Limits of Integration Re: Joint Probability Functions

In summary, the conversation discusses the process of showing that a given function, f(x,y)=24xy for 0<= x<=1, 0<=y<=1, 0<=x+y<=1, otherwise x=0, is a joint probability function by double integration and obtaining a result of 1. The conversation also explores the limits of integration and how they relate to the problem at hand. Another related problem is discussed, involving the determination of independence between two variables and the process of finding marginal distributions. The question of adjusting limits of integration is also raised.
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insixyears
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Edit: I'm not sure if this post was deleted by an admin or if I just didn't click on the submit button. Apologies in advance if it was the former, but I reposted since I couldn't think of a reason why this post would be deleted

Homework Statement



f(x,y)=24xy for 0<= x<=1, 0<=y<=1, 0<=x+y<=1, otherwise x=0

The goal is to show that f(x,y) is a joint probability fxn and I believe we do that by doubly integrating f(x,y) and showing the result equals 1.

The Attempt at a Solution



I believe that the answer is: [tex]\int_0^1\int_0^{1-y} \! 24xy \, \mathrm{d}x{d}y[/tex], however I'm having trouble understanding the limits of integration. Why is it that we integrate from 0 to 1-y on x and from 0 to 1 on y?

Here's a similar problem I had earlier:

Homework Statement



f(x,y)=2 0<x<y, 0<y<1
The problem asks the student to determine whether X,Y are independent.

The Attempt at a Solution



So, I set out to find the marginal distribution of x:

[tex]\int_x^1 \! 2 \, \mathrm{d}y[/tex] Why do we integrated from x to 1
[tex]\int_0^y \! 2 \, \mathrm{d}x[/tex] Why do we integrate from 0 to y

Could we switch the limits of integration as long as we adjust them for both X and Y? For example, could we integrate from 0 to 1 instead of x to 1 if we change the limits of integration on Y?
 
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I'm not entirely sure if this is the right place to ask this question, but I figured it was worth a shot. Thank you in advance for any help!
 

FAQ: Limits of Integration Re: Joint Probability Functions

What is a joint probability function?

A joint probability function is a mathematical model that describes the probability of two or more events occurring simultaneously. It is used in statistics to analyze the relationship between multiple variables and their probabilities of occurrence.

What is the purpose of determining limits of integration for joint probability functions?

Determining the limits of integration for joint probability functions is important because it allows us to calculate the probability of an event occurring within a specified range. This is useful in understanding the likelihood of certain outcomes and making informed decisions based on those probabilities.

How do you determine the limits of integration for a joint probability function?

The limits of integration for a joint probability function are determined by the range of values for each variable that are relevant to the event being measured. These limits are typically determined by the problem at hand and can be found through statistical analysis or by examining the given data.

What are the limitations of using joint probability functions?

One limitation of using joint probability functions is that they assume independence between variables. In reality, many events may be dependent on one another, which can affect the accuracy of the calculated probabilities. Additionally, joint probability functions can become complex and difficult to interpret when dealing with a large number of variables.

How can joint probability functions be applied in real-world situations?

Joint probability functions have a wide range of applications in fields such as finance, economics, and social sciences. They can be used to analyze risk and make predictions based on historical data, as well as to understand the relationship between multiple variables in a given situation. For example, joint probability functions can be used to determine the likelihood of certain diseases in a population based on factors such as age, gender, and lifestyle habits.

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