1. Discrete Probability Distributions
Chapter 6

2. Learning Objectives
LO6-1 Identify the characteristics of a probability
distribution.
LO6-2 Distinguish between discrete and continuous
random variables.
LO6-3 Compute the mean, variance, and standard
deviation of a discrete probability distribution.
LO6-4 Explain the assumptions of the binomial distribution
and apply it to calculate probabilities.

3. What is a Probability Distribution?
LO6-1 Identify the characteristics of a probability distribution.
What is a Probability Distribution?
PROBABILITY DISTRIBUTION A listing of all the outcomes of an experiment and the probability associated with each outcome.

4. Characteristics of a Probability Distribution
LO6-1
Characteristics of a Probability Distribution
The probability of a particular outcome is between 0 and 1 inclusive.
The outcomes are mutually exclusive events.
The list is exhaustive. So the sum of the probabilities of the various events is equal to 1.

5. Probability Distribution - Example
LO6-1
Probability Distribution - Example
Experiment: Toss a coin three times. Observe the number of heads.
The possible experimental outcomes are: zero heads, one head, two heads, and three heads.
What is the probability distribution for the number of heads?

6. Probability Distribution: Number of Heads in 3 Tosses of a Coin
LO6-1
Probability Distribution: Number of Heads in 3 Tosses of a Coin

7. LO6-2 Distinguish between discrete and continuous random variables.
RANDOM VARIABLE A quantity resulting from an experiment that, by chance, can assume different values.

8. Types of Random Variables
LO6-2
Types of Random Variables
DISCRETE RANDOM VARIABLE A random variable that can assume only certain clearly separated values. It is usually the result of counting something.
CONTINUOUS RANDOM VARIABLE A random variable that can assume an infinite number of values within a given range. It is usually the result of some type of measurement.

9. Discrete Random Variable
LO6-2
Discrete Random Variable
DISCRETE RANDOM VARIABLE A random variable that can assume only certain clearly separated values. It is usually the result of counting something.
EXAMPLES:
The number of students in a class
The number of children in a family
The number of cars entering a carwash in a hour
The number of home mortgages approved by Coastal Federal Bank last week

10. Continuous Random Variable
LO6-2
Continuous Random Variable
CONTINUOUS RANDOM VARIABLE A random variable that can assume an infinite number of values within a given range. It is usually the result of some type of measurement
EXAMPLES:
The length of each song on the latest Tim McGraw CD
The weight of each student in this class
The amount of money earned by each player in the National Football League

11. The Mean of a Discrete Probability Distribution
LO6-3 Compute the mean, variance, and standard deviation of a discrete probability distribution.
The Mean of a Discrete Probability Distribution
The mean is a typical value used to represent the central location of a probability distribution.
The mean of a probability distribution is also referred to as its expected value.

12. The Mean of a Discrete Probability Distribution - Example
LO6-3
The Mean of a Discrete Probability Distribution - Example
John Ragsdale sells new cars for Pelican Ford. John usually sells the largest number of cars on Saturday. He has developed the following probability distribution for the number of cars he expects to sell on a particular Saturday.

13. The Mean of a Discrete Probability Distribution - Example
LO6-3
The Mean of a Discrete Probability Distribution - Example

14. LO6-3
The Variance and Standard Deviation of a Discrete Probability Distribution
Measures the amount of spread in a distribution.
The computational steps are:
Subtract the mean from each value, and square this difference.
Multiply each squared difference by its probability.
Sum the resulting products to arrive at the variance.

15. LO6-3
The Variance and Standard Deviation of a Discrete Probability Distribution - Example

16. Binomial Probability Distribution
LO6-4 Explain the assumptions of the binomial distribution and apply it to calculate probabilities.
Binomial Probability Distribution
A widely occurring discrete probability distribution
Characteristics of a binomial probability distribution:
There are only two possible outcomes on a particular trial of an experiment.
The outcomes are mutually exclusive.
The random variable is the result of counts.
Each trial is independent of any other trial.

17. Characteristics of a Binomial Probability Experiment
LO6-4
Characteristics of a Binomial Probability Experiment
The outcome of each trial is classified into one of two mutually exclusive categories—a success or a failure.
The random variable, x, is the number of successes in a fixed number of trials.
The probability of success and failure stay the same for each trial.
The trials are independent, meaning that the outcome of one trial does not affect the outcome of any other trial.

18. Binomial Probability Formula
LO6-4
Binomial Probability Formula

19. Binomial Probability - Example
LO6-4
Binomial Probability - Example
There are five flights daily from Pittsburgh via US Airways into the Bradford Regional Airport. Suppose the probability that any flight arrives late is 0.20.
What is the probability that none of the flights are late today?
Recall: 0! = 1, and, any variable with a 0 exponent is equal to one.

20. Binomial Distribution Probability
LO6-4
Binomial Distribution Probability
The probabilities for each value of the random variable, number of late flights (0 through 5), can be calculated to create the entire binomial probability distribution.

21. Mean and Variance of a Binomial Distribution
LO6-4
Mean and Variance of a Binomial Distribution
Knowing the number of trials, n, and the probability of a success, , for a binomial distribution, we can compute the mean and variance of the distribution.

22. Mean and Variance of a Binomial Distribution - Example
LO6-4
Mean and Variance of a Binomial Distribution - Example
For the example regarding the number of late flights, recall that  =.20 and n = 5.
What is the average number of late flights?
What is the variance of the number of late flights?

23. Mean and Variance of a Binomial Distribution – Example
LO6-4
Mean and Variance of a Binomial Distribution – Example
Using the general formulas for discrete probability distributions:

24. Binomial Probability Distribution – MegaStat Example
LO6-4
Binomial Probability Distribution – MegaStat Example
Statistical software, such as Megastat, can also create the values and graph for any binomial probability distribution.
Five percent of the worm gears produced by an automatic, high-speed Carter-Bell milling machine are defective. What is the binomial probability distribution of the number defective when six gears are selected?

25. Binomial – Shapes or Skewness for Varying  and n=10
LO6-4
Binomial – Shapes or Skewness for Varying  and n=10
The shape of a binomial distribution changes as n and  change.

26. Binomial – Shapes or Skewness for Constant  and Varying n
LO6-4
Binomial – Shapes or Skewness for Constant  and Varying n