Distribution of Bernoulli random variable

In summary: Proof_of_the_binomial_distributionIn summary, the Binomial distribution is a probability distribution that describes the outcome of a random experiment where X represents the number of successes in an experiment, and p is the probability of success.
  • #1
trojansc82
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Homework Statement



a) Let X1, X2, ...XN be a collection of independent Bernoulli random variables. What is the distribution of Y = [itex]\sum[/itex]Ni = 1 Xi


b) Show E(Y) = np

Homework Equations



Bernoulli equations f(x) = px(1-p)1-x

The Attempt at a Solution



a)X1 + X2 + ... + XN = p

b) Not sure. I'm assuming the expectation would mean when each probability is multiplied by "x", the x's go from 0 to n, meaning they represent the n.
 
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  • #2
trojansc82 said:

Homework Statement



a) Let X1, X2, ...XN be a collection of independent Bernoulli random variables. What is the distribution of Y = [itex]\sum[/itex]Ni = 1 Xib) Show E(Y) = np

Homework Equations



Bernoulli equations f(x) = px(1-p)1-x

The Attempt at a Solution



a)X1 + X2 + ... + XN = p

b) Not sure. I'm assuming the expectation would mean when each probability is multiplied by "x", the x's go from 0 to n, meaning they represent the n.

Have you stated the question carefully and correctly? What is p? Are the independent random variables of the same distribution? Have you studied binomial distributions yet?
 
  • #3
LCKurtz said:
Have you stated the question carefully and correctly? What is p? Are the independent random variables of the same distribution? Have you studied binomial distributions yet?

Yeah, I have studied the binomial. I'm going back and looking at old tests before my final, so I'm trying to remember the way the solution worked.

I was easily able to derive the mean and variance from a Bernoulli distribution, but I'm having trouble with these two problems.

Also, p is the expected outcome. I'm assuming you're just deriving the mean of the Binomial distribution here, but I'm not sure how that is done.
 
  • #4

FAQ: Distribution of Bernoulli random variable

What is a Bernoulli random variable?

A Bernoulli random variable is a discrete random variable that takes on only two possible outcomes, typically represented as success (1) or failure (0). It is a fundamental concept in probability and statistics, and is commonly used to model binary events.

How is a Bernoulli random variable distributed?

A Bernoulli random variable follows a Bernoulli distribution, which is a type of discrete probability distribution. It is characterized by a single parameter, p, which represents the probability of success. The probability mass function of a Bernoulli distribution is P(X=x) = p^x * (1-p)^(1-x), where x=0 or 1.

What is the expected value of a Bernoulli random variable?

The expected value, or mean, of a Bernoulli random variable is equal to p, the probability of success. This means that if the experiment is repeated many times, the average number of successes will approach p. It is a measure of the central tendency of the distribution.

How is a Bernoulli random variable related to a binomial distribution?

A Bernoulli random variable can be thought of as a special case of a binomial distribution, which is the distribution of the number of successes in a series of n independent Bernoulli trials. A binomial distribution is characterized by two parameters, n and p, where n represents the number of trials and p represents the probability of success in each trial.

How can a Bernoulli random variable be used in real-world applications?

Bernoulli random variables can be used to model binary events in a variety of fields, such as economics, psychology, and biology. For example, they can be used to model the success or failure of a medical treatment, the outcome of a coin toss, or the likelihood of a customer making a purchase. They are also an important building block for more complex statistical models.

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