Negative binomial distribution

In summary, the task is to repeatedly roll a fair die until a 3 is achieved on the fourth roll. X represents the number of rolls needed to achieve this goal. The question is asking for the expected value (E(X)) and variance (Var(X)) of X. However, there is some confusion about the details of the task and how successes are defined. Further clarification is needed in order to accurately solve the problem.
  • #1
DotKite
81
1

Homework Statement



Repeatdly roll a fair die until the outcome 3 has accurred on the 4th roll. Let
X be the number of times needed in order to achieve this goal. Find E(X)
and Var(X)

Homework Equations





The Attempt at a Solution



I am having trouble deciphering this question. Is the first sentence saying to roll a die until you get a 3 on the fourth roll? thus the event of a success is when you get a 3 on the fourth roll? but what is number of successes? which you need to know in order to find the mean in a negative binomial distribution.

Apparently E(X) = 24.
 
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  • #2
DotKite said:

Homework Statement



Repeatdly roll a fair die until the outcome 3 has accurred on the 4th roll. Let
X be the number of times needed in order to achieve this goal. Find E(X)
and Var(X)

Homework Equations





The Attempt at a Solution



I am having trouble deciphering this question. Is the first sentence saying to roll a die until you get a 3 on the fourth roll? thus the event of a success is when you get a 3 on the fourth roll? but what is number of successes? which you need to know in order to find the mean in a negative binomial distribution.

Apparently E(X) = 24.

Please clarify: do you mean the first 1, 2 or 3 tosses could also result in a "3", and that you look only at the result of the fourth toss? Or, do you mean that you count the rolls only if the first three are non-3s and the fourth is a 3? It makes a huge difference!
 

Related to Negative binomial distribution

1. What is the Negative Binomial Distribution?

The Negative Binomial Distribution is a probability distribution that is used to model the number of successes in a series of independent and identically distributed Bernoulli trials before a specified number of failures occur. It is a discrete probability distribution that is often used in situations where the outcome of interest is the number of failures before a certain number of successes is reached.

2. What are the main characteristics of the Negative Binomial Distribution?

There are three main characteristics of the Negative Binomial Distribution:

  • It is a discrete probability distribution, meaning that it can only take on certain values.
  • It has two parameters: the number of successes (r) and the probability of success (p).
  • It is right-skewed, meaning that the majority of the data is concentrated on the left side of the distribution.

3. How is the Negative Binomial Distribution different from the Binomial Distribution?

The main difference between the Negative Binomial Distribution and the Binomial Distribution is that the Binomial Distribution models the number of successes in a fixed number of trials, while the Negative Binomial Distribution models the number of failures before a specified number of successes is reached. Additionally, the Binomial Distribution has only one parameter (the probability of success), while the Negative Binomial Distribution has two parameters (the number of successes and the probability of success).

4. In what real-world situations is the Negative Binomial Distribution commonly used?

The Negative Binomial Distribution is often used in situations where the outcome of interest is the number of failures before a certain number of successes is reached. This can include things like the number of attempts it takes to successfully complete a task, the number of customers that need to be contacted before making a sale, or the number of trials it takes to observe a specific event.

5. How is the Negative Binomial Distribution related to the Poisson Distribution?

The Negative Binomial Distribution is closely related to the Poisson Distribution. In fact, the Negative Binomial Distribution can be thought of as a generalization of the Poisson Distribution, where the number of successes in a series of trials is modeled instead of just the number of occurrences in a fixed period of time. As the number of successes (r) in the Negative Binomial Distribution approaches infinity, it converges to the Poisson Distribution.

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