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# Bernoullis Random Variables And Binomial Distribution

## on Feb 17, 2010

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Bernoullis Random Variables And Binomial Distribution

Bernoullis Random Variables And Binomial Distribution

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## Bernoullis Random Variables And Binomial DistributionPresentation Transcript

• 1.10 Bernoulli’s random Variables & Binomial Distribution
• Bernoulli Random Variable
Suppose that a trial, or an experiment, whose outcome can be classified as either a success or a failure is performed. If we let X=1 when the outcome is a success and X=0 when the outcome is a failure, then the pmf of X is given by
• Bernoulli Random Variable
A random variable X is said to be a Bernoulli random variable (after the Swiss mathematician James Bernoulli) if its probability mass function is given by
• Binomial Random Variable
Suppose now that n independent trials, each of which results in a success with probability p and in a failure with probability 1-p, are to be performed. If X represents the number of successes that occur in the n trials, then X is said to be a Binomial random variable with parameters (n,p) . Thus a Bernoulli random variable is just a binomial random variable with parameters (1,p) .
• Binomial Distribution
Bernoulli Trials
There are only two possible outcomes for each trial.
The probability of a success is the same for each trial.
There are n trials, where n is a constant.
The n trials are independent.
• Binomial Distribution
Let X be the random variable that equals the number of successes in n trials.
If p and 1 – p are the probabilities of success and failure on any one trial then the probability of getting x successes and n – x failures in some specific order is
px(1- p)n – x
The number of ways in which one can select the x trials on which there is to be a success is
• Binomial Distribution
Thus the probability of getting x successes in n trials is given by
This probability distribution is called the binomial
distribution because for x = 0, 1, 2, …, and n the
value of the probabilities are successive terms of
binomial expansion of [p + (1 – p)]n;
• Binomial Distribution
for the same reason, the combinatorial quantities
are referred to as binomial coefficients.
The preceding equation defines a family of probability distributions with each member characterized by a given value of the parameterp and the number of trials n.
• Binomial Distribution
Distribution function for binomial distribution
• Binomial Distribution
The value of b(x;n,p) can be obtained by formula
since the two cumulative probabilities B(x; n, p) and B(x - 1; n, p) differ by the single term b(x; n,p).
If n is large the calculation of binomial probability can become quite tedious.
• Binomial Distribution Function
Table for n = 2 and 3 and p = .05 to .25
• Example
• The Mean and the Variance of a Probability Distribution
Mean of discrete probability distribution
The mean of a probability distribution is the mathematical expectation of a corresponding random variable.
If a random variable X takes on the values x1, x2, …, or xk, with the probability f(x1), f(x2),…, and f(xk), its mathematical expectation or expected value is
 = x1· f(x1) + x2· f(x2) + … + xk· f(xk)
• The Mean and the Variance of a Probability Distribution
Mean of binomial distribution
p  probability of success
n  number of trials
Variance of binomial distribution
• The Mean and the Variance of a Probability Distribution
Mean of binomial distribution
p  probability of success
n  number of trials
Proof:
• The Mean and the Variance of a Probability Distribution
Put x – 1= y and n – 1 = m, so n – x = m – y,
• Computing formula for the variance
Variance of binomial distribution
Proof:
• Put x – 1 = y and n – 1 = m
The Mean and the Variance of a Probability Distribution
• The Mean and the Variance of a Probability Distribution
• Put y – 1 = z and m – 1 = l in first summation
The Mean and the Variance of a Probability Distribution
• Moment Generating function for Binomial distribution
• Second ordinary/raw moment (moment about origin)
Moment Generating function for Binomial distribution
• Moment Generating function for Binomial distribution
Moment Generating function for Binomial distribution