Probability and Stochastic Processes A Friendly Introduction for Electrical and Computer Engineers 3rd Edition Yates Solutions Manual

Probability and Stochastic Processes
A Friendly Introduction for Electrical and Computer Engineers
Third Edition
INSTRUCTOR’S SOLUTION MANUAL
Roy D. Yates, David J. Goodman, David Famolari
September 8, 2014
Comments on this Solutions Manual
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other documents of interest are also available for download:
– A manual probmatlab3e.pdf describing the matcode3e .m functions
– The quiz solutions manual quizsol.pdf.
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Problem Solutions – Chapter 1
Problem 1.1.1 Solution
Based on the Venn diagram on the right, the complete Gerlandas
pizza menu is
• Regular without toppings
• Regular with mushrooms
• Regular with onions
• Regular with mushrooms and onions
• Tuscan without toppings
• Tuscan with mushrooms
M O
T
Based on the Venn diagram on the right, the answers are mostly
fairly straightforward. The only trickiness is that a pizza is either
Tuscan (T) or Neapolitan (N) so {N, T} is a partition but they
are not depicted as a partition. Speciﬁcally, the event N is the
region of the Venn diagram outside of the “square block” of event
T. If this is clear, the questions are easy.
M O
T
(a) Since N = Tc, N ∩ M = φ. Thus N and M are not mutually exclusive.
(b) Every pizza is either Neapolitan (N), or Tuscan (T). Hence N ∪ T = S so
that N and T are collectively exhaustive. Thus its also (trivially) true that
N ∪ T ∪ M = S. That is, R, T and M are also collectively exhaustive.
(c) From the Venn diagram, T and O are mutually exclusive. In words, this
means that Tuscan pizzas never have onions or pizzas with onions are never
Tuscan. As an aside, “Tuscan” is a fake pizza designation; one shouldn’t
conclude that people from Tuscany actually dislike onions.
(d) From the Venn diagram, M ∩ T and O are mutually exclusive. Thus Ger-
landa’s doesn’t make Tuscan pizza with mushrooms and onions.
(e) Yes. In terms of the Venn diagram, these pizzas are in the set (T ∪ M ∪ O)c.
2

At Ricardo’s, the pizza crust is either Roman (R) or Neapolitan
(N). To draw the Venn diagram on the right, we make the fol-
lowing observations:
R N
M
OW
• The set {R, N} is a partition so we can draw the Venn diagram with this
partition.
• Only Roman pizzas can be white. Hence W ⊂ R.
• Only a Neapolitan pizza can have onions. Hence O ⊂ N.
• Both Neapolitan and Roman pizzas can have mushrooms so that event M
straddles the {R, N} partition.
• The Neapolitan pizza can have both mushrooms and onions so M ∩O cannot
be empty.
• The problem statement does not preclude putting mushrooms on a white
Roman pizza. Hence the intersection W ∩ M should not be empty.
(a) An outcome speciﬁes whether the connection speed is high (h), medium (m),
or low (l) speed, and whether the signal is a mouse click (c) or a tweet (t).
The sample space is
S = {ht, hc, mt, mc, lt, lc} . (1)
(b) The event that the wi-ﬁ connection is medium speed is A1 = {mt, mc}.
(c) The event that a signal is a mouse click is A2 = {hc, mc, lc}.
(d) The event that a connection is either high speed or low speed is A3 =
{ht, hc, lt, lc}.
3

(e) Since A1 ∩ A2 = {mc} and is not empty, A1, A2, and A3 are not mutually
exclusive.
(f) Since
A1 ∪ A2 ∪ A3 = {ht, hc, mt, mc, lt, lc} = S, (2)
the collection A1, A2, A3 is collectively exhaustive.
(a) The sample space of the experiment is
S = {aaa, aaf, afa, faa, ffa, faf, aff, fff} . (1)
(b) The event that the circuit from Z fails is
ZF = {aaf, aff, faf, fff} . (2)
The event that the circuit from X is acceptable is
XA = {aaa, aaf, afa, aff} . (3)
(c) Since ZF ∩ XA = {aaf, aff} = φ, ZF and XA are not mutually exclusive.
(d) Since ZF ∪ XA = {aaa, aaf, afa, aff, faf, fff} = S, ZF and XA are not
collectively exhaustive.
(e) The event that more than one circuit is acceptable is
C = {aaa, aaf, afa, faa} . (4)
The event that at least two circuits fail is
D = {ffa, faf, aff, fff} . (5)
(f) Inspection shows that C ∩ D = φ so C and D are mutually exclusive.
(g) Since C ∪ D = S, C and D are collectively exhaustive.
4

The sample space is
S = {A♣, . . . , K♣, A♦, . . . , K♦, A♥, . . . , K♥, A♠, . . . , K♠} . (1)
The event H that the first card is a heart is the set
H = {A♥, . . . , K♥} . (2)
The event H has 13 outcomes, corresponding to the 13 hearts in a deck.
The sample space is
S =



1/1 . . . 1/31, 2/1 . . . 2/29, 3/1 . . . 3/31, 4/1 . . . 4/30,
5/1 . . . 5/31, 6/1 . . . 6/30, 7/1 . . . 7/31, 8/1 . . . 8/31,
9/1 . . . 9/31, 10/1 . . . 10/31, 11/1 . . . 11/30, 12/1 . . . 12/31



