Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Exercise 2.3 Probability

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Probability

Page 71 Problem 1 Answer

Given- 8 bottles of zinfandel, 10 of merlot, and 12 of cabernet all from different wineries.

To find- ways to serve 3bottles of zinfandel and serving order is important.We will use permutation formula P(n,r)=n!

(n−r)! to solve the problem.

If 3 bottles of zinfandel are to be served, so the number of ways for selecting 3 out of 8 are P(8,3).

P(8,3)=8!

(8−3)!=8!

5!=8×7×6×5!

5!=8×7×6=336

​There are 336 ways to serve 3 bottles of zinfandel and serving order is important.

Page 71 Problem 2 Answer

Given-8 bottles of zinfandel,10  of merlot, and 12 of cabernet all from different wineries. To find- ways to serve randomly selected 6 bottles of wine from the 30.

Since the order is not important we will use combination formula C(n,r)=n!

r!(n−r)! to solve the problem.

The number of ways of to serve 6 bottles of randomly selected wine out of 30 servings-

​C(30,6)​=30!

6!(30−6)!=30!

6!×24!=593,775

​The number of ways of to serve 6 bottles of randomly selected wine out of 30 servings are 593,775.

Page 71 Problem 3 Answer

Given-8 bottles of zinfandel, 10 of merlot, and 12 of cabernet all from different wineries.

To find- ways to obtain two bottles of each variety when 6bottles are randomly selected.

We will use combination formula C(n,r)=n!

r!(n−r)! to solve the problem because we need two of each variety thus order will matter.

For 6 bottles we need 2 from 8 bottles of zinfandel, 2 from 10 bottles of merlot, 2 from 12 bottles of cabernet, so total number of ways -8C2×10

C2×12

C2=8!

2!(8−2)!×10!

2!(10−2)!×12!

2!(12−2)!=8!

2!×6!×10!

2!×8!×12!

2!×10!=12!

2!×2!×2!×6!

=83,160

​There are 83,160 ways to obtain two bottles of each variety when 6 bottles are randomly selected.

Page 71 Problem 4 Answer

Given- 8 bottles of zinfandel, 10 of merlot, and 12  of cabernet all from different wineries.

Number of ways of selecting two bottles of each variety being chosen when 6 bottles are randomly selected obtained in c): 83160.

To find- probability that two bottles of each variety being chosen when 6 bottles are randomly selected.

We will find the probability of the event which is given by: Number of favourable outcomes.

Total number of outcomes

As we know that there are 83,160  ways of selecting two bottles of each variety being chosen when  6 bottles are randomly selected.

So, the probability that two bottles of each variety being chosen when 6 bottles are randomly selected.

P( Two bottles of each variety )=83,160/30

C6=0.14​

The probability that two bottles of each variety being chosen when 6 bottles are randomly selected is 0.14.

Page 71 Problem 5 Answer

Given-8 bottles of zinfandel, 10 of merlot, and 12 of cabernet all from different wineries.

To find-  the probability that all of the bottles are of same variety when 6 bottles are randomly selected.

We will use combination to solve the problem of all of them being the same variety and then use probability to find the answer.

If 6 bottles are randomly selected from 30 bottles, then the probability that all of them are of the same variety is,

P( all of them are the same variety )=8

C6+10

C6+12

C6/30

C6=28+210+924/593,775

=0.002

​The probability that all of the bottles are of same variety when 6 bottles are randomly selected is0.002 .

Page 71 Problem 6 Answer

Given- A production facility employs 20 workers on the day shift, 15 workers on the swing shift, and 6

workers on the graveyard shift. A quality control consultant is to select 6 of the workers in-depth interview.

To find- number of selections that 6 of the workers to be selected for an in-depth interviews will be from the day shift and the probability that all 6 selected workers will be from the day shift.

We will firstly find the combination to select the workers using C(n,r)=n!

r!(n−r)! the find the probability.

The 6 workers are selected from 20 day shift workers. So, the number of ways are(20/6) by using combination formula-

C(n,r)=n!

r!(n−r)!

=(20/6)

=20!

6!×14!

=38760 ways

​The probability that all the 6 members are selected from day shift is-

P(all 6 selected workers will  be from the day shift )

​=(20/6)/(45/6)

=38,760/8,145,060

=0.0048

​The number of selections that 6 of the workers to be selected for an in-depth interviews will be from the day shift are 38760 .

The probability that all 6 selected workers will be from the day shift is 0.0048.

Page 71 Problem 7 Answer

Given:  Given- A production facility employs 20 workers on the day shift, 15 workers on the swing shift, and 10 workers on the graveyard shift.

A quality control consultant is to select 6of the workers in-depth interview.

To find the probability that all 6selected workers will be from the same shift.

We will first use the formula n/Cr=n!

r!(n−r)! to choose from different shifts and then take the probability.

Calculate the probability of all the chosen people to be in day shift,

D=20

C6/20

C6=20!

6!(20−6)!

20/C6=38760

​Calculate the probability of all the chosen people to be in night shift,

N=15/C6

15/C6=15!

6!(15−6)!

15/C6=5005

​Calculate of probability of all the chosen people to be in the graveyard shift,

G=10/C6

10/C6=10!

6!(10−6)!

10/C6=210

​Calculate the total number of the probability of picking 6 people out of 45,

T=45/C6

45/C6=45!

6!(45−6)!

45/C6=814506

​Now substitute the values in the formula,

P=38760+5005+210/814506

P=0.00539

​The probability that all 6 selected workers will be from the same shift is:0.00539.

Page 71 Problem 8 Answer

Given- A production facility employs 20 workers on the day shift, 15 workers on the swing shift, and  10 workers on the graveyard shift.

A quality control consultant is to select 6 of the workers in-depth interview.

To find the probability that at least two different shifts will be represented among the selected workers The probability that all 6 selected workers will be from the same shift is obtained as: 0.00539 Hence by using this we will find the required probability.

The probability of all 6 chosen can be from one shift was calculated from the previous question,

P=0.00539

Hence the probability that at least two different shifts will be represented among the selected workers

=1−Probability that all 6 selected workers will be from the same shift.

=1−0.00539

=0.9946​

The total probability of at least two different shifts represented among the selected workers is 0.9946.

Page 71 Problem 9 Answer

Given- A production facility 20 employs workers on the day shift, 15 workers on the swing shift, and 10 workers on the graveyard shift.

A quality control consultant is to select 6 of the workers in-depth interview.

To find the probability that at least one of the shifts will be unrepresented in the sample of workers.

We will first find the number of ways of selection for different shifts not represented using nCr=n!

r!(n−r)! and then get the probability.

Finding the number of ways in which day shift is not represented,

A1=15+10

C6/25

C6=25!

6!(25−6)!

25/C6=177100

​Finding the number of ways in which  swing shift is not represented,

A2=10+20/C6

30/C6=30!

6!(30−6)!

30/C6=593775

​Finding the probability of graveyard shift not represented,

A3=20+15

C6/35

C6=35!

6!(35−6)!

35/C6=1623160

​Hence we get:P(A)=25

C6/45

C6=0.022

P(A2)=30

C6/45

C6=0.073

P(A3)=35

C6,45

C6=0.199

​Similarly we get:

Now add all the possibilities and subtract individual probabilities to find the solution,

P3=A1+A2+A3−(P(A1∩A2)+P(A2∩A3)+P(A1∩A3))+P(A1∩A2∩A3)

Since P(A1∩A2)+P(A2∩A3)+P(A1∩A3)is nothing but the probability of all the swifts together, which was calculated earlier, which is 312.

The value of P(A1∩A2∩A3)=0

Now substitute the value of all the values calculated in the formula,

Hence the required probability will be:

P3=P(A1)+P(A2)+P(A3)−(P(A1∩A2)+P(A2∩A3)+P(A1∩A3))+P(A1∩A2∩A3)

=0.022+0.073+0.199+0.00003+0.0006+0.005+0

=0.289

​The probability that at least one of the shifts will be unrepresented in the sample of workers is 0.289.

Page 72 Problem 10 Answer

Given: A box in a certain supply room contains four 40-W lightbulbs, five 60-W bulbs, and six 75-W bulbs.

Given: three bulbs are randomly selected.To find the probability that exactly two of the selected bulbs are rated 75-W.

First, we will find the number of outcomes that is in how many ways two bulbs of 75−W can be removed by using combination Cr,n=(n/r) and then find the probability.

Number of 40 W bulbs-4

Number of 60 W bulbs-5

Number of 75 W bulbs-6

Hence 2 bulbs shall be taken from a group of 6 items and remaining one can be taken from 4+5=9

Hence the number of ways to do so is:

C2,6×C1,9=(6/2)×(9/1)

⇒C2,6×C1,9

=135​

The total outcomes are:

C3,15=(15/3)

C3,15=455

Hence if we consider A to be the event where exactly two bulbs of 75−W are selected then:

P(A)= number of favorable outcomes in A number of outcomes in the sample space

P(A)=135/455

P(A)=0.297

​The probability that exactly two of the selected bulbs are rated 75-W is 0.297.

Page 72 Problem 11 Answer

Given: A box in a certain supply room contains four 40-W lightbulbs, five 60-W bulbs, and six 75-W bulbs.

Given: Three bulbs are randomly selected.to find the probability that all three bulbs selected have the same rating.

We will use the formula for disjoint events: P(A∪B∪C)=P(A)+P(B)+P(C)

It is possible that the 3 selected light bulbs are all from either of the groups 40 W, 60 W and 75 W.

That is, denote events B1,B2,B3 as all 3 light bulbs are from 40W,60W , and 75W, respectively.

The event B is the initial one – all 3 randomly selected are from the same group.

Hence, as the 3 events B1,B2,B3 are disjoint  we can apply:

P(A∪B∪C)=P(A)+P(B)+P(C)

⇒P(B)=P(B1∪B2∪B3)

P(B1∪B2∪B3)=P(B1)+P(B2)+P(B3)=C3,4/C3,15+C3,5/C3,15+C3,6/C3,15

=4/455+10/455+20/455

=34/455

=0.075.

​The probability that all three of the selected bulbs have the same rating is 0.075.

Page 72 Problem 12 Answer

Given: A box in a certain supply room contains four 40-W lightbulbs, five 60-W bulbs, and six 75

-W bulbs Given: Three bulbs are randomly selected.

To find the probability that one bulb of each type is selected.

We will solve this by getting the favorable outcomes and using (n/1)=n.

We denote with C event that the 3 randomly selected light bulbs have representative in each of the three groups.

Then the required probability is given by:

P(C)= number of favorable outcomes in C number of outcomes in the sample space

P(C)=(4/1)×(5/1​)×(​6/1​)(​15/3​)

P(C)=4×5×6/455

P(C)=0.2637

​The probability that one bulb of each type is selected is 0.2637.

Page 72 Problem 13 Answer

Given: A box in a certain supply room contains four 40-W lightbulbs, five 60-W bulbs, and six 75-W bulbs Given: Bulbs are to be selected one by one until a 75-W bulb is found.

To find the probability that it is necessary to examine at least six bulbs.

We will use complement rule P(A)=1−P(Ac) to find the required probability.

Let D be the event that is is necessary to examine at least six bulbs

The probability of the required event may be calculated using the facts that the light bulbs are selected one-by-one, and that the selected light bulb before sixth is from group of 75 W (complement of D , in 1./2./3./4 or 5. turn 75W is selected) Therefore we have:

P{ At least six bulbs selected  to get 75W Bulb }=1−P{75W bulb selected  within first five bulbs }

P(D)=1−P(Dc)

=1−6/15−9/15×6/14−9⋅8/15⋅14×6/13−9⋅8⋅7

15⋅14⋅13×6

12−9⋅8⋅7⋅6

15⋅14⋅13⋅12×6/11

=1−0.958

=0.042

​The probability that it is necessary to examine at least six bulbs is 0.042.

Page 72 Problem 14 Answer

Given: An ATM personal identification number (PIN) consists of four digits, each a0,1,2,…8, or 9, in succession.To find the number of different possible PINs if there are no restrictions on the choice of digits.

We will use the product rule for k−tuples.

Finding the number of ways the PIN can be arranged,When there is no restrictions each digit can be selected in 10 ways.

So possible numbers of pin are: P=10×10×10×10

P=10000​

The total number of different possible PINs can be generated, if there are no restrictions on the choice of digits is 10000.

Page 72 Problem 15 Answer

Given:  An ATM personal identification number (PIN) consists of four digits, each a0,1,2,…8 , or 9 , in succession.

Given:  According to a representative at the author’s local branch of Chase Bank, there are in fact restrictions on the choice of digits. The following choices are prohibited:

(1) all four digits identical

(2) sequences of consecutive ascending or descending digits, such as 6543

(3) any sequence starting with 19

To find the probability that the sequence of pin will be legitimate.

Calculating the probability of all the conditions,. The identical numbers are totally 10.

There are 7 combinations of ascending and 7 descending combinations. A total of 14 .

The first two numbers are 1and 9, then the probability is 10=100.

Now remove all the invalid probabilities from the total,

P=10000−10−14−100/10000

P=0.9876

The probability that the random pick will be a legitimate PIN is 0.9876

Page 72 Problem 16 Answer

Given:  An ATM personal identification number (PIN) consists of four digits, each0,1,2,…8, or 9, in succession.

Given: Someone has stolen an ATM card and knows that the first and last digits of the PIN are 8 and 1, respectively.

He has three tries before the card is retained by the ATM (but does not realize that).

So he randomly selects the 2nd and 3rd digits for the first try, then randomly selects a different pair of digits for the second try, and yet another randomly selected pair of digits for the third try .

To find the probability that the individual gains access to the account.

First, we will find the favorable outcomes by using the product rule and then find the probability.

Since there is only two slots left and 10 digits, the number of favorable outcomes will be

S=10⋅10

S=100

​There was three attempt for trying a combination,

P=3/100

P=0.03 .

The probability that the individual gains access to the account is 0.03

Page 72 Problem 17 Answer

Given: An ATM personal identification number (PIN) consists of four digits, each0,1,2,…8, or 9, in succession.

Obtained probability in part c) 0.03

To find the probability that the individual gains access to the account if the first and last digits are 1 and 1, respectively.

We will get the favorable outcomes and then find the required probability.

Since the numbers are identical, there is a possibility of violation in the password.

It can not be1111 and can not contain continues numbers (1901,1902,1991).

Therefore, the total number of violation is 10+1=11

The total probability with three possible combination is,

P=3/100−11

P=3/89

P=0.0337

The probability for the individual to gain access to the account is 0.0337.

Page 72 Problem 18 Answer

Given:  A starting lineup in basketball consists of two guards, two forwards, and a center.

Given:  A certain college team has on its roster three centers, four guards, four forwards, and one individual (X) who can play either guard or forward.

To find the number of different lineups that can be created.

We shall use the combination formula nCr=n!

r!(n−r)! for the possibilities for which X can be picked or not.

There are three possibilities of player(X),

When (X) is not picked,

P=(2 out of 4 guards )+(2 out of 4 frowards )+(1 out of 3 center )

P=(4/C2)⋅(4/C2)⋅(3/C1)

P=6⋅6⋅3

P=108

When (X) is picked as guard,

P=(1 out of 4 guards )+(2 out of 4 frowards )+(1 out of 3 center )

P=(4/C1)⋅(4/C2)⋅(3/C1)

P=4⋅6⋅3

P=72

The same number is when the player is picked as forward.

P=(2 out of 4 guards )+(1 out of 4 forwards )+(1 out of 3 center )

P=(4/C2)⋅(4/C1)⋅(3/C1)

P=4⋅6⋅3

P=72

​Now add all the found variables,

T=108+72+72

T=252​

The number of starting lineups that can be created is 252.

Page 72 Problem 19 Answer

Given: A starting lineup in basketball consists of two guards, two forwards, and a center.

Given:  the roster has 5 guards, 5 forwards, 3 centers, and 2 “swing players” (X and Y) who can play either guard or forward.

To find the probability that the team consists of a legitimate starting lineup.

We will find the combination with different position of the individual player X (old team member) or (given team member) and them sum up all.

When the starting lineup is made without X or Y: C2,5⋅C2,5⋅C1,3

=(​5/2​)⋅(​5/2​)⋅(​3/1​)

=5!/2(5−2)!⋅5!/2!(5−2)!⋅3!/1!(3−1)!

=10⋅10⋅3

=300

X is guard and there is no Y:​C1,5⋅C2,5⋅C1,3

=(​5/1​)⋅(​5/2​)⋅(​3/1​)

=5!/1((5−1)!⋅5!/2(5−2)!⋅3!/1(3−1)!

=5⋅10⋅3

=150

Xis guard and there is no Y:C2,5⋅C1,5⋅C1,3

=(​5/2​)⋅(​5/1​)⋅(​3/1​)

=5!/2(5−2)!⋅5!/1!(5−1)!⋅3!/1!(3−1)!

=10⋅5⋅3

=150

Y is guard and there is no X:

​C1,5⋅C2,5⋅C1,3

=(​5/1​)⋅(​5/2​)⋅(​3/1​)

=5!/1((5−1)!⋅5!/2!(5−2)!⋅3!/1(3−1)!

=5⋅10⋅3

=150

Y is forward and there is no X:C2,5⋅C1,5⋅C1,3

=(​5/2​)⋅(​5/1​)⋅(​3/1​)

=5!/2!(5−2)!⋅5!/1!(5−1)!⋅3!/1(3−1)!

=10⋅5⋅3

=150

X is guard and Y is also guard:

1⋅C2,5⋅C1,3

=1⋅(​5/2​)⋅(​3/1​)

=1⋅5!/2!(5−2)!⋅3!/1!(3−1)!

=1⋅10⋅3

=30

X is guard and Y  is forward:

C1,5⋅C1,5⋅C1,3

=(​5/1​)⋅(​5/1​)⋅(​3/1​)

=5!/1(5−1)!⋅5!/1(5−1)!⋅3!/1(3−1)!

=5⋅5⋅3

=75

​Xis forward and Y is guard: C1,5⋅C1,5⋅C1,3

=(​5/1​)⋅(​5/1​)⋅(​3/1​)

=5!/1!(5−1)!⋅5!/1!(5−1)!⋅3!/1(3−1)!

=5⋅5⋅3

=75

X is forward and Y is forward:

C2,5⋅1⋅C1,3

=(​5/2​)⋅1⋅(​3/1​)

=5!/2!(5−2)!⋅1⋅3!/1!(3−1)!

=10⋅1⋅3

=30

​We will now sum up all the favorable possibilities.

300+4⋅150+2⋅30+2⋅75=1110

Total number of ways to choose 5 players from 15 members:

​(​15/5​)

​=15!

5!(15−5)!

=3003

The required probability of five constituting the legitimate starting lineup is:

P(A)= Number of favorable outcomes in A

Number of outcomes in the sample space

=1110/3003

=0.3696

​The probability of five constituting the legitimate starting lineup is 0.3696.

Page 72 Problem 20 Answer

Given: In five-card poker, a straight consists of five cards with adjacent denominations. You are dealt with a five-card hand.

To find the probability that it will be a straight with high card 10.

To find the probability that it will be straight.

To find the probability that it will be a straight flush.

We will use the combination formula Ck,n=n!

k!(n−k)! to find the favorable outcomes for each case and then find the probability.

Total ways to select 5 cards out of 52are

C5,52 =2,598,960

​(​52/5​)/52!

= 5!(52−5)!

We have a 10 high card, straight consists of six, seven, eight, nine, ten.

We will have 4 different types of cards of each type.

