1. P(L′) = P(S)
2. P(M OR S)
3. P(F AND L)
4. P(M|L)
5. P(L|M)
6. P(S|F)
7. P(F|L)
8. P(F OR L)
9. P(M AND S)
10. P(F)

P(N) = $\frac{15}{42}$ = $\frac{5}{14}$ = .36

P(C) = $\frac{5}{42}$ = .12

P(G) = $\frac{20}{150}$ = $\frac{2}{15}$ = .13

P(R) = $\frac{22}{150}$ = $\frac{11}{75}$ = .15

P(O) = $\frac{150-22-38-20-28-26}{150}$ = $\frac{16}{150}$ = $\frac{8}{75}$ = .11

P(E) = $\frac{47}{194}$ = .24

P(N) = $\frac{23}{194}$ = .12

P(S) = $\frac{12}{194}$ = $\frac{6}{97}$ = .06

$\frac{13}{52}$ = $\frac{1}{4}$ = .25

$\frac{3}{6}$ = $\frac{1}{2}$ = .5

$P(R)=\frac{4}{8}=.5$

P(O OR H)

P(H|I)

P(N|O)

P(I OR N)

P(I)

The likelihood that an event will occur given that another event has already occurred.

1

the probability of landing on an even number or a multiple of three

P(J) = .3

P(Q AND R) = P(Q)P(R)

.1 = (.4)P(R)

P(R) = .25

0.376

C|L means, given the person chosen is a Latino Californian, the person is a registered voter who prefers life in prison without parole for a person convicted of first degree murder.

L AND C is the event that the person chosen is a voter of the ethnicity in question who prefers life without parole over the death penalty for a person convicted of first degree murder.

.6492

No, because P(L AND C) does not equal 0.

P(musician is a male AND had private instruction) = $\frac{15}{130}$ = $\frac{3}{26}$ = .12

P(being a female musician AND learning music in school) = $\frac{38}{130}$ = $\frac{19}{65}$ = .29

P(being a female musician)P(learning music in school) = $\left(\frac{72}{130}\right)\left(\frac{62}{130}\right)$ = $\frac{4,464}{16,900}$ = $\frac{1,116}{4,225}$ = .26

No, they are not independent because P(being a female musician AND learning music in school) is not equal to P(being a female musician)P(learning music in school).

$\frac{\mathrm{35,065}}{\mathrm{100,450}}$

To pick one person from the study who is Japanese American AND uses the product 21 to 30 times a day means that the person has to meet both criteria: both Japanese American and uses the product 21 to 30 times a day. The sample space should include everyone in the study. The probability is $\frac{\mathrm{4,715}}{\mathrm{100,450}}$.

To pick one person from the study who is Japanese American given that person uses the product 21 to 30 times a day, means that the person must fulfill both criteria and the sample space is reduced to those who uses the product 21 to 30 times a day. The probability is $\frac{4715}{\mathrm{15,273}}$.

1. You can't calculate the joint probability knowing the probability of both events occurring, which is not in the information given; the probabilities should be multiplied, not added; and probability is never greater than 100 percent
2. A home run by definition is a successful hit, so he has to have at least as many successful hits as home runs.

0

.3571

.2142

Physician (83.7)

83.7 − 79.6 = 4.1

P(Occupation < 81.3) = .5

1. The Forum Research surveyed 1,046 Torontonians.
2. 58 percent
3. 42 percent of 1,046 = 439 (rounding to the nearest integer)
4. .57
5. .60.

1. yes; P(getting a pork chop) = P(not getting a chicken breast)
2. getting a pork chop and getting a chicken breast
3. no

1. 20/40 = 1/2
2. 5/40 = 1/8
3. 39/40
4. 4/40 = 1/10
5. 33/40
6. 15/40 = 3/8
7. 0/40 = 0

Compute the probabilities.

1. 20/40 = 1/2
2. 8/40 = 1/5
3. 40/40 = 1
4. 16/40 = 2/5
5. 18/40 = 9/20
6. 40/40 = 1

1. {G1, G2, G3, G4, G5, Y1, Y2, Y3}
2. $\frac{5}{8}$
3. $\frac{2}{3}$
4. $\frac{2}{8}$
5. $\frac{6}{8}$
6. No, because P(G AND E) does not equal 0.

1. {(G,H) (G,T) (B,H) (B,T) (R,H) (R,T)}
2. P(A) = P(blue)P(head) = $\left(\frac{3}{10}\right)$$\left(\frac{1}{2}\right)$ = $\frac{3}{20}$
3. Yes, A and B are mutually exclusive because they cannot happen at the same time; you cannot pick a card that is both blue and also (red or green). P(A AND B) = 0.
4. No, A and C are not mutually exclusive because they can occur at the same time. In fact, C includes all of the outcomes of A; if the card chosen is blue it is also (red or blue). P(A AND C) = P(A) = $\frac{3}{20\text{.}}$

1. S = {(HHH), (HHT), (HTH), (HTT), (THH), (THT), (TTH), (TTT)}
2. $\frac{4}{8}$
3. Yes, because if A has occurred, it is impossible to obtain two tails. In other words, P(A AND B) = 0.

