1. Discrete Probability Distributions
Systems Engineering Program
Department of Engineering Management, Information and Systems
EMIS 7370/5370 STAT 5340 :
PROBABILITY AND STATISTICS FOR SCIENTISTS AND ENGINEERS
Discrete Probability Distributions
Binomial, Negative Binomial, Geometric Distributions
Dr. Jerrell T. Stracener, SAE Fellow
Leadership in Engineering

2. Binomial Distribution

3. The Binomial Model:
Let X be the number of “successes” in n trials. If
1. The trials are identical and independent
2. Each trial results in one of two possible
outcomes success or failure
3. The probability of success on a single trial is p,
and is constant from trial to trial,
X ~ B (n,p)

4. The Binomial Model:
then X has the Binomial Distribution with Probability
Mass Function given by:
where
and

5. Binomial Distribution
Rule:
for x = 0, …, n - 1

6. Binomial Distribution
Mean or Expected Value
 = np
Standard Deviation

7. The Binomial Model in Excel
Excel provides an easier way of finding probabilities:
Click the Insert button on the menu bar (at the top of the Excel page)
Go to the function option
Choose Statistical from the Function Category window (a list of all available statistical functions will appear in the Function Name window)
Choose the BINOMDIST function
Type in parameters:
number_s => X
Trials => N
Probability_s => p
Cumulative (logical) => TRUE for cumulative function, FALSE for mass function.

8. Example - Binomial Distribution
When circuit boards used in the manufacture of compact disc players are tested, the long-run percentage of defects is 5%. Let X=the number of defective boards in a random sample size n=25, so X~B(25,0.05).
a) Determine P(X  2).
b) Determine P(X  5).
c) Determine P(1  X  4).
d) What is the probability that none of the 25
boards are defective?
e) Calculate the expected value and standard
deviation of X.

9. Solution – Binomial Distribution

10. Negative Binomial Distribution

11. The Negative Binomial Model:
The negative binomial distribution is based on an
experiment satisfying the following conditions:
1. The experiment consists of a sequence of
independent and identical trials
2. Each trial can result in either a success, S, or a
Failure, F.
3. The probability of success is constant from trial
to trial, so P(S on trial i) = p for i = 1, 2, 3, …

12. The Negative Binomial Model:
4. The experiment continues (trials are performed)
until a total of r successes have been observed,
where r is a specified positive integer.
The associated random variable is:
X = number of failures that precede the rth success
X is called the negative binomial random variable
because, in contrast to the binomial random
variable, the number of successes is fixed and the
number of trials is random.
Possible values of X are x = 0, 1, 2, ...

13. The Negative Binomial Model:
The probability mass function of the negative
binomial random variable X with parameters
r = number of successes and
p = probability of success on a single trial

14. The Negative Binomial Model:
The negative binomial model can also be
expressed as:
X = total number of trials to get k successes
k = number of successes and
p = probability of success on a single trial

15. Negative Binomial Probability Mass Function Derivation
Consider the probability of a success on the trial preceded by successes and failures in some specified order.
Since the trials are independent, we can multiply all the probabilities corresponding to each desired outcome.
Each success occurs with probability and each failure with probability
Therefore, the probability for the specified order, ending in a success, is

16. The total number of sample points in the experiment ending in a success, after the occurrence of successes and failures in any order, is equal to the number of partitions of trials into two groups with successes corresponding to one group and failures corresponding to the other group.
The sample space consists of points, each
mutually exclusive and occurring with equal probability
We obtain the general formula by multiplying by

17. The Negative Binomial Model: Example
Find the probability that a person tossing three coins will get either all heads or all tails for the second time on the fifth toss.
Solution

18. Since a success must occur on the fifth toss of the 3 coins, the following outcomes are possible:
probability
Toss 1 2 3 4 5 S F

19. Therefore, the probability of obtaining all heads or all tails for the second time on the fifth toss is

20. The Negative Binomial Model:
Example Solution continued
Or, using the Negative Binomial Distribution with
r = 2, p = 0.25, and x = 3 gives

21. The Negative Binomial Model
If X is a negative binomial random variable with
probability mass function nb(x;r,p) then
and

22. The Negative Binomial Model
Note: By expanding the binomial coefficient in
front of pr(1 - p)x and doing some cancellation, it
can be seen that NB(x;r,p) is well defined even
when r is not an integer. This generalized negative
binomial distribution has been found to fit the
observed data quite well in a wide variety of
applications.

23. The Negative Binomial Model in Excel
Similar to the Binomial Dist in Excel:
Click the Insert button on the menu bar (at the top of the Excel page)
Go to the function option
Choose Statistical from the Function Category window (a list of all available statistical functions will appear in the Function Name window)
Choose the NEGBINOMDIST function
Type in parameters:
number_f => X
Number_s => r
Probability_s => p

24. The Geometric Distribution
If repeated independent and identical trials can result in a success with probability p and a failure with probability q = 1 - p, then the probability mass function of the random variable x, the number of the trial on which the first success occurs, is:
g(x;p) = pqx-1, x = 1,2,3…

25. The Geometric Distribution
The mean and variance of a random variable following the geometric distribution are

26. The Geometric Distribution - Example
At “busy time” a telephone exchange is very near capacity, so callers have difficulty placing their calls. It may be on interest to know the number of attempts necessary in order to gain a connection. Suppose that we let p = 0.05 be the probability of a connection during a busy time. We are interested in knowing the probability that 5 attempts are necessary for a successful call.

27. The Geometric Distribution - Example Solution
The random variable X is the number of attempts for a successful call. Then X~G(0.05),
So that for with x = 5 and p = 0.05 yields:
P(X=x) = g(5;0.05)
= (0.05)(0.95)4
= 0.041
And the expected number of attempts is