So, favorable outcomes will be 4⋅4⋅4⋅4⋅4=45

=1024

So, probability that out of 5 dealt cards we get a straight with high card10:

Number of favorable outcomes in A

Number of outcomes in the sample space =1024/2,598,960 = 0.000394

We have the straights with high card,5 high card,6 high card,7 high card,8 high card,9 high card,10 high card,J high card,Q high card K and high card A.So, there are10 cards.

We have calculated the desired outcomes to be 1024

Hence, we get P({ straight })=10⋅1024/2,598,960=0.00394.

​The number of ways to select straight flush is 10.4=40 as there are 10 different straights and we have 4 different straight flush for each straight.

We know that total ways of selecting 5 cards out of 52 is 2,598,960.

Therefore, the probability that the card is a straight flush is P({ straight flush })=40/2,598,960=0.00001539​

The probability that out of 5 dealt cards, it is straight with high card 10 is 0.000394.

The probability that the selected c.

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Exercise 2.2 Probability

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Probability

Page 62 Problem 1 Answer

Given: A mutual fund company offers its customers a variety of funds: a money-market fund, three different bond funds, two stock funds and a balanced fund.

Among customers who own shares in just one fund, the percentages of customers in the different funds are as follows:

Money-market 20%

High-risk stock 18%

Short bond 15%

Moderate-risk stock 25%

Intermediate bond 10%

Balanced 7%

Long bond 5%

A customer who owns shares in just one fund is randomly selected.

To find the probability that the selected individual owns shares in the balanced fund.

We will make use of the percentages of customers in different funds given to us.

Let A be the event:

A=” Selected individual owns shares in the balanced fund”

Hence we have:

P(A)= The sum of all given percentage is =20%+18%+15%+25%+10%+7%+5%=100%

The probability of balanced fund is already given that is =7%=0.07

The probability that the selected individual owns shares in the balanced fund is 0.07.

Page 62 Problem 2 Answer

Given: A mutual fund company offers its customers a variety of funds: a money-market fund, three different bond funds two stock funds and a balanced fund.

Among customers who own shares in just one fund, the percentages of customers in the different funds are as follows:

Money-market  20%

High-risk stock 18%

Short bond 15%

Moderate-risk stock 25%

Intermediate bond 10%

Balanced 7%

Long bond 5%

A customer who owns shares in just one fund is randomly selected.

To find the probability that the individual owns shares in a bond fund We will make use of the percentages of customers in different funds given to us.

Let B be the event:

B= ”Individual owns shares in a bond fund” and let

B1=”Individual owns shares in short bond funds”

B2=”Individual owns shares in intermediate bond funds”

B3=” Individual owns shares in long-term bond funds”

Hence we get

Probability of bond fund = short bond + Intermediate bond + long bond

⇒P(B)=P(B1)+P(B2)+P(B3)

=15%+10%+5%

=30%

=0.30

​The probability that the individual owns shares in a bond fund is 0.30.

Page 62 Problem 3 Answer

Given: A mutual fund company offers its customers a variety of funds: a money-market fund, three different bond funds (short, intermediate, and long-term), two stock funds (moderate and high-risk), and a balanced fund.

Among customers who own shares in just one fund, the percentages of customers in the different funds are as follows:

Money-market 20%

High-risk stock 18%

Short bond 15%

Moderate-risk stock 25%

Intermediate bond 10%

Balanced 7%

Long bond 5%

A customer who owns shares in just one fund is randomly selected.

To find the probability that the selected individual does not own shares in a stock fund.

We will make use of the Complement proposition P(A)+P(A′)=1.

Let C be the event that:

C=”Selected individual does not own shares in a stock fund”

Probability of stock fund  =high risk stock + moderate risk stock

Hence P(C)=18%+25%

=43%

=0.43

Now, using complement rule: ​P(A)+P(A′)=1

⇒P(A′)=1−P(A)

​P( not stock fund )=1−P( stock fund )

P(C′)=1−P(C)

=1−0.43

​P(C′)=0.57

​The probability that the selected individual does not own shares in a stock fund is 0.57.

Page 62 Problem 4 Answer

Given: A computer consulting firm presently has bids out on three projects.

Given: Ai={ awarded project i}

for i=1,2,3and

P(A1)=.22

P(A2)=.25

P(A3)=.28

P(A1∩A2)=.11

P(A1∩A3)=.05

P(A2∩A3)=.07

P(A1∩A2∩A3)=.01

​To express in words A1∪A2 and compute its probability.We will use P(A∪B)=P(A)+P(B)−P(A∩B) to compute the probability.

To describe A1∪A2 in words as:

A1∪A2= {awarded project one or project two or both the projects}.

We know that P(A∪B)=P(A)+P(B)−P(A∩B)

Hence the probability of A1∪A2 is:

P(A1∪A2)=P(A1)+P(A2)−P(A1∩A2)

=0.22+0.25−0.11

=0.36

​A1∪A2 in words is described as {awarded project one or project two or both the projects}.

Probability A1∪A2 is 0.36

Page 62 Problem 5 Answer

Given: A computer consulting firm presently has bids out on three projects.

Given: Ai={ awarded project i} , for

i=1,2,3 and ​P(A1)=.22

P(A2)=.25

P(A3)=.28

P(A1∩A2)=.11

P(A1∩A3)=.05

P(A2∩A3)=.07

P(A1∩A2∩A3)=.01

​To express A1′∩A2′ in words and compute its probability.

We will use ​P(A′∩B′)=P((A∪B)′)=1−P(A∪B) to compute the probability.

Describing A1′∩A2′ in words as:

A1′∩A2′={awarded project neither 1or 2}

By De Morgan’s law

(A1∪A2)′=A1′∩A2′ is given.

Now the probability of A1′∩A2′ is:

P(A1′∩A2′)=P((A1∪A2)′)=1−P(A1∪A2)

=1−0.36

=0.64​

A1′∩A2′  in words is described as {awarded project neither 1 or 2}

Probability of  A1′∩A2′ is 0.64

Page 62 Problem 6 Answer

Given: A computer consulting firm presently has bids out on three projects.

Given: Ai={ awarded project i} for i=1,2,3 and suppose that

P(A1)=.22

P(A2)=.25

P(A3)=.28

P(A1∩A2)=.11

P(A1∩A3)=.05

P(A2∩A3)=.07

P(A1∩A2∩A3)=.01

​To express A1∪A2∪A3 in words and compute its probability.

We will use P(A∪B∪C)=P(A)+P(B)+P(C)−P(A∩B)−P(A∩C)−P(B∩C)+P(A∩B∩C) to compute the probability.

Describing A1∪A2∪A3 in words as:

A1∪A2∪A3 {awarded project 1 or project 2 or project 3}.

Now the probability of A1∪A2∪A3 is:

P(A1∪A2∪A3)=P(A1)+P(A2)+P(A3)−P(A1∩A2)−P(A1∩A3)−P(A2∩A3)+P(A1∩A2∩A3)

=0.22+0.25+0.28+0.11−0.05−0.07+0.01

=0.53​

A1∪A2∪A3 in words is described as {awarded project 1 or project 2 or project3 } Probability of  A1∪A2∪A3 is 0.53.

Page 62 Problem 7 Answer

Given: A computer consulting firm presently has bids out on three projects.

Given:Ai={ awarded project i} , for i=1,2,3 and suppose that

P(A1)=.22

P(A2)=.25

P(A3)=.28

P(A1∩A2)=.11

P(A1∩A3)=.05

P(A2∩A3)=.07

P(A1∩A2∩A3)=.01​

To expressA1′∩A2′∩A3′3 in words and compute its probability.We will use​

P(A′∩B′∩C′)=P(A∪B∪C)′

=1−P(A∪B∪C) to compute the probability.

Describing A1′∩A2′∩A3′  in words as:

A1′∩A2′∩A3′={none of the three projects was awarded}.

We know that ​

P(A′∩B′∩C′)=P(A∪B∪C)′

=1−P(A∪B∪C)​

Hence probability of A1′∩A2′∩A3′ is:

P(A1′∩A2′∩A3′)=P(A1∪A2∪A3)′

=1−P(A1∪A2∪A3)

=1−0.53

=0.47​

Describing A1′∩A2′∩A3′  in words as:

A1′∩A2′∩A3′= {none of the three projects was awarded}.

We know that ​

P(A′∩B′∩C′)=P(A∪B∪C)′

=1−P(A∪B∪C)​

Hence probability of A1′∩A2′∩A3′ is:

P(A1′∩A2′∩A3′)=P(A1∪A2∪A3)′

=1−P(A1∪A2∪A3)

=1−0.53

=0.47

Page 62 Problem 8 Answer

Given: A computer consulting firm presently has bids out on three projects.

Given: Ai={ awarded project i}, for i=1,2,3 and suppose that

P(A1)=.22

P(A2)=.25

P(A3)=.28

P(A1∩A2)=.11

P(A1∩A3)=.05

P(A2∩A3)=.07

P(A1∩A2∩A3)=.01

To expressA1′∩A2′∩A3 in words and compute its probability.We will use P(A′∩B′∩C)=P(C)−P(A∩C)−P(B∩C)+P(A∩B∩C) to compute the probability.

Describing A1′∩A2′∩A3 in words as A1′∩A2′∩A3= {awarded project 3 and neither awarded project 1 and project 2}.

We know that: P(A′∩B′∩C)=P(C)−P(A∩C)−P(B∩C)+P(A∩B∩C)

Hence probability of A1′∩A2′∩A3 is: P(A1′∩A2′∩A3)=P(A3)−P(A1∩A3)−P(A2∩A3)+P(A1∩A2∩A3)

=0.28−0.05−0.07+0.01

=0.17​

A1′∩A2′∩A3 in words described as {awarded project 3 and neither awarded project 1 and project 2 }.

Probability of  A1′∩A2′∩A3 is 0.17

Page 62 Problem 9 Answer

Given: A computer consulting firm presently has bids out on three projects.

Given: Let Ai={ awarded project i} , for i=1,2,3 and suppose that

P(A1)=.22

P(A2)=.25

P(A3)=.28

P(A1∩A2)=.11

P(A1∩A3)=.05

P(A2∩A3)=.07

P(A1∩A2∩A3)=.01

​To express (A1′∩A2′)∪A3 in words and compute its probability.We will use P((A′∩B′)∪C)=P(A′∩B′∩C′)+P(C) to compute the probability.

Describing (A1′∩A2′)∪A3 in words as:

(A1′∩A2′)∪A3={awarded neither project 1 and project 2 or awarded project 3 }

We know that P((A′∩B′)∪C)=P(A′∩B′∩C′)+P(C)

Hence probability of (A1′∩A2′)∪A3 is:

P((A1′∩A2′)∪A3)=P(A1′∩A2′∩A3′)+P(A3)

=0.47+0.28

=0.75

​(A1′∩A2′)∪A3  in words described as {awarded neither project 1 and project 2 or awarded project 3 }

Probability of (A1′∩A2′)∪A3  is 0.75 .

Page 62 Problem 10 Answer

Given: 55% of all adults regularly consume coffee, 45% regularly consume carbonated soda, and 70% regularly consume at least one of these two products.

To find the probability that a randomly selected adult regularly consumes both coffee and soda.We will make use of the property: P(A∪B)=P(A)+P(B)−P(A∩B)

Let A =Event that the elderly consumes coffee.

B=Event that adult regularly consume carbonated soda.

C=Event that adult consumes coffee, carbonated soda or both.

Hence:​

P(A)=0.55

P(B)=0.45

P(C)=0.7

​Now to find the probability that a randomly selected adult regularly consumes both coffee and soda means we will have to fins: P(A∩B)

We know that: P(A∪B)=P(A)+P(B)−P(A∩B)

⇒P(A∩B)=P(A)+P(B)−P(A∪B)

​We can see that the event C=A∪B

Hence P(A∩B)=P(A)+P(B)−P(C)

⇒P(A∩B)=0.55+0.45−0.7

⇒P(A∩B)=0.3

​The probability that a randomly selected adult regularly consumes both coffee and soda is 0.3.

Page 62 Problem 11 Answer

Given: Suppose that 55% of all adults regularly consume coffee,  regularly consume carbonated soda, and 70% regularly consume at least one of these two products.

To find the probability that a randomly selected adult doesn’t regularly consume at least one of these two products.

We will use the property P((A∪B)′)=1−P(A∪B)

Let, A= Event that adult regularly consumes coffee

B=Event that adult regularly consumes carbonated soda

C=Event that adult regularly consumes coffee, soda or both

Now, the probability of these events is given as:

P(A)=0.55

P(B)=0.45

P(C)=0.7​

The probability that a randomly selected adult doesn’t regularly consume at least one of these two products is the probability that the selected adult consumes only coffee or consumes only carbonated soda or does not consume both products that is (A∪B)′

Now, the probability that a randomly selected adult doesn’t regularly consume at least one of these two products is: P((A∪B)′)=1−P(A∪B)

=1−0.7

=0.3​

The probability that a randomly selected adult doesn’t regularly consume at least one of these two products is 0.3

Page 63 Problem 12 Answer

Given: A denotes the event that the next request for assistance from a statistical software consultant relates to the SPSS package and B denotes the event that the next request for assistance from a statistical software consultant relates to the SAS package.

Given:P(A)=0.30

P(B)=0.50​

To check why it is not that case that P(A)+P(B)=1

There are three conditions for P(A)+P(B)=1even if one is not satisfied then the result cannot hold.

Now A and B should be complementary but It does not hold here, as these days some new programs like Python , Mat Lab are used and different people have different opinions hence SPSS and SAS are not the only events that can occur.

Therefore we can say that they are not not complementary events

Hence, it does not hold all the conditions, therefore P(A)+P(B)≠1.

We described why it is not a case that P(A)+P(B)=1.

Page 63 Problem 13 Answer

Given: A denote the event that the next request for assistance from a statistical software consultant relates to the SPSS package and:B denote the event that the next request for assistance from a statistical software consultant relates to the SAS package.

Given P(A)=0.30

P(B)=0.50​

To find:P(A′).We will use: P(A)+P(A′)=1

Since for any event A we have P(A)+P(A′)=1

This implies,

P(A′)=1−P(A)

P(A′)=1−0.3

P(A′)=0.7

​We calculated that P(A′)=0.7

Page 63 Problem 14 Answer

Given: A denote the event that the next request for assistance from a statistical software consultant relates to the SPSS package and B denote the event that the next request for assistance from a statistical software consultant relates to the SAS package.

Given: P(A)=0.3

P(B)=0.5​

To find: P(A∪B).

We will use : P(A∪B)=P(A)+P(B)−P(A∩B)

Since A and B cannot occur at the same time, they are mutually exclusive events .

This implies that, A∩B=∅

Also, P(A∪B)=P(A)+P(B)−P(A∩B)

Then, we have P(A∪B)=P(A)+P(B)−P(∅)

Since,

P(∅)=0

P(A)=0.3

P(B)=0.5

Thus,P(A∪B)=P(A)+P(B)

P(A∪B)=0.3+0.5

P(A∪B)=0.8

​We calculated that. P(A∪B)=0.8

Page 63 Problem 15 Answer

Given: A denote the event that the next request for assistance from a statistical software consultant relates to the SPSS package and B denote the event that the next request for assistance from a statistical software consultant relates to the SAS package,Given: P(A)=0.3

P(B)=0.5​

From part c) we obtain: P(A∪B)=0.8

To find:P(A′∩B′).We will use: De Morgan’s law and the complement property.

Using De Morgan’s law,A′∩B′=(A∪B)′

we get,P(A′∩B′)=P((A∪B)′)

Also, since P(A)+P(A′)=1

P((A∪B)′)=1−P(A∪B)

Thus, we get, P(A′∩B′)=1−P(A∪B)

Since, P(A∪B)=0.8

Therefore,

P(A′∩B′)=1−0.8

P(A′∩B′)=0.2

​We calculated that P(A′∩B′)=0.2.

Page 63 Problem 16 Answer

A box contains six 40 W bulbs , five 60W and four 75 W bulbs . Bulbs are selected at random .

To find the probability that at least two bulbs must be selected to obtain one that is rated 75W.

We will use the definition of probability and complement rule.

Consider, an event E such that

E= at least two bulbs must be selected to obtain a first 75W bulb

Then, the complementary event of E will be E′ the first bill is 75W bulb

Also, since P(E)+P(E′)=1

This implies, P(E)=1−P(E′)

Thus, P(at least two bills must be selected to obtain a first 75W bulb)

=1−P ( the first bill is 75Wbulb ).

It is given that there are:

Using probability formula, P(E′)= number of 75 W bulbs  total number of bulbs

Total bills in a wallet are 5+4+6=15 bills.

Number of  75 W bulb =4

Thus, P(E′)=4/15

Therefore, P(E)=1−4/15

P(E)=11/15

P( at least two bills must be selected to obtain a first 75 W bulb)=11/15=0.7333

​The probability that at least two bulbs must be selected to obtain a first 75W bulb is0.7333.

Page 63 Problem 17 Answer

Given: Total number of joints in one batch =10,000

Number of defective joints judged by Inspector A=724

Number of defective joints judged by Inspector B=751

Number of defective joints judged by at least one of the inspectors =1159.

To find the probability that the selected joint was judged to be defective by neither of the two inspectors.

Consider X= Event that selected defective joints were judged by Inspector A

Y= Event that selected defective joints were judged by Inspector B

Hence we get:

X∪Y={ selected defective joints were judged by inspectorA or B or both }

(X∪Y)′={ selected defective joints were judged neither by inspector A or B} to find P((X∪Y)′).

Since, P(E)+P(E′)=1

Then, P((X∪Y)′)=1−P(X∪Y)

Using, probability formula, we have P(X∪Y)= number of defective joints judged by at least one of the inspectors  total number of joints

P(X∪Y)=1159/10000

Therefore,P((X∪Y)′)=1−1159/10000

P((X∪Y)′)=8841/10000

P((X∪Y)′)=0.8841​

The probability that the selected joint was judged to be defective by neither of the two inspectors is 0.8841

Page 63 Problem 18 Answer

Given: Total number of joints in one batch =10,000

Number of defective joints judged by Inspector A=724

Number of defective joints judged by Inspector B=751

Number of defective joints judged by at least one of the inspectors=1159

To find: the probability that the selected joint was judged to be defective by the inspector B but not by inspector A.

consider X=Event that selected defective joints were judged by InspectorA

Y=Event that selected defective joints were judged by Inspector B

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 18

Hence X∪Y={selected defective joints were judged by inspectorA or B or both }

X∩Y={selected defective joints were judged by inspector A and B}

X′={selected defective joints were not judged by Inspector A}

(X′∩Y)={ selected defective joints were not judged by Inspector A and were judged by Inspector B}.i

We need to find the probability of X′∩Y.

Venn diagram for(X′∩Y) i.e., the selected joint was judged to be defective by inspector B but not by inspector A is:

From the Venn Diagram above, we can observe that,

P(X′∩Y)=P(Y)−P(X∩Y)

Also, sinceP(X∪Y)=P(X)+P(Y)−P(X∩Y)

This implies, P(X∩Y)=P(X)+P(Y)−P(X∪Y)

Using probability formula, P(X)= number of defective joints judged by inspector A

Total number of joints

P(X)=724/10000

P(X)=0.0724

​P(Y)= number of defective joints judged by inspector B

Total number of joints

P(Y)=751/10000

P(Y)=0.0751

P(X∪Y)= number of defective joints judged by inspector A or B or both

Total number of joints

P(X∪Y)=1159/10000

P(X∪Y)=0.1159

P(X∩Y)=0.0724+0.0751−0.1159

P(X∩Y)=0.0316

​Thus, P(X′∩Y)=0.0751−0.0316

P(X′∩Y)=0.0435

​The probability that the selected joint was judged to be defective by inspector B but not by inspector A=0.0435

Page 63 Problem 19 Answer

Given: The route used by a certain motorist in commuting to work contains two intersections with traffic signals.

