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التركية - الإنجليزية

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binomial distribution

The discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p

(İstatistik) In probability theory and statistics, the binomial distribution is the discrete probability distribution of the number of successes in a sequence of n independent yes/no experiments, each of which yields success with probability p. Such a success/failure experiment is also called a Bernoulli experiment or Bernoulli trial. In fact, when n = 1, the binomial distribution is a Bernoulli distribution. The binomial distribution is the basis for the popular binomial test of statistical significance

In statistics, the probability of finding exactly k successes in n independent Bernoulli trials, when each trial has success probability p: n k n-k P(k,n,p) = ( ) p (1-p) k This ideal distribution is produced by evaluating the probability function for all possible k, from 0 to n If we have an experiment which we think should produce a binomial distribution, and then repeatedly and systematically find very improbable test values, we may choose to reject the null hypothesis that the experimental distribution is in fact binomial Also see the binomial section of the Ciphers By Ritter / JavaScript computation pages

The distribution in which each of a number of independent trials results in one of two mutually exclusive outcomes

The distribution of the probability of a specified number of successes in a given number of independent trials, in each of which the probability of success is the same Useful in performing certain statistical significance tests on dichotomous data

The probability of exactly k independent events each with a probability p in M trials is M choose k pk (1-p)M-k, where M choose k = M!/(k! (M-k)!) The mean of the Binomial distribution is pM and its variance is p(1-p)M

The probability distribution describing the number of events (e g , heads for coin flips) in a given number of trials for a specified probability of the event happening on each trial

A random variable has a binomial distribution (with parameters n and p) if it is the number of "successes" in a fixed number n of independent random trials, all of which have the same probability p of resulting in "success " Under these assumptions, the probability of k successes (and n-k failures) is nCk pk(1-p)n-k, where nCk is the number of combinations of n objects taken k at a time: nCk = n!/(k!(n-k)!) The expected value of a random variable with the Binomial distribution is n×p, and the standard error of a random variable with the Binomial distribution is (n×p×(1 - p))½ This page shows the probability histogram of the binomial distribution

a frequency distribution between two modes; refers to the shape of the frequency plot graph

Binomial distributions model (some) discrete random variables When a coin is flipped, the outcome is either a head or a tail ie there are two mutually exclusive possible outcomes For convenience, one of the outcomes can be termed 'success' and the other outcome termed 'failure' If a coin is flipped N times, then the binomial distribution can be used to determine the probability of obtaining exactly r successes in the N outcomes The formula that is used assumes that the events: fall into only two categories (ie are dichotomous); are mutually exclusive; are independent and are randomly selected

The frequency distribution of the probability of a specified number of successes in an arbitrary number of repeated independent Bernoulli trials. Also called Bernoulli distribution

probability distribution based on events for which there are only two possible outcomes on each occurrence

The theoretical frequency distribution of events that have two possible outcomes

The probability model for the number of successes x in a fixed number n of two-outcome ("success," "failure") trials that are independent and are of equal probability p; the distribution is computed as

A discrete distribution describing the results of an experiment known as Bernoulli’s Trial

A calculation that measures the likelihood of events taking place where the probability is measured between 0 (the event will certainly not occur) and 1 (the event is absolutely certain)

"A statistical distribution giving the probability of obtaining a specific number of successes in a binomial experiment" (Borwein, Watters, & Borowski, 1997)

a theoretical distribution of the number of successes in a finite set of independent trials with a constant probability of success

In probability, a binomial distribution gives the probabilities of k outcomes A (or n-k outcomes B) in n independent trials for a two-outcome experiment in which the possible outcomes are denoted A and B

A distribution which gives the probability of observing X successes in a fixed number (n) of independent Bernoulli trials p represents the probability of a success on a single trial Parameters: event probability 0<=p<=1 number of trials: n>0 Domain: X=0,1, ,n Mean: np Variance: np(1-p)

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