In this section we present several relationships that will be helpful in computing probabil- ities. The relationships are the complement of an event, the addition law, conditional prob- ability, and the multiplication law.
Complement of an Event
For an event A, the complement of event Ais the event consisting of all sample points in sample space S that are not in A. The complement of A is denoted by Ac. Figure 2.2 pro- vides a diagram, known as a Venn diagram, that illustrates the concept of a complement.
The rectangular area represents the sample space for the experiment and as such contains all possible sample points. The circle represents event A and contains only the sample points that belong to A. The remainder of the rectangle contains all sample points not in event A, which by definition is the complement of A.
In any probability application, event A and its complement Acmust satisfy the condition P(A) ⫹P(Ac) ⫽1
Solving for P(A), we have
P(A) ⫽1 ⫺P(Ac) (2.3)
NOTES AND COMMENTS
1. The sample space, S, is itself an event. It con- tains all the experimental outcomes, so it has a probability of 1; that is, P(S) ⫽1.
2. When the classical method is used to assign probabilities, the assumption is that the experi-
mental outcomes are equally likely. In such cases, the probability of an event can be com- puted by counting the number of experimental outcomes in the event and dividing the result by the total number of experimental outcomes.
Note: The shaded region depicts the complement of A, denoted Ac
Ac Event A
Sample Space S
. FIGURE 2.2 COMPLEMENT OF EVENT A
©Cengage Learning 2013
Equation (2.3) shows that the probability of an event A can be computed by subtraction if the probability of its complement, P(Ac), is known. Similarly, subtraction can be used to compute the probability of the compliment Acof an event A if the probability of the event P(A), is known.
Consider the case of a sales manager who, after reviewing sales reports, states that 80%
of new customer contacts result in no sale. By letting A denote the event of a sale and Ac denote the event of no sale, the manager is stating that P(Ac)⫽0.80. Using equation (2.3), we see that
P(A) ⫽1 ⫺P(Ac) ⫽1 ⫺0.80 ⫽0.20
which shows that there is a 0.20 probability that a sale will be made on a new customer contact.
In another case, a purchasing agent states a 0.90 probability that a supplier will send a ship- ment that is free of defective parts. Using the complement, we can conclude a 1⫺0.90⫽0.10 probability that the shipment will contain some defective parts.
Addition Law
The addition law is a useful relationship when we have two events and are interested in knowing the probability that at least one of the events occurs. That is, with events A and B, we are interested in knowing the probability that event A or event B or both will occur.
Before we present the addition law, we need to discuss two concepts concerning combina- tions of events: the union of events and the intersection of events.
For two events A and B, theunion of events A and Bis the event containing all sam- ple points belonging to A or B or both. The union is denoted A´B.The Venn diagram shown in Figure 2.3 depicts the union of events A and B; the shaded region contains all the sample points in event A, as well as all the sample points in event B. The fact that the cir- cles overlap (or intersect) indicates that some sample points are contained in both A and B.
For two events A and B, the intersection of events A and Bis the event containing the sample points belonging to both A and B. The intersection is denoted by A¨B. The Venn diagram depicting the intersection of the two events is shown in Figure 2.4. The area where the two circles overlap is the intersection; it contains the sample points that are in both A and B.
2.4 Some Basic Relationships of Probability 35
Event A
Sample Space S
Event B FIGURE 2.3 UNION OF EVENTS A AND B (SHADED REGION)
Key words for the union of events (A´B) are “either A or B occurs” or “at least one of the two events occurs.” Note that the conjunction “or” commonly indicates a union of events.
Key words for the intersection of events (A¨B) are “both A and B occur.” Note that the conjunction “and”
commonly indicates an intersection of events.