Given probability to stop at the first signal: 0.4

Given probability to stop at the second signal: 0.5

Given probability to stop at at least one of the two signals: 0.6

To find the probability that he must stop at both the signals.We will use: P(A∩B)=P(A)+P(B)−P(A∪B)

Let us define event A to be when the motorist stops at the first signal with probability P(A)=0.4.

Let us also define event B to be when the motorist stops at the second signal with probability P(B)=0.5.

The probability of the event that he must stop at at least one of the two signals isP(A∩B)=0.7

By using P(A∩B)=P(A)+P(B)−P(A∪B) we get:

P(A∩B)=P(A)+P(B)−P(A∪B)

⇒P(A∩B)=0.4+0.5−0.7

⇒P(A∩B)=0.2

​Hence the probability that he must stop at both of the signals is P(A∩B)=0.2.

Page 63 Problem 20 Answer

Given : The route used by a certain motorist in commuting to work contains two intersections with traffic signals.

Given probability to stop at the first signal(event A): 0.4

Given probability to stop at the second signal(event B):0.5

Given probability to stop at at least one of the two signals: 0.6

To find the probability that he must stop at the first signal but not at the second signal.We will use: P(A∩Bc)=P(A)−P(A∩B)

We are given,

P(A)=0.4

P(B)=0.5

P(A∪B)=0.7

We are asked to calculate P(A∩Bc).

Using the formula for the intersection of the event A and the complement of the event B:P(A∩Bc)=P(A)−P(A∩B)∣

Substituting the values from Step1, we get:

P(A∩Bc)=P(A)−P(A∩B)

⇒0.4−0.2

⇒0.2

​Therefore the probability that he stops at the first signal but not at the second signal is P(A∩Bc)=0.2 .

Hence the probability that he stops at the first signal but not at the second signal is P(A∩Bc)=0.2 .

Page 63 Problem 21 Answer

Given: The route used by a certain motorist in commuting to work contains two intersections with traffic signals.

Given probability to stop at the first signal (event A): 0.4

Given probability to stop at the second signal(event B): 0.5

Given probability to stop at at least one of the two signals:

To find the probability that he stops at exactly one signal.

We are given,

P(A)=0.4

P(B)=0.5

P(A∪B)=0.7​

We are asked to calculate

P({ at exactly one signal })=P(A∪B)−P(A∩B)

⇒P=[P(A)−P(A∩B)]+[P(B)−P(A∩B)]

Substituting values from Step1, we get:

P=[P(A)−P(A∩B)]+∣P(B)−P(A∩B)]

⇒[0.4−0.2]+[0.5−0.2]

⇒[0.2]+[0.3]

⇒0.2+0.3

⇒0.5

​Therefore the probability that he will stop at exactly one signal is P=0.5.

Hence the probability that he will stop at exactly one signal is P=0.5.

Page 64 Problem 22 Answer

Given: The three most popular options on a certain type of new car are a built-in GPS (A), a sunroof (B), and an automatic transmission (C).

Given: Request for A: 40%

Request for B:55%

Request for C:70%

Request for A or B:63%

Request for A or C:77%

Request for B or C:85%

To determine the probability that next purchaser will request at least one of the three options.

We will use P(A∪B∪C)=P(A)+P(B)+P(C)−P(A∩B)−P(A∩C)−P(B∩C)+P(A∩B∩C)

The probability that the next purchaser will request at least one of the three options can be written as:

P(A∪B∪C)

We can also write this as: P(A∪B∪C)=P(A)+P(B)+P(C)−P(A∩B)−P(A∩C)−P(B∩C)+P(A∩B∩C)

where P(A)=0.4

P(B)=0.55

P(C)=0.70

P(A∪B)=0.63

P(A∪C)=0.77

P(B∪C)=0.85

​In this step, we will calculate P(A∩B),P(A∩C),P(B∩C), and P(A∩B∩C)

P(A∩B)=P(A)+P(B)−P(A∪B)∣

⇒0.4+0.55−0.63∣

⇒0.32 I

P(A∩C)=P(A)+P(C)−P(A∪C)∣

⇒0.4+0.7−0.77∣

⇒0.33 I

P(B∩C)=P(B)+P(C)−P(B∪C)∣

⇒0.55+0.7−0.8∣

⇒0.45 I

P(A∩B∩C)=P(A∪B∪C)−P(A)−P(B)−P(C)+P(A∩B)+P(A∩C)+P(B∩C)∣

⇒0.85−0.4−0.55−0.7+0.32+0.33+0.45∣

⇒0.3​

The Venn Diagram representation is:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 22

Using the formula for intersection and substituting values, we get:

P(A∪B∪C)=P(A)+P(B)+P(C)−P(A∩B)−P(A∩C)−P(B∩C)+P(A∩B∩C)∣

⇒0.4+0.55+0.7−0.32−0.33−0.45+0.33

⇒0.85

Therefore the probability that the next purchaser will request at least one of the three options is 0.85.

Hence the probability that the next purchaser will request at least one of the three options is 0.85

Page 64 Problem 23 Answer

Given: The three most popular options on a certain type of new car are a built-in GPS (A), a sunroof (B), and an automatic transmission (C). Given:

Request for A:40%

Request for B:55%

Request for C:70%

Request for A or B:63%

Request for A or C:77%

Request for B or C:85%

​To determine the probability that the next purchaser will select none of the three options.

We will use: P(A∪B∪C)=P(A)+P(B)+P(C)−P(A∩B)−P(A∩C)−P(B∩C)+P(A∩B∩C)

The probability that the next purchaser will request at least one of the three options can be written as:

P(A∪B∪C)

We can also write this as:

P(A∪B∪C)=P(A)+P(B)+P(C)−P(A∩B)−P(A∩C)−P(B∩C)+P(A∩B∩C)

⇒0.4+0.55+0.7−0.32−0.33−0.45+0.33

⇒0.85​

The probability of the next purchaser selecting none of the three options can be written as:

P((A∪B∪C)c)

We can also write this as:

P((A∪B∪C)c)=1−P(A∪B∪C)

⇒1−0.85

⇒0.15​

The Venn Diagram for the following exercise prompt would look like this:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 23

Hence the probability of the next purchaser selecting none of the three options is 0.15

Page 64 Problem 24 Answer

Given: The three most popular options on a certain type of new car are a built-in GPS (A), a sunroof (B), and an automatic transmission (C). Given:

Request for A:40%

Request for B:55%

Request for C:70%

Request for A or B:63%

Request for A or C:77%

Request for B or C:85%

​To find the probability that next purchaser will request only an automatic transmission and not either of the other two options.

We will draw the Venn diagram and then solve.

The probability that the next purchaser will request only an automatic transmission option and not either of the other two can be written as P(A′∩B′∩C)

The probability that the next purchaser will request only an automatic transmission option and not either of the other two can be written as P(A′∩B′∩C)

We can substitute the respective values:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 24

P(A′∩B′∩C)=P(C)−P(A∩C)−P(B∩C)+P(A∩B∩C)

⇒0.7−0.33−0.45+0.3

⇒0.22

​Hence the probability that the next purchaser will request only an automatic transmission option and not either of the other two options is 0.22

Page 64 Problem 25 Answer

Given: The three most popular options on a certain type of new car are a built-in GPS (A), a sunroof (B), and an automatic transmission (C).

Given: Request for A:40%

Request for B:55%

Request for C:70%

Request for A or B:63%

Request for A or C:77%

Request for B or C:85%

​To find the probability that the next purchaser will choose exactly of the three options.

We will draw a Venn diagram and then solve.

The complete Venn Diagram for the exercise prompt is:

Since we want to find the probability that the next purchaser will select exactly one of these three options, we will shade our Venn Diagram accordingly.

After shading, the resulting Venn Diagram is:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 25

Taking the summation of the shaded area we get:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 25 1

P(exactly one of the three )

⇒P(A)+P(B)+P(C)−P(A∩B)−P(B∩C)−P(A∩B)+3P(A∩B∩C)∣

⇒0.05+0.08+0.22∣

⇒0.35​

Therefore the probability that the next purchaser will select exactly one of these three options is 0.35

Page 64 Problem 26 Answer

Given: An academic department with five faculty members— Anderson, Box, Cox, Cramer, and Fisher—must select two of its members to serve on a personnel review committee.

Because the work will be time-consuming, no one is anxious to serve, so it is decided that the representative will be selected by putting the names on identical pieces of paper and then randomly selecting two.

To find the probability that both Anderson and Box will be selected.

We will find the total number of events that is possible in this case.

After which we will take the total number of events of “both Anderson and Box being selected”.

Let this be denoted by P(A).We will then find P(A)

In this step, we will find the total number of sample spaces possible. The sample space in this case is:

{A,B},{A,Cox},{A,Cr},{A,F},{B,Cox}

{B,Cr},{B,F},{Cox,Cr},{Cox,F},{Cr,F}​

Therefore the sample space has 10 events.

Let A= Event that both Anderson and Box are selected.

Then the events can be:{A,B}

Therefore, we can write P(A) to be: P({A,B})=1/10

Hence the probability that both Anderson and Box will be selected is 1/10.

Page 64 Problem 27 Answer

Given: An academic department with five faculty members— Anderson, Box, Cox, Cramer, and Fisher—must select two of its members to serve on a personnel review committee.

Because the work will be time-consuming, no one is anxious to serve, so it is decided that the representative will be selected by putting the names on identical pieces of paper and then randomly selecting two.

To find the probability that at least one of the two members whose name begins with C is selectedWe will find the total number of events that is possible in this case.

After which we will take the total number of events of ” at least one of the two members whose name begins with C being selected”.

Let this be denoted by P(C).We will the find P(C)

In this step, we will find the total number of sample spaces possible. The sample space in this case is:

{A,B},{A,Cox},{A,Cr},{A,F},{B,Cox},

{B,Cr},{B,F},{Cox,Cr},{Cox,F},{Cr,F}

​Therefore the sample space has 10 events.

Let P(C)= Event that at least one of the two members whose name begins with C is selected.

We are told that at least one of the two members whose name begins with C is selected. Then the disjoint events can be: {A,Cox},{A,Cr},{B,Cox},{B,Cr},{Cox,Cr},{Cox,F},{Cr,F}

Therefore, we can write P(C) to be:

P(C)=P({A,Cox}+{A,Cr}+{B,Cox}+{B,Cr}+{Cox,Cr}+{Cox,F}+{Cr,F})

⇒7/10,​

Hence the probability that at least one of the two members whose name begins with C is selected is 7/10 .

Page 64 Problem 28 Answer

Given: An academic department with five faculty members— Anderson, Box, Cox, Cramer, and Fisher—must select two of its members to serve on a personnel review committee.

Because the work will be time-consuming, no one is anxious to serve, so it is decided that the representative will be selected by putting the names on identical pieces of paper and then randomly selecting two.

Given: Five faculties have taught for 3,6,7,10, and 14 years, respectively.

To find the probability that the two chosen representatives have a total of at least15 years’ teaching experience there.

We will find the total number of events that is possible in this case.

After which we will take the total number of events of “at least 15 years’ teaching experience”.

Let this be denoted by P(B). We will then find P(B)

In this step, we will find the total number of sample spaces possible. The sample space in this case is:

{3,6},{3,7},{3,10},{3,14},{6,7},{6,10},{6,14},{7,10},{7,14},{10,14}∣

Therefore the sample space has 10 events.

We are told that at least 15  years of teaching experience is required. Then the disjoint events can be:

{3,14},{6,10},{6,14},{7,10},{7,14},{10,14}

Let B= Event that representative have at least 15 years of teaching experience

Therefore, we can write P(B) to be:

P(B)=P({3,14}+{6,10}+{6,14}+{7,10}+{7,14}+{10,14})

⇒6/10

Hence the probability  that the two chosen representatives have a total of at least 15 years’ teaching experience is 6/10.

Page 64 Problem 29 Answer

Given : A family consisting of three members, A, B , and C goes to a medical clinic that always has a doctor at each stations 1,2, and 3.

During a certain week each member of the family visits a clinic and is assigned a random station.

Given: Any incoming individual is equally likely to be assigned to any of the three stations irrespective of where other individuals have been assigned.

To find the probability that all three family members are assigned to the same station.We will find the total number of sample space that is possible in this case.

After which we will take the total number of events of “all the three family members are assigned to the same station”.

Let this be denoted by P(A). We will then find P(A)

In this step, we will find the total number of sample spaces possible.

Since there are 3 stations and 3 family members. Then the sample space will consist of:

n=33

n=27

Therefore the sample space is 27 events.

Let A= Event that all three family members go to the same station.

Then the events can be: (1,1,1),(2,2,2),(3,3,3)

We can write P(A) to be:

P(A)=P((1,1,1)+(2,2,2)+(3,3,3))

=3/27​

⇒P(A)=1/9

Hence the probability that all three family members are assigned to the same station is 1/9.

Page 64 Problem 30 Answer

Given: A family consisting of three members, A,B , and C goes to a medical clinic that always has a doctor at each stations 1,2 and 3.

During a certain week each member of the family visits a clinic and is assigned a random station.

Given: Any incoming individual is equally likely to be assigned to any of the three stations irrespective of where other individuals have been assigned.

To find the probability that at most two family members are assigned to the same station.

We will find the total number of events that is possible in this case.

After which we will take the total number of events of “at most two family members are assigned to the same station”.

Let this be denoted by P(B). We will find the total number of events that is possible in this case.

After which we will take the total number of events of “at most two family members are assigned to the same station”.

Let this be denoted by P(B). We will then find p (B)

In this step, we will find the total number of sample spaces possible.

Since there are 3 stations and 3 family members. Then the sample space will consist of:

n=33

n=27

Therefore the sample space is 27 events.

Let B be the event that at most two family members go to the same station.

Then the events which do not follow can be:

B{c}=(1,1,1),(2,2,2),(3,3,3)

We can write P(B) as:

P(B)=1−P(Bc)

=1−1/9

=8/9

Hence the probability that at most two family members are assigned to the same station is 8/9⋅

Page 64 Problem 31 Answer

Given: A family consisting of three members, A,B , and C goes to a medical clinic that always has a doctor at each stations 1,2 and 3.

During a certain week each member of the family visits a clinic and is assigned a random station.

Given: Any incoming individual is equally likely to be assigned to any of the three stations irrespective of where other individuals have been assigned.

To find the probability that every family member is assigned to a different station.

We will find the total number of events that is possible in this case.

After which we will take the total number of events of “every family member is assigned to a different station”.

Let this be denoted by P(C). We will then find P(C)

In this step, we will find the total number of sample spaces possible.

Since there are 3 stations and 3 family members. Then the sample space will consist of:

n=33

n=27

Therefore the sample space is 27 events.

Let C be the event that every family member is assigned a different station.

Then the events can be:

(1,2,3),(1,3,2),(2,1,3),(2,3,1),(3,1,2),(3,2,1)∣

We can write P© to be P/(C)

=P({(1,2,3),(1,3,2),(2,1,3),(2,3,1),(3,1,2),(3,2,1)}⟩

=6/27​

⇒P(C)=2/9

Hence the probability that every family member is assigned to a different station is 2/9.

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Exercise 2.4 Probability

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Probability

Page 80 Problem 1 Answer

Given: The population of a particular country consists of three ethnic groups. Each individual belongs to one of the four major blood groups.

Given  probability table is:

Blood Group O A B AB
Ethnic Group 1 0.082 0.106 0.008 0.004
2 0.135 0.141 0.018 0.006
3 0.215 0.2 0.065 0.02

Given events:A={ type A selected },B={ type B selected }, and C={ ethnic group 3 selected }

​To calculate P(A),P(C), and P(A∩C)

We will make use of the given table in order to find the probabilities.

The probability of selecting type A is the sum of column A of joint probability table.

P(A)=0.106+0.141+0.2

=0.447

The probability of selecting group 3 is the sum of row 3 of the given joint probability table.

The probability of A∩C is row 3 and column A

P(A∩C)=0.2

We obtain:

P(A)=0.447

P(C)=0.5

P(A∩C)=0.2

Page 80 Problem 2 Answer

Given: The population of a particular country consists of three ethnic groups. Each individual belongs to one of the four major blood groups.

Given: Joint probability table:

Blood Group O A B AB
Ethinc Group 1 0.082 0.106 0.008 0.004
2 0.135 0.141 0.018 0.006
3 0.215 0.2 0.065 0.02

Given events:

A={ type A selected }

B ={ type B selected }

and C={ ethnic group 3 selected }

​To calculate: P(A∣C) and P(C∣A)

To explain in context what each of these probabilities represents.

We will calculate the required conditional probabilities by using the formula: P(A∣B)=P(A∩B)

P(B) where P(B)>0.

From previous part P(A∩C)=0.2

By using the  definition of conditional probability

P(A∣C)=P(A∩C)/P(C)

=0.2/0.5

=0.4

This represents that if we know that an individual comes from ethnic group 3, the probability that the individual has blood type A is 0.4.

and P(C∣A)= P(C∩A)/P(A)

0.2/0.447

= 0.447​

This represents that if we know that an individual has blood type A the probability that the individual comes from ethnic group 3 is 0.447.

We obtain:

P(A∣C)=0.4

P(C∣A)=0.447​

P(A∣C) represents that if we know that an individual comes from ethnic group 3 , the probability that the individual has blood type A is 0.4.

P(C∣A) represents that if we know that an individual has blood type A the probability that the individual comes from ethnic group3 is 0.447.

Page 80 Problem 3 Answer

Given: The population of a particular country consists of three ethnic groups. Each individual belongs to one of the four major blood groups.

We are given the table:

Blood Group O A B AB
Ethnic Group 1 82 0.106 0.008 0.004
2 0.135 0.141 0.018 0.006
3 0.215 0.2 0.065 0.02

Given events: ​

A={ type A selected }

B={ type B selected }  and C={ ethnic group 3 selected }

​To find the probability a given individual is from ethnic group 1 if he or she does not have type B blood.

We will first find the probability of not have a B blood then use conditional probability.

We need to find P(D∣B′).

P(D∩B′)=0.082+0.106+0.004

=0.192

We know P(B′)=1−P(B)

=1−(0.008+0.018+0.065)

=0.909

By using the definition of conditional probability, we get

P(D∣B′)=P(D∩B′)

P(B′)= 0.192/0.909

=0.211

​The probability that the given individual is from ethnic group 1 given that he or she does not have type B blood is 0.211.

Page 80 Problem 4 Answer

Given:  An individual is randomly selected from the population of all adult males living in the United States Given the following events:

A={ Individual person is 6 feet tall }

B={ Individual person is a professional basketball player}

​To check among P(A∣B) and P(B∣A), which is larger.We will check using the conditional probability formula: P(A∣B)=P(A∩B)

P(B),P(B)>0

We are not given the exact data of how many people are taller than 6 feet.

By logic, we can say that number of people who are taller than 6 feet are more than the number of professional basketball players.

