Page 94 - Applied Statistics And Probability For Engineers

PQ220 6234F.Ch 03 13/04/2002 03:19 PM Page 72

72 CHAPTER 3 DISCRETE RANDOM VARIABLES AND PROBABILITY DISTRIBUTIONS

3-6 BINOMIAL DISTRIBUTION

Consider the following random experiments and random variables:
1. Flip a coin 10 times. Let X number of heads obtained.
2. A worn machine tool produces 1% defective parts. Let X number of defective parts
in the next 25 parts produced.
3. Each sample of air has a 10% chance of containing a particular rare molecule. Let
X the number of air samples that contain the rare molecule in the next 18 samples
analyzed.
4. Of all bits transmitted through a digital transmission channel, 10% are received in
error. Let X the number of bits in error in the next five bits transmitted.

5. A multiple choice test contains 10 questions, each with four choices, and you guess
at each question. Let X the number of questions answered correctly.
6. In the next 20 births at a hospital, let X the number of female births.
7. Of all patients suffering a particular illness, 35% experience improvement from a
particular medication. In the next 100 patients administered the medication, let X
the number of patients who experience improvement.
These examples illustrate that a general probability model that includes these experiments as
particular cases would be very useful.
Each of these random experiments can be thought of as consisting of a series of repeated,
random trials: 10 flips of the coin in experiment 1, the production of 25 parts in experiment 2,
and so forth. The random variable in each case is a count of the number of trials that meet a
specified criterion. The outcome from each trial either meets the criterion that X counts or it
does not; consequently, each trial can be summarized as resulting in either a success or a fail-
ure. For example, in the multiple choice experiment, for each question, only the choice that is
correct is considered a success. Choosing any one of the three incorrect choices results in the
trial being summarized as a failure.
The terms success and failure are just labels. We can just as well use A and B or 0 or 1.
Unfortunately, the usual labels can sometimes be misleading. In experiment 2, because X
counts defective parts, the production of a defective part is called a success.
A trial with only two possible outcomes is used so frequently as a building block of a
random experiment that it is called a Bernoulli trial. It is usually assumed that the trials that
constitute the random experiment are independent. This implies that the outcome from one
trial has no effect on the outcome to be obtained from any other trial. Furthermore, it is
often reasonable to assume that the probability of a success in each trial is constant. In
the multiple choice experiment, if the test taker has no knowledge of the material and just
guesses at each question, we might assume that the probability of a correct answer is 1 4
for each question.
Factorial notation is used in this section. Recall that n! denotes the product of the integers
less than or equal to n:

p
n! n1n 121n 22 122112
For example,

5! 152142132122112 120 1! 1