Interpretation of random variable

In summary, the given probability mass function represents a binomial random variable with a success probability of p and r-1 successes out of r-1+k trials, with each success having a probability of p. This can be interpreted as the probability of obtaining a certain number of successes in a series of trials with a given probability of success.
  • #1
Obraz35
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Homework Statement


The probability mass function of a random variable X is:
P(X=k) = (r+k-1 C r-1)pr(1-p)k
Give an interpretation of X.

Homework Equations





The Attempt at a Solution


The PMF looks like the setup for a binomial random variable. The first combination looks like you are arranging r-1 successes in r+k-1 slots. And the pr seems like it is giving the probability of r successes occurring. But I don't see what X as a whole is standing for.
 
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  • #2
i think you;re almost there, so i assume you mean
[tex] P(X=k) = C_{r-1}^{r+k-1}p^r(1-p)^k [/tex]

can re-write this as
[tex] P(X=k) = p.C_{(r-1)}^{k+(r-1)}p^{r-1}(1-p)^{k} [/tex]

which as you say comparing with the binomial distribution is for n trials, m success, and probabilty of success of p

can re-write this as
[tex] P(M=m) = p.C_{m}^{n}p^{n-m}(1-p)^{m} [/tex]

so your distribution is effectively p (the probabilty of a single success) times the probability of r-1 successes out of r-1+k trials (with probability of success p).

any ideas for an interpretation of this as a total entity?
 

1. What is a random variable?

A random variable is a numeric quantity that takes on different values in a random manner. This means that the value of the variable cannot be predicted with certainty, but rather follows a probability distribution.

2. How is a random variable interpreted in statistical analysis?

In statistical analysis, a random variable is used to model the uncertainty in a particular outcome. It is often used to represent the possible outcomes of a random experiment or to describe the variability in a population.

3. What is the difference between a discrete and a continuous random variable?

A discrete random variable can only take on a finite or countably infinite number of values, while a continuous random variable can take on any value within a certain range. For example, the number of heads when flipping a coin is a discrete random variable, while the weight of a person is a continuous random variable.

4. How is the probability distribution of a random variable determined?

The probability distribution of a random variable is determined by the values that the variable can take on and the corresponding probabilities of each value occurring. This can be represented in a table, graph, or mathematical formula.

5. What is the central limit theorem and how is it related to random variables?

The central limit theorem states that the sum of a large number of independent and identically distributed random variables will follow a normal distribution, regardless of the distribution of the individual variables. This theorem is important in statistical analysis as it allows for the use of normal distribution assumptions in many cases.

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