- #1
flughafen
- 2
- 0
- Homework Statement
- statistics
- Relevant Equations
- pmf
I am new to the topic so I do need your help here. Thanks in advance
Understanding the PMF of a Random Variable: A Brief Overview
- Thread starter flughafen
- Start date
In summary, the conversation is about a user seeking help with a topic on a forum. However, the forum requires an effort from the user before providing assistance. The user has not made much progress and is considering using the provided answer as a base for future questions. The forum member suggests sketching a cdf to better understand the topic and asks if the user knows the difference between a continuous and discrete probability distribution.
- #2
BvU
Science Advisor
Homework Helper
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Hello @flughafen, 
!
Unfortunately for you, PF requires an effort from you before we are allowed to assist. So: what have you got thus far ?
!Unfortunately for you, PF requires an effort from you before we are allowed to assist. So: what have you got thus far ?
- #3
flughafen
- 2
- 0
BvU said:Hello @flughafen,
Unfortunately for you, PF requires an effort from you before we are allowed to assist. So: what have you got thus far ?
I haven't gotten much far yet. Just left it there, thinking that I could use the answer of this as base for other alike questions.
- #4
etotheipi
flughafen said:
Have you tried sketching the cdf as in part a? This should help you to see what's going on.
Last edited by a moderator:
- #5
HallsofIvy
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Do you not at least know the difference between a "continuous" and a "discrete" probability distribution? That is one of the questions asked that you did not answer.
Related to Understanding the PMF of a Random Variable: A Brief Overview
1. What is the PMF of a random variable Y?
The PMF (Probability Mass Function) of a random variable Y is a function that assigns probabilities to each possible value of Y. It describes the probability distribution of Y and can be represented as a graph or a table.
2. How is the PMF of Y different from the PDF of Y?
The PMF of Y is used for discrete random variables, while the PDF (Probability Density Function) is used for continuous random variables. The PMF gives the probability of a specific value of Y occurring, while the PDF gives the probability of Y falling within a certain range of values.
3. How do you calculate the PMF of Y?
The PMF of Y can be calculated by determining the probability of each possible value of Y using the formula P(Y=y) = number of outcomes where Y=y / total number of outcomes. This can be done using a table or a graph, depending on the distribution of Y.
4. What does the PMF of Y tell us about the random variable?
The PMF of Y provides information about the probability distribution of Y. It tells us the likelihood of different values of Y occurring and can help us understand the characteristics and behavior of the random variable.
5. Can the PMF of Y be used to calculate expected values?
Yes, the PMF of Y can be used to calculate the expected value of Y, which is the average value that we would expect to obtain if we repeatedly sampled Y. The formula for calculating expected value using the PMF is E(Y) = Σ y * P(Y=y), where y represents each possible value of Y.
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