So, N(A)>N(B)

N(A)>N(B)

We know P(A)= Number of favourable outcomes of A/ Number of outcomes in sample space

=N(A)/N>N(B)/N

=P(B)

​Therefore,⇒P(A)>P(B)

⇒1

P(A)<1/P(B)

Obviously, P(A∩B)>0

So,  P(A∩B)

P(A)<P(A∩B)

P(B) which is equivalent to   P(B∣A)<P(A∣B)

For events

A={ Individual person is 6 feet tall }

B={ Individual person is a professional basketball player}

​we obtain P(B∣A)<P(A∣B) because N(A)>N(B)

Page 80 Problem 5 Answer

Given: A certain system can experience three different types of defects. Given: that Ai={ event of the type i ;=1,2,3}

Given probabilities:

P(A1)=0.12

P(A2)=0.07

P(A3)=0.05

P(A1∪A2)=0.13

P(A1∪A3)=0.14

P(A2∪A3)=0.1

P(A1∩A2∩A3)=0.01

​To find the probability that the system has type 2 defect, given that it has type 1 defect.

We will use the Conditional probability formula P(A∣B)=P(A∩B)

P(B),P(B)>0

We need to find P(A2∣A1).

By using conditional probability and data given in the question, we get

P(A2∩A1) =0.06

=P(A1)+P(A2)−P(A1∪A2)

= 0.12+0.07−0.13

​= P(A2∣A1)=P(A​2∩A​1)/P(A​1)

=0.06/0.12

=0.5​

The probability  that the system have a defect of type 2 given that the system has type 1 defect is 0.5

Page 80 Problem 6 Answer

Given: A certain system can experience three different types of defects.

Given: that Ai ={ event of the type i ;=1,2,3}

Given probabilities:

P(A1)=0.12

P(A2)=0.07

P(A3)=0.05

P(A1∪A2)=0.13

P(A1∪A3)=0.14

P(A2∪A3)=0.1

P(A1∩A2∩A3)=0.01​

To find the probability the system has all three types of defects given that it has type 1

defect. We will use the conditional prob

We need to find P(A1∩A2∩A3∣A3).

Using the definition of conditional probability and the data given in the question, we get

P(A1∩A2∩A3∣A1)=P[(A​1∩A​2∩A​3)∩A​3]/P(A​1)

=P(A​1∩A​2∩A​3)/P(A​1)

=0.01/0.12

=0.0833

​The probability that system has all three types of defects given that it has the type 1 defect is 0.0833.

Page 80 Problem 7 Answer

Given:  A certain system can experience three different types of defects.

Given: that Ai ={ event of the type i ;=1,2,3}

Given probabilities:

P(A1)=0.12

P(A2)=0.07

P(A3)=0.05

P(A1∪A2)=0.13

P(A1∪A3)=0.14

P(A2∪A3)=0.1

P(A1∩A2∩A3)=0.01

​To find the probability that the system has exactly one type of defect given that it has at least one type of defect.

We will use the Venn diagrams to obtain the probabilities of the individual events and then use the conditional probability.

We will obtain the following probabilities in order to solve further.

P(A1∩A2)=P(A1)+P(A2)−P(A1∪A2)

=0.12+0.07−0.13

=0.06

P(A1∩A3)=P(A1)+P(A3)−P(A1∪A3)

=0.12+0.05−0.14

=0

P(A2∩A3)=P(A2)+P(A3)−P(A2∪A3)

=0.07+0.05−0.1

=0.02​

We observe that A∩B=B as B⊂A.

We also see that B is the union of A1∩A2′∩A3′,A1′∩A2∩A3′,A1′∩A2′∩A3.

We will now find the value of probabilities of these individual events and unite then in order to get.P(B).

For,A1∩A2′∩A3′

we will first draw the Venn diagram as follows.

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 7

Using the data given in the question, we get

P(A1∩A2′∩A3′)=

=0.04

P(A1)−P(A1∩A2)−P(A1∩A3)+P(A1∩A2∩A3)

= 0.12−0.06−0.03+0.01

​For, we will first draw the Venn diagram as follows.

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 7 1

Using the data given in the question, we get

P(A1′∩A2∩A3′)

=0.07−0.06−0.02+0.01

=0

=P(A2)−P(A1∩A2)−P(A2∩A3)+P(A1∩A2∩A3)

​For, we will first draw the Venn diagram as follows.

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 7 2

Using the data given in the question, we get

P(A1′∩A2′∩A3)=P(A3)−P(A1∩A3)−P(A2∩A3)+P(A1∩A2∩A3)

=0.05−0.03−0.02+0.01

=0.01

​We will combine all these to get P(B)

P(B)=0.04+0+0.01

=0.05

​Using the definition of conditional probability, we get

P(B∣A)=P(B∩A)/P(A)

=P(B)/P(A)

=0.05/0.14

=0.3571

​Given that the system has at least one type of defect, the probability that it has exactly one type of defect is 0.3571.

Page 80 Problem 8 Answer

Given:  A certain system can experience three different types of defects. Given: that Ai={ event of the type i ;=1,2,3}

Given probabilities:

P(A1)=0.12P(A2)=0.07P(A3)=0.05

P(A1∪A2)=0.13

P(A1∪A3)=0.14

P(A2∪A3)=0.1

P(A1∩A2∩A3)=0.01

​To find the probability that the system does not have third type of defect given that it has both of the first two ty

We need to find P(A3′∣A1∩A2). we will first construct its Venn diagram to get the desired formula.

By using the definition of conditional probability, we get

P(A3′∣A1∩A2)=P(A1∩A2∩A3′)

P(A1∩A2)=P(A1∩A2)−P(A1∩A2∩A3)

P(A1∩A2)=0.06−0.01/0.06

=0.833​

The probability that it does not have the third type of defect given that it has both of the first two types of defects is 0.833.

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 8

Page 81 Problem 9 Answer

Given: Deer ticks can be carriers of either Lyme disease or human granulocytic ehrlichiosis (HGE).

Based on a recent study, suppose that 16% of all ticks in a certain location carry Lyme disease, 10% carry HGE, and 10% of the ticks that carry at least one of these diseases in fact carry both of them.

Given: A randomly selected tick is found to have carried HGE.

To find the probability that the selected tick is also a carrier of Lyme disease.

We will find this using the conditional probability formula: P(A∣B)=P(A∩B)/P(B)

We define,

A={ Carrier of Lyme disease }

B={ Carrier of HGE disease }

​So, we have

P(A)=0.16

P(B)=0.1

P(A∩B∣A∪B)=0.1

​We will calculate the probability of A given that B has already occurred.

We  have​P(A∩B∣A∪B)

=P[(A∩B)∩(A∪B)]/P(A∪B)

=P(A∩B)

P(A∪B) ( as intersection is the subset of union ).

Given, P(A∩B∣A∪B)=0.1.

Putting this in above equation, we get

P(A∩B)/P(A∪B)

=0.1

Applying the formula of union of two sets, we get

P(A∩B)=0.1⋅P(A∪B)

P(A∩B)=0.1⋅[P(A)+P(B)−P(A∩B)]

=0.1⋅[0.16+0.1−P(A∩B)]

=0.016+0.01−0.1⋅P(A∩B)

=0.026−0.1⋅P(A∩B)

​Hence, 1.1⋅P(A∩B)=0.026

P(A∩B)=0.026/1.1

=0.02364

Putting this value in the formula for conditional property,

P(A∣B)

=0.02364/0.1

=P(A∩B)/P(B)

=0.2364

​The probability that is also a carrier of Lymph disease given that it is a carrier of HGE is 0.2364.

Page 81 Problem 10 Answer

Given any events A and B.

To show that: P(A∣B)+P(A′∣B)=1

We will use the conditional probability formula: P(A∣B)=P(A∩B)/P(B),P(B)>0

We have P(B∣A)=P(A∩B)/P(A).

Also, we know that A′represents the complement of A.

Hence, we obtain

P(A∣B)=P(A∩B)

P(B)⋯(1)

P(B∣A)=P(A​′∩B)/P(B)​

Also,A′∩B is that part of B where no element of A is present.

So, P(A′∩B)=P(B)−P(A∩B)

On combining the results,

We get P(A∣B)+P(A′∣B)=P(A∩B)

P(B)+P(A​′∩B)

P(B)=P(A∩B)

P(B)+P(B)−P(A∩B)

P(B)=P(A∩B)+P(B)−P(A∩B)/P(B)

=P(B)/P(B)

=1

Hence, P(A∣B)+P

(A′∣B)=P(B)/P(B)=1

​We proved that P(A∣B)+P(A′∣B)=1

Page 81 Problem 11 Answer

Given: Any three events A,B,C with P(C)>0.

To show that: P(A∪B∣C)=P(A∣C)+P(B∣C)−P(A∩B∣C).

We will use the conditional probability and P(A∪B)=P(A)+P(B)−P(A∩B).

We will find the value of P(A∪B∣C).

P(A∪B∣C)=P[(A∪B)∩C]/P(C)

[Now using the property (A∪B)∩C=(A∩C)∪(B∩C)

P(A∪B∣C)=P[(A∪B)∩C]/P(C)

=P[(A∩C)∪(B∩C)]/P(C)

=P(A∣C)+P(B∣C)+P(A∩B∣C)

=P(A∩C)+P(B∩C)−P(A∩B∩C)/P(C)

= P(A∩C)/P(C)+P(B∩C)

P(C)−P(A∩B)∩C/P(C)

Hence,P(A∪B∣C)=P(A∣C)+P(B∣C)+P(A∩B∣C)

We have shown that P(A∪B∣C)=P(A∣C)+P(B∣C)+P(A∩B∣C)

Page 81 Problem 12 Answer

Given: Seventy percent of the light aircraft that disappear while in flight in a certain country are subsequently discovered.

Of the aircraft that are discovered, 60% have an emergency locator, whereas 90% of the aircraft not discovered do not have such a locator.

Given : a light aircraft has disappeared.

To find that the aircraft will not be discovered if it has an emergency locator.We will use conditional probability P(A∣B)=P(B∣A)P(A)

P(B),P(B)>0

The given data is, D=0.7,L∣D=0.6 and NL∣ND=0.9

Finding the probability of the airplane being not discovered, and has an emergency locator,

P(ND∣L)=P(L∣ND)P(ND)/P(L)

P(ND)=1−P(D)

P(ND)=1−0.7

P(ND)=0.3

​The probability assembly rule can be used,

P(L)=P(L∣D)P(D)+P(L∣ND)P(ND)

P(L∣ND)=1−P(NL∣ND)

P(L∣ND)=1−0.90

P(L∣ND)=0.10

​Substitute the values in the equation,

P(L)=(0.6)(0.7)+(0.1)(0.3)

P(L)=0.45​

Find the value of P(ND) by subtracting the value of P(D) from 1,

P(ND)=1−P(D)

P(ND)=1−0.45

P(ND)=0.55​

Substitute the values in the conditional probability formula,

P(ND∣L)=(0.1)(0.3)/(0.45)

P(ND∣L)=0.067

​The probability that the aircraft will not be discovered, but has an emergency locator is 0.067.

Page 81 Problem 13 Answer

Given: Seventy percent of the light aircraft that disappear while in flight in a certain country are subsequently discovered.

Of the aircraft that are discovered, 60% have an emergency locator, whereas 90% of the aircraft not discovered do not have such a locator.

Given : a light aircraft has disappeared.To find the probability that the plane will be discovered given that it does not have an emergency locator.

We will use the conditional probability

P(A|B)=P(B|A)P(A)/P(B),P(B)>0

The formula can be rewritten too suit the situation,

P(D∣NL)=P(NL∣D)P(D)/P(NL)

Find the rest of the unknown values,

P(NL∣D)=1−P(L∣D)

P(NL∣D)=1−0.6

P(NL∣D)=0.4​

Now substitute all the calculated values in the equation,

P(D∣NL)=P(NL∣D)P(D)/P(NL)

P(D∣NL)=(0.4)⋅(0.7)/0.55

P(D∣NL)=0.509

​The probability that it will be discovered and did not have an emergency locator is 0.509

Page 82 Problem 14 Answer

Given:  A company that manufactures video cameras produces a basic model and a deluxe model.

Over the past year, 40% of the cameras sold have been of the basic model.

Of those buying the basic model, 30% purchase an extended warranty, whereas 50% of all deluxe purchasers do so.

A randomly selected purchaser has an extended warranty.

To find the probability that he or she has a basic model.

We will use law of total probability.

Let B be the event that the basic model has been produced.

Let D be the event that the deluxe model has be produces.

Let EW be the evet that the extended warranty is chosen by the customer.

Then we can see:

P(B)/E(D)=0.4,

P(EW∣B)=0.6,

P(EW∣D)=0.3,=0.5.​

The required probability is of the event B∣EW

We get: P(B∣EW)=P(B∩EW)/P(Ew)

Using the property P(A∩B)=P(A∣B)⋅P(B)

we get: P(B∣EW)=P(B∣EW)

Now using law of total probability we get:

P(B∣EW)=P(EW∣B)P(B)

P(EW∣B)P(B)+P(EW∣D)P(D)

=0.3⋅0.4/0.3⋅0.4+0.5⋅0.6

=0.29.

​The probability that the purchaser has a basic model given that he or she has an extended warranty is 0.29.

Page 82 Problem 15 Answer

Given: For customers purchasing a refrigerator at a certain appliance store, let A be the event that the refrigerator was manufactured in the U.S.,

B be the event that the refrigerator had an ice maker, and  C be the event that the customer purchased an extended warranty.

Given probabilities are :

P(A)=.75

P(B∣A)=.9

P(B∣A′)=.8

P(C∣A∩B)=.8

P(C∣A∩B′)=.6

P(C∣A′∩B)=.7

P(C∣A′∩B′)=.3

To construct a tree diagram consisting of first-, second-, and third-generation branches, and place an event label and appropriate probability next to each branch.

We will first find the required probabilities to draw the diagram and then draw it.

In tree diagram,

In the initial branch there will be first layer of events , and in the diagram the adequate probabilities are given.

In the second branch there will be second layer of events with the given adequate conditional probabilities.

And in the third branch there will be third layer of events with the given adequate conditional probabilities.

The first layer probabilities :

P(A)=0.75

P(A′)=1−P(A)

P(A′)=0.25​

The second layer probabilities :

P(B∣A)=0.9

P(B′∣A)=1−P(B∣A)

P(B′∣A)=0.1

P(B∣A′)=0.8

P(B′∣A′)=1−P(B∣A′)

P(B′∣A′)=0.2

The third layer probabilities : All the events in this will be the probability of intersection of adequate three three events that leads to that  point.

P(C∣A∩B)=0.8

P(C′∣A∩B)=0.2

P(C∣A∩B′)=0.6

P(C′∣A∩B′)=0.4

P(C∣A′∩B)=0.7

P(C′∣A′∩B)=0.3

P(C∣A′∩B′)=0.3

P(C′∣A′∩B′)=0.7

​The tree diagram is :

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 15 1

The tree diagram is :

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 15

Page 82 Problem 16 Answer

Given: For customers purchasing a refrigerator at a certain appliance store, let A be the event that the refrigerator was manufactured in the U.S.,B  be the event that the refrigerator had an ice maker, and C be the event that the customer purchased an extended warranty.

Given probabilities are :

P(A)=.75

P(B∣A)=.9

P(B∣A′)=.8

P(C∣A∩B)=.8

P(C∣A∩B′)=.6

P(C∣A′∩B)=.7

P(C∣A′∩B′)=.3

To compute : P(A∩B∩C)

We will use the multiplication rule P(A∩B)=P(A∣B)⋅P(B)

If there are three events then we will use multiplication rule twice.

Now, using multiplication rule :

P(A∩B∩C)=P(C∣A∩B)⋅P(A∩B)

=P(C∣A∩B)⋅P(B∣A)⋅P(A)∣

=0.75⋅0.9⋅0.8

=0.54

Hence, P(A∩B∩C)=0.54

​We obtain:  P(A∩B∩C)=0.54

Page 82 Problem 17 Answer

Given: For customers purchasing a refrigerator at a certain appliance store, let A be the event that the refrigerator was manufactured in the U.S.,B  be the event that the refrigerator had an ice maker, and C be the event that the customer purchased an extended warranty.

Given probabilities are :

P(A)=.75

P(B∣A)=.9

P(B∣A′)=.8

P(C∣A∩B)=.8

P(C∣A∩B′)=.6

P(C∣A′∩B)=.7

P(C∣A′∩B′)=.3

​To compute: P(B∩C)

We will use the multiplication rule and the property P(B∩C)=P(B∩C∩A)+P(B∩C∩A′)

for three events.

Using property :

P(B∩C)=P(B∩C∩A)+P(B∩C∩A′)

Now, we will use the multiplication rule twice we get :

=0.54+0.25⋅0.8⋅0.7

=0.68

Hence, P(B∩C)=0.68

​We obtain: P(B∩C)=0.68

Page 82 Problem 18 Answer

Given: For customers purchasing a refrigerator at a certain appliance store, let A be the event that the refrigerator was manufactured in the U.S., B be the event that the refrigerator had an ice maker, and C be the event that the customer purchased an extended warranty.

Given probabilities are :

P(A)=.75

P(B∣A)=.9

P(B∣A′)=.8

P(C∣A∩B)=.8

P(C∣A∩B′)=.6

P(C∣A′∩B)=.7

P(C∣A′∩B′)=.3

​To compute P(C).

We will use the property P(C)=P(A∩B∩C)+P(A′∩B∩C)+P(A∩B′∩C)+P(A′∩B′∩C)

Using property ,

P(C)=P(A∩B∩C)+P(A′

∩B∩C)+P(A∩B′∩C)+P(A′∩B′∩C)

=0.75⋅0.9⋅0.8+0.75⋅0.1⋅0.6+0.25⋅0.8⋅0.7+0.25⋅0.2⋅0.3

=0.54+0.045+0.14+0.015

=0.74

Hence, P(C)=0.74

​We obtain:  P(C)=0.74

Page 82 Problem 19 Answer

Given: For customers purchasing a refrigerator at a certain appliance store, let A be the event that the refrigerator was manufactured in the U.S., B be the event that the refrigerator had an ice maker, and C  be the event that the customer purchased an extended warranty.

Given probabilities are :

P(A)=.75

P(B∣A)=.9

P(B∣A′)=.8

P(C∣A∩B)=.8

P(C∣A∩B′)=.6

P(C∣A′∩B)=.7

P(C∣A′∩B′)=.3

To compute P(A∣B∩C), the probability of U.S purchase given that an icemaker and an extended warranty are also purchased.

We will use the conditional property given by: P(A∣B)=P(A∩B)

P(B) where P(B)>0.

Using conditional property,

P(A∣B∩C)=P(A∩B∩C)/P(B∩C)

=0.54/0.68

=0.7941

Hence, P(A∣B∩C)=0.7941.

​The probability of a U.S purchase given that an icemaker and an extended warranty are also purchased is: P(A∣B∩C)=0.7941.

Page 82 Problem 20 Answer

Given : The Reviews editor for a certain scientific journal decides whether the review for any particular book should be short (1−2pages), medium (3−4 pages), or long (5−6pages).

Data on recent reviews indicates that 60% of them are short, 30% are medium, and the other 10% are long.

Reviews are submitted in either Word or LaTeX.

For short reviews, 80% are in Word, whereas 50%of medium reviews are in Word and 30% of long reviews are in Word.

Given: a recent review is randomly selected.To find the probability that the selected review was submitted in Word format.

We will draw a tree diagram and then with the help of that use that law total of total probability.

In tree diagram,

In the initial branch there will be first layer of events , and in the diagram the adequate probabilities are given.

In the second branch there will be second layer of events with the given adequate conditional probabilities.

Then, using the multiplication rule to the right of the second generation branches then we will display the product of the particular probabilities that leads to the point which is the probability of intersections.

