How Do You Determine the Joint CDF for Joy and Ethan's Arrival Times?

In summary: F_{X,Y}(x,y) &= \frac{x^2}{2x+y}-\frac{x^2}{2x+20}\\&= -\frac{x^2}{4x+40}.\end{align}So the joint CDF is $$F_{X,Y}(x,y)=-40x^2.$$In summary, the joint CDF of $X$ and $Y$ is $-40x^2$.
  • #1
marcadams267
21
1
So I have this word problem as seen below:

Joy and Ethan have agreed to meet for dinner between 8:00 PM and 9:00 PM. Suppose that Ethan may
arrive at any time between the set meeting. Joy on the other hand will arrive at the set meeting under the
following conditions:
• Joy will always arrive earlier than Ethan.
• Joy will never arrive later than 20 minutes.
• Joy’s arrival time added to Ethan’s arrival time will never exceed an hour.
Let X be the arrival time (in minutes) of Ethan and Y be the arrival time (in minutes) of Joy after 8:00
PM. Assume that all possible arrival times under the specified conditions are equally likely to occur.

I need to find the joint PDF of X and Y, which is easy enough as all that requires is differentiating the joint CDF with respect to both x and y. However, I'm not entirely sure how I would go about finding the joint CDF from the word problem above.
So far, I've gotten the bounds of the two variables as 0<X< 20 and X<Y<60 and X+Y <=60.
However, I do not know what to do next. I would appreciate any help on how to go about solving this problem. Thank you.
 
Last edited:
Physics news on Phys.org
  • #2
A:The joint CDF is$$F_{X,Y}(x,y) = P\{X \leq x, Y \leq y\}$$and the PDF is$$f_{X,Y}(x,y) = \frac{\partial^2 F_{X,Y}}{\partial x \partial y}.$$For $y>x$, we have\begin{align}F_{X,Y}(x,y) &= \int_0^x\int_t^y f_{X,Y}(s,t)\;ds\;dt\\&= \int_0^x\int_t^y f_X(s) f_Y(t|s)\;ds\;dt\\&= \int_0^x f_X(s)\left[\int_t^y f_Y(t|s)\;dt\right]\;ds\\&= \int_0^x f_X(s)(1-F_Y(t|s))\;ds.\end{align}Where$$f_X(x) = \frac{1}{20},\quad 0 < x < 20,$$is the PDF of $X$ and$$F_Y(y|x) = \begin{cases}0, & 0 \leq y \leq x,\\\frac{y-x}{60-x}, & x < y \leq 60,\end{cases}$$is the conditional CDF of $Y$ given $X$. The PDF of $Y$ conditioned on $X$ is$$f_Y(y|x) = \frac{1}{60-x},\quad x < y \leq 60.$$So\begin{align}F_{X,Y}(x,y) &= \int_0^x \frac{1}{20}\left(1-\frac{y-s}{60-s}\right)\;ds\\&= \frac{1}{20}\left[x - \frac{y-x}{60-x}\right].\end{align}Differentiating twice with respect to $x$ and
 

FAQ: How Do You Determine the Joint CDF for Joy and Ethan's Arrival Times?

What is a joint CDF in a word problem?

A joint CDF, or cumulative distribution function, is a mathematical function that describes the probability of a random variable being less than or equal to a certain value. In a word problem, it is used to calculate the probability of multiple variables occurring together.

How is the joint CDF calculated in a word problem?

The joint CDF is calculated by finding the probability of all possible combinations of the variables in the word problem. This can be done by using a joint probability table or by using the formula P(X ≤ x, Y ≤ y) = P(X ≤ x) * P(Y ≤ y).

What is the difference between a joint CDF and a marginal CDF in a word problem?

A joint CDF describes the probability of multiple variables occurring together, while a marginal CDF describes the probability of a single variable occurring. In a word problem, the joint CDF is used to calculate the probability of multiple events happening together, while the marginal CDF is used to calculate the probability of a single event.

How is the joint CDF used to solve a word problem?

The joint CDF is used to solve a word problem by calculating the probability of all possible combinations of the variables involved. This can help determine the likelihood of a certain outcome or event occurring.

What are some common applications of joint CDF in word problems?

Joint CDFs are commonly used in word problems involving multiple variables, such as in statistics, probability, and economics. They can also be used in real-world scenarios, such as predicting the likelihood of a certain event happening based on various factors.

Similar threads

Replies
20
Views
847
Replies
7
Views
2K
Replies
1
Views
3K
Replies
29
Views
3K
Back
Top