. (1)
The event H defined by the event of a July birthday is given by the following et
with 31 sample outcomes:
H = {7/1, 7/2, . . . , 7/31} . (2)
Of course, there are many answers to this problem. Here are four partitions.
1. We can divide students into engineers or non-engineers. Let A1 equal the
set of engineering students and A2 the non-engineers. The pair {A1, A2} is a
partition.
2. To separate students by GPA, let Bi denote the subset of students with GPAs
G satisfying i − 1 ≤ G < i. At Rutgers, {B1, B2, . . . , B5} is a partition. Note
that B5 is the set of all students with perfect 4.0 GPAs. Of course, other
schools use different scales for GPA.
3. We can also divide the students by age. Let Ci denote the subset of students
of age i in years. At most universities, {C10, C11, . . . , C100} would be an event
space. Since a university may have prodigies either under 10 or over 100, we
note that {C0, C1, . . .} is always a partition.
5

4. Lastly, we can categorize students by attendance. Let D0 denote the number
of students who have missed zero lectures and let D1 denote all other students.
Although it is likely that D0 is an empty set, {D0, D1} is a well deﬁned
partition.
Let R1 and R2 denote the measured resistances. The pair (R1, R2) is an outcome
of the experiment. Some partitions include
1. If we need to check that neither resistance is too high, a partition is
A1 = {R1 < 100, R2 < 100} , A2 = {R1 ≥ 100} ∪ {R2 ≥ 100} . (1)
2. If we need to check whether the ﬁrst resistance exceeds the second resistance,
a partition is
B1 = {R1 > R2} B2 = {R1 ≤ R2} . (2)
3. If we need to check whether each resistance doesn’t fall below a minimum
value (in this case 50 ohms for R1 and 100 ohms for R2), a partition is
C1, C2, C3, C4 where
C1 = {R1 < 50, R2 < 100} , C2 = {R1 < 50, R2 ≥ 100} , (3)
C3 = {R1 ≥ 50, R2 < 100} , C4 = {R1 ≥ 50, R2 ≥ 100} . (4)
4. If we want to check whether the resistors in parallel are within an acceptable
range of 90 to 110 ohms, a partition is
D1 = (1/R1 + 1/R2)−1
< 90 , (5)
D2 = 90 ≤ (1/R1 + 1/R2)−1
≤ 110 , (6)
D2 = 110 < (1/R1 + 1/R2)−1
. (7)
6

(a) A and B mutually exclusive and collectively exhaustive imply P[A]+P[B] = 1.
Since P[A] = 3 P[B], we have P[B] = 1/4.
(b) Since P[A ∪ B] = P[A], we see that B ⊆ A. This implies P[A ∩ B] = P[B].
Since P[A ∩ B] = 0, then P[B] = 0.
(c) Since it’s always true that P[A ∪ B] = P[A] + P[B] − P[AB], we have that
P[A] + P[B] − P[AB] = P[A] − P[B]. (1)
This implies 2 P[B] = P[AB]. However, since AB ⊂ B, we can conclude that
2 P[B] = P[AB] ≤ P[B]. This implies P[B] = 0.
The roll of the red and white dice can be assumed to be independent. For each
die, all rolls in {1, 2, . . . , 6} have probability 1/6.
(a) Thus
P[R3W2] = P[R3] P[W2] =
1
36
.
(b) In fact, each pair of possible rolls RiWj has probability 1/36. To find P[S5],
we add up the probabilities of all pairs that sum to 5:
P[S5] = P[R1W4] + P[R2W3] + P[R3W2] + P[R4W1] = 4/36 = 1/9.
An outcome is a pair (i, j) where i is the value of the first die and j is the value of
the second die. The sample space is the set
S = {(1, 1), (1, 2), . . . , (6, 5), (6, 6)} . (1)
with 36 outcomes, each with probability 1/36 Note that the event that the absolute
value of the difference of the two rolls equals 3 is
D3 = {(1, 4), (2, 5), (3, 6), (4, 1), (5, 2), (6, 3)} . (2)
Since there are 6 outcomes in D3, P[D3] = 6/36 = 1/6.
7

(a) FALSE. Since P[A] = 1 − P[Ac] = 2 P[Ac] implies P[Ac] = 1/3.
(b) FALSE. Suppose A = B and P[A] = 1/2. In that case,
P [AB] = P [A] = 1/2 > 1/4 = P [A] P [B] . (1)
(c) TRUE. Since AB ⊆ A, P[AB] ≤ P[A], This implies
P [AB] ≤ P [A] < P [B] . (2)
(d) FALSE: For a counterexample, let A = φ and P[B] > 0 so that A = A∩B = φ
and P[A] = P[A ∩ B] = 0 but 0 = P[A] < P[B].
The sample space of the experiment is
S = {LF, BF, LW, BW} . (1)
From the problem statement, we know that P[LF] = 0.5, P[BF] = 0.2 and
P[BW] = 0.2. This implies P[LW] = 1 − 0.5 − 0.2 − 0.2 = 0.1. The questions
can be answered using Theorem 1.5.
(a) The probability that a program is slow is
P [W] = P [LW] + P [BW] = 0.1 + 0.2 = 0.3. (2)
(b) The probability that a program is big is
P [B] = P [BF] + P [BW] = 0.2 + 0.2 = 0.4. (3)
(c) The probability that a program is slow or big is
P [W ∪ B] = P [W] + P [B] − P [BW] = 0.3 + 0.4 − 0.2 = 0.5. (4)
8

