Is the Proof of Joint CDF at NTNU Correct?

In summary, the conversation is about finding the probability of a certain event using the notation G_{x,y} and breaking it down into smaller events. The individual steps in the process are outlined and an error in the original proof is identified.
  • #1
Zen
10
0
http://folk.ntnu.no/bronner/temp/temp1178774511.57813.png

Or am I missing somthing obvious?
 
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  • #2
OK
So here's how we did this:
Let [tex]G_{x,y}=( \omega \in \Omega|X(\omega)\leq x,Y(\omega)\leq y)=(X\leq x,Y\leq y)[/tex]
then
[tex]P(G_{x,y})=P(X\leq x,Y\leq y)=F(x,y)[/tex]
then
[tex]P(a<X<b,c<Y<d)=G_{b,d}\setminus (G_{a,d}\cup G_{b,c})[/tex]
then break up the RHS:
[tex]P(G_{b,d})-[P(G_{a,d})+P(G_{b,c})-P(G_{a,d}\cap G_{b,c})][/tex]
can you go from there?
 
Last edited:
  • #3
Thanks, I got it know. I also found the error in my original proof: P((B intersect D) U (A intersect C)) != 1
 

Related to Is the Proof of Joint CDF at NTNU Correct?

1. What is "Proof of Joint CDF - NTNU"?

"Proof of Joint CDF - NTNU" is a mathematical concept used in statistics to determine the probability of two random variables occurring simultaneously. It is often used in data analysis and modeling to understand the relationship between two variables.

2. How is "Proof of Joint CDF - NTNU" calculated?

The "Proof of Joint CDF - NTNU" is calculated by taking the integral of the joint probability density function (PDF) over a specific region of the two variables. This region is defined by the values of the two variables and can range from a single point to an entire range of values.

3. What does the "Proof of Joint CDF - NTNU" tell us?

The "Proof of Joint CDF - NTNU" provides information about the relationship between two variables. It can tell us the probability of both variables occurring within a specific range and can also provide insights into the correlation between the two variables.

4. How is "Proof of Joint CDF - NTNU" used in data analysis?

"Proof of Joint CDF - NTNU" is often used in data analysis to determine the likelihood of a specific event occurring when two or more variables are involved. It can also be used to model the relationship between variables and make predictions based on the data.

5. Are there any limitations to "Proof of Joint CDF - NTNU"?

One limitation of "Proof of Joint CDF - NTNU" is that it assumes a linear relationship between the two variables. It also requires that the data follows a specific distribution, such as a normal distribution. Additionally, it can be challenging to interpret the results, especially if there are multiple variables involved.

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