bennie d waller, longwood university statistics bennie waller [email protected] 434-395-2046...

Bennie D Waller, Longwood University

Statistics

Bennie Waller

[email protected]

434-395-2046Longwood University

201 High StreetFarmville, VA 23901

mailto:[email protected]


Discrete Probability

Bennie Waller

[email protected]

434-395-2046Longwood University

201 High StreetFarmville, VA 23901

mailto:[email protected]


Discrete Probability Distributions

DISCRETE RANDOM VARIABLE - A variable that can assume only certain clearly separated values and is the typically the result of counting something.

CHARACTERISTICS OF A PROBABILITY DISTRIBUTION

1.The probability of a particular outcome is between 0 and 1 inclusive.

2. The outcomes are mutually exclusive events.

3. The list is exhaustive. So the sum of the probabilities of the various events is equal to 1.

CONTINUOUS RANDOM VARIABLE – A variable that can assume an infinite number of values within a given range. It is usually the result of some type of measurement


Experiment:

Toss a coin three times. Observe the number of heads. The possible results are: Zero heads, One head, Two heads, andThree heads.

What is the probability distribution for the number of heads?

PROBABILITY DISTRIBUTION A listing of all the outcomes of an experiment and the probability associated with each outcome.

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Probability Distributions


Discrete Probability Distribution

Mean

X P(x)

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.06 .20

.07 .20

.08 .20

.09 .20



Variance

X P(x)

.05 .20

.06 .20

.07 .20

.08 .20

.09 .20


Binomial Probability Distribution• A Widely occurring discrete probability distribution• Characteristics of a Binomial Probability Distribution

1. There are only two possible outcomes on a particular trial of an experiment.

2. The outcomes are mutually exclusive,

3. The random variable is the result of counts.

4. Each trial is independent of any other trial

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Binomial Distribution


Probability Distribution of Number of Heads Observed in 3 Tosses of a Coin

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Calculating means and variances of a binominal distribution


Poisson Probability Distribution• The Poisson probability distribution is characterized by the

number of times an event happens during some interval or continuum.

Examples include:

• The number of misspelled words per page in a newspaper.

• The number of calls per hour received by Dyson Vacuum Cleaner Company.

• The number of vehicles sold per day at Hyatt Buick GMC in Durham, North Carolina.

• The number of goals scored in a college soccer game.

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Poisson Distribution


Assume baggage is rarely lost by Northwest Airlines. Suppose a random sample of 1,000 flights shows a total of 300 bags were lost. Thus, the arithmetic mean number of lost bags per flight is 0.3 (300/1,000). If the number of lost bags per flight follows a Poisson distribution with u = 0.3, find the probability of not losing any bags.

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Binomial – Shapes for Varying n ( constant)


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Discrete Probability Distributions

Problem: The arrival of customers at a service desk follows a Poisson distribution. If they arrive at a rate of two every five minutes, what is the probability that no customers arrive in a five-minute period?


ExampleProblem: Elly's hot dog emporium is famous for its chilidogs. Elly's latest sales indicate that 30% of the customers order their chilidogs with hot peppers. Suppose 18 customers are selected at random. What is the probability that exactly ten customers will ask for hot peppers?


Example

Problem: For the following probability distribution, what is the variance?


ExampleProblem: There are eight flights from Minneapolis to St. Cloud each day. The probability that any one flight is late is 0.10. Using the binomial probability formula, what is the probability that 1 or more are late?


ExampleProblem: There are eight flights from Minneapolis to St. Cloud each day. The probability that any one flight is late is 0.10. Using the binomial probability formula, what is the probability that none are late?


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