Understanding Joint PDF and Independence in Probability: Solving for P(X+Y<=2)

In summary, the question asks to find the probability of the sum of two variables, X and Y, being less than or equal to 2. The teacher's solution involves using the joint probability density function and separating the integral into two parts based on the given limits for x and y. This is possible because the function is only dependent on x and y and can be divided into two independent functions. Drawing a sketch of the regions of integration can help visualize this concept.
  • #1
cjaylee
7
0
Let the joint PDF of (X,Y) be of the form:
f(x,y) = 1/8x(x-y), 0<=x<=2, |y|<=x
f(x,y) = 0 elsewhere

Find P(X+Y<=2).

The answer that my teacher gave was

P(X+Y<=2)=∫01dx ∫-xx 1/8x(x-y)dy + ∫12dx ∫-x2-x 1/8x(x-y)dy

I do not understand how my teacher could separate the integral like that ∫ dx ∫ dy when the function has both the variable x and y. Shouldn't that be only possible when we can separate the function into 2 separate functions that are independent of each other?

Thanks for the help!
 
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  • #2
Hint: draw a sketch of the regions of integration in (x,y).
 

Related to Understanding Joint PDF and Independence in Probability: Solving for P(X+Y<=2)

1. What is a Joint Probability Density Function (PDF)?

A joint probability density function is a mathematical function that describes the probability of two or more continuous random variables occurring simultaneously. It is represented as a function of the variables and can be used to calculate the likelihood of specific outcomes.

2. How is a Joint PDF different from a regular PDF?

A regular PDF describes the probability distribution of a single random variable, while a joint PDF describes the probability distribution of two or more random variables. A joint PDF takes into account the relationship between the variables and how they affect each other's probabilities.

3. What is the importance of independence in a Joint PDF?

Independence in a Joint PDF means that the two variables are not affected by each other and their probabilities are completely separate. This simplifies the calculation of the joint probability and allows for easier analysis of the individual variables.

4. How do you determine if two random variables are independent in a Joint PDF?

If the joint probability of two variables can be expressed as the product of their individual probabilities, then they are considered independent in the Joint PDF. Mathematically, this can be represented as P(X,Y) = P(X) * P(Y).

5. Can a Joint PDF be used for discrete random variables?

Yes, a Joint PDF can be used for both continuous and discrete random variables. However, the calculation and interpretation of probabilities may differ between the two types of variables.

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