Nothing really. It concerns an experiment with identified success and failure probabilities (p and q), or Bernoulli trials, like the conventional binomial distribution. In an negative binomial experiment, the experiment is stopped after "r" successes occur in n trials. Thus, there must be r-1 successes in the first n-1 trials, and the final trial must be a success. This stopping event causes a n-1 and r-1 terms to appear in the factorial expressions of the distribution, which I suspect is the origins of calling this distribution a "negative binomial distribution." I would prefer to call this a Bernoulli experiment distibution with a stopping rule, but that's probably much too long. Some excellent websites provide examples and more discussion: http://mathworld.wolfram.com/NegativeBinomialDistribution.html http://stattrek.com/Lesson2/NegBinomial.aspx http://en.wikipedia.org/wiki/Negative_binomial_distribution Stattrek has very good examples. Note the distribution can be expressed in a number of forms.
First i will explain the binomial expansion
It is necessary to use a continuity correction when using a normal distribution to approximate a binomial distribution because the normal distribution contains real observations, while the binomial distribution contains integer observations.
Binomial distribution is the basis for the binomial test of statistical significance. It is frequently used to model the number of successes in a sequence of yes or no experiments.
Poisson and Binomial both the distribution are used for defining discrete events.You can tell that Poisson distribution is a subset of Binomial distribution. Binomial is the most preliminary distribution to encounter probability and statistical problems. On the other hand when any event occurs with a fixed time interval and having a fixed average rate then it is Poisson distribution.
The mean of a binomial probability distribution can be determined by multiplying the sample size times the probability of success.
what is meant by a negative binomial distribution what is meant by a negative binomial distribution
what are the uses of binomial distribution
You distribute the binomial.
The binomial distribution is defined by two parameters so there is not THE SINGLE parameter.
In a symmetric binomial distribution, the probabilities of success and failure are equal, resulting in a symmetric shape of the distribution. In a skewed binomial distribution, the probabilities of success and failure are not equal, leading to an asymmetric shape where the distribution is stretched towards one side.
Normal distribution is the continuous probability distribution defined by the probability density function. While the binomial distribution is discrete.
First i will explain the binomial expansion
The negative binomial can be applied in any situation in which there is a series of independent trials, each of which can result in either of just two outcomes. The distribution applies to the number of trials that occur before the designated outcome occurs. For example, if you start flipping a fair coin repeatedly the negative binomial distribution gives the number of times you must flip the coin until you see 'heads'. There are also 'everyday' applications in inventory control and the insurance industry. Please see the link.
It is necessary to use a continuity correction when using a normal distribution to approximate a binomial distribution because the normal distribution contains real observations, while the binomial distribution contains integer observations.
Binomial distribution is the basis for the binomial test of statistical significance. It is frequently used to model the number of successes in a sequence of yes or no experiments.
Poisson and Binomial both the distribution are used for defining discrete events.You can tell that Poisson distribution is a subset of Binomial distribution. Binomial is the most preliminary distribution to encounter probability and statistical problems. On the other hand when any event occurs with a fixed time interval and having a fixed average rate then it is Poisson distribution.
The distribution depends on what the variable is. If the key outcome is the number on the top of the die, the distribution in multinomial (6-valued), not binomial. If the key outcome is the number of primes, composite or neither, the distribution is trinomial. If the key outcome is the number of sixes, the distribution is binomial with unequal probabilities of success and failure. If the key outcome is odd or even the distribution is binomial with equal probabilities for the two outcomes. Thus, depending on the outcome of interest the distribution may or may not be binomial and, even when it is binomial, it can have different parameters and therefore different shapes.