If a microchip manufacturer claims that only 4% of his chips are defective, what is the probability that among the 60 chips chosen, exactly three are defective?
If
denotes the probability that the chip is defective, and
the probability that the chip is not defective, then
,
,
, and
.
If a telemarketing executive has determined that 15% of the people contacted will purchase the product, what is the probability that among the 12 people who are contacted, 2 will buy the product?
If S denoted the probability that a person will buy the product, and F the probability that the person will not buy the product, then
,
,
, and
.
In this section, we will develop and use Bayes' Formula to solve an important type of probability problem. Bayes' formula is a method of calculating the conditional probability
from
. The ideas involved here are not new, and most of these problems can be solved using a tree diagram. However, Bayes' formula does provide us with a tool with which we can solve these problems without a tree diagram.
We begin with an example.
Suppose you are given two jars. Jar I contains one black and 4 white marbles, and Jar II contains 4 black and 6 white marbles. If a jar is selected at random and a marble is chosen,
What is the probability that the marble chosen is a black marble?
If the chosen marble is black, what is the probability that it came from Jar I?
If the chosen marble is black, what is the probability that it came from Jar II?
Let
I be the event that Jar I is chosen,
be the event that Jar II is chosen,
be the event that a black marble is chosen and
the event that a white marble is chosen.
We illustrate using a tree diagram.
The probability that a black marble is chosen is
.
To find
, we use the definition of conditional probability, and we get
Similarly,
In parts b and c, the reader should note that the denominator is the sum of all probabilities of all branches of the tree that produce a black marble, while the numerator is the branch that is associated with the particular jar in question.