Probability Distribution Function

In summary, the probability density function for this example is F(x) = 1 - e^{-2x} and the range is x < 0.
  • #1
p.mather
19
0

Homework Statement



Given the probability distribution function:

See attachment.

Determine the:

1. Probability density function.
2. The mean.
3. The median.

Homework Equations



Hello,

I am really struggling with this subject area, this is an example I have found, would someone be able to go through a solution so I can begin to understand it a bit more.

Appreciate any help.

Thanks.

The Attempt at a Solution

 

Attachments

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  • #2
It's pretty hard to work with that "example" since it is neither a probability density function nor a cumulative distribution function.

Question for you: How do I know that?
 
  • #3
The probability density function is F'(x) , the derivative of F(x).
The mean is [tex] \int x F'(x) dx [/tex] taken over the interval where F'(x) is not zero.
The median is found by solving for x in the equation F(x) = 1/2

(The mode (or modes) is found by finding the value (or values) where F'(x) is maximum. )

I don't have time to go through this in detail now. If you have further questions, ask.
 
  • #4
Stephen Tashi said:
The probability density function is F'(x) , the derivative of F(x).
The mean is [tex] \int x F'(x) dx [/tex] taken over the interval where F'(x) is not zero.
The median is found by solving for x in the equation F(x) = 1/2

(The mode (or modes) is found by finding the value (or values) where F'(x) is maximum. )

I don't have time to go through this in detail now. If you have further questions, ask.

As i was saying i am not good at this at all and its something i need to begin to understand. Would you please be able to provide a worked solution so i can begin to understand. It would be greatly appreciated. Thanks.
 
  • #5
LCKurtz said:
It's pretty hard to work with that "example" since it is neither a probability density function nor a cumulative distribution function.

Question for you: How do I know that?

Do not mindlessly start taking derivatives of that function. First Re-read LCKurtz post.

What is the range of a CDF?, and what are the restriction for the range of a PDF?

What are the requirements for a function to be a CDF or a PDF?
 
  • #6
The cumulative distribution (sometimes simply called "the distribution") of a random variable [itex] X [/itex] is the function [itex] F(x) [/itex] that gives the probability that [itex] X \leq x [/itex].

The example you gave:

[tex] F(x) = 1 - e^{2x} [/tex] for [tex] x \geq 0 [/tex] doesn't make sense as a cumulative distribution because for positive values of [itex] x [/itex] , [itex] F(x) < 0 [/itex] and probabilities must be non-negative numbers.

One way to fix the typo in the example is say that [itex] F(x) [/itex] is defined by:

[tex] F(x) = 1 - e^{-2x} [/tex] for [itex] x \geq 0 [/itex][tex] F(x) = 0 [/tex] for [itex] x < 0 [/itex]

Is this much clear?
 

Related to Probability Distribution Function

1. What is a probability distribution function (PDF)?

A probability distribution function (PDF) is a mathematical function that describes the probability of a random variable taking on a certain value or falling within a certain range of values. It is used to model and analyze data in various fields, such as statistics, physics, and finance.

2. What is the difference between a PDF and a cumulative distribution function (CDF)?

A PDF gives the probability that a random variable will take on a specific value, while a CDF gives the probability that the random variable will be less than or equal to a specific value. In other words, a PDF describes the likelihood of individual outcomes, whereas a CDF describes the likelihood of a range of outcomes.

3. How is a PDF calculated?

A PDF is calculated by taking the derivative of the corresponding CDF. This means that the PDF is the slope of the CDF at a particular point. It is also important to note that the area under the PDF curve must be equal to 1, as the total probability of all possible outcomes must equal 1.

4. What are the main types of probability distribution functions?

The main types of probability distribution functions include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. Each type is used to model different types of data and has its own unique set of characteristics and properties.

5. How are probability distribution functions used in data analysis?

Probability distribution functions are used in data analysis to describe and analyze the characteristics of a dataset. They can be used to calculate the likelihood of certain outcomes, make predictions, and identify patterns and trends in the data. They are also used to compare datasets and determine the significance of differences between them.

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