Finding Joint Density Function for Region R: Graph and Calculation

In summary, the conversation discussed finding the area and joint density function for a region R = {(x,y): |x| + |y| <= 1}. The area was determined to be 2 by multiplying the area of one triangle by four, and it was suggested that the joint density function could be f(x,y) = 1/2 for 0 < x < 1 - |x| and for y within the limits of R. However, it was also mentioned that x could be negative and that the region R was diamond shaped, so the description of R itself should be used to determine where f = 1/2.
  • #1
squenshl
479
4
I have a region R = {(x,y): |x| + |y| <= 1}
I have sketched the graph on the x-y plane and shaded in the region R (I got a diamond).
How do I find the area of R and hence find the joint density function f(x,y) and how do I specify the region for which f(x,y) is non-zero.
 
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  • #2
A diamond is 4 triangles: Area(R) = 4 times the area of one triangle.

Are x and y random variables?
 
  • #3
They are just random points chosen uniformly on the region
 
  • #4
So the area is 0.5 x 4 = 2, how does that relate to finding the joint density function.
So f(x,y) = 2 for -1 < x < 1 and -1 < y < 1, 0 otherwise
 
Last edited:
  • #5
No. Wouldn't it be f(x,y) = x + y for 0 < x < 1 and 0 < y < 1 and 0 otherwise
because when I do the double integral, the integral equals 1 which it should.
 
  • #6
squenshl said:
So the area is 0.5 x 4 = 2, how does that relate to finding the joint density function.
So f(x,y) = 2 for -1 < x < 1 and -1 < y < 1, 0 otherwise

squenshl said:
No. Wouldn't it be f(x,y) = x + y for 0 < x < 1 and 0 < y < 1 and 0 otherwise
because when I do the double integral, the integral equals 1 which it should.

You said your region is R = {(x,y): |x| + |y| <= 1}. That is not the same as you are describing as -1 < x < 1 and -1 < y < 1. And a uniform distribution gives a constant density, not a variable like x + y. You need to have f(x,y) be a constant value on R such that the integral of f(x,y) over R is 1.
 
  • #7
f(x,y) = 1/2 for 0 < x < 1 - |x|, but I don't know the limits of y.
 
  • #8
squenshl said:
f(x,y) = 1/2 for 0 < x < 1 - |x|, but I don't know the limits of y.

No. Can't x be negative?

Didn't you say you had drawn a picture of your region R and it was diamond shaped?

Use the description of R itself to say where f = 1/2.
 

1. What is a joint density function?

A joint density function is a mathematical function that describes the probability distribution of two or more random variables. It is often used to model the relationship between multiple variables in a system.

2. How is a joint density function different from a marginal density function?

A marginal density function describes the probability distribution of a single random variable, while a joint density function describes the distribution of multiple variables together. In other words, a joint density function takes into account the relationship between variables, whereas a marginal density function does not.

3. What is the importance of a joint density function in statistics?

A joint density function is important in statistics because it allows us to calculate the probability of various outcomes for multiple variables simultaneously. This can be useful in many real-world applications, such as risk assessment and predictive modeling.

4. How is a joint density function related to a joint probability distribution?

A joint density function and a joint probability distribution are essentially the same thing, but they are described using different mathematical notation. In general, a joint density function is used when the variables being studied are continuous, while a joint probability distribution is used for discrete variables.

5. How do you calculate the joint density function for two independent variables?

If two variables are independent, the joint density function can be calculated by multiplying their individual density functions. For example, if X and Y are independent, the joint density function would be f(x,y) = f(x) * f(y).

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