A sample outcome indicates whether the cell phone is handheld (H) or mobile (M)
and whether the speed is fast (F) or slow (W). The sample space is
S = {HF, HW, MF, MW} . (1)
The problem statement tells us that P[HF] = 0.2, P[MW] = 0.1 and P[F] = 0.5.
We can use these facts to find the probabilities of the other outcomes. In particular,
P [F] = P [HF] + P [MF] . (2)
This implies
P [MF] = P [F] − P [HF] = 0.5 − 0.2 = 0.3. (3)
Also, since the probabilities must sum to 1,
P [HW] = 1 − P [HF] − P [MF] − P [MW]
= 1 − 0.2 − 0.3 − 0.1 = 0.4. (4)
Now that we have found the probabilities of the outcomes, finding any other prob-
ability is easy.
(a) The probability a cell phone is slow is
P [W] = P [HW] + P [MW] = 0.4 + 0.1 = 0.5. (5)
(b) The probability that a cell hpone is mobile and fast is P[MF] = 0.3.
(c) The probability that a cell phone is handheld is
P [H] = P [HF] + P [HW] = 0.2 + 0.4 = 0.6. (6)
A reasonable probability model that is consistent with the notion of a shuffled deck
is that each card in the deck is equally likely to be the first card. Let Hi denote
the event that the first card drawn is the ith heart where the first heart is the ace,
9

the second heart is the deuce and so on. In that case, P[Hi] = 1/52 for 1 ≤ i ≤ 13.
The event H that the ﬁrst card is a heart can be written as the mutually exclusive
union
H = H1 ∪ H2 ∪ · · · ∪ H13. (1)
Using Theorem 1.1, we have
P [H] =
13
i=1
P [Hi] = 13/52. (2)
This is the answer you would expect since 13 out of 52 cards are hearts. The point
to keep in mind is that this is not just the common sense answer but is the result
of a probability model for a shuﬄed deck and the axioms of probability.
Let si denote the outcome that the down face has i dots. The sample space is
S = {s1, . . . , s6}. The probability of each sample outcome is P[si] = 1/6. From
Theorem 1.1, the probability of the event E that the roll is even is
P [E] = P [s2] + P [s4] + P [s6] = 3/6. (1)
Let si equal the outcome of the student’s quiz. The sample space is then composed
of all the possible grades that she can receive.
S = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10} . (1)
Since each of the 11 possible outcomes is equally likely, the probability of receiving
a grade of i, for each i = 0, 1, . . . , 10 is P[si] = 1/11. The probability that the
student gets an A is the probability that she gets a score of 9 or higher. That is
P [Grade of A] = P [9] + P [10] = 1/11 + 1/11 = 2/11. (2)
The probability of failing requires the student to get a grade less than 4.
P [Failing] = P [3] + P [2] + P [1] + P [0]
= 1/11 + 1/11 + 1/11 + 1/11 = 4/11. (3)
10

Each statement is a consequence of part 4 of Theorem 1.4.
(a) Since A ⊂ A ∪ B, P[A] ≤ P[A ∪ B].
(b) Since B ⊂ A ∪ B, P[B] ≤ P[A ∪ B].
(c) Since A ∩ B ⊂ A, P[A ∩ B] ≤ P[A].
(d) Since A ∩ B ⊂ B, P[A ∩ B] ≤ P[B].
Specifically, we will use Theorem 1.4(c) which states that for any events A and B,
P [A ∪ B] = P [A] + P [B] − P [A ∩ B] . (1)
To prove the union bound by induction, we first prove the theorem for the case of
n = 2 events. In this case, by Theorem 1.4(c),
P [A1 ∪ A2] = P [A1] + P [A2] − P [A1 ∩ A2] . (2)
By the first axiom of probability, P[A1 ∩ A2] ≥ 0. Thus,
P [A1 ∪ A2] ≤ P [A1] + P [A2] . (3)
which proves the union bound for the case n = 2.Now we make our induction
hypothesis that the union-bound holds for any collection of n − 1 subsets. In this
case, given subsets A1, . . . , An, we define
A = A1 ∪ A2 ∪ · · · ∪ An−1, B = An. (4)
By our induction hypothesis,
P [A] = P [A1 ∪ A2 ∪ · · · ∪ An−1] ≤ P [A1] + · · · + P [An−1] . (5)
This permits us to write
P [A1 ∪ · · · ∪ An] = P [A ∪ B]
≤ P [A] + P [B] (by the union bound for n = 2)
= P [A1 ∪ · · · ∪ An−1] + P [An]
≤ P [A1] + · · · P [An−1] + P [An] (6)
which completes the inductive proof.
11

