Uniform distribution- probabilities

In summary, the student is trying to solve a homework problem but is having difficulty understanding the notation and is looking for help. The student finds that the density function for X is correct but does not know how to find the cumulative distribution function.
  • #1
Dassinia
144
0
Hello, I am stuck at this exercise:

1. Homework Statement

X ~ U(0, a), a > 0 and Y = min(X; a=2).
- Find the cumulative distribution function of Y
-Is the variable Y continuous ?

Homework Equations


3. The Attempt at a Solution [/B]
The density function for X is
f(t)= 1/a if 0≤t≤a , 0 elsewhere
Is it correct to write that :
∀t < 0, P(Y ≤ t) = 0
∀0 ≤ t < a/2, P(Y ≤ t) = P(X ≤ t) = t/a
∀t ≥ a/2, P(Y ≤ t) = 1

And then don't know what to do
Thanks
 
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  • #2
I'm not familiar with the notation min(X; a=2). What does it mean?
 
  • #3
Dassinia said:
Hello, I am stuck at this exercise:

1. Homework Statement

X ~ U(0, a), a > 0 and Y = min(X; a=2).
- Find the cumulative distribution function of Y
-Is the variable Y continuous ?

Homework Equations


3. The Attempt at a Solution [/B]
The density function for X is
f(t)= 1/a if 0≤t≤a , 0 elsewhere
Is it correct to write that :
∀t < 0, P(Y ≤ t) = 0
∀0 ≤ t < a/2, P(Y ≤ t) = P(X ≤ t) = t/a
∀t ≥ a/2, P(Y ≤ t) = 1

And then don't know what to do
Thanks

You have an '##a##' in the definition of ##X## itself, and an '##a##' in the "definition" of ##Y## in terms of ##X##. Are they supposed to be the same '##a##' in both places? If so, I cannot make any sense out of what you wrote.

On the other hand, if you really mean that ##Y = \min(X,2)##, then that would have meaning. In that case it is important to distinguish between the two cases ##0 < a \leq 2## and ##a > 2##.
 
  • #4
Oh sorry, I didn't notice that there is a mistake in the expression of Y

It is Y=min(X; a/2)
 
  • #5
Dassinia said:
Oh sorry, I didn't notice that there is a mistake in the expression of Y

It is Y=min(X; a/2)
In that case your CDF for Y is correct. Draw it. Is it continuous?
 
  • #6
No, it is not continuous !
But how to find the cumulative distribution function ?
Thanks !
 
  • #7
Dassinia said:
No, it is not continuous !
But how to find the cumulative distribution function ?
Thanks !
The CDF is what you wrote in the OP. You specified P(Y<=t) for all three ranges of t.
 
  • #8
Oh, right ! I don't know why I thought that I had to find something else :eek:
Thanks for your answers !
 

Related to Uniform distribution- probabilities

1. What is a uniform distribution?

A uniform distribution is a probability distribution where all possible outcomes have an equal chance of occurring. This means that the probability of any given outcome is the same as any other outcome.

2. How is a uniform distribution represented mathematically?

A uniform distribution is typically represented using the notation U(a,b), where a and b are the parameters of the distribution. The formula for calculating the probability of a given outcome x is P(x) = 1/(b-a), where a ≤ x ≤ b.

3. What is the difference between a discrete and continuous uniform distribution?

A discrete uniform distribution has a finite number of possible outcomes with equal probabilities, while a continuous uniform distribution has an infinite number of possible outcomes with equal probabilities. In other words, a discrete uniform distribution can only take on whole number values, while a continuous uniform distribution can take on any value within a specified range.

4. What is the mean and variance of a uniform distribution?

The mean of a uniform distribution is the average of the two parameters, (a+b)/2. The variance is calculated as (b-a)^2 / 12. This means that the spread of the data is determined by the range between a and b.

5. How is a uniform distribution used in real-world applications?

Uniform distributions are commonly used in situations where all outcomes are equally likely, such as in rolling a fair die or flipping a fair coin. They can also be used in modeling random variables in fields such as finance, economics, and engineering.

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