The first layer of events are given below :

A1={ short book }

A2={ medium book }

A3={ long book }​

Their adequate properties are :

P(A1)=0.6

P(A2)=0.3

P(A3)=0.1​

The second layer of events are given below :

W={ submited in word }

L={ submited in LaTeX }​

Now, the adequate conditional probabilities are :

P(W∣A1)=0.8

P(L∣A1)=1−P(W∣A1)∣

P(L∣A1)=0.2

P(W∣A2)=0.5

P(L∣A2)=1−P(W∣A2)

P(L∣A2)=0.5

P(W∣A3)=0.3

P(L∣A3)=1−P(W∣A3)

P(L∣A3)=0.7

​Now, we will calculate the every of the four intersections using the multiplication rule :

P(A1∩W)=P(A1)⋅P(W∣A1)

=0.6⋅0.8

=0.48

P(A1∩L)=P(A1)⋅P(L∣A1)

=0.6⋅0.2

=0.12​

Similarly, P(A2∩W)=0.15P(A2∩L)=0.15

P(A3∩W)=0.03

P(A3∩L)=0.07​

Now, the tree diagram is :

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 20

Now, using the law of total probability :

P(W)=P(W∣A1)P(A1)+P(W∣A2)P(A2)+P(W∣A3)P(A3)

=0.48+0.15+0.03

=0.66

Hence, P(W)=0.66

The probability that the selected review was submitted in Word format is

P(W)=0.66

Page 82 Problem 21 Answer

Given : The Reviews editor for a certain scientific journal decides whether the review for any particular book should be short ( 1−2pages), medium (3−4pages), or long ( 5−6pages).

Data on recent reviews indicates that60% of them are short,  30%are medium, and the other 10% are long.

Reviews are submitted in either Word or LaTeX.

For short reviews, 80% are in Word, whereas 50%

of medium reviews are in Word and 30% of long reviews are in Word.

Given: a recent review is randomly selected.To find the probabilities for the review to be short , medium and long given that it was submitted in Word format.

We will draw the tree diagram and then use Bayes’ theorem in order to get the answer.

In tree diagram,

In the initial branch there will be first layer of events , and in the diagram the adequate probabilities are given.

In the second branch there will be second layer of events with the given adequate conditional probabilities.

Then, using the multiplication rule to the right of the second generation branches then we will display the product of the particular probabilities that leads to the point which is the probability of intersections.

The first layer of events are given below :

A1={ short book }

A2={ medium book

A3={ long book }

​Their adequate properties are :

P(A1)=0.6

P(A2)=0.3

P(A3)=0.1

​The second layer of events are given below :

W={ submited in word }

L={ submited in LaTeX }

​Now, the adequate conditional probabilities are :

P(W∣A1)=0.8

P(L∣A1)=1−P(W∣A1)

P(L∣A1)=0.2

P(W∣A2)=0.5

P(L∣A2)=1−P(W∣A2)

P(L∣A2)=0.5

P(W∣A3)=0.3

P(L∣A3)=1−P(W∣A3)

P(L∣A3)=0.7

​Now, we will calculate the every of the four intersections using the multiplication rule :

P(A1∩W)=P(A1)⋅P(W∣A1)

=0.6⋅0.8

=0.48

P(A1∩L)=P(A1)⋅P(L∣A1)

=0.6⋅0.2

=0.12

​Similarly, P(A2∩W)=0.15

P(A2∩L)=0.15

P(A3∩W)=0.03

P(A3∩L)=0.07

​Now, the tree diagram is :

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 21

Now using Bayes’ theorem,

The posterior probabilities are :

P(A1∣W)=P(A​1∩W)/P(W)

=0.48/0.66

=0.727

P(A2∣W)=P(A​2∩W)/P(W)

=0.15/0.66

=0.227

P(A3∣W)=P(A​3∩W)/P(W)

=0.03/0.66

=0.046

​The posterior probability for the review to be short given that it is submitted in Word is:0.727

The posterior probability for the review to be medium given that it is submitted in Word is:0.227

The posterior probability for the review to be long given that it is submitted in Word is:0.046

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Exercise 2.5 Probability

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Probability

Page 86 Problem 1 Answer

Given: Random student is selected from a university.

Given: A={ Visa } .

B={ MasterCard }

​Given: P(A)=0.5

P(B)=0.4

P(A∩B)=0.25

​To show that A and B are dependent by using the definition of independence.

To verify that the multiplication property does not hold.We will have to show that: P(A∣B)≠P(A) and P(A∩B)≠P(A)⋅P(B)

​Firstly, we will find P(A∣B) with the definition of conditional probability.

P(A∣B)=P(A∩B)/P(B)

=0.3/0.4

=0.75​

Also, we are given P(A)=0.6.

We clearly see that 0.75=P(A∣B)≠P(A)=0.6

Hence, events A and B are dependent.

We will verify this by using multiplication rule.

We know that if the two events are independent then they will satisfy P(A∩B)=P(A)⋅P(B)

From the given data, we see, P(A∩B)=0.3;P(A)⋅P(B)=0.6⋅0.4

=0.24

​Thus, we get P(A∩B)≠P(A)⋅P(B)

∴It is verified that these two events are independent.

We have shown that the events A and B are dependent and that the multiplication property does not hold.

Page 86 Problem 2 Answer

Given: An oil exploration company currently has two active projects, one in Asia and the other in Europe.

Let A be the event that the Asian project is successful and B be the event that the European project is successful.

Given: A and B are independent events with P(A)=0.4 and P(B)=0.7

To find the probability that the European project is not successful given that the Asian project is not successful.

We will use the above stated propositions to find the probability.

A and B are given to be independent.

Now, using above preposition we know that A​′ and B​′ are also independent.

We will now find the conditional probability of B​′ and given that the event A​′ has occurred and is denoted by P(B′∣A′).

Here, event B​′ is European project is successful and event A​′ is the Asian project is not successful.

if P(A∣B)=P(A)  ,where A and B are the two events then this implies that they are independent.

Now, ​P(B′∣A′)=P(B′)

=1−P(B)

=1−0.7

=0.3

​The probability that the European project is also not successful given that the Asian project is not successful is : P(B′∣A′)=0.3

Page 86 Problem 3 Answer

Given: An oil exploration company currently has two active projects, one in Asia and the other in Europe.

Let A be the event that the Asian project is successful and B be the event that the European project is successful.

Given: A and B are independent events with P(A)=0.4 andP(B)=0.7

To find the probability that at least one of the two projects will be successful.

We will use the property P(A∪B)=P(A)+P(B)−P(A∩B)

The probability that at least one of the two projects will be successful is :

P(A∪B)=P(A)+P(B)−P(A∩B)

=0.4+0.7−0.4⋅0.7

=0.82​

From the above preposition when two sets A and B are independent then :

P(A∩B)=P(A)⋅P(B)

=0.4⋅0.7

=0.28​

The probability that at least one of the two projects will be successful is :P(A∪B)=0.82.

Page 86 Problem 4 Answer

Given: An oil exploration company currently has two active projects, one in Asia and the other in Europe.

Let A be the event that the Asian project is successful and B be the event that the European project is successful.

Given: A and B are independent events with P(A)=0.4 and P(B)=0.7

To find the probability only the Asian project is successful given that at least one of the two is successful.

We will use conditional probability P(A∣B)=P(A∩B)

P(B) where P(B)>0

We have A and B are independent which implies that A and B​′ are independent.

A∩B′  is the event only the Asian project is successful.

Now, we will find the conditional probability of A∪B given that the event  A∩B′ has occurred is denoted by P(A∩B′∣A∪B).

P(A∩B′∣A∪B)=P[(A∩B​′)∩(A∪B)]/P(A∪B)

=P(A∩B​′)/P(A∪B)

=P(A)⋅P(B​′)/P(A∪B)

=0.4⋅(1−0.7)/0.82

=0.146

​The probability that only the Asian project is successful is :P(A∩B′∣A∪B)=0.146

Page 86 Problem 5 Answer

Given: A and B events are independent.To show that events A​′ and B are independent.

We will show that: P(A′∣B)=P(A′)

To prove that event A​′ and B are independent.

Using conditional probability we have,

P(A′∣B)=P(A′∩B)/P(B) [Now using, P(B)=P[(A′∩B)∪(A∩B)]=P(A′∩B)+P(A∩B)

That is P(A′∩B)=P(B)−P(A∩B)]

=P(B)−P(A∩B)/P(B) [Now using multiplication property where events A and B are independent.]

=P(B)−P(A)P(B)/P(B)

=P(B)(1−P(A))/P(B)​ [Now, for any event C we know that P(C)+P(C′)=1 .]

=1−P(A)

=P(A′)​

Hence we get: P(A′∣B)=P(A′)

Therefore events A​′ and B are independent.

We have shown that the events A′ and B are independent if A and B are independent.

Page 87 Problem 6 Answer

Given: One of the assumptions underlying the theory of control charting is that successive plotted points are independent of one another.

Each plotted point can signal either that a manufacturing process is operating correctly or that there is some sort of malfunction.

Given: The probability that a particular point will signal a problem with the process is 0.5.

To find the probability that at least one of 10 successive points indicates a problem when in fact the process is operating correctly.

To find the probability for 25 successive points.

We will use multiplication property for independent events and the fact that if A and B are independent then A′ and B′are also independent events.

Let us denote the event as :Ai={ point i error was signaled incorrectly }

where i=1,2,…,25

The probability of each of the event is given 0.05 is same for all events.

Now, the probability that at least one of 10 successive points indicates a problem when in fact the process is operating correctly is denoted as the union of the events A1,A2,…,A10.

Now, using De Morgan’s Law we have :

P(A1∪A2∪…∪A10)=P[(A1′∩A2′∩…∩A10′)′]

[Now, using the property when A be an event P(A′)+P(A)=1 ]

=1−P(A1′∩A2′∩…∩A10′) [Now using multiplication property]

=1−P(A1′)⋅P(A2′)⋯…P(A10′)∣ [Again using P(A′)+P(A)=1]

=1−(1−0.05)⋅(1−0.05)⋅…⋅(1−0.05)

=1−0.9510

=0.401

​Similarly for 25 successive points we get :

P(A1∪A2∪…∪A25)=P[(A1′∩A2′∩…∩A25′)′]

=1−P(A1′∩A2′∩…∩A25′)

=1−P(A1′)⋅P(A2′)⋯…P(A25′)

=1−(1−0.05)⋅(1−0.05)⋅…(1−0.05)

=1−0.9525

=0.723.​

The probability that at least one of 10 successive points indicates a problem when in fact the process is operating correctly is 0.401.

The probability for 25 successive points is 0.723.

Page 87 Problem 7 Answer

Given:  In October, 1994, a flaw in a certain Pentium chip installed in computers was discovered that could result in a wrong answer when performing a division.

The manufacturer initially claimed that the chance of any particular division being incorrect was only 1 in 9 billion, so that it would take thousands of years before a typical user encountered a mistake.

However, statisticians are not typical users; some modern statistical techniques are so computationally intensive that a billion divisions over a short time period is not outside the realm of possibility.

Given that the 1 in 9 billion figure is correct and that results of different divisions are independent of one another.

To find the probability that at  least one error occurs in one billion divisions with the chip.

We will use multiplication of independent events and the fact that if A and B are independent then A′ and B′ are also independent.

We will denote the event as :

Denote event A={ the division is incorrect }.

And the probability of event A is given as P(A)=0.0000000009

And the probability of the same of one in nine billion is :

P(A)=1/9,000,000,000

=a

Now, we have assume that we have one billion divisions with the same chip.

Let us denote this event as Ai={ the ith division is incorrect } , from i=1 to billion,  where (i=1,2,…,109) .

​Now, the probability that at least one error occurs in one billion divisions with this chip is the union of the billion Ai events is :

Using De Morgan’s Law,

P(A1∪A2∪…∪A10​9)=P[(A1′∩A2′∩…∩A10​9′)′

=1−P(A1′∩A2′∩…∩A10​9′)

=1−P(A1′)⋅P(A2′)⋯⋅⋅P(A10​9′)

=1−(1−a)⋅(1−a)⋅…⋅(1−a)

=1−0.895

=0.105

​The probability that at least one error occurs in one billion divisions with the chip is 0.105

Page 87 Problem 8 Answer

Given: An aircraft seam requires 25 rivets.

The seam will have to be reworked if any of these rivets is defective.

Suppose rivets are defective independently of one another, each with the same probability.

To find the probability that a rivet is defective if 20% of all seams need reworking.

We will use the multiplicative property of independent events.

Given that an aircraft seam requires 25 rivets.

Let us consider the probability that a rivet is defected is p.

Now, let us consider the probability that a rivet is not defected is 1−p

And it is given that the rivets are defective independently of one another, each with the same probability.

The probability that the seam is to be reworked is :

P( reworked )=1−P( not reworked )

=1−P( all 25 rivets are non−defective )

=1−[P( rivet is non−defective )]25

(​ Since defective  probabilities are independent ​)

P( reworked )=1−[(1−p)25]

P( reworked )=0.2 is given then:

​0.2(1−p)25/1−p

p=1−(1−p)25

=0.8

=25/√0.8

=0.0088.

​⇒p≈0.009

​The probability that a rivet is defective if 20%  of all seams need reworking is 0.009

Page 87 Problem 9 Answer

Given: An aircraft seam requires 25 rivets.

The seam will have to be reworked if any of these rivets is defective.

Suppose rivets are defective independently of one another, each with the same probability.

To find how small the probability of a defective rivet should be to ensure that only 10% of all seams need reworking.

We will use the multiplicative property of independent events

Given that an aircraft seam requires 25 rivets…

Let us consider the probability that a rivet is defected is p.

Now, let us consider the probability that a rivet is not defected is 1−p.

And it is given that the rivets are defective independently of one another, each with the same probability.

The probability of a defective rivet be to ensure that only 10% of all seams need reworking is :

​P( reworked )=1−∣(1−p)25∣

⇒0.10=1−[(1−p)]{25}

⇒[1−p]25

=1−0.10

⇒(1−p)=0.901/25

⇒p=1−0.90{1/25}

⇒p=1−0.99579

p=0.00421

​The probability of a defective rivet should be 0.00421 to ensure that only 10% of all seams need reworking.

Page 87 Problem 10 Answer

Given: A boiler has five identical relief valves.

Given: The probability that any particular valve will open on demand is 0.96.

To assume independent operation of the valves and find P(at least one valve opens) and P(at least one valve fails to open).

We will multiplication rule on independent events and the fact that if two events are independent then their complements are also independent events.

Lets us consider the event A be that at least one valve open and event B be that at least one valve fails to open.

And let Vi be the events that i th valve open.

We have P(A)=1−P(A′) Where event A​′  be that has no valve opens. Now, the probability of A​′is :

P(A′)=P(V1′∩V2′∩V3′∩V4′∩V5′)

Now, using the multiplication rule we have :

P(A′)=P(V1′∩V2′∩V3′∩V4′∩V5′)=P(V1′)⋅P(V2′)⋅P(V3′)⋅P(V4′)⋅P(V5′)

=[P(Vi′)]5

=[1−P(Vi)]5

=[1−0.95]{5}

=[0.05]{5}

=0.0000003125

Now, the probability that at least one valve open is :

P(A)=1−P(A′)

=1−(0.0000003125)

=0.99999969

≈0.9999

​Now, we have P(B)=1−P(B′) where event B​′ is that all valves open.

Now, the probability of B​′is :

P(B′)=P(V1∩V2∩V3∩V4∩V5)

The probability that at least one valve fails to open is P(B)=1−P(B′)

By using multiplication rule,

P(B′)=P(V1∩V2∩V3∩V4∩V5)

=P(V1)⋅P(V2)⋅P(V3)⋅P(V4)⋅P(V5)

=[P(Vi)]5

=0.95{5}

=0.7737

Now, P(B)=1−P(B′)

=1−(0.774)

=0.2262

​The probability that at least one valve opens is 0.9999. The probability that at least one valve fails to open is0.2262

Page 87 Problem 11 Answer

Given: Two pumps connected in parallel fail independently of one another on any given day.

Given: The probability that only the older pump will fail is 0.10, and the probability that only the newer pump will fail is 0.05.

To find the probability that the pumping system will fail on any given day (which happens if both pumps fail).

Let us denote the events as :

A={ older pump fails }

B={ newer pump fails }​

Now, the probabilities are given :

P(A∩B′)=P(A)−P(A∩B)

=0.1/P(B∩A′)=P(B)−P(A∩B)

=0.05

Now, lets consider x=P(A∩B) , then

P(A)=0.1+x and

P(B)=0.05+x

Now, using the multiplication property we have :

P(A∩B)=P(A)⋅P(B)

=(0.1+x)⋅(0.05+x)

The quadratic equations will be :

x=0.005+0.1⋅x+0.05⋅x+x2/x2−0.85⋅x+0.005=0

​Now, the roots of this quadratic equation is :

x12=−b±√b2−4ac/2a

=0.85±√0.852−4⋅1⋅0.005/2⋅1

x1=0.85+√0.852−4⋅0.005/2

x{1}=0.8441

x2=0.85−√0.852−4⋅0.005/2

x{2}=0.0059.

​We have P(A∪B)=P(A)+P(B)−P(A∩B).

Lets assume x1 is true then :

P(A∪B)=0.1+0.8441+0.05+0.8441−0.8441

P(A∪B)=0.9941

​Now, lets assume x2is true then :

P(A∪B)=0.1+0.0059+0.05+0.0059−0.0059

P(A∪B)=0.1559

Both are the possible solutions.

If x1 is true then we have P(A∪B)>1 which is not possible.x2 is only possible solution.

The probability that the pumping system will fail on any given day (which happens if both pumps fail) is 0.0059.

Page 87 Problem 12 Answer

Given: Consider the system of components connected as in the accompanying picture.

Components 1 and 2 are connected in parallel, so that subsystem works iff either 1 or 2 works; since 3 and 4 are connected in series, that subsystem works iff both 3 and 4 work.

Given figure:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 2 Probability 12

To find probability that the system works given that components work independently of one another.

We will use the multiplication property for independent events.

The given components are independent.

Their probability is :​P( component 1 works )=0.9

P( component 2 works )=0.9

P( component 3 works )=0.8

P( component 4 works )=0.8

​Now, using the multiplication rule :

P( component 1 and 2 work )=P( component 1 works )×P( component 2 works )

=0.9⋅0.9

=0.81​

Now, using the general addition rule for any two events –

If component 1 or component 2 works then the top subsystem works that is :

P( top subsystem works )=P( component 1 works )+P( component 2 works )−P( component 1 and 2 work )

=0.9+0.9−0.81

=0.99​

Similarly, if both components 3 and component 4 works then the bottom subsystem works that is :

P( bottom subsystem works )=P( component 3 works )×P( component 4 works )

=0.8⋅0.8

=0.64

​Each component is given independent which implies that the two subsystems are independent.

Using the multiplication rule for independent events we get :

P (both subsystems work) =P (top subsystem works) ×P (bottom subsystem works)

=0.99×0.64

=0.6336​

When the top subsystem works or the bottom subsystem works then the system will work.

P( system works )=P( top subsystem works )+P( bottom subsystem works )−P( both subsystems work )

=0.99+0.64−0.6336

=0.9964

=99.64%

​The probability that system works : P(system works) is 0.9964.

Page 87 Problem 13 Answer

Given: Components arriving at a distributor are checked for defects by two different inspectors (each component is checked by both inspectors).

The first inspector detects 90% of all defectives that are present, and the second inspector does likewise.

Given: At least one inspector does not detect a defect on  20% of all defective components.

To find the probability that a defective component will be detected only by the first inspector.

