Understanding Discrete and Continuous PMFs: Exploring the Differences

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In summary, the conversation discusses a problem involving the pmf of a discrete and continuous Pmf function. The solution to all three parts is 3/15, as the only relevant x values are 1 and 2. The conversation also clarifies the difference between discrete and continuous Pmf functions.
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Homework Statement


Let [tex]P_{x}(x) = \frac{x}{15}, x = 1,2,3,4,5 ; 0 elsewhere [/tex]
be the pmf of X. Find P(X=1 or 2), P(1/2 < X < 5/2), P(1 ≤X≤2).

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The Attempt at a Solution


I believe what this problem is trying to show is the difference between discrete and continuous Pmf functions.

First, I believe the answer to ALL three parts is 3/15 as this is a discrete interval for x.
However, what is throwing me off is the continuous intervals in P(x).

Let me explain my answers:
1) If X = 1 or 2 then this is the easy solution of 1/15+2/15.
2) if we have the interval 1/2 < x < 5/2 well, the only x values that matter are still x = 1 or x = 2. Same answer.
3) Similarly, we have 1 and 2 included, so 3/15.

Am I doing anything wrong here? Is my thinking correct?

Thank you.
 
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RJLiberator said:
Is my thinking correct?
You have the correct answers. Your thinking is correct - unless you are taking a very advanced course that expects you to derive the answers using generalized definitions of integration.
 
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Perfect, thank you much for the confirmation.
I just didn't want it to end up being a trick question considering all three parts have the same answer. So the question just wanted to clarify discrete vs. continuous after all.

Cheers.
 

FAQ: Understanding Discrete and Continuous PMFs: Exploring the Differences

What is a discrete PMF in statistics?

A discrete PMF (Probability Mass Function) is a function that maps each possible value of a discrete random variable to its corresponding probability. In other words, it tells us the probability of each possible outcome of a discrete random variable.

How is a discrete PMF different from a continuous PDF?

A discrete PMF is used for discrete random variables, which can only take on a finite or countably infinite number of values. On the other hand, a continuous PDF (Probability Density Function) is used for continuous random variables, which can take on any value within a certain range. Additionally, the values of a discrete PMF are probabilities, while the values of a continuous PDF are probabilities per unit of measurement.

How do you calculate the PMF of a discrete random variable X?

To calculate the PMF of a discrete random variable X, you need to know the possible values that X can take and their corresponding probabilities. Then, you can use the formula PMF(X = x) = P(X = x) to calculate the probability of X taking on a specific value x.

What is the relationship between a PMF and a CDF?

A PMF (Probability Mass Function) and a CDF (Cumulative Distribution Function) are both used to describe the probabilities associated with a random variable. The PMF gives the probabilities of specific values of the random variable, while the CDF gives the probability that the random variable is less than or equal to a certain value. The CDF can be calculated by summing up the probabilities from the PMF for all values less than or equal to a given value.

Can a discrete PMF be used to calculate the mean and variance of a random variable?

Yes, a discrete PMF can be used to calculate the mean and variance of a random variable. The mean of a random variable X is given by E(X) = ∑xP(X = x), where x is each possible value of X and P(X = x) is the corresponding probability. The variance of X is given by Var(X) = ∑(x-E(X))^2P(X = x).

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