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what is meant by a negative binomial distribution what is meant by a negative binomial distribution

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what are the uses of binomial distribution

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You distribute the binomial.

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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.

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Normal distribution is the continuous probability distribution defined by the probability density function. While the binomial distribution is discrete.

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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.

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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.

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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.

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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.

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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.

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Use the continuity correction when using the normal distribution to approximate a binomial distribution to take into account the binomial is a discrete distribution and the normal distribution is continuous.

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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.

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There is no such thing. The Normal (or Gaussian) and Binomial are two distributions.

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No it is a "discrete" distribution because the outcomes can only be integers.

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The Poisson distribution with parameter np will be a good approximation for the binomial distribution with parameters n and p when n is large and p is small. For more details See related link below

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The binomial distribution is defined by two parameters so there is not THE SINGLE parameter.

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The mean of a binomial probability distribution can be determined by multiplying the sample size times the probability of success.

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Binomial distribution is learned about in most statistic courses. You could use them in experiments when there are two possible outcomes and each experiment is independent.

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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?

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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.

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Sol Weintraub has written:

'Tables of the cumulative binomial probability distribution for small values of p' -- subject(s): Binomial distribution, Tables

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The binomial distribution is a discrete probability distribution. The number of possible outcomes depends on the number of possible successes in a given trial. For the Poisson distribution there are Infinitely many.

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The Poisson distribution is characterised by a rate (over time or space) of an event occurring. In a binomial distribution the probability is that of a single event (outcome) occurring in a repeated set of trials.

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The binomial distribution can be approximated with a normal distribution when np > 5 and np(1-p) > 5 where p is the proportion (probability) of success of an event and n is the total number of independent trials.

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A small partial list includes:

-normal (or Gaussian) distribution

-binomial distribution

-Cauchy distribution

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The statement is false. The binomial distribution (discrete) or uniform distribution (discrete or continuous) are symmetrical but they are not normal. There are others.

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The binomial probability distribution is discrete.

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n(p)(1-p) n times p times one minus p, where n is the number of outcomes in the binomial distribution, and p is the probability of a success.

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The binomial distribution is a discrete probability distribution which describes the number of successes in a sequence of draws from a finite population, with replacement. The hypergeometric distribution is similar except that it deals with draws without replacement. For sufficiently large populations the Normal distribution is a good approximation for both.

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The F distribution can't be negative.

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Yes, and the justification comes from the Central Limit Theorem.

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is median

a chafractoristic of population

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The central limit theorem basically states that for any distribution, the distribution of the sample means approaches a normal distribution as the sample size gets larger and larger. This allows us to use the normal distribution as an approximation to binomial, as long as the number of trials times the probability of success is greater than or equal to 5 and if you use the normal distribution as an approximation, you apply the continuity correction factor.

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No. The binomial distribution (discrete) or uniform distribution (discrete or continuous) are symmetrical but they are not normal. There are others.

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The Poisson distribution is a limiting case of the binomial distribution when the number of trials is very large and the probability of success is very small. The Poisson distribution is used to model the number of occurrences of rare events in a fixed interval of time or space, while the binomial distribution is used to model the number of successful outcomes in a fixed number of trials.

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[(1 - p)/(1 - pet)]r for t < -ln(p)

where

p = probability of success in each trial,

r = number of failures before success.

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Rolling a six with a die or not rolling a six.

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No.

I am using "normalization" as used in probability theory as application of a normalizing constant to a value, to make it conform to a certain distribution.

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You can use a normal distribution to approximate a binomial distribution if conditions are met such as n*p and n*q is > or = to 5 & n >30.

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Yes, the chi-square test can be used to test how well a binomial fits, provided the observations are independent of one another and all from the same (or identical) binomial distribution.

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The number of 6s in 37 rolls of a loaded die and binomial.

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