To find the probability that a defective component will be detected only by exactly one of the two inspectors.We will use the property P(A′)+P(A)=1

Let us denoted the events as :

A1={ first inspector detects }

A2={ second inspector detects}

​And the probabilities of the above events are given as :

P(A1)=0.9

P(A2)=0.9

​The probability that at least one inspector does not detect a defect is the union of the events A1′ and A2′ is given to be :

P(A1′∪A2′)=0.2

From the above given probability we can obtain :

P(A1∩A2)=1−P[(A1∩A2)′]

Now, using the property P(A′)+P(A)=1

we get :=1−P(A1′∪A2′)

=1−0.2

=0.8

​Now, the probability of event A1 intersection event A2 is :

P(A1∩A2′)=P(A1)−P(A1∩A2)

=0.9−0.8

=0.1

​Now, the probability that one of the two inspectors detect is the sum of the probability we just calculate above and the probability that a  defective component will be detected only by the second inspector  is :

P(A1′∩A2)=P(A2′)−P(A1∩A2)

=0.9−0.8

=0.1​

Now, we will define an event C as:C={ exactly one inspector detects }

And the probability of the event C is:

P(C)=P(A1∩A2′)+P(A1′∩A2)

=0.1+0.1

=0.2

​The probability that  a defective component will be detected only by the first inspector isP(A1

∩A2′)=0.1 .The probability that  a defective component will be detected by exactly one of the two inspectors is P(C)=0.2.

Page 87 Problem 14 Answer

Given: Components arriving at a distributor are checked for defects by two different inspectors (each component is checked by both inspectors).

The first inspector detects 90% of all defectives that are present, and the second inspector does likewise.

Given: At least one inspector does not detect a defect on  20%of all defective components.

To find the probability that all three defective components in a batch escape detection by both inspectors.

We will use the multiplication of independent events.

Let us denoted the events as :

A1={ first inspector detects }

A2={ second inspector detects }

​And the probabilities of the above events are given as :

P(A1)=0.9

P(A2)=0.9​

The probability that at least one inspector does not detect a defect is the union of the events A1′and A2′  is given to be :

P(A1′∪A2′)=0.2

From the above given probability we can obtain :

P(A1∩A2)=1−P∣∣(A1∩A2)′∣∣

Now using the property P(A′)+P(A)=1

we get :

=1−P(A1′∪A2′)

=1−0.2

=0.8

​The probability that neither of inspectors detect a defect is the complement of union of events A1 and A2 is : ( from part )

Using identity P(A)+P(A′)=1 we have,

P[(A1∪A2)′]=1−P(A1∪A2)

The union of two events can always be represented as the sum of these three events. So, we have

=1−[P(A1∩A2)+P(C)](P(C) from part (a) )

=1−0.8−0.2

=0

​Now we consider an event A3  which is denoted as :

A3={ all three defective components escape detection }

Now, the probability of event A3 is :

Using multiplication property we have,

P(A3)=P[(A1∪A2)′]⋅P[(A1∪A2)′∣⋅P[(A1∪A2)′]

=0⋅0⋅0

=0​

The probability that all three defective components in a batch escape detection by both inspectors is : P(A3)=0

Page 87 Problem 15 Answer

Given: Seventy percent of all vehicles examined at a certain emissions inspection station pass the inspection.

Given: Successive vehicles pass or fail independently of one another.

To calculate the probability: P(all of the next three vehicles inspected pass)We will use the multiplication rule for independent events.

We have: P( Pass )=0.7

Now we are given that the successive events are independent, hence we can apply multiplicative rule of multiplication for independent evets.

Therefore we get:

P( All three pass )

=P( Pass )×P( Pass )×P( Pass )

=0.7×0.7×0.7

=0.343

=34.3%

​We obtain: P(all of the next three vehicles inspected pass)=0.343

Page 87 Problem 16 Answer

Given: Seventy percent of all vehicles examined at a certain emissions inspection station pass the inspection.

Given: Successive vehicles pass or fail independently of one another.

To calculate the probability : P(at least one of the next three inspected fails)We will use the complement rule to calculate.

We have obtained in part a)

P (all of the next three vehicles inspected pass) =0.343

Hence by using complement rule we get:

P( At least one fail )

=1−P( All three pass )

=1−0.343

=0.657

=65.7%

​We obtain: P(at least one of the next three inspected fails)=0.657

Page 87 Problem 17 Answer

Given: Seventy percent of all vehicles examined at a certain emissions inspection station pass the inspection.

Given: Successive vehicles pass or fail independently of one another.

To calculate: P(exactly one of the next three inspected passes)We will use complement rule to calculate.

We have: P( Pass )=0.7 by using complement rule we get: P( Fail )

=1−P( Pass )

=1−0.7

=0.3

​We are given that the events are independent.

Hence by using the multiplicative rule for independent events we get:

P( Exactly one pass )

=3×P( Pass )×P( Fail )×P( Fail )

=3×0.7×0.3×0.3

=0.189

​We obtain: P(exactly one of the next three inspected passes)=0.189

Page 87 Problem 18 Answer

Given: Seventy percent of all vehicles examined at a certain emissions inspection station pass the inspection.

Given: Successive vehicles pass or fail independently of one another.

To calculate: P(at most one of the next three vehicles inspected passes)We will use addition rule for mutually exclusive events.

We have obtained in part c) that: P( Fail )=0.3

The events are independent.

Hence by using the multiplication rule:

P( None pass )

=P( Fail )×P( Fail )×P( Fail )

=0.3×0.3×0.3

=0.027​

We know from c that: P (exactly one of the next three inspected passes) =0.189

At most one vehicle passes when none of the vehicle passes OR exactly one vehicle passes.

These events are mutually exclusive hence we have: P( At most one pass )

=P( None pass )+P( Exactly one pass )

=0.027+0.189

=0.216​

We obtain: P(at most one of the next three vehicles inspected passes)=0.216.

Page 87 Problem 19 Answer

Given: Seventy percent of all vehicles examined at a certain emissions inspection station pass the inspection.

Given: Successive vehicles pass or fail independently of one another.

To find the probability all three vehicles pass given that at least one of the next three vehicles passes inspection.

We will use conditional probability formula: P(B∣A)=P(A∩B)

P(A),P(A)>0

From d) we have that: P( None pass )=0.027

Hence using complement rule we get:

P( At least one pass )

=1−P( None pass )

=1−0.027

=0.973

Now using conditional probability we get:

​​P( All three pass ∣ At least one pass )

=P( All three pass and At least one pass /P( At least one pass )

=P( All three pass )/P( At least one pass )

=0.343/0.973

​⇒P ( All three pass ∣ At least one pass )=0.3525

​The probability all three vehicles pass given that at least one of the next three vehicles passes inspection is 0.3525.

Page 88 Problem 20 Answer

Given: Professor Stan der Deviation can take one of two routes on his way home from work.

On the first route, there are four railroad crossings.

The probability that he will be stopped by a train at any particular one of the crossings is 0.1, and trains operate independently at the four crossings.

The other route is longer but there are only two crossings, independent of one another, with the same stoppage probability for each as on the first route.

On a particular day, Professor Deviation has a meeting scheduled at home for a certain time.

Whichever route he takes, he calculates that he will be late if he is stopped by trains at at least half the crossings encountered.

To determine which route to take so that he can minimize the probability of being late to the meeting.

We will use the probability mass function of binomial random variable.

Let R1 and R2 be the events that the professor is late when taking the first route and the second route respectively.

Hence we have:

P(R1)=P(2 crossings )+P(3 crossings )+P(4 crossing )

=P(X=2)+P(X=3)+P(X=4)

=(​4/2​)p2/(1−p)4−2+(​4/3​)p3/(1−p)4−3+(​4/4​)p4/(1−p)4−4

=6⋅0.12⋅0.92+4⋅0.13⋅0.9+0.14

=0.05.

​For the second rule we will have :P(R2)

=P(1 crossings )+P(2 crossings )

=P(X=1)+P(X=2)

=(​2/1​)p1(1−p)2−1+(​2/2​)p2/(1−p)2−2

=0.1⋅0.9+0.9⋅0.1+0.1⋅0.1

=0.19.

We can see that the probability to get late of route one is lesser than that of route 2 the professor must choose the first route.

The professor should take the first route to minimize the probability of being late to the meeting.

Page 88 Problem 21 Answer

Given: Professor Stan der Deviation can take one of two routes on his way home from work. On the first route, there are four railroad crossings.

The probability that he will be stopped by a train at any particular one of the crossings is 0.1, and trains operate independently at the four crossings.

The other route is longer but there are only two crossings, independent of one another, with the same stoppage probability for each as on the first route.

On a particular day, Professor Deviation has a meeting scheduled at home for a certain time.

Whichever route he takes, he calculates that he will be late if he is stopped by trains at at least half the crossings encountered.

Given: The professor tosses a fair coin to decide on a route and he is late.

To find the probability that he took the four crossing route.We will use the law of total probability.

Let L denote the event that the professor is late home.

R1 and R2 are  the events that the professor is late when taking the first route and the second route respectively

Hence we need to find the probability of event R1∣L

Now we have:

P(R1∣L)=P(L∩R1)/P(L)

By using the multiplicative rule P(A∩B)=P(A∣B)⋅P(B)

we get: P(L∩R1)

P(L)=P(L∣R1)P(R1)/P(L)

By Law of total probability we get:

​P(L∣R1)P(R1)/P(L)

=P(L∣R1)P(R1)/P(L∣R1)P(R1)+P(L∣R2)P(R2)

=0.5⋅0.05/0.5⋅0.05+0.5⋅0.19

=0.22.

Hence P(R1∣L)=0.22

The probability that the professor took the four crossing route is 0.22.

 

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Supplementary Exercise Probability

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 2 Probability

Page 88 Problem 1 Answer

Given: A small manufacturing company will start operating a night shift.

There are 20 machinists employed by the company.

To find how many different crews are possible if a night crew consists of 3 machinists.

We will use combination formula: ​

Ck,n=(​n/k​)=n!/k!(n−k)!

We have n=20 individuals

We have to get a subset of k=3

Hence:

C3,20=(​20/3​)

=20!/3!(20−3)!

=1140.

​1140 different crews are possible if a night crew consists of 3 machinists.

Page 88 Problem 2 Answer

Given: A small manufacturing company will start operating a night shift.

There are 20 machinists employed by the company.

To find the number of crews not having the best machinist given that the machinist are ranked 1,2,…,20 in order of competence.

We will use combination formula: ​

Ck,n=(​n/k​)

=n!/k!(n−k)!

We have to take out the best machinists .

Hence: n=19

We need to crew crew of 3

Hence k=3

Therefore we have:

​C3,19=(​19/3​)

=19!/3!(19−3)!

=969

​The number of crews not having the best machinist given that the machinist are ranked1,2,…,20 in order of competence is 969.

Page 88 Problem 3 Answer

Given: A small manufacturing company will start operating a night shift.

There are 20 machinists employed by the company.

To find the number of crews that will have at least 1 of the 10 best machinists.

We will use combination formula: ​

Ck,n=(​n/k​)

=n!/k!(n−k)!

There are a total of 1140

Hence if we subtract 1140 from the number of to select 3 machinists from of group of 10 bottom machinists we will get the required answer.

Hence we have:

C3,20−C3,10

=1140−(​10/3​)

=1140−120

=1020​

The number of crews that will have at least 1 of the 10 best machinists is 1020.

Page 88 Problem 4 Answer

Given: A small manufacturing company will start operating a night shift.

There are 20 machinists employed by the company.

To find the probability that the best machinist will not work that night given that one of these crews is selected at random to work on a particular night.

We will use combination formula: ​

Ck,n=(​n/k​)

=n!/k!(n−k)!

We know that for an event A we have

P(A)= number of favorable outcomes in A number of outcomes in the sample space

Now here A is the event  that the best machinist will not work that night

We have found the favorable outcomes in part b) 969 and the total number of outcomes in part a) 1140

Hence P(A)=C3,19/C3,20

=969/1140

=0.85

​The probability that the best machinist will not work that night is 0.85.

Page 89 Problem 5 Answer

Given: One satellite is scheduled to be launched from Cape Canaveral in Florida, and another launching is scheduled for Vandenberg Air Force Base in California.

Given: A denotes the event that the Vandenberg launch goes off on schedule, and B represent the event that the Cape Canaveral launch goes off on schedule.

Given that A and B are independent events.Given:​P(A)>P(B),P(A∪B)=.626, and P(A∩B)=.144

​To determine: P(A) and P(B)

We will use the properties:  P(A∪B)=P(A)+p(B)-P(A∩B) and

P(A∩B)=P(A).P(B)

Since there are two questions and two unknowns,

Substitute the values in one of the equation,

P(A∩B)=P(A)⋅P(B)

0.144=P(A)⋅P(B)

P(A)=0.144

P(B)

Substitute the values in the other equation,

P(A∪B)=P(A)+P(B)−P(A∩B)

0.626=P(A)+P(B)−0.144

Now replace the value of P(A),

0.626=0.144

P(B)+P(B)−0.144/0.626⋅P(B)=0.144+P(B)2−0.144⋅P(B)

0=0.144+P(B)2−0.77⋅P(B)

After solving the quadratic equation, the solutions are,

P(A) or P(B)=0.45 or 0.32

Since P(A)>P(B),

P(A)=0.45.

P(B)=0.32.

The values of P(A) and P(B), calculated using the given values was found to be 0.45 and 0.32 respectively.

Page 89 Problem 6 Answer

Given: There are six friends namely, A,B,C,D,E,and F.A has heard a rumor and wants to share it with all the friends.

A selects one of the five at random and tells the rumor to the chosen individual.

Given: individual then selects at random one of the four remaining individuals and repeats the rumor. Continuing, a new individual is selected from those not already having heard the rumor by  the individual who has just heard it, until everyone has been told.

To find the probability that the rumor is repeated in the order B,C,D,E and F.

We will use permutation to find the favorable outcomes and then find the probability.

The number of ways to arrange all the friends is,P5,5

=5!/(5−5)!

P=5!.

P=120.

But the required condition is a very specific order (B,C,D,E,and ,F),

T=1/120

T=0.0083.

The probability that the rumor is repeated in the order (B,C,D,E,and ,F) is 0.0083.

Page 89 Problem 7 Answer

There are six friends namely,A,B,C,D,E,and F A has heard a rumor and wants to share it with all the friends.

A selects one of the five at random and tells the rumor to the chosen individual.

Given:  individual then selects at random one of the four remaining individuals and repeats the rumor.

Continuing, a new individual is selected from those not already having heard the rumor by the individual who has just heard it, until everyone has been told.

To find the probability that F is the third person at the party to be told the rumor.

We will use permutation to find the favorable outcomes and then find the probability.

The probability of F being the third person,

P4,4=4!/(4−4)!

P=4⋅3⋅1⋅2⋅1

P=24.

The probability of the above situation to occur is,

T=24/120

T=0.2.

The probability that F is the third person at the party to be told the rumor is 0.2.

Page 89 Problem 8 Answer

There are six friends namely, A,B,C,D,E,and F.A has heard a rumor and wants to share it with all the friends.

A selects one of the five at random and tells the rumor to the chosen individual.

Given:  individual then selects at random one of the four remaining individuals and repeats the rumor.

Continuing, a new individual is selected from those not already having heard the rumor by the individual who has just heard it, until everyone has been told.

To find the probability that F is the last person to hear the rumor.

We will use permutation to find the favorable outcomes and then find the probability.

The probability of F being the last person,

P4,4=4!/(4−4)!

P=4⋅3⋅2⋅1⋅1

P=24.

The probability of the situation to occur is,

T=24/120

T=0.2.

The probability that F  is the last person to hear the rumor is 0.2.

Page 89 Problem 9 Answer

There are six friends namely, A,B,C,D,E,and F.A has heard a rumor and wants to share it with all the friends,  A selects one of the five at random and tells the rumor to the chosen individual.

Given:  individual then selects at random one of the four remaining individuals and repeats the rumor.

Continuing, a new individual is selected from those not already having heard the rumor by the individual who has just heard it, until everyone has been told.

Given: at each stage the person who currently “has” the rumor does not know who has already heard it and selects the next recipient at random from all five possible individuals, To find the probability that F has still not heard the rumor after it has been told ten times at the party.

We will use the definition of probability.

The probability of F to have not heard after 10 times,

F=410

F=1048576.

The total probability of everyone hearing the rumor,T=510

T=9565625.

Now the probability of the event happening is,

P=F/T

P=1058576/9565625

P=0.1074.

The probability that F has still not heard the rumor after it has been told for 10 times is 0.1074.

Page 89 Problem 10 Answer

Given: Each contestant on a quiz show is asked to specify one of six possible categories from which questions will be asked Given: P( contestant requests category i)=1/6

Given: successive contestants choose their categories independently of one another.

To find the probability that exactly one contestant has selected category 1 given that there are three contestants on each show and all three contestants on a particular show select different categories.We will use combination formula n

Cr=(​nr​)n!/r!(n−r)! in order to find the favorable and total number of outcomes and then find the probability.

We have:

​P( contestant requests category i)

i=1/6

=1,2,3,4,5,6

​We need 3 of the 6 categories which can be done in C6,3

=(​6/3​) ways

Hence the number of possible outcomes are:

(​6/3​)=6!/3!(6−3)!

=20

There are C5,2=(​5/2​) ways to select the remaining two categories of the five excluding 1

Hence the number of favorable outcomes is:

(​5/2​)=5!/2!(5−2)!

=10

​Therefore the probability that category 1 is selected is:

P( Category 1 is selected )= number of favorable outcomes/ number of possible outcomes

=10/20

=1/2

=0.5

​The probability that exactly one contestant has selected category 1 is 0.5.

Page 90 Problem 11 Answer

Given: One percent of all individuals in a certain population are carriers of a particular disease.

A diagnostic test for this disease has a 90% detection rate for carriers and a 5% detection rate for noncarriers

Given: The test is applied independently to two different blood samples from the same randomly selected individual.

To find the probability that both the tests yield the same result.

We will use the complement rule and general multiplication rule: P(A∩B)=P(A)×P(B∣A)

Let: A=Event that the person has the disease

B=Event that the test is positive.

Hence:

P(A)=0.01

P(B∣A)=0.9

P(B∣A′)=0.05​

Therefore by using complement rule we have:

P(A′)=1−P(A)

=1−0.01

=0.99

P(B′∣A)=1−P(B∣A)

=1−0.9

=0.1 and

P(B′∣A′)=1−P(B∣A′)

=1−0.05

=0.95

​Using the multiplication rule we have:

P(A∩B)=P(A)×P(B∣A)

=0.01×0.9

=0.009

P(A∩B′)=P(A)×P(B′∣A)

=0.01×0.1

0.001

P(A′∩B)=P(A′)×P(B∣A′)

=0.99×0.05

=0.0495

P(A′∩B′)=P(A′)×P(B′∣A′)

=0.99×0.95

=0.9405

​Hence we have:

P(B)=P(A∩B)+P(A′∩B)

=0.009+0.0495

=0.0585

P(B′)=P(A∩B′)+P(A′∩B′)

=0.001+0.9405

=0.9415

​The tests are independent and therefore we get:

P( Both B)=0.0585×0.0585

≈0.0034

P( Both B′)

=0.9415×0.9415

≈0.8864

​Hence P( Same result )

=P( Both B)+P( Both B′)

=0.0034+0.8864

=0.8898​

The probability that both the tests yield the same result is 0.8898.

Page 90 Problem 12 Answer

Given: One percent of all individuals in a certain population are carriers of a particular disease.

A diagnostic test for this disease has a 90% detection rate for carriers and a 5% detection rate for noncarriers.