©Cengage Learning 2013
62345_02_ch02_p027-061.qxd 12/22/11 4:04 PM Page 35
The addition law provides a way to compute the probability of event A or B or both oc- curring. In other words, the addition law is used to compute the probability of the union of two events, A´B. The addition lawis formally stated as follows:
(2.4)
To obtain an intuitive understanding of the addition law, note that the first two terms in the addition law, P(A) ⫹P(B), account for all the sample points in A´B. However, as the sample points in the intersection A¨Bare in both A and B, when we compute P(A) ⫹P(B), we in effect are counting each of the sample points in A¨Btwice. We correct for this dou- ble counting by subtracting P(A¨B).
To apply the addition law, let us consider the following situations in a college course in quantitative methods for decision making. Of 200 students taking the course, 160 passed the midterm examination and 140 passed the final examination; 124 students passed both exams. Let
A⫽event of passing the midterm exam B⫽event of passing the final exam
This relative frequency information leads to the following probabilities:
After reviewing the grades, the instructor decided to give a passing grade to any student who passed at least one of the two exams; note that this implies the instructor will give a passing grade to any student who passed the midterm exam or passed the final exam. That is, any student who passed the midterm, any student who passed the final, and any student who passed both exams would receive a passing grade. What is the probability of a student receiving a passing grade in this course?
P(A傽B)⫽ 124 200⫽0.62 P(B)⫽ 140
200⫽0.70 P(A)⫽160
200⫽0.80 P(A傼B)⫽P(A)⫹P(B)⫺P(A傽B)
Note that P(A´B) ⫽ P(B´A); that is, the order of events in a union does not affect the probability of the union.
Note that P(A¨B) ⫽ P(B ¨A); that is, the order of events in an intersection does not affect the probability of the intersection.
Event
Event A 傽 B
A
Sample Space S
Event B FIGURE 2.4 INTERSECTION OF EVENTS A AND B (SHADED REGION)
©Cengage Learning 2013
Your first reaction may be to try to count how many of the 200 students passed at least one exam, but note that the probability question is about the union of the events A and B.
That is, we want to know the probability that a student passed the midterm (A), passed the final (B), or passed both. Thus we want to know P(A´B). Using the addition law (2.4) for the events A and B, we have
Knowing the three probabilities on the right-hand side of this equation, we obtain
This result indicates an 88% chance of a student passing the course because of the 0.88 probability of passing at least one of the exams.
Now consider a study involving the television-viewing habits of married couples. It was reported that 30% of the husbands and 20% of the wives were regular viewers of a partic- ular Friday evening program. For 12% of the couples in the study, both husband and wife were regular viewers of the program. What is the probability that at least one member of a married couple is a regular viewer of the program?
Let
H⫽husband is a regular viewer W⫽wife is a regular viewer
We have P(H) ⫽0.30, P(W) ⫽0.20, and P(H¨W) ⫽0.12; thus, the addition law yields
This result shows a 0.38 probability that at least one member of a married couple is a reg- ular viewer of the program.
Before proceeding, let us consider how the addition law is applied to mutually exclu- sive events. Two or more events are said to be mutually exclusive if the events do not have any sample points in common—that is, there are no sample points in the intersection of the events. For two events A and B to be mutually exclusive, P(A¨B) ⫽0. Figure 2.5 provides
P(H傼W)⫽P(H)⫹P(W)⫺P(H傽W)⫽0.30⫹0.20⫺0.12⫽0.38 P(A傼B)⫽0.80⫹0.70⫺0.62⫽0.88
P(A傼B)⫽P(A)⫹P(B)⫺P(A艚B)
2.4 Some Basic Relationships of Probability 37
An event and its complement are mutually exclusive and their union is the entire sample space.
Event A Event B
FIGURE 2.5 MUTUALLY EXCLUSIVE EVENTS
©Cengage Learning 2013
62345_02_ch02_p027-061.qxd 12/22/11 4:04 PM Page 37
a Venn diagram depicting two mutually exclusive events. Because P(A¨B)⫽0 for the special case of mutually exclusive events,the addition law becomes
(2.5)
To compute the probability of the union of two mutually exclusive events, we simply add the corresponding probabilities.