1. If Y and Z are independent, then P(Y AND Z) = P(Y)P(Z), so P(Y OR Z) = P(Y) + P(Z) – P(Y)P(Z).
2. .5

iii i iv ii

1. P(R) = .44
2. P(R|E) = .56
3. P(R|O) = .31
4. No, whether the money is returned is not independent of which class the money was placed in. There are several ways to justify this mathematically, but one is that the money placed in economics classes is not returned at the same overall rate; P(R|E) ≠ P(R).
5. No, this study definitely does not support that notion; in fact, it suggests the opposite. The money placed in the economics classrooms was returned at a higher rate than the money place in all classes collectively; P(R|E) > P(R).

1. P(type O OR Rh–) = P(type O) + P(Rh–) – P(type O AND Rh–)

   0.52 = 0.43 + 0.15 – P(type O AND Rh–); solve to find P(type O AND Rh–) = .06

   6 percent of people have type O, Rh– blood

2. P(NOT(type O AND Rh–)) = 1 – P(type O AND Rh–) = 1 – .06 = .94

   94 percent of people do not have type O, Rh– blood

1. Let C = be the event that the cookie contains chocolate. Let N = the event that the cookie contains nuts.
2. P(C OR N) = P(C) + P(N) – P(C AND N) = .36 + .12 – .08 = .40
3. P(NEITHER chocolate NOR nuts) = 1 – P(C OR N) = 1 – .40 = .60

0

$\frac{10}{67}$

$\frac{10}{34}$

d

b

1. $\frac{26}{106}$
2. $\frac{33}{106}$
3. $\frac{21}{106}$
4. $\left(\frac{26}{106}\right)$ + $\left(\frac{33}{106}\right)$ – $\left(\frac{21}{106}\right)$ = $\left(\frac{38}{106}\right)$
5. $\frac{21}{33}$

a

1. P(C) = .4567
2. not enough information
3. not enough information
4. no, because over half (0.51) of men have at least one false-positive text

1. P(J OR K) = P(J) + P(K) − P(J AND K); .45 = .18 + .37 – P(J AND K); solve to find P(J AND K) = .10
2. P(NOT (J AND K)) = 1 – P(J AND K) = 1 – 010 = .90
3. P(NOT (J OR K)) = 1 – P(J OR K) = 1 – .45 = .55

1. 

   Figure 3.24
2. P(GG) = $\left(\frac{5}{8}\right)\left(\frac{5}{8}\right)$ = $\frac{25}{64}$
3. P(at least one green) = P(GG) + P(GY) + P(YG) = $\frac{25}{64}$ + $\frac{15}{64}$ + $\frac{15}{64}$ = $\frac{55}{64}$
4. P(G|G) = $\frac{5}{8}$
5. Yes, they are independent because the first card is placed back in the bag before the second card is drawn. The composition of cards in the bag remains the same from draw one to draw two.

1. 	<20	20–64	>64	Totals
   Female	.0244	.3954	.0661	.486
   Male	.0259	.4186	.0695	.514
   Totals	.0503	.8140	.1356	1

   Table 3.27
2. P(F) = .486
3. P(>64|F) = .1361
4. P(>64 and F) = P(F) P(>64|F) = (.486)(.1361) = .0661
5. P(>64|F) is the percentage of female drivers who are 65 or older and P(>64 and F) is the percentage of drivers who are female and 65 or older.
6. P(>64) = P(>64 and F) + P(>64 and M) = .1356
7. No, being female and 65 or older are not mutually exclusive because they can occur at the same time P(>64 and F) = .0661.

1. 	Car, Truck or Van	Walk	Public Transportation	Other	Totals
   Alone	.7318
   Not Alone	.1332
   Totals	.8650	.0390	.0530	.0430	1

   Table 3.28
2. If we assume that all walkers are alone and that none from the other two groups travel alone (which is a big assumption) we have: P(Alone) = .7318 + .0390 = .7708.
3. Make the same assumptions as in (b) we have: (.7708)(1,000) = 771
4. (.1332)(1,000) = 133

The completed contingency table is as follows:

1. $\frac{255}{3059}$
2. $\frac{196}{3059}$
3. $\frac{718}{3059}$
4. 0
5. $\frac{463}{3059}$
6. $\frac{136}{196}$

7. 

   Figure 3.25