Given: The test is applied independently to two different blood samples from the same randomly selected individual.

To find the probability that the selected individual is a carrier given that both tests are positive.

We will use conditional probability.

We have:

A=Event that the person has the disease

B=Event that the test is positive.

From part a we have: P( Both B)≈0.0034

Hence P( Both (A∩B))

=P(A∩B)×P(A∩B)

=0.009×0.009

=0.000081

​Using the definition of conditional probability we have:P(A∣ Both B)

=P(A∩ Both B)/P( Both B)

=P( Both (A∩B))/P( Both B)

=0.000081/0.0585×0.0585

≈0.02367

=2.367%

​The probability that the selected individual is a carrier given that both tests are positive is 0.02367.

Page 90 Problem 13 Answer

Given: System consists of two components.

Given: The probability that the second component functions in a satisfactory manner during its design life=0.9.

The probability that at least one of the two components does so=0.96.

The probability that both components do so=0.75. the first component functions in a satisfactory manner throughout its design life.

To find the probability that the second one also functions in a satisfactory manner throughout its design life.We will use conditional probability P(B∣A)=P(A∩B)/P(A)

Let us consider following events,

A={ the event of the first component functioning}

B={ the event of the second component functioning}

We are given that,

P(B)=0.9

P(A∪B)=0.96

P(A∩B)=0.75

Using Addition Rule,

P(A∪B)=P(A)+P(B)−P(A∩B)

This implies, P(A)=−P(B)+P(A∩B)+P(A∪B)

Thus,P(A)=−0.9+0.75+0.96

P(A)=0.81

Using definition of conditional probability,

P(B∣A)=P(A∩B)/P(A)

P(B∣A)=0.75/0.81

P(B∣A)≈0.9259

The probability that the second component functions in a satisfactory manner throughout its design life given that the first one does is 0.9259.

Page 90 Problem 14 Answer

Given: A certain company sends 40% of its overnight mail parcels via express mail service E1.

of which 2% arrive after the guaranteed delivery time .

Given: a record of an overnight mailing is randomly selected from the company’s file.

To find the probability that the parcel went via E1and was late.

We will use the multiplication property: P(A∩B)=P(A∣B)⋅P(B)

We have: P(E1)=0.4

Now if L denotes the event :late arrived” then:

P(L∣E1)=0.02

Hence by using multiplication rule we have:

P(L∩E1)=P(L∣E1)⋅P(E1)

=(0.02)⋅(0.4)

=0.008

​The probability that the parcel went via E1 and was late is 0.008.

Page 90 Problem 15 Answer

Given percentages of the assembly lines that they need rework:

A1=5%

A2=8%

A3=10%

Given percentages of components produced:

A1=50%

A2=30%

A3=20%

Given: a randomly selected component needs rework

To find: the probability that it came from lineA1.

To find: the probability that it came from line A2.

To find: the probability that it came from lineA3.

We will use Bayes’ theorem in order to find the required probability.

Let us consider the following event,

R={Component needs rework}

It is given that,

P(A1)=0.5

P(A2)=0.3

P(A3)=0.2

P(R∣A1)=0.05;

P(R∣A2)=0.08

P(R∣A3)=0.1

​Now, we need to find the probability such that if a random selected component needs rework, it is from the lineA1

i.e.,P(A1∣R)

Using Bayes’ theorem, we get,

P(A1∣R)=P(A1∩R)/P(R)=P(A1)⋅P(R∣A1)

P(A1)⋅P(R∣A1)+P(A2)⋅P(R∣A2)+P(A2)⋅P(R∣A2)

=0.5⋅0.05/0.5⋅0.05+0.3⋅0.08+0.2⋅0.1

=0.025/0.025+0.024+0.02

=0.025/0.069

P(A1∣R)=0.377

Now, we need to find the probability such that if a random selected component needs rework, it is from the lineA2

i.e.,P(A2∣R).

Using Bayes’ Theorem, we get

P(A2∣R)=P(A2∩R)/P(R)

=P(A2)⋅P(R∣A2)

P(A1)⋅P(R∣A1)+P(A2)⋅P(R∣A2)+P(A2)⋅P(R∣A2)

=0.3⋅0.08/0.5⋅0.05+0.3⋅0.08+0.2⋅0.1

=0.024/0.025+0.024+0.02

=0.024/0.069

P(A2∣R)=0.348

Now, we need to find the probability such that if a random selected component needs rework, it is from the line A3

i.e.,P(A3∣R).

Using Bayes’ Theorem, we get

P(A3∣R)=P(A3∩R)/P(R)

=P(A3)⋅P(R∣A3)

P(A1)⋅P(R∣A1)+P(A2)⋅P(R∣A2)+P(A2)⋅P(R∣A2)

=0.2⋅0.1/0.5⋅0.05+0.3⋅0.08+0.2⋅0.1

=0.02/0.025+0.024+0.02

=0.02/0.069

P(A3∣R)=0.29 the probability that the component  came from lineA1 is 0.377 the probability that the component  came from line A2 is 0.348 the probability that the component  came from line A3 is 0.29

Page 90 Problem 16 Answer

Given: A subject is allowed a sequence of glimpses to detect a target where Gi={ the target is detected on the ith glimpse } with  h pi=P(Gi)

Given: Gi′s are independent events.

To write an expression for the probability that the target has been detected by the end of the nth glimpse.

We will use the multiplication property of mutually independent events.

An event Gi is defined as follows,

Gi= the target is detected on theith glimpse

Also, the probability of the event Gi is denoted by pi, then the probability that the target is not observed on ithglimpse is denoted by(1−pi).

The objective is to find the probability that the target has been detected by the end of thenth glimpse.

As it is given thatGi′s are independent, therefore their complementary events are also independent.

Now, the probability that the target has been detected by the end of then th glimpse is equal to the combined probability that the target is not detected by first′n−1′ trials.

Therefore,

P(Gn) = P(target that is not detected in previous ′n′ trials)

P(Gn) = 1 − P(G1∪G2∪……∪Gn)

P(Gn) = 1 − P(G1​′∪G2​′∪……∪Gn′)

P(Gn) = 1 − [P(G1​′)×P(G2​′)×……P(Gn​′)]

P(Gn) = 1 − [(1−p1)×(1−p2)×….…×(1−pn)]

​The expression for the probability that the target has been detected by the end of then th glimpse is P(Gn)=1−[(1−p1)×(1−p2)×….…×(1−pn)]=1−i=1∏n(1−pi)

Page 91 Problem 17 Answer

Given : A particular airline has 10 A.M. flights from Chicago to New York, Atlanta, and Los Angeles and the events are describes as:

A= New York flight is full

B =The Atlanta flight is full

C =The Los Angeles flight is full

Given: All the three events are independent events.

Given probabilities: ​P(A)=0.9

P(B)=0.7

P(C)=0.8​

To find the probability that all three flights are full.To find the probability that at least one flight is not full.

We will use the complement rule and multiplication property for independent events in order to find the required probabilities.

Now, we will find the probability that all three flights are full.

This means that all the events are occurring together, since all the events are independent, so by using the multiplication rule of probability

Let probability that all the fights are full be P(A∩B∩C)

As P(A1∩A2∩A3∩………An)=P(A1)⋅P(A2)⋅P(A3)⋅…………P(An)

Therefore, P(A∩B∩C)=P(A)⋅P(B)⋅P(C)

P(A∩B∩C)=0.9⋅0.7⋅0.8

P(A∩B∩C)=0.504

​Now, we will calculate the probability if at least one flight is not full.

We will calculate it using the definition of the complement of an event as the event that at least one flight is not full is a complement to that event that all the three flights are full (let the probability that all the flights are full be P(A) )

​ P(Z) = 1 − P(A)

P(Z) = 1 − 0.504

P(Z) = 0.496

​The probability that all three flights are full is 0.0504 .

The probability that at least one flight is not full is0.496.

Page 91 Problem 18 Answer

Given : A particular airline has 10 A.M. flights from Chicago to New York, Atlanta, and Los Angeles and the events are describes as:

A= New York flight is full

B = The Atlanta flight is full

C =The Los Angeles flight is full

Given: All three events are independent events.Given probabilities:

P(A)=0.9

P(C)=0.8

P(B)=0.7

To find the probability that only the New York flight is full.To find the probability that exactly one of the three flights is full.

We will use the complement rule and multiplication property for independent events in order to find the required probabilities.

First, we will find the probability that only the New York flight is full. It means that event A will occur but at the same time events, A and B will not occur.

The probability that only the New York flight is full-

By using the multiplication rule of probability –

P(A∩Bˉ∩Cˉ)=P(A)⋅P(Bˉ)⋅P(Cˉ)

we have ,P(Aˉ) = 1−P(A)

P(A∩Bˉ∩Cˉ)=0.9⋅ (1 − 0.7)⋅(1−0.8)

P(A∩Bˉ∩Cˉ)=0.9⋅0.3⋅0.2

P(A∩Bˉ∩Cˉ)=0.054​

Now, we need to find the probability that exactly one of the three flights is full.

There are three possible ways either the New York flight is full, or The Atlanta flight is full or The Los Angeles flight is full.

The probability that exactly one of the three flights is full –

P(exactly one of the three flights is full) = P(A∩Bˉ∩Cˉ)+ P(Aˉ∩B∩Cˉ)+ P(Aˉ∩Bˉ∩C)

P(exactly one of the three flights is full) = P(A)P(Bˉ)P(Cˉ)+ P(Aˉ)P(B)P(Cˉ) + P(A)ˉP(B)ˉP(C)

Now, we will use the multiplication rule of probability,

P(exactly one of the three flights is full)= ​(0.9)⋅(1−0.7)⋅(1−0.8?) + (1−0.9)⋅(0.7)⋅(1−0.8) + (1−0.9)⋅(1−0.7)⋅(0.8)

​P(exactly one of the three flights is full)= 0.9⋅0.3⋅0.2 + 0.1⋅0.7⋅0.2 + 0.1⋅0.3⋅0.8

P(exactly one of the three flights is full)= 0.092

The probability that only the New York flight is full is0.054 .

The probability that exactly one of the three flights is full is0.092 .

Page 91 Problem 19 Answer

Given: a box is containing four slips and each having same dimension

Given events:A1 = {win prize 1}

A2 = {win prize 2}

A3 = {win prize 3}

​To show that A1 and A2 are independent,A2  and A3 are independent and A1 and A3 are also independent.

To showP(A1∩A2∩A3) ≠ P(A1)⋅P(A2)⋅P(A3), so the three are not mutually independent.

We will use the multiplication property is not hold to prove that they are not mutually independent.

P(A1)=win prize 1(A1)+win prize 1,2,3(A4)

Therefore,P(A1) = 1/2,  P(A2) = 1/2   ,   P(A3) = 1/2

P(A1∩A2) = A4

A1+A2+A3+A4

P(A1∩A2) = 1/4

As, P(A1)⋅P(A2) = 1/2⋅1/2

P(A1)⋅P(A2)= 1/4 which is equal to P(A1∩A2)

This shows that A1 and A2 are independent events.

​Similarly, we will solve

P(A1∩A3) = P(A1)⋅P(A3)

P(A1∩A3) = 1/2 ⋅ 1/2

P(A1∩A3) = 1/4 which is equal to P(A1 ∩A3)

This shows that A1 and A3 are independent events

​Now, for P(A2∩A3) = P(A2)⋅P(A3)

P(A2∩A3) = 1/2 ⋅ 1/2

P(A2∩A3) = 1/4 which is equal to P(A2∩A3)

This shows that A2 and A3 are independent events

​P(A1∩A2∩A3) = A4

A1+A2 +A3+A4

P(A1∩A2∩A3) = 1/4

And P(A1)⋅P(A2)⋅P(A3) = 1/2 ⋅ 1/2 ⋅ 1/2

P(A1)⋅P(A2)⋅P(A3) = 1/8

Therefore, it shows that P(A1∩A2∩A3)≠ P(A1)⋅P(A2)⋅P(A3)

Hence Proved.​

We have proved that P(A1∩A2∩A3) ≠ P(A1)⋅P(A2)⋅P(A3) and also we have shown that the three events are pairwise independent.

Page 91 Problem 20 Answer

Given: A1,A2, and A3are independent events.To show that: P(A1∣A2∩A3)=P(A1)

We will use Bayes’ Theorem in order to show the required.

We have to consider that A1,A2 and A3 are independent events

By using Bayes Theorem, we will simplify  for P(A1∣A2∩A3).

P(A1∣A2∩A3) = P(A1∩A2∩A3)

P(A2∩A3)

As all the events are independent, then it follows the multiplication rule,

P(A1∩A2∩A3) = P(A1)P(A2)P(A3)

P(A2∩A3) = P(A2)P(A3)

Therefore, P(A1∣A2∩A3) = P(A1∩A2∩A3)

P(A2∩A3)

P(A1∣A2∩A3) = P(A1)P(A2)P(A3)

P(A2)P(A3)

P(A1∣A2∩A3) = P(A1)

Hence proved.

​We have shown that if A1,A2 , and A3 are independent events then P(A1∣A2∩A3)=P(A1).

Page 91 Problem 21 Answer

Given: a box is containing four slips and each having same dimension

Given events:A1 = {win prize 1}

A2 = {win prize 2}

A3 = {win prize 3}

​To show that A1 and A2 are independent,A2  and A3 are independent and A1 and A3 are also independent.To show P(A1∩A2∩A3) ≠ P(A1)⋅P(A2)⋅P(A3), so the three are not mutually independent.

We will use the multiplication property is not hold to prove that they are not mutually independent.

P(A1)=win prize 1(A1)+win prize 1,2,3(A4)

Therefore,P(A1) = 1/2,  P(A2) = 1/2   ,  P(A3) = 1/2

P(A1∩A2) = A4

A1+A2+A3+A4

P(A1∩A2) = 1/4

As, P(A1)⋅P(A2) = 1/2⋅1/2

P(A1)⋅P(A2)= 1/4 which is equal to P(A1∩A2)

This shows that A1 and A2 are independent events.

Similarly, we will solve

P(A1∩A3) = P(A1)⋅P(A3)

P(A1∩A3) = 1/2 ⋅ 1/2

P(A1∩A3) = 1/4 which is equal to P(A1 ∩A3)

This shows that A1 and A3 are independent events

​Now, for P(A2∩A3) = P(A2)⋅P(A3)

P(A2∩A3) = 1/2 ⋅ 1/2

P(A2∩A3) = 1/4 which is equal to P(A2∩A3)

This shows that A2 and A3 are independent events

​P(A1∩A2∩A3) = A4

A1+A2 +A3+A4

P(A1∩A2∩A3) = 1/4 And P(A1)⋅P(A2)⋅P(A3) = 1/2 ⋅ 1/2 ⋅ 1/2

P(A1)⋅P(A2)⋅P(A3) = 1/8

Therefore, it shows that P(A1∩A2∩A3)≠ P(A1)⋅P(A2)⋅P(A3)

Hence Proved.

​We have proved that P(A1∩A2∩A3) ≠ P(A1)⋅P(A2)⋅P(A3) and also we have shown that the three events are pairwise independent.

Page 91 Problem 22 Answer

Given: A1,A2, and A3 are independent events.To show that: P(A1∣A2∩A3)=P(A1)

We will use Bayes’ Theorem in order to show the required.

We have to consider that A1,A2and A3 are independent events

By using Bayes Theorem, we will simplify  for P(A1∣A2∩A3).

P(A1∣A2∩A3) = P(A1∩A2∩A3)

P(A2∩A3)

As all the events are independent, then it follows the multiplication rule ,

P(A1∩A2∩A3) = P(A1)P(A2)P(A3)

P(A2∩A3) = P(A2)P(A3)

Therefore, P(A1∣A2∩A3) = P(A1∩A2∩A3)

P(A2∩A3)

P(A1∣A2∩A3) = P(A1)P(A2)P(A3)

P(A2)P(A3)

P(A1∣A2∩A3) = P(A1)

Hence proved.

​We have shown that if A1,A2, and A3 are independent events then P(A1∣A2∩A3)=P(A1).

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 3 Exercise 3.2 Discrete Random Variables and Probability Distributions

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 3 Discrete Random Variables and Probability Distributions

Page 104 Problem 1 Answer

Given -An automobile service facility specializing in engine tune-ups knows that 45% of all tune-ups are done on four-cylinder automobiles,40% on six-cylinder automobiles, and 15% on eight cylinder automobiles.

We assume number of cylinders on the next car to betuned.

We will find the pmf of X.

We note that the probabilities are given in question as percentages.

We will convert it in integers to obtain the pmf.

Hence, the probability mass function is given by

​P(X=4)=0.45

P(X=6)=0.4

P(X=8)=0.15 and

P(X=x)=0

for every other x.

We obtain:

​P(X=4)=0.45

P(X=6)=0.4

P(X=8)=0.15

For other numbers, the probability is zero.

Page 104 Problem 2 Answer

Given -An automobile service facility specializing in engine tune-ups knows that 45% of all tune-ups are done on four-cylinder automobiles, 40% on six-cylinder automobiles, and 15% on eight cylinder automobiles.

We suppose number of cylinders on the next car to be tuned.

We will draw both a line graph and a probability histogram for the pmf of parta).

From part a), we know the probability values.

We will plot the graph as follows:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 2

We will plot the histogram as follows :

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 2 1

The required graph is:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 2 2

The required Histogram is:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 2 3

Page 104 Problem 3 Answer

Given -An automobile service facility specializing in engine tune-ups knows that 45% of all tune-ups are done on four-cylinder automobiles,40% on six-cylinder automobiles, and 15% on eight cylinder automobiles.

We suppose number of cylinders on the next car to be tuned.

We will find the probability that the next car tuned has at least six cylinders and more than six cylinders.

We will obtain the probability that the next car tuned has at least six cylinders as shown below:

P( at least 6)

=P6+P8

=0.4+0.15

=0.55

The probability that the next car tuned has more than six cylinders is given as:

P8=15/100

P8=0.15

We obtain:

P(X≥6)=0.55

P(X>6)=0.15

Page 105 Problem 4 Answer

Given – In some parts of California are particularly earthquake-prone.

We suppose that in one metropolitan area, 25% of all homeowners are insured against earthquake damage.

Four homeowners are to be selected at random; let X denote the number among the four who have earthquake insurance.

We will find the Probability Distribution of X.

We suppose that S denote a homeowner who has insurance and F one who does not.

Now, one possible outcome is SFSS , with probability (.25)(.75)(.25)(.25) and associated X value 3.

There are 15 other outcomes.

We have:

n=4

p=25%

=0.25

​The possible outcomes are {SSSS, SSSF, SSFS, SSFF, SFSS, SFSF, SFFS, SFFF, FSSS, FSSF, FSFS,FSFF, FFSS, FFSF, FFFS, FFFF}

The event SSSS corresponds to X=4.

Also, the events SSSF, SSFS, SFSS, FSSS  corresponds toX=3.

The events SSFF , SFSF, SFFS, FSSF, FSFS, FFSS  correspond toX=3.

Events  FFFS, FFSF,FSFF,SFFF corresponds to X=1 .

Event FFFF corresponds to X=0.

Using “Binomial Probability”, we get:

P(X=k)=4!/k!(4−k)!×0.25k×(1−0.2)4−k

We obtain:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 4

We obtain probability distribution of X as follows:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 4 1

Page 105 Problem 5 Answer

Given – In some parts of California are particularly earthquake-prone.

We suppose that in one metropolitan area,25% of all homeowners are insured against earthquake damage.

Four homeowners are to be selected at random;X will denote the number among the four who have earthquake insurance.

