makes sense, the problem I have is how to set it up. Would I just do the double integral from 0 to 1 of the function with respect to x and y and then subtract?
 
Last edited:
  • #4
Ray Vickson said:
The complement of the event "at least one component has a lifetime of >= 1 year" is "both components have lifetimes < 1 year".

Actually nevermind I got it, thanks for the help!
 

1. What is a marginal probability distribution?

A marginal probability distribution is a probability distribution that shows the probabilities of different values for a single variable, ignoring the values of other variables. It is obtained by summing or integrating the joint probability distribution over all possible values of the other variables.

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

A joint probability distribution shows the probabilities of different combinations of values for two or more variables, while a marginal probability distribution shows the probabilities of different values for a single variable.

3. How can a marginal probability distribution be represented?

A marginal probability distribution can be represented in various forms, such as a probability mass function for discrete variables or a probability density function for continuous variables.

4. What information can be obtained from a marginal probability distribution?

A marginal probability distribution can provide information about the likelihood of different values for a single variable, including the most likely value and the range of values that are more or less likely to occur.

5. How is a marginal probability distribution used in statistical analysis?

A marginal probability distribution is used in statistical analysis to understand the relationship between different variables and to make predictions about the values of one variable based on the values of other variables. It can also be used to calculate other important statistics, such as expected values and variances.

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