Conditional Probability
In many probability situations, being able to determine the probability of one event when another related event is known to have occurred is important. Suppose that we have an event Awith probability P(A) and that we obtain new information or learn that another event, de- noted B, has occurred. If A is related to B, we will want to take advantage of this informa- tion in computing a new or revised probability for event A.
This new probability of event A is written P(A冟B). The “冟” denotes the fact that we are considering the probability of event A given the condition that event B has occurred. Thus, the notation P(A冟B) is read “the probability of A given B.”
With two events A and B, the general definitions of conditional probabilityfor A given Band for B given A are as follows:
(2.6)
(2.7)
Note that for these expressions to have meaning, P(B) cannot equal 0 in equation (2.6) and P(A) cannot equal 0 in equation (2.7). Also note that P(A冟B) P(B冟A), unless P(A)⫽P(B).
To obtain an intuitive understanding of the use of equation (2.6), consider the Venn diagram in Figure 2.6. The shaded region (both light gray and dark gray) denotes that
⫽ P(B 冟 A)⫽ P(A傽B)
P(A) P(A 冟 B)⫽ P(A傽B)
P(B) P(A傼B)⫽P(A)⫹P(B)
For a conditional probability such as P(A | B)⫽0.25, the probability value of 0.25 refers only to the probability of event A. No information is provided about the probability of event B.
Event A
Event A 傽 B
Event B FIGURE 2.6 CONDITIONAL PROBABILITY P(A冟B) ⫽P(A¨B)/P(B)
For practice, try solving Problem 7.
©Cengage Learning 2013
event B has occurred; the dark gray shaded region denotes the event (A¨B). We know that once B has occurred, the only way that we can also observe event A is for event (A¨B) to occur. Thus, the ratio P(A¨B)/P(B) provides the probability that we will observe event Awhen event B has already occurred.
We can apply conditional probability to the promotional status of male and female of- ficers of a major metropolitan police force. The force consists of 1200 officers: 960 men and 240 women. Over the past two years, 324 officers have been promoted. Table 2.1 shows the specific breakdown of promotions for male and female officers. Such a table is often called a contingency table or a crosstabulation.
After reviewing the promotional record, a committee of female officers filed a dis- crimination case on the basis that only 36 female officers had received promotions during the past two years. The police administration argued that the relatively low number of pro- motions for female officers is due not to discrimination but to the fact that few female officers are on the force. We use conditional probability to evaluate the discrimination charge.
Let
M⫽event an officer is a man W⫽event an officer is a woman
B⫽event an officer is promoted
Dividing the data values in Table 2.1 by the total of 1200 officers permits us to summarize the available information as follows:
Because each of these values gives the probability of the intersection of two events, these probabilities are called joint probabilities. Table 2.2, which provides a summary of the probability information for the police officer promotion situation, is referred to as a joint probability table.
probability that an officer is a woman and is not promoted P(W傽Bc)⫽ 204
1200⫽0.17
probability that an officer is a woman and is promoted P(W傽B)⫽ 36
1200⫽0.03
probability that an officer is a man and is not promoted
P(M傽Bc)⫽ 672
1200⫽0.56
probability that an officer is a man and is promoted
P(M傽B)⫽ 288
1200⫽0.24
2.4 Some Basic Relationships of Probability 39
Promoted Not Promoted Total
Men 288 672 960
Women 36 204 240
Total 324 876 1200
TABLE 2.1 CONTINGENCY TABLE FOR PROMOTIONAL STATUS OF POLICE OFFICERS DURING THE PAST TWO YEARS
Try Problem 12 for practice computing conditional probabilities.
