How can I find the CDF and PDF of Y?

In summary, for a uniform(0,1) random variable X, with Y=e^−X, the CDF of Y is 0 for y < 1/e and 1 for y > 1, and increasing in between. The PDF of Y is 1/y and the expected value of Y is 1/2.
  • #1
Jonobro
7
0
Problem

Let X be a uniform(0,1) random variable, and let Y=e^−X.
Find the CDF of Y.
Find the PDF of Y.
Find EY.

Relevant Equations
5b04f0e6f9.png

http://puu.sh/kAVJ8/0f2b1e7b22.png


My attempt at a solution

If I solve for the range of y I get (1, 1/e), but because Y is not an increasing function, my second bound is smaller than my first. I am really confused as to how I would be able to solve for the CDF and PDF in this case... Any help would be greatly appreciated.
 
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  • #3
To get CDF:
[itex]P(Y<y)=P(e^{-X}<y)=P(-X<lny)=P(X\ge -lny)=1+lny[/itex]
PDF = [itex]\frac{1}{y}[/itex]
 
  • #4
This is very helpful. Thanks. However, would the CDF be 0 for y < 1 and 1 for y > 1/e? This part does not really make sense.
 
  • #5
Mathman's formula is pretty clear for the cdf. It is equal to 0 at y=1/e, and 1 at y=1, and increasing in between.
 
  • #6
RUber said:
Mathman's formula is pretty clear for the cdf. It is equal to 0 at y=1/e, and 1 at y=1, and increasing in between.
... which is just what we expect since X certainly lies between 0 and 1, hence Y = exp(-X) lies between 1/e and 1. The density of Y is zero left of 1/e and right of 1. And in between it's the derivative of mathman's cumulative distribution function, hence 1/y.

Check: 1/y integrates to 1 when you integrate it from 1/e to 1
 
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FAQ: How can I find the CDF and PDF of Y?

1. How do I find the CDF and PDF of Y?

The CDF (cumulative distribution function) and PDF (probability density function) of Y can be found by first determining the distribution of Y. Depending on the type of distribution, there are different methods for finding the CDF and PDF. For example, if Y follows a normal distribution, the CDF and PDF can be calculated using mathematical formulas, while for other distributions, numerical methods or statistical software may be needed.

2. What information do I need to find the CDF and PDF of Y?

In order to find the CDF and PDF of Y, you will need to know the distribution of Y, as well as any parameters that define the distribution. For example, for a normal distribution, you would need to know the mean and standard deviation, while for a binomial distribution, you would need to know the probability of success and number of trials.

3. Can I find the CDF and PDF of Y if I only have a set of data points?

No, in order to find the CDF and PDF of Y, you will need to know the distribution of Y. Having a set of data points can help determine the distribution, but it is not sufficient on its own.

4. Why is it important to find the CDF and PDF of Y?

The CDF and PDF of Y provide important information about the distribution of Y. The CDF gives the probability of Y being less than or equal to a certain value, while the PDF gives the probability of Y falling within a certain range. This information can be used to make predictions and analyze data.

5. Is there a difference between the CDF and PDF of Y?

Yes, the CDF and PDF of Y serve different purposes. The CDF gives the cumulative probability of Y, while the PDF gives the probability density at a specific point. The CDF can be used to find the probability of Y falling within a range, while the PDF can be used to find the probability of Y being a specific value.

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