The answer depends on whether or not the events are independent, whether or not the probabilities are the same for each event etc. Without that information, there cannot be an answer.
It is used when repeated trials are carried out , in which there are only two outcomes (success and failure) and the probability of success is a constant and is independent of the outcomes in other trials.
A number of independent trials such that there are only two outcomes and the probability of "success" remains constant.
A geometric distribution comes from a binary probability which does not have a set number of trials. It seeks to determine how many trials must be conducted before success is achieved. For example, instead of saying, "If I shoot the ball 5 times, what is my probability of success," a geometric probability would question, "How many times will I have to shoot the ball before I make a basket?"
The binomial distribution has two parameter, denoted by n and p. n is the number of trials. p is the constant probability of "success" at each trial.
A number of trials, each of which has only two outcomes: these are usually termed "success" and "failure". The trials must be independent and the probability of success must remain constant.
Consider a binomial distribution with 10 trials What is the expected value of this distribution if the probability of success on a single trial is 0.5?
It is used when repeated trials are carried out , in which there are only two outcomes (success and failure) and the probability of success is a constant and is independent of the outcomes in other trials.
A number of independent trials such that there are only two outcomes and the probability of "success" remains constant.
The requirements are that there are repeated trials of the same experiment, that each trial is independent and that the probability of success remains the same.
An empirical rule indicates a probability distribution function for a variable which is based on repeated trials.
The binomial distribution is one in which you have repeated trials of an experiment in which the outcomes of the experiment are independent, the probability of the outcome is constant.If there are n trials and the probability of "success" in each trail is p, then the probability of exactly r successes is (nCr)*p^r*(1-p)^(n-r) :where nCr = n!/[r!*(n-r)!]and n! = n*(n-1)*...*3*2*1
A geometric distribution comes from a binary probability which does not have a set number of trials. It seeks to determine how many trials must be conducted before success is achieved. For example, instead of saying, "If I shoot the ball 5 times, what is my probability of success," a geometric probability would question, "How many times will I have to shoot the ball before I make a basket?"
The binomial distribution has two parameter, denoted by n and p. n is the number of trials. p is the constant probability of "success" at each trial.
Binomials are used when the total of n independent trials take place and one wants to find the probability of r successes, when each success has a probability "p" of occurring. There should be independent trails, Probability of success stays the same for all trials, Fixed number of trials and Two different classifications in order to use binomial distribution.
A number of trials, each of which has only two outcomes: these are usually termed "success" and "failure". The trials must be independent and the probability of success must remain constant.
For the binomial, it is independent trials and a constant probability of success in each trial.For the Poisson, it is that the probability of an event occurring in an interval (time or space) being constant and independent.
The probability that is based on repeated trials of an experiment is called empirical or experimental probability. It is calculated by dividing the number of favorable outcomes by the total number of trials conducted. As more trials are performed, the empirical probability tends to converge to the theoretical probability.