©Cengage Learning 2013
62345_02_ch02_p027-061.qxd 12/22/11 4:04 PM Page 39
The values in the margins of the joint probability table provide the probabilities of each single event separately: P(M)⫽0.80, P(W)⫽0.20, P(B)⫽0.27, and P(Bc)⫽0.73, which indicate that 80% of the force is male, 20% of the force is female, 27% of all officers received promotions, and 73% were not promoted. These probabilities are referred to as marginal probabilities because of their location in the margins of the joint probability table. Returning to the issue of discrimination against the female officers, we see that the probability of promotion of an officer is P(B)⫽0.27 (regardless of whether that officer is male or female). However, the critical issue in the discrimination case involves the two con- ditional probabilities P(B冟M) and P(B冟W); that is, what is the probability of a promotion given that the officer is a man and what is the probability of a promotion given that the officer is a woman? If these two probabilities are equal, the discrimination case has no basis because the chances of a promotion are the same for male and female officers. However, different conditional probabilities will support the position that male and female officers are treated differently in terms of promotion.
Using equation (2.7), the conditional probability relationship, we obtain
What conclusions do you draw? The probability of a promotion for a male officer is 0.30, which is twice the 0.15 probability of a promotion for a female officer. Although the use of conditional probability does not in itself prove that discrimination exists in this case, the conditional probability values strongly support the argument presented by the female officers.
In this illustration, P(B) ⫽0.27, P(B冟M) ⫽0.30, and P(B冟W) ⫽0.15. Clearly, the probability of promotion (event B) differs by gender. In particular, as P(B 冟M) ZP(B), events B and M are dependent events. The probability of event B (promotion) is higher when M (the officer is male) occurs. Similarly, with P(B冟W) ZP(B), events B and W are dependent events. But, if the probability of event B was not changed by the existence of
P(B 冟 W)⫽ P(B傽W) P(W) ⫽ 0.03
0.20⫽0.15 a⫽ 36冫1200 240冫1200 ⫽ 36
240b P(B 冟 M)⫽P(B傽M)
P(M) ⫽ 0.24
0.80⫽0.30 a⫽ 288冫1200 960冫1200⫽ 288
960b
Promoted Not Promoted Total
Men 0.24 0.56 0.80
Women 0.03 0.17 0.20
Total 0.27 0.73 1.00
TABLE 2.2 JOINT PROBABILITY TABLE FOR POLICE OFFICER PROMOTIONS
Marginal probabilities appear in the margins of the table.
Joint probabilities appear in the body of the table.
©Cengage Learning 2013
event M—that is, P(B冟M) ⫽P(B)—events B and M would be independent events. Two events A and B are independent if
P(B冟A) ⫽P(B) or
P(A冟B) ⫽P(A) Otherwise, the events are dependent.
The Q.M. in Action, Product Testing for Quality Control at Morton International, describes how a subsidiary of Morton International used conditional probability to help decide to implement a quality control test.
2.4 Some Basic Relationships of Probability 41
For practice, try solving Problem 13.
Morton International is a company with businesses in salt, household products, rocket motors, and specialty chemicals. Carstab Corporation, a subsidiary of Morton, produces a variety of specialty chemical products de- signed to meet the unique specifications of its customers.
For one particular customer, Carstab produced an ex- pensive catalyst used in chemical processing. Some, but not all, of the product produced by Carstab met the cus- tomer’s specifications.
Carstab’s customer agreed to test each lot after re- ceiving it to determine whether the catalyst would per- form the desired function. Lots that did not pass the customer’s test would be returned to Carstab. Over time, Carstab found that the customer was accepting 60% of the lots and returning 40%. In probability terms, each Carstab shipment to the customer had a 0.60 probability of being accepted and a 0.40 probability of being returned.
Neither Carstab nor its customer was pleased with these results. In an effort to improve service, Carstab ex- plored the possibility of duplicating the customer’s test prior to shipment. However, the high cost of the special testing equipment made that alternative infeasible.
Carstab’s chemists then proposed a new, relatively low- cost test designed to indicate whether a lot would pass the customer’s test. The probability question of interest was:
What is the probability that a lot will pass the customer’s test given that it passed the new Carstab test?
