Joint Density Probability Question

In summary, the problem asks to find F(z) and f(z) where Z=X+Y, given that the joint density f(x,y) is equal to 1 in a shaded area and 0 elsewhere. The attempt at a solution involves computing double integrals, with the solutions being z-(1/2) and -z-(1/2). However, adding them together would result in F(z) equaling -1, which is not possible. Further help and guidance is requested.
  • #1
shespuzzling
7
0

Homework Statement



I am having trouble understanding the results of a homework problem and want to make sure I'm doing it right. The problem states that the joint density f(x,y) is equal to 1 in the shaded area and 0 elsewhere. The graph for f(x,y) looks like the attachment. The problem asks you to find F(z) and f(z) where Z=X+Y.

Homework Equations



I started to solve this by computing the double integrals (sorry, don't know how to use math fonts here).

Integral as y goes from 0 to 1, and x goes from 0 to (z-y) of f(x,y)
Integral as y goes from -1 to 0, and x goes from (z-y) to 0 of f(x,y)

I got as the solutions z-(1/2) and -z-(1/2), respectively. So adding them together you'd get F(z) to equal -1, which doesn't make any sense.

Any help/guidance would be apprecaited.
 

Attachments

  • f(x,y).JPG
    f(x,y).JPG
    4.2 KB · Views: 372
Physics news on Phys.org
  • #2
Thanks! The attempt at a solutionI started to solve this by computing the double integrals (sorry, don't know how to use math fonts here).Integral as y goes from 0 to 1, and x goes from 0 to (z-y) of f(x,y) Integral as y goes from -1 to 0, and x goes from (z-y) to 0 of f(x,y)I got as the solutions z-(1/2) and -z-(1/2), respectively. So adding them together you'd get F(z) to equal -1, which doesn't make any sense. Any help/guidance would be apprecaited. Thanks!
 

FAQ: Joint Density Probability Question

What is joint density probability?

Joint density probability is a concept in statistics that measures the likelihood of two or more random variables occurring simultaneously. It is represented by a function that assigns a probability to each possible combination of values for the variables.

How is joint density probability different from individual probability?

Individual probability measures the likelihood of a single event occurring, while joint density probability measures the likelihood of multiple events occurring together. In other words, individual probability focuses on one variable, while joint density probability takes into account the relationship between multiple variables.

What is the difference between joint density probability and joint distribution?

Joint density probability is a function that assigns probabilities to combinations of values for multiple variables, while joint distribution is the probability distribution of those variables. In other words, joint density probability is a way to represent the joint distribution.

How is joint density probability used in data analysis?

Joint density probability is used to model the relationship between variables in a dataset, and can be used to make predictions about the likelihood of certain combinations of values occurring. It is often used in regression analysis and other statistical models to understand the relationship between variables and make predictions based on that relationship.

What is the difference between joint density probability and conditional probability?

Joint density probability measures the likelihood of multiple events occurring together, while conditional probability measures the likelihood of one event occurring given that another event has already occurred. In other words, joint density probability takes into account all possible combinations of events, while conditional probability focuses on a specific scenario.

Back
Top