Cumulative distribution function

In summary, the sample space for the quadrilateral defined by {(x,y): 0 < x < b, 0 < x < b} contains any x or y within 0 to b in both directions. The coordinate Z is denoted by the smallest value between x and y, with a minimum at 0 and a maximum at b. To find the region in the square corresponding to {Z < z}, the values of X and Y must both be greater than z. This means that Z (the smaller of X and Y) would be greater than z in this case. Therefore, the entire area of the square is not Z, but rather any values of z in (0,b) where both X and Y are greater
  • #1
magnifik
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A dart is thrown towards a quadrilateral defined by {(x,y): 0 < x < b, 0 < x < b}. Assume the dart is equally likely to land anywhere within this shape. Let Z be denoted by the (x,y) coordinate with the least value. Find the region in the square corresponding to {Z < z}

so i know the sample space contains any x or y within 0 to b in both directions. and i know that Z = whichever coordinate is the smallest, with a minimum at 0 and a maximum at b. i don't understand how to find where {Z < z} inside the square... wouldn't all the values be within Z? this makes no sense to me
 
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  • #2
magnifik said:
A dart is thrown towards a quadrilateral defined by {(x,y): 0 < x < b, 0 < x < b}. Assume the dart is equally likely to land anywhere within this shape. Let Z be denoted by the (x,y) coordinate with the least value. Find the region in the square corresponding to {Z < z}

so i know the sample space contains any x or y within 0 to b in both directions. and i know that Z = whichever coordinate is the smallest, with a minimum at 0 and a maximum at b. i don't understand how to find where {Z < z} inside the square... wouldn't all the values be within Z? this makes no sense to me

If I give you a value of z in (0,b) it is quite possible for both X and Y to both take values > z, so Z (the smaller of X and Y) would be > z in that case. By the way, you should distinguish between X (the random variable) and x (possible numerical value for X), and between Y and y.

RGV
 
  • #3
i'm still confused on how this relates to the area of the square
 
  • #4
is z the entire area of the square since it can take on any value from 0 to b in both the x and y directions?
 

1. What is a cumulative distribution function (CDF)?

A cumulative distribution function (CDF) is a statistical concept that represents the probability of a random variable taking on a value less than or equal to a given value. It is a function that maps a probability distribution to a cumulative probability, and is often used to understand the likelihood of certain outcomes in a dataset.

2. How is a cumulative distribution function different from a probability distribution function?

The main difference between a cumulative distribution function (CDF) and a probability distribution function (PDF) is that a CDF gives the probability of a random variable taking on a value less than or equal to a given value, while a PDF gives the probability of a random variable taking on a specific value. In other words, a PDF represents the relative likelihood of each possible outcome, while a CDF represents the overall likelihood of all outcomes up to a given value.

3. What is the purpose of using a cumulative distribution function?

A cumulative distribution function (CDF) is often used to analyze and summarize data, as it provides information about the likelihood of certain outcomes in a dataset. It can also be used to compare different datasets or to make predictions about future outcomes based on past data.

4. How is a cumulative distribution function calculated?

A cumulative distribution function (CDF) is calculated by taking the integral of the probability density function (PDF) of a given distribution. This integration gives a function that represents the cumulative probabilities of all possible outcomes in the dataset.

5. Can a cumulative distribution function be used for any type of data?

Yes, a cumulative distribution function (CDF) can be used for any type of data that can be represented by a probability distribution. This includes continuous data, such as height or weight measurements, as well as discrete data, such as the number of students in a classroom. However, the specific type of CDF used may vary depending on the type of data and the assumptions made about its distribution.

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