1. Discrete Probability Distributions
CHAPTER 5
Discrete Probability Distributions
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Objectives
Construct a probability distribution for a random variable.
Find the mean, variance, and expected value for a discrete random variable.
Find the exact probability for X successes in n trials of a binomial experiment.
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Objectives (cont’d.)
Find the mean, variance, and standard deviation for the variable of a binomial distribution.
Find probabilities for outcomes of variables using the Poisson, hypergeometric, and multinomial distributions.
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Introduction
Many decisions in business, insurance, and other real-life situations are made by assigning probabilities to all possible outcomes pertaining to the situation and then evaluating the results.
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5. Introduction (cont’d.)
This chapter explains the concepts and applications of probability distributions. In addition, special probability distributions, such as the binomial, multinomial, Poisson, and hypergeometric distributions are explained.
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Random Variables
A random variable is a variable whose values are determined by chance.
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7. Discrete Probability Distribution
A discrete probability distribution consists of the values a random variable can assume and the corresponding probabilities of the values. The probabilities are determined theoretically or by observation.
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Calculating the Mean
In order to find the mean for a probability distribution, one must multiply each possible outcome by its corresponding probability and find the sum of the products.
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Rounding Rule
The mean, variance, and standard deviation should be rounded to one more decimal place than the outcome, X.
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10. Variance of a Probability Distribution
The variance of a probability distribution is found by multiplying the square of each outcome by its corresponding probability, summing those products, and subtracting the square of the mean.
The formula for calculating the variance is:
The formula for the standard deviation is:
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Expected Value
Expected value or expectation is used in various types of games of chance, in insurance, and in other areas, such as decision theory.
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12. Expected Value (cont’d.)
The expected value of a discrete random variable of a probability distribution is the theoretical average of the variable. The formula is:
The symbol E(X) is used for the expected value.
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13. The Binomial Distribution
Many types of probability problems have only two possible outcomes or they can be reduced to two outcomes.
Examples include: when a coin is tossed it can land on heads or tails, when a baby is born it is either a boy or girl, etc.
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14. The Binomial Experiment
The binomial experiment is a probability experiment that satisfies these requirements:
1. Each trial can have only two possible outcomes—success or failure.
2. There must be a fixed number of trials.
3. The outcomes of each trial must be independent of each other.
4. The probability of success must remain the same for each trial.
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15. The Binomial Experiment (cont’d.)
The outcomes of a binomial experiment and the corresponding probabilities of these outcomes are called a binomial distribution.
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16. Notation for the Binomial Distribution
The symbol for the probability of success
The symbol for the probability of failure
The numerical probability of success
The numerical probability of failure
and
The number of trials
The number of successes
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17. Binomial Probability Formula
In a binomial experiment, the probability of exactly X successes in n trials is
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18. Binomial Distribution Properties
mean
variance
standard deviation
The mean, variance, and standard deviation of a variable that has the binomial distribution can be found by using the following formulas.
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19. Other Types of Distributions
The multinomial distribution is similar to the binomial distribution but has the advantage of allowing one to compute probabilities when there are more than two outcomes.
The multinomial distribution is a general distribution, and the binomial distribution is a special case of the multinomial distribution.
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Poisson Distribution
The Poisson distribution is a discrete probability distribution that is useful when n is large and p is small and when the independent variables occur over a period of time.
The Poisson distribution can be used when there is a density of items distributed over a given area or volume, such as the number of defects in a given length of videotape.
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21. Formula for the Poisson Distribution
The probability of X occurrences in an interval of time, volume, area, etc., for a variable where  is the mean number of occurrences per unit (area, time, volume, etc.) is
where
The letter e is a constant approximately equal to
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22. Poisson Distribution (cont’d.)
The Poisson distribution can also be used to approximate the binomial distribution when n is large and is small (e.g., less than 5).
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23. Hypergeometric Distribution
When sampling is done without replacement, the binomial distribution does not give exact probabilities, since the trials are not independent. The smaller the size of the population, the less accurate the binomial probabilities will be. The hypergeometric distribution is a distribution of a variable that has two outcomes when sampling is done without replacement.
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24. Formula for the Hypergeometric Distribution
Given a population with only two types of objects (females and males, defective and nondefective, etc.) such that there are a items of one kind and b items of another kind and equals the total population, the probability of selecting without replacement a sample of size n with X items of type a and items of type b is
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25. Applications of Hypergeometric Distribution
Objects are often manufactured and shipped to a company. The company selects a few items and tests to see whether they are satisfactory or defective. The company must know the probability of getting a specific number of defects to make the decision to accept or reject the whole shipment based on a small sample. To do this, the company uses the hypergeometric distribution.
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Summary
A probability distribution can be graphed, and the mean, variance, and standard deviation can be found.
The mathematical expectation can also be calculated for a probability distribution.
Expectation is used in insurance and games of chance.
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Summary (cont’d.)
The most common probability distributions are the binomial, multinomial, Poisson, and hypergeometric distributions.
The binomial distribution is used when there are only two outcomes for an experiment, a fixed number of trials, the probability is the same for each trial, and the outcomes are independent of each other.
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Summary (cont’d.)
The multinomial distribution is an extension of the binomial distribution and is used when there are three or more outcomes for an experiment.
The hypergeometric distribution is used when sampling is done without replacement.
The Poisson distribution is used in special cases when independent events occur over a period of time, area, or volume.
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Conclusion
Many decisions in business, insurance, and other real-life situations are made by assigning probabilities to all possible outcomes pertaining to the situation and then evaluating the results.
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