Probability question - Binomial distribution

In summary, the conversation discusses finding the expression for the probability of obtaining two heads in a row while tossing two unbiased coins repeatedly. The random variable X represents the number of throws required, and it is likely a binomial distribution. The solution may involve an infinite geometric series.
  • #1
thereddevils
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Homework Statement



A game is played by tossing two unbiased coins repeatedly until two heads are obtained in the same throw. The random variable X denotes the number of throws required. Find the expression for P(X=r).

Homework Equations





The Attempt at a Solution



It looks to be a binomial distribution but the number of trials could be infinity. I have no idea to class this into which distribution(so far i have learned binomial, poisson and normal).

The best i can get is

P(X=r)=nCr (1/4)^r (3/4)^(n-r)
 
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  • #2


What is n supposed to be?

The number if trials could be infinite, but the probability of that happening is zero. The setup is similar to if someone just flipped one coin until they got a heads; do you know how to solve that problem?
 
  • #3


Office_Shredder said:
What is n supposed to be?

The number if trials could be infinite, but the probability of that happening is zero. The setup is similar to if someone just flipped one coin until they got a heads; do you know how to solve that problem?

It's an infinite geometric series probability(not sure what to call that).

1/2 + (1/2)^2 + (1/2)^3 + (1/2)^4 + ...

but now two coins are flipped together, i am not sure how to do that here.
 

Related to Probability question - Binomial distribution

1. What is a binomial distribution?

A binomial distribution is a probability distribution that describes the possible outcomes of a certain number of independent trials, where each trial can result in a success or failure. It is characterized by two parameters, the number of trials and the probability of success for each trial.

2. How is a binomial distribution different from other probability distributions?

A binomial distribution is different from other probability distributions because it only considers two possible outcomes for each trial (success or failure) and assumes that each trial is independent from the others. Other distributions, such as the normal distribution, can have more than two outcomes and may not assume independence between trials.

3. What is the formula for calculating the probability of a specific number of successes in a binomial distribution?

The formula for calculating the probability of exactly k successes in n trials in a binomial distribution is P(X = k) = nCk * p^k * (1-p)^(n-k), where nCk is the number of combinations of k objects from a set of n objects, p is the probability of success for each trial, and (1-p) is the probability of failure for each trial.

4. How can the binomial distribution be used in real life?

The binomial distribution can be used in real life to model and predict the outcomes of events with two possible outcomes, such as the probability of flipping a coin and getting a certain number of heads in a certain number of flips. It can also be used in fields such as genetics, where it can be used to calculate the probability of inheritance patterns for certain traits.

5. What are some assumptions that must be met for a binomial distribution to be applicable?

Some assumptions that must be met for a binomial distribution to be applicable include: each trial must have two possible outcomes, the outcomes must be mutually exclusive, the trials must be independent, and the probability of success must be constant for each trial. Additionally, the number of trials must be fixed and the trials must be performed under the same conditions.

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