We need to draw the corresponding probability distribution.

We suppose that S denote a homeowner who has insurance and F one who does not.

Then one possible outcome is SFSS , with probability (.25)(.75)(.25)(.25) and associated X value 3.

There are 15 other outcomes.

We note that the width of bars should be same and the length of bars is equal to probability.

We can draw the bars sa follows:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 5 1

We obtain probability distribution histogram as follows:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 5

Page 105 Problem 6 Answer

Given – In some parts of California are particularly earthquake-prone.

We assume that in one metropolitan area,25% of all homeowners are insured against earthquake damage.

Four homeowners are to be selected at random.

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 6

We suppose that X denotes the number among the four who have earthquake insurance.

We will find the most likely value for X.

We note that the most likely value for X is 1.

This is due to the fact that the probability for X=1 is highest.

We can check this by constructing the probability distribution table that X=1:

We conclude that the most likely value for X is 1.

Page 105 Problem 7 Answer

Given that in some parts of California are particularly earthquake-prone.

We assume that in one metropolitan area,25% of all homeowners are insured against earthquake damage.

Four homeowners are to be selected at random; let X denote the number among the four who have earthquake insurance.

We will find the probability that at least two of the four selected have earthquake insurance.

We will use the addition rule.

With the help of the the addition rule for disjoint events, we havet :

P(A or B)=P(A)+P(B) .

Now, adding the possibilities of the corresponding events, we get:

P(X≥2)=P(X=2)+P(X=3)+P(X=4)

=0.2109+0.0469+0.0039

=0.2617.​

We obtained that the probability that at least two of the four selected have earthquake insurance is 0.2617.

Page 105 Problem 8 Answer

We note that probability mass function= probability distribution.

We are given in the question thatY= the number of days beyond Wednesday that it takes for both magazines to arrive.

Hence,

P(Wed.) = .3

P(Thurs.) = .4

P(Fri.) = .2

P(Sat.) = .1

In order to get the probability mass function (pmf) of Y

we note that the possible values of Y are0,1,2,3.

We note that the possible outcomes are:

(W,W),(W,T),(W,F),(W,S)

(T,W),(T,T),(T,F),(T,S)

(F,W),(F,T),(F,F),(F,S)

(S,W),(S,T),(S,F),(S,S)

​For y=0, we get:

P(Y=0)

=P(W,W)

=P(W)×P(W)

=0.3×0.3

=0.09

Fory=1, we get:

P(Y=1)=P[(W,T),(T,W),(T,T)]

=[P(W)×P(T)]+[P(T)×P(W)]+[P(T)×P(T)]

=[0.3×0.4]×[0.4×0.3]+[0.4×0.4]

=0.12+0.12+0.16

=0.4

​Fory=2, we get:

P(Y=2)=P[(W,F),(T,F),(F,W),(F,T),(F,F)]

=[P(W)×P(F)]+[P(T)×P(F)]+[P(F)×P(W)]+[P(F)×P(T)]+[P(F)×P(F)]

=[0.3×0.2]+[0.4×0.2]+[0.2×0.3]+[0.2×0.4]+[0.2×0.2]

=0.06+0.08+0.06+0.08+0.04

=0.32

​Fory=3, we get:

​P(Y=3)=P[(W,S),(T,S),(F,S),(S,W),(S,T),(S,F),(S,S)]

[P(W)×P(S)]+[P(T)×P(S)]+[P(F)×P(S)]+[P(S)×P(W)]+[P(S)×P(T)]+[P(S)×P(F)]+[P(S)×P(S)]

=[0.3×0.1]+[0.4×0.1]+[0.2×0.1]+[0.1×0.3]+[0.1×0.4]+[0.1×0.2]+[0.1×0.1]

=0.03+0.04+0.02+0.03+0.04+0.02+0.01

=0.19

​The probability mass function (pmf) of Y are given below:

P(Y=0)=0.09​

P(Y=1)=0.4

P(Y=2)=0.32

P(Y=3)=0.19

Page 105 Problem 9 Answer

We have three couples and two single individuals.We will denote events as:

Ci={ couple i arrives late }

Aj={ individual j arrives late }

​Here i:1,2,3 and j:4,5, we are given the probabilities Ci,Aj=0.4​.

We assumeX= the number of people who arrive late for the seminar.

We note that the probability mass function (pmf) of a discrete random variable X Is p(x)=P(X:x)

​=P(ω∈S:X(ω):x)

​It is true for every number x .

When none are late, x=0  , we have

p(0)=P(X=0)

​=P(C1′C2′C3′A4′A5′)

​=P(C1′)⋅P(C2′)⋅P(C3′)⋅P(A4′)⋅P(A5′)

=(1−0.4)⋅(1−0.4)⋅(1−0.4)⋅(1−0.4)⋅(1−0.4)

=0.07776

​When one individual arrives late, we have x=1.

So we get

p(1)=P(X=1)

=P[(C1′C2′C3′A4A5′)∪(C1′C2′C3′A4′A5)

=P(C1′C2′C3′A4A5′)+(C1C2C3′A4′A5)

=[(1−0.4)⋅(1−0.4)⋅(1−0.4)⋅(0.4)⋅(1−0.4)]+[(1−0.4)⋅(1−0.4)⋅(1−0.4)⋅(1−0.4)⋅(0.4)]

=0.10368

When x=2, we obtain:

p(2)=P(X:2)

=P(C1′C2′C3′A4A5)+P(C1C2′C3′A4′A5)+P(C1′C2C3′A4A5′)+P(C1′C2′C3A4′A5)

=[(1−0.4)⋅(1−0.4)⋅(1−0.4)⋅(0.4)⋅(0.4)]+[(1−0.4)⋅(1−0.4)⋅(1−0.4)⋅(0.4)⋅(0.4)]+[(1−0.4)⋅(1−0.4)⋅(1−0.4)⋅(0.4)⋅(0.4)]+[(1−0.4)⋅(1−0.4)⋅(1−0.4)⋅(0.4)⋅(0.4)]

​=0.19008​​

When x=3 , we obtain

p(3)=P(X=3)

=P(C1C2′C3′A4A5′)+P(C1C2′C3′A4′A5)+P(C1′C2C3′A4A5′)+P(C1′C2C3′A4′A5)+P(C1′C2C3A4A5′)+P(C1′C2′C3A4′A5)

=6⋅(1−0.4)3⋅(0.4)2

=0.20736

​In the same way, we get:

p(4)=P(X=4)​=3⋅(1−0.4)3⋅(0.4)2+3⋅(1−0.4)2⋅(0.4)3

=0.1728

​Also, we obtain:

p(5)=P(X=5)=6.(1−0.4)2⋅(0.4)3

=0.13824

​In the same way, we get:

p(6)=P(X=6)=(1−0.4)2⋅(0.4)3+3.(1−0.4)⋅(0.4)4

=0.06912

p(7)=2.(1−0.4)⋅(0.4)4

=0.03072

p(8)=(0.4)5

=0.01024

​Summing up, we get rhe probability mass function (pmf) of X as follows:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 9

Page 105 Problem 10 Answer

Using the part a) , we get the pmf of x as :

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 10

We need to calculate P(2≤X≤6).

Using definition, we get the cumulative distribution function (cdf) of Xas follows:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 10 1

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 10 2

In order to get P(2≤x≤6)

​we will use the definition of cdf as follows:

here,P(a≤x≤b)=F(b)−F(a−1)

​SoP(2≤x≤6)=F(6)−F(2−1)

=F(6)−F(1)

=0.95904−0.18144

=0.7776

​We obtain: P(2≤x≤6)=0.7776.

Page 105 Problem 11 Answer

We have:

P(x)=log10(x+1/x)

​Here,x:1,2,3,…,9 is termed as Benford’s law.

We are given that:

P(x)=log10

(x+1/x)

x=1,2,3,…,9

​Now, summing up both sides, we get:

∑P(x)=1

⇒∑x=1/9log10(x+1/x)=1

Hence, using the logarithmic property, we get:

⇒x=1∑9log10(x+1/x)=log10

(1+1/1)+log10(2+1/2)+….+log10(9+1/9)

​=log10(2)+log10(3/2)+log10(4/3)+log10(5/4)+log10(6/5)+log10(7/6)+log10(8/7)+log10(9/8)+log10(10/9)

​=log10[2×3/2×43×5/4×6/5×7/6×87×9/8×10/9]

=log10[10]

=1

​We proved the legitimate probability mass function.

It is,

∑P(x)=1

⇒∑x=1/9log10(x+1/x)=1

Page 105 Problem 12 Answer

For the given data, we need to find the probability of corresponding to X values.

We need to compare with actual solution.

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 12

We will use the pdf of uniform distribution.

We will obtain the individual probabilities using the probability distribution function as follows:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 12 1

The probability distribution value decreases as the corresponding X values increases and the discrete uniform distribution value is constant the corresponding X values are increasing.

Page 105 Problem 13 Answer

The Benford’s law is given as :

p(x)=log10((x+1)/x)

​Here, we note that the leading digits of each number from the given possibilities x=1,2,3…….9.

We need to find the Cumulative Distribution Function (CDF).

We will calculate the probability for each leading digit and by summing up all the probabilities we can arrive at the desired output.

Firstly, we will take the leading number asx=1:

Hence p(1)=log10((1+1)/1)

p(1)=log10(2/1)

​Now, taking the leading number x=2 :

p(2)=log10((2+1)/2)

p(2)=log10(3/2)

Taking the leading numbers of3,4,5… up to 9, we can obtain the required values in the similar way.

We get:

p(3)=log10(4/3)

p(4)=log10(5/4)

p(5)=log10(6/5)

Similarly,

p(6)=log10(7/6)

p(7)=log10(8/7)

p(8)=log10(9/8)

p(9)=log10(10/9)

​Now, we will determine the CDF of the given leading numbers.

We will sum up the individual probabilities from 0 to 9

to get:cdf(1≤x≤9)=p(1)+p(2)+p(3)+p(4)+p(5)+p(6)+p(7)+p(8)+p(9)

Hence, putting the values, we get:

log10(2/1)+log10(3/2)+log10(4/3)+log10(5/4)+log1(6/5)+log1(7/6)+log10(8/7)+log10(9/8)+log10(10/9)cdf(1≤x≤9)=log10(10)=1

​We obtain the Cumulative Distribution Function (CDF) for the given leading numbers as1.

Page 105 Problem 14 Answer

The Benford’s law probability function is given by:p(x)=log10((x+1)/x)

​The leading digits of every number from the given possibilities is x=1,2,3….8,9.

​We will find the probability that the leading digit will be at most3 or P(x≤3) ​and at least 5 or P(x≥5).We will use Benford’s law.

The probability that the leading digit will be at most 3 can be calculated as follows:

P(x≤3): p(x≤3)=p(1)+p(2)+p(3)

=log10(2/1)+log10(3/2)+log10(4/3)

=log10(4)

=0.6020

​The probability that the leading digit is at least 5 is calculated by P(x≥5) :

We get: p(x≥5)=p(5)+p(6)+p(7)+p(8)+p(9)

=cdf(9)−cdf(5)

=1−log10(5)

=0.3010

The probability that the leading digit is at most 3 is0.6020

The probability that the leading digit is at least 5 will be 0.3010.

Page 106 Problem 15 Answer

Given – In the New York city there are six ATM machines for a certain bank.

We suppose X represents number of ATM’s in use at some of point time in a day.

We have: F(x)=0;x<0

=0.06;0≤x<1

=0.19;1≤x<2

=0.39;2≤x<3

=0.67;3≤x<4

=0.92;4≤x<5

=0.97;5≤x<6

=1.00;6≤x

​We will find the respective probability.

We will use the cdf values given above based on the conditions laid.

We will find p(2) as follows:

We will calculate this as follows:

p(2) , that is p(X=2)

p(2)=F(2)−F(1)

=0.39−0.19

=0.20

​The cdf forp(2) is 0.20.

Page 106 Problem 16 Answer

Given – In the New York city there are six ATM machines for a certain bank.

We suppose X represents number of ATM’s in use at some of point time during a day.

So, F(x)=0;x<0

=0.06;0≤x<1

=0.19;1≤x<2

=0.39;2≤x<3

=0.67;3≤x<4

=0.92;4≤x<5

=0.97;5≤x<6

=1.00;6≤x

​We will find the respective probability .We will use the cdf values given above based on the conditions laid.

Using the definition of cdf, we obtain:

p(X>3)=p(X=4,5,6)

=F(6)−F(3)

=1.00−0.67

=0.33

​The probability using the cdf for p(X>3) is 0.33,

Page 106 Problem 17 Answer

Given – In the New York city there are six ATM machines for a certain bank.

We suppose X represents number of ATMs in use at some of point time in a day.

We have F(x)=0;x<0

=0.06;0≤x<1

=0.19;1≤x<2

=0.39;2≤x<3

=0.67;3≤x<4

=0.92;4≤x<5

=0.97;5≤x<6

=1.00;6≤x

​We will find the probability from the cdf values given above based on the conditions required.

We will use the definition of cumulative density function.

We know that p(a≤X≤b)=F(b)−F(a).

Using this, we get:

p(2≤X≤5)=p(X=2,3,4,5)

=F(5)−F(2)

=0.97−0.19

=0.78​

We obtain the probability​p(2≤X≤5) as 0.78.

Page 106 Problem 18 Answer

Given – In a New York city a bank consists of six ATM machines.

We suppose X represents number of ATM’s in use at any instant in a day.

We have F(x)=0;x<0

=0.06;0≤x<1

=0.19;1≤x<2

=0.39;2≤x<3

=0.67;3≤x<4

=0.92;4≤x<5

=0.97;5≤x<6

=1.00;6≤x

​We will calculate the respective probability from the cdf values.

We will use the definition of the cumulative density function.

Using the definition of cumulative density function, we get:

p(2<X<5)=p(X=3,4)

=F(4)−F(2)

=0.92−0.39

=0.53​

For the given data, the probability​p(2<X<5) is equal to 0.53.

Page 106 Problem 19 Answer

Given -X

represents the number of months between successive payments.

We have :F(x)=0;x<1

=0.30;1≤x<3

=0.40;3≤x<4

=0.45;4≤x<6

=0.60;6≤x<12

=1.00;12≤x

​​We will find the pmf of X.

We will use the definition of Probability Mass Function to get the pmf of X.

We need to find Probability Mass Function of X:

Here, we know that X= number of months between successive payments can be taken from the data.

X will be 1,3,4,6 and 12.

Using the definition, we get:

p(1)=F(1)−F(0)

=0.30−0

=0.30

p(3)=F(3)−F(1)

=0.40−0.30

p(4)=F(4)−F(3)

=0.45−0.40

=0.05

​Similarly, we can get the following values:

p(6)=F(6)−F(4)

=0.60−0.45

=0.15

p(12)=F(12)−F(6)

=1.00−0.60

=0.40

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 19

Now, we will sum up the data obtained in the tabular form:

The desired Probability mass function of X is given below:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 19 1

Page 106 Problem 20 Answer

Given -X represents the number of months between successive payments.

We have: F(x)=0;x<1

=0.30;1≤x<3

=0.40;3≤x<4

=0.45;4≤x<6

=0.60;6≤x<12

=1.00;12≤x​

​We will find the values of P(3≤X≤6)

We will also find P(4≤X)

We know that:X= number of months between successive payments can be taken from the data.

Now, we note that X∈{1,3,4,6,12}

Using the definitions, we get:

p(3≤x≤6)

=p(x=3,4,6)

=F(6)−F(1)

=0.60−0.30

=0.30

​We will now find P(4≤X) as follows:

P(4≤X)=1−P(X<4)

=1−(0.40)

=0.60

​The probability for p(3≤x≤6) is 0.30.

The probability for p(4≤x) is 0.60.

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 3 Exercise 3.1 Discrete Random Variables and Probability Distributions

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 3 Discrete Random Variables and Probability Distributions

Page 95 Problem 1 Answer

Given – A concrete beam may fail either by shear(S) or flexure(F). We also have that randomly3

failed beams are selected and the type of failure is determined for each one.

Here,X =the number of beams among the three selected that failed by shear.

We will obtain each outcome in the sample space along with the associated value of X.

We will construct a table to summarize the values obtained.

We have:

X= the number of beams among the three selected that failed by shear

S= shear

F= flexure

We will construct the table as follows :

 OUTCOME  BEAM 1  BEAM 2   BEAM 3      X
     Outcome 1 S      S      S 3
     Outcome 2 S S F 2
     Outcome 3 S F S 2
     Outcome 4 F S S 2
     Outcome 5 S F F 1
     Outcome 6 F S F 1
     Outcome 7 F F S 1
     Outcome 8 F F F 0

 

Hence, we obtained each outcome in the sample space along with the associated value of X.

We get total 8 outcomes in the sample space.

The values of X associated with the outcomes are listed below:

 OUTCOME  BEAM 1  BEAM 2   BEAM 3      X
     Outcome 1 S      S      S 3
     Outcome 2 S S F 2
     Outcome 3 S F S 2
     Outcome 4 F S S 2
     Outcome 5 S F F 1
     Outcome 6 F S F 1
     Outcome 7 F F S 1
     Outcome 8 F F F 0

 

Page 95 Problem 2 Answer

Given -X=the number of nonzero digits in a randomly selected 4-digit PIN that has no restriction on the digits.

We will find the possible values of X.We will also give three possible outcomes and their associated X values.

We note that U.S. zip codes can have 4 digit PIN.

The values of X can be: X = 0,1,2,3,4

This is because the pin can have at most 4 nonzero digits.

We have X= the number of nonzero digits in a PIN.

We are given that there is no restriction on the digits of a pin, the three possible outcomes will be-

9022,when  X=3

2356, when X=4

1000, when X=1

Possible values of X are:

X = 0,1,2,3,4

Possible outcomes are :

​9022, when X= 3

2356, when X=4

1000, when X= 1

Page 95 Problem 3 Answer

Given – The sample space S is an infinite set We have to find that if we have any random variable,X defined from S will have an infinite set of possible values or not.

We will assume any infinite set for the following case.

Here we will suppose that S is a sample space which is an infinite set, where a randomly a number has been selected from N which includes digits 1,3 or 5.

X={1 if a randomly selected number from N includes digits 1 or 3 or 5; 0 otherwise

We see that X has only two values, but the sample space is infinite.

So, it means that it is not necessary that if sample space is an infinite set, then any random variable X defined from that sample spaceS will have an infinite number of possible values.

If the sample spaceS is an infinite set, it is not necessarily imply that any random variable X defined from S will have an infinite set of possible values.

Page 95 Problem 4 Answer

Given – The variable X is the number of unbroken eggs in a randomly chosen standard egg carton.

We will find the set of possible values for the variable .

We will also need to check whether they are discrete or not.

Generally, a standard egg carton contains a dozen of eggs(dozen means12).

It means that standard egg carton has 12 eggs.

Hence, the number of unbroken eggs can be:

{0,1,2,3,4,5,6,7,8,9,10,11,12}

(at most12)(This is because we cannot take any negative or decimals values as it is not possible to have number of eggs in negative or decimal values).

Hence, we get the set of possible values as:

{0,1,2,3,4,5,6,7,8,9,10,11,12}.

We will now check whether the set is discrete or not.

We note that a variable is said to be discrete if it has a specific values and are restricted to separate values like integers or we can say if the variable has counts.

A variable is said to be continuous if it can have any value over a continuous range like decimals, real numbers, etc.

We know the unbroken eggs are restricted to separate values and cannot be a decimal(any value over continuous range).

Hence, we can say that X is discrete or countable.

ForX= number of unbroken eggs in a