A sample of lots was tested under both the customer’s procedure and Carstab’s proposed procedure. Results were that 55% of the lots passed Carstab’s test, and 50%
of the lots passed both the customer’s and Carstab’s test.
In probability notation, we have
A⫽the event the lot passes the customer’s test B⫽the event the lot passes Carstab’s test where
P(B) ⫽0.55 and P(A¨B) ⫽0.50
The probability information sought was the conditional probability P(A | B) given by
Prior to Carstab’s new test, the probability that a lot would pass the customer’s test was 0.60. However, the new results showed that given that a lot passed Carstab’s new test, it had a 0.909 probability of passing the cus- tomer’s test. This result was good supporting evidence for the use of the test prior to shipment. Based on this probability analysis, the preshipment testing procedure was implemented at the company. Immediate results showed an improved level of customer service. A few lots were still being returned; however, the percentage was greatly reduced. The customer was more satisfied and return shipping costs were reduced.
P(A 冟 B)⫽ P(A傽B) P(B) ⫽ 0.50
0.55⫽0.909 PRODUCT TESTING FOR QUALITY CONTROL AT MORTON INTERNATIONAL*
Q.M. in ACTION
*Based on information provided by Michael Haskell of Morton International.
62345_02_ch02_p027-061.qxd 12/22/11 4:04 PM Page 41
Multiplication Law
The multiplication lawcan be used to find the probability of an intersection of two events.
The multiplication law is derived from the definition of conditional probability. Using equa- tions (2.6) and (2.7) and solving for P(A¨B), we obtain the multiplication law:
(2.8) (2.9)
The multiplication law is useful in situations for which probabilities such as P(A), P(B), P(A冟B), and/or P(B冟A) are known but P(A¨B) is not. For example, suppose that a news- paper circulation department knows that 84% of its customers subscribe to the daily edition of the paper. Let D denote the event that a customer subscribes to the daily edition; hence, P(D)⫽0.84. In addition, the department knows that the conditional probability that a cus- tomer who already holds a daily subscription also subscribes to the Sunday edition (event S) is 0.75; that is, P(S冟D)⫽0.75. What is the probability that a customer subscribes to both the daily and Sunday editions of the newspaper? Using equation (2.9), we compute P(D¨S):
This result tells us that 63% of the newspaper’s customers subscribe to both the daily and Sunday editions.
Before concluding this section, let us consider the special case of the multiplication law when the events involved are independent. Recall that independent events exist whenever P(B冟A) ⫽P(B) or P(A冟B) ⫽P(A). Returning to the multiplication law, equations (2.8) and (2.9), we can substitute P(A) for P(A冟B) and P(B) for P(B冟A). Hence, for the special case of independent events,the multiplication law becomes
(2.10)
Thus, to compute the probability of the intersection of two independent events, we sim- ply multiply the corresponding probabilities. For example, a service station manager knows from past experience that 40% of her customers use a credit card when purchasing gaso- line. What is the probability that the next two customers purchasing gasoline will both use a credit card? If we let
A⫽the event that the first customer uses a credit card B⫽the event that the second customer uses a credit card
the event of interest is A¨B. With no other information, it is reasonable to assume A and Bare independent events. Thus,
P(A傽B)⫽P(A)P(B)⫽(.40)(.40)⫽0.16 P(A傽B)⫽P(A)P(B)
P(D傽S)⫽P(S 冟 D)P(D)⫽0.75(0.84)⫽0.63 P(A傽B)⫽P(B 冟 A)P(A)
P(A傽B)⫽P(A 冟 B)P(B)
NOTES AND COMMENTS
1. Do not confuse mutually exclusive events with independent events. Two events with nonzero probabilities cannot be both mutually exclusive and independent. If one mutually exclusive event
is known to occur, the probability of the other oc- curring is reduced to zero. Thus, they cannot be independent.