It is tempting to use the following proof:
Since S and φ are mutually exclusive, and since S = S ∪ φ,
1 = P [S ∪ φ] = P [S] + P [φ] .
Since P[S] = 1, we must have P[φ] = 0.
The above “proof” used the property that for mutually exclusive sets A1 and A2,
P [A1 ∪ A2] = P [A1] + P [A2] . (1)
The problem is that this property is a consequence of the three axioms, and thus
must be proven. For a proof that uses just the three axioms, let A1 be an arbitrary
set and for n = 2, 3, . . ., let An = φ. Since A1 = ∪∞
i=1Ai, we can use Axiom 3 to
write
P [A1] = P [∪∞
i=1Ai] = P [A1] + P [A2] +
∞
i=3
P [Ai] . (2)
By subtracting P[A1] from both sides, the fact that A2 = φ permits us to write
P [φ] +
∞
n=3
P [Ai] = 0. (3)
By Axiom 1, P[Ai] ≥ 0 for all i. Thus, ∞
n=3 P[Ai] ≥ 0. This implies P[φ] ≤ 0.
Since Axiom 1 requires P[φ] ≥ 0, we must have P[φ] = 0.
Following the hint, we deﬁne the set of events {Ai|i = 1, 2, . . .} such that i =
1, . . . , m, Ai = Bi and for i > m, Ai = φ. By construction, ∪m
i=1Bi = ∪∞
i=1Ai.
Axiom 3 then implies
P [∪m
i=1Bi] = P [∪∞
i=1Ai] =
∞
i=1
P [Ai] . (1)
12

For i > m, P[Ai] = P[φ] = 0, yielding the claim P[∪m
i=1Bi] = m
i=1 P[Ai] =
m
i=1 P[Bi].
Note that the fact that P[φ] = 0 follows from Axioms 1 and 2. This problem is
more challenging if you just use Axiom 3. We start by observing
P [∪m
i=1Bi] =
m−1
i=1
P [Bi] +
∞
i=m
P [Ai] . (2)
Now, we use Axiom 3 again on the countably inﬁnite sequence Am, Am+1, . . . to
write
∞
i=m
P [Ai] = P [Am ∪ Am+1 ∪ · · ·] = P [Bm] . (3)
Thus, we have used just Axiom 3 to prove Theorem 1.3:
P [∪m
i=1Bi] =
m
i=1
P [Bi] . (4)
Each claim in Theorem 1.4 requires a proof from which we can check which axioms
are used. However, the problem is somewhat hard because there may still be
a simpler proof that uses fewer axioms. Still, the proof of each part will need
Theorem 1.3 which we now prove.
For the mutually exclusive events B1, . . . , Bm, let Ai = Bi for i = 1, . . . , m and
let Ai = φ for i > m. In that case, by Axiom 3,
P [B1 ∪ B2 ∪ · · · ∪ Bm] = P [A1 ∪ A2 ∪ · · ·]
=
m−1
i=1
P [Ai] +
∞
i=m
P [Ai]
=
m−1
i=1
P [Bi] +
∞
i=m
P [Ai] . (1)
13

Now, we use Axiom 3 again on Am, Am+1, . . . to write
∞
i=m
P [Ai] = P [Am ∪ Am+1 ∪ · · ·] = P [Bm] . (2)
Thus, we have used just Axiom 3 to prove Theorem 1.3:
P [B1 ∪ B2 ∪ · · · ∪ Bm] =
m
i=1
P [Bi] . (3)
(a) To show P[φ] = 0, let B1 = S and let B2 = φ. Thus by Theorem 1.3,
P [S] = P [B1 ∪ B2] = P [B1] + P [B2] = P [S] + P [φ] . (4)
Thus, P[φ] = 0. Note that this proof uses only Theorem 1.3 which uses only
Axiom 3.
(b) Using Theorem 1.3 with B1 = A and B2 = Ac, we have
P [S] = P [A ∪ Ac
] = P [A] + P [Ac
] . (5)
Since, Axiom 2 says P[S] = 1, P[Ac] = 1 − P[A]. This proof uses Axioms 2
and 3.
(c) By Theorem 1.8, we can write both A and B as unions of mutually exclusive
events:
A = (AB) ∪ (ABc
), B = (AB) ∪ (Ac
B). (6)
Now we apply Theorem 1.3 to write
P [A] = P [AB] + P [ABc
] , P [B] = P [AB] + P [Ac
B] . (7)
We can rewrite these facts as
P[ABc
] = P[A] − P[AB], P[Ac
B] = P[B] − P[AB]. (8)
Note that so far we have used only Axiom 3. Finally, we observe that A ∪ B
can be written as the union of mutually exclusive events
A ∪ B = (AB) ∪ (ABc
) ∪ (Ac
B). (9)
14

Once again, using Theorem 1.3, we have
P[A ∪ B] = P[AB] + P[ABc
] + P[Ac
B] (10)
Substituting the results of Equation (8) into Equation (10) yields
P [A ∪ B] = P [AB] + P [A] − P [AB] + P [B] − P [AB] , (11)
which completes the proof. Note that this claim required only Axiom 3.
(d) Observe that since A ⊂ B, we can write B as the mutually exclusive union
B = A ∪ (AcB). By Theorem 1.3 (which uses Axiom 3),
P [B] = P [A] + P [Ac
B] . (12)
By Axiom 1, P[AcB] ≥ 0, hich implies P[A] ≤ P[B]. This proof uses Axioms 1
and 3.
Each question requests a conditional probability.
(a) Note that the probability a call is brief is
P [B] = P [H0B] + P [H1B] + P [H2B] = 0.6. (1)
The probability a brief call will have no handoffs is
P [H0|B] =
P [H0B]
P [B]
=
0.4
0.6
=
2
3
. (2)
(b) The probability of one handoff is P[H1] = P[H1B] + P[H1L] = 0.2. The
probability that a call with one handoff will be long is
P [L|H1] =
P [H1L]
P [H1]
=
0.1
0.2
=
1
2
. (3)
(c) The probability a call is long is P[L] = 1 − P[B] = 0.4. The probability that
a long call will have one or more handoffs is
P [H1 ∪ H2|L] =
P [H1L ∪ H2L]
P [L]
=
P [H1L] + P [H2L]
P [L]
=
0.1 + 0.2
0.4
=
3
4
. (4)
15

Let si denote the outcome that the roll is i. So, for 1 ≤ i ≤ 6, Ri = {si}. Similarly,
Gj = {sj+1, . . . , s6}.
(a) Since G1 = {s2, s3, s4, s5, s6} and all outcomes have probability 1/6, P[G1] =
5/6. The event R3G1 = {s3} and P[R3G1] = 1/6 so that
P [R3|G1] =
P [R3G1]
P [G1]
=
1
5
. (1)
(b) The conditional probability that 6 is rolled given that the roll is greater than
3 is
P [R6|G3] =
P [R6G3]
P [G3]
=
P [s6]
P [s4, s5, s6]
=
1/6
3/6
. (2)
(c) The event E that the roll is even is E = {s2, s4, s6} and has probability 3/6.
The joint probability of G3 and E is
P [G3E] = P [s4, s6] = 1/3. (3)
The conditional probabilities of G3 given E is
P [G3|E] =
P [G3E]
P [E]
=
1/3
1/2
=
2
3
. (4)
(d) The conditional probability that the roll is even given that it’s greater than
3 is
P [E|G3] =
P [EG3]
P [G3]
=
1/3
1/2
=
2
3
. (5)
Since the 2 of clubs is an even numbered card, C2 ⊂ E so that P[C2E] = P[C2] =
1/3. Since P[E] = 2/3,
P [C2|E] =
P [C2E]
P [E]
=
1/3
2/3
= 1/2. (1)
The probability that an even numbered card is picked given that the 2 is picked is
P [E|C2] =
P [C2E]
P [C2]
=
1/3
1/3
= 1. (2)
16

Let Ai and Bi denote the events that the ith phone sold is an Apricot or a Banana
respectively. Our goal is to find P[B1B2], but since it is not clear where to start,
we should plan on filling in the table
A2 B2
A1
B1
This table has four unknowns: P[A1A2], P[A1B2], P[B1A2], and P[B1B2]. We start
knowing that
P [A1A2] + P [A1B2] + P [B1A2] + P [B1B2] = 1. (1)
We still need three more equations to solve for the four unknowns. From “sales of
Apricots and Bananas are equally likely,” we know that P[Ai] = P[Bi] = 1/2 for
i = 1, 2. This implies
P [A1] = P [A1A2] + P [A1B2] = 1/2, (2)
P [A2] = P [A1A2] + P [B1A2] = 1/2. (3)
The final equation comes from “given that the first phone sold is a Banana, the sec-
ond phone is twice as likely to be a Banana,” which implies P[B2|B1] = 2 P[A2|B1].
Using Bayes’ theorem, we have
P [B1B2]
P [B1]
= 2
P [B1A2]
P [B1]
=⇒ P [B1A2] =
1
2
P [B1B2] . (4)
Replacing P[B1A2] with P[B1B2]/2 in the the first three equations yields
P [A1A2] + P [A1B2] +
3
2
P [B1B2] = 1, (5)
P [A1A2] + P [A1B2] = 1/2, (6)
P [A1A2] +
1
2
P [B1B2] = 1/2. (7)
Subtracting (6) from (5) yields (3/2) P[B1B2] = 1/2, or P[B1B2] = 1/3, which is
the answer we are looking for.
17

At this point, if you are curious, we can solve for the rest of the probability table.
From (4), we have P[B1A2] = 1/6 and from (7) we obtain P[A1A2] = 1/3. It then
follows from (6) that P[A1B2] = 1/6. The probability table is
A2 B2
A1 1/3 1/6
B1 1/6 1/3
.
The first generation consists of two plants each with genotype yg or gy. They are
crossed to produce the following second generation genotypes, S = {yy, yg, gy, gg}.
Each genotype is just as likely as any other so the probability of each genotype is
consequently 1/4. A pea plant has yellow seeds if it possesses at least one dominant
y gene. The set of pea plants with yellow seeds is
Y = {yy, yg, gy} . (1)
So the probability of a pea plant with yellow seeds is
P [Y ] = P [yy] + P [yg] + P [gy] = 3/4. (2)
Define D as the event that a pea plant has two dominant y genes. To find the
conditional probability of D given the event Y , corresponding to a plant having
yellow seeds, we look to evaluate
P [D|Y ] =
P [DY ]
P [Y ]
. (1)
Note that P[DY ] is just the probability of the genotype yy. From Problem 1.4.5,
we found that with respect to the color of the peas, the genotypes yy, yg, gy, and
gg were all equally likely. This implies
P [DY ] = P [yy] = 1/4 P [Y ] = P [yy, gy, yg] = 3/4. (2)
Thus, the conditional probability can be expressed as
P [D|Y ] =
P [DY ]
P [Y ]
=
1/4
3/4
= 1/3. (3)
18

The sample outcomes can be written ijk where the first card drawn is i, the second
is j and the third is k. The sample space is
S = {234, 243, 324, 342, 423, 432} . (1)
and each of the six outcomes has probability 1/6. The events E1, E2, E3, O1, O2,
O3 are
E1 = {234, 243, 423, 432} , O1 = {324, 342} , (2)
E2 = {243, 324, 342, 423} , O2 = {234, 432} , (3)
E3 = {234, 324, 342, 432} , O3 = {243, 423} . (4)
(a) The conditional probability the second card is even given that the first card
is even is
P [E2|E1] =
P [E2E1]
P [E1]
=
P [243, 423]
P [234, 243, 423, 432]
=
2/6
4/6
= 1/2. (5)
(b) The conditional probability the first card is even given that the second card
is even is
P [E1|E2] =
P [E1E2]
P [E2]
=
P [243, 423]
P [243, 324, 342, 423]
=
2/6
4/6
= 1/2. (6)
(c) The probability the first two cards are even given the third card is even is
P [E1E2|E3] =
P [E1E2E3]
P [E3]
= 0. (7)
(d) The conditional probabilities the second card is even given that the first card
is odd is
P [E2|O1] =
P [O1E2]
P [O1]
=
P [O1]
P [O1]
= 1. (8)
(e) The conditional probability the second card is odd given that the first card
is odd is
P [O2|O1] =
P [O1O2]
P [O1]
= 0. (9)
19

The problem statement yields the obvious facts that P[L] = 0.16 and P[H] = 0.10.
The words “10% of the ticks that had either Lyme disease or HGE carried both
diseases” can be written as
P [LH|L ∪ H] = 0.10. (1)
(a) Since LH ⊂ L ∪ H,
P [LH|L ∪ H] =
P [LH ∩ (L ∪ H)]
P [L ∪ H]
=
P [LH]
P [L ∪ H]
= 0.10. (2)
Thus,
P [LH] = 0.10 P [L ∪ H] = 0.10 (P [L] + P [H] − P [LH]) . (3)
Since P[L] = 0.16 and P[H] = 0.10,
P [LH] =
0.10 (0.16 + 0.10)
1.1
= 0.0236. (4)
(b) The conditional probability that a tick has HGE given that it has Lyme
disease is
P [H|L] =
P [LH]
P [L]
=
0.0236
0.16
= 0.1475. (5)
From the table we look to add all the mutually exclusive events to find each prob-
ability.
(a) The probability that a caller makes no hand-offs is
P [H0] = P [LH0] + P [BH0] = 0.1 + 0.4 = 0.5. (1)
(b) The probability that a call is brief is
P [B] = P [BH0] + P [BH1] + P [BH2] = 0.4 + 0.1 + 0.1 = 0.6. (2)
(c) The probability that a call is long or makes at least two hand-offs is
P [L ∪ H2] = P [LH0] + P [LH1] + P [LH2] + P [BH2]
= 0.1 + 0.1 + 0.2 + 0.1 = 0.5. (3)
20

(a) From the given probability distribution of billed minutes, M, the probability
that a call is billed for more than 3 minutes is
P [L] = 1 − P [3 or fewer billed minutes]
= 1 − P [B1] − P [B2] − P [B3]
= 1 − α − α(1 − α) − α(1 − α)2
= (1 − α)3
= 0.57. (1)
(b) The probability that a call will billed for 9 minutes or less is
P [9 minutes or less] =
9
i=1
α(1 − α)i−1
= 1 − (0.57)3
. (2)
(a) For convenience, let pi = P[FHi] and qi = P[V Hi]. Using this shorthand, the
six unknowns p0, p1, p2, q0, q1, q2 ﬁll the table as
H0 H1 H2
F p0 p1 p2
V q0 q1 q2
. (1)
However, we are given a number of facts:
p0 + q0 = 1/3, p1 + q1 = 1/3, (2)
p2 + q2 = 1/3, p0 + p1 + p2 = 5/12. (3)
Other facts, such as q0+q1+q2 = 7/12, can be derived from these facts. Thus,
we have four equations and six unknowns, choosing p0 and p1 will specify the
other unknowns. Unfortunately, arbitrary choices for either p0 or p1 will lead
21

to negative values for the other probabilities. In terms of p0 and p1, the other
unknowns are
q0 = 1/3 − p0, p2 = 5/12 − (p0 + p1), (4)
q1 = 1/3 − p1, q2 = p0 + p1 − 1/12. (5)
Because the probabilities must be nonnegative, we see that
0 ≤ p0 ≤ 1/3, (6)
0 ≤ p1 ≤ 1/3, (7)
1/12 ≤ p0 + p1 ≤ 5/12. (8)
Although there are an inﬁnite number of solutions, three possible solutions
are:
p0 = 1/3, p1 = 1/12, p2 = 0, (9)
q0 = 0, q1 = 1/4, q2 = 1/3. (10)
and
p0 = 1/4, p1 = 1/12, p2 = 1/12, (11)
q0 = 1/12, q1 = 3/12, q2 = 3/12. (12)
and
p0 = 0, p1 = 1/12, p2 = 1/3, (13)
q0 = 1/3, q1 = 3/12, q2 = 0. (14)
(b) In terms of the pi, qi notation, the new facts are p0 = 1/4 and q1 = 1/6.
These extra facts uniquely specify the probabilities. In this case,
p0 = 1/4, p1 = 1/6, p2 = 0, (15)
q0 = 1/12, q1 = 1/6, q2 = 1/3. (16)
22

This problems asks whether A and B can be independent events yet satisfy A = B?
By deﬁnition, events A and B are independent if and only if P[AB] = P[A] P[B].
We can see that if A = B, that is they are the same set, then
P [AB] = P [AA] = P [A] = P [B] . (1)
Thus, for A and B to be the same set and also independent,
P [A] = P [AB] = P [A] P [B] = (P [A])2
. (2)
There are two ways that this requirement can be satisﬁed:
• P[A] = 1 implying A = B = S.
• P[A] = 0 implying A = B = φ.
From the problem statement, we learn three facts:
P [AB] = 0 (since A and B are mutually exclusive) (1)
P [AB] = P [A] P [B] (since A and B are independent) (2)
P [A] = P [B] (since A and B are equiprobable) (3)
Applying these facts in the given order, we see that
0 = P [AB] = P [A] P [B] = (P [A])2
. (4)
It follows that P[A] = 0.
Let Ai and Bi denote the events that the ith phone sold is an Apricot or a Banana
respectively. The works “each phone sold is twice as likely to be an Apricot than
a Banana” tells us that
P [Ai] = 2 P [Bi] . (1)
23

However, since each phone sold is either an Apricot or a Banana, Ai and Bi are a
partition and
P [Ai] + P [Bi] = 1. (2)
Combining these equations, we have P[Ai] = 2/3 and P[Bi] = 1/3. The probability
that two phones sold are the same is
P [A1A2 ∪ B1B2] = P [A1A2] + P [B1B2] . (3)
Since “each phone sale is independent,”
P [A1A2] = P [A1] P [A2] =
4
9
, P [B1B2] = P [B1] P [B2] =
1
9
. (4)
Thus the probability that two phones sold are the same is
P [A1A2 ∪ B1B2] = P [A1A2] + P [B1B2] =
4
9
+
1
9
=
5
9
. (5)
A
B
In the Venn diagram, assume the sample space has
area 1 corresponding to probability 1. As drawn,
both A and B have area 1/4 so that P[A] = P[B] =
1/4. Moreover, the intersection AB has area 1/16
and covers 1/4 of A and 1/4 of B. That is, A and B
are independent since
P [AB] = P [A] P [B] . (1)
(a) Since A and B are mutually exclusive, P[A ∩ B] = 0. Since P[A ∩ B] = 0,
P [A ∪ B] = P [A] + P [B] − P [A ∩ B] = 3/8. (1)
24

A Venn diagram should convince you that A ⊂ Bc so that A ∩ Bc = A. This
implies
P [A ∩ Bc
] = P [A] = 1/4. (2)
It also follows that P[A ∪ Bc] = P[Bc] = 1 − 1/8 = 7/8.
(b) Events A and B are dependent since P[AB] = P[A] P[B].
(a) Since C and D are independent,
P [C ∩ D] = P [C] P [D] = 15/64. (1)
The next few items are a little trickier. From Venn diagrams, we see
P [C ∩ Dc
] = P [C] − P [C ∩ D] = 5/8 − 15/64 = 25/64. (2)
It follows that
P [C ∪ Dc
] = P [C] + P [Dc
] − P [C ∩ Dc
] (3)
= 5/8 + (1 − 3/8) − 25/64 = 55/64. (4)
Using DeMorgan’s law, we have
P [Cc
∩ Dc
] = P [(C ∪ D)c
] = 1 − P [C ∪ D] = 15/64. (5)
(b) Since P[CcDc] = P[Cc] P[Dc], Cc and Dc are independent.
(a) Since A ∩ B = ∅, P[A ∩ B] = 0. To ﬁnd P[B], we can write
P [A ∪ B] = P [A] + P [B] − P [A ∩ B] (1)
or
5/8 = 3/8 + P [B] − 0. (2)
Thus, P[B] = 1/4. Since A is a subset of Bc, P[A ∩ Bc] = P[A] = 3/8.
Furthermore, since A is a subset of Bc, P[A ∪ Bc] = P[Bc] = 3/4.
25

(b) The events A and B are dependent because
P [AB] = 0 = 3/32 = P [A] P [B] . (3)
(a) Since C and D are independent P[CD] = P[C] P[D]. So
P [D] =
P [CD]
P [C]
=
1/3
1/2
= 2/3. (1)
In addition, P[C ∩ Dc] = P[C] − P[C ∩ D] = 1/2 − 1/3 = 1/6. To find
P[Cc ∩ Dc], we first observe that
P [C ∪ D] = P [C] + P [D] − P [C ∩ D] = 1/2 + 2/3 − 1/3 = 5/6. (2)
By De Morgan’s Law, Cc ∩ Dc = (C ∪ D)c. This implies
P [Cc
∩ Dc
] = P [(C ∪ D)c
] = 1 − P [C ∪ D] = 1/6. (3)
Note that a second way to find P[Cc ∩ Dc] is to use the fact that if C and D
are independent, then Cc and Dc are independent. Thus
P [Cc
∩ Dc
] = P [Cc
] P [Dc
] = (1 − P [C])(1 − P [D]) = 1/6. (4)
Finally, since C and D are independent events, P[C|D] = P[C] = 1/2.
(b) Note that we found P[C ∪ D] = 5/6. We can also use the earlier results to
show
P [C ∪ Dc
] = P [C] + P [D] − P [C ∩ Dc
] (5)
= 1/2 + (1 − 2/3) − 1/6 = 2/3. (6)
(c) By Definition 1.6, events C and Dc are independent because
P [C ∩ Dc
] = 1/6 = (1/2)(1/3) = P [C] P [Dc
] . (7)
26

For a sample space S = {1, 2, 3, 4} with equiprobable outcomes, consider the events
A1 = {1, 2} A2 = {2, 3} A3 = {3, 1} . (1)
Each event Ai has probability 1/2. Moreover, each pair of events is independent
since
P [A1A2] = P [A2A3] = P [A3A1] = 1/4. (2)
However, the three events A1, A2, A3 are not independent since
P [A1A2A3] = 0 = P [A1] P [A2] P [A3] . (3)
There are 16 distinct equally likely outcomes for the second generation of pea plants
based on a first generation of {rwyg, rwgy, wryg, wrgy}. These are:
rryy rryg rrgy rrgg
rwyy rwyg rwgy rwgg
wryy wryg wrgy wrgg
wwyy wwyg wwgy wwgg
A plant has yellow seeds, that is event Y occurs, if a plant has at least one dominant
y gene. Except for the four outcomes with a pair of recessive g genes, the remaining
12 outcomes have yellow seeds. From the above, we see that
P [Y ] = 12/16 = 3/4 (1)
and
P [R] = 12/16 = 3/4. (2)
To find the conditional probabilities P[R|Y ] and P[Y |R], we first must find P[RY ].
Note that RY , the event that a plant has rounded yellow seeds, is the set of
outcomes
RY = {rryy, rryg, rrgy, rwyy, rwyg, rwgy, wryy, wryg, wrgy} . (3)
27

Since P[RY ] = 9/16,
P [Y |R] =
P [RY ]
P [R]
=
9/16
3/4
= 3/4 (4)
and
P [R |Y ] =
P [RY ]
P [Y ]
=
9/16
3/4
= 3/4. (5)
Thus P[R|Y ] = P[R] and P[Y |R] = P[Y ] and R and Y are independent events.
There are four visibly diﬀerent pea plants, corresponding to whether the peas are
round (R) or not (Rc), or yellow (Y ) or not (Y c). These four visible events have
probabilities
P [RY ] = 9/16 P [RY c
] = 3/16, (6)
P [Rc
Y ] = 3/16 P [Rc
Y c
] = 1/16. (7)
(a) For any events A and B, we can write the law of total probability in the form
of
P [A] = P [AB] + P [ABc
] . (1)
Since A and B are independent, P[AB] = P[A] P[B]. This implies
P [ABc
] = P [A] − P [A] P [B] = P [A] (1 − P [B]) = P [A] P [Bc
] . (2)
Thus A and Bc are independent.
(b) Proving that Ac and B are independent is not really necessary. Since A and
B are arbitrary labels, it is really the same claim as in part (a). That is,
simply reversing the labels of A and B proves the claim. Alternatively, one
can construct exactly the same proof as in part (a) with the labels A and B
reversed.
(c) To prove that Ac and Bc are independent, we apply the result of part (a)
to the sets A and Bc. Since we know from part (a) that A and Bc are
independent, part (b) says that Ac and Bc are independent.
28

A AC
AB ABC C
BCB
In the Venn diagram at right, assume the sample space
has area 1 corresponding to probability 1. As drawn, A,
B, and C each have area 1/2 and thus probability 1/2.
Moreover, the three way intersection ABC has probabil-
ity 1/8. Thus A, B, and C are mutually independent
since
P [ABC] = P [A] P [B] P [C] . (1)
A AB B
AC C BC
In the Venn diagram at right, assume the sample space
has area 1 corresponding to probability 1. As drawn, A,
B, and C each have area 1/3 and thus probability 1/3.
The three way intersection ABC has zero probability,
implying A, B, and C are not mutually independent since
P [ABC] = 0 = P [A] P [B] P [C] . (1)
However, AB, BC, and AC each has area 1/9. As a result, each pair of events is
independent since
P [AB] = P [A] P [B] , P [BC] = P [B] P [C] , P [AC] = P [A] P [C] . (2)
We can generate the 200 × 1 vector T, denoted T in Matlab, via the command
T=50+ceil(50*rand(200,1))
Keep in mind that 50*rand(200,1) produces a 200×1 vector of random numbers,
each in the interval (0, 50). Applying the ceiling function converts these random
numbers to rndom integers in the set {1, 2, . . . , 50}. Finally, we add 50 to produce
random numbers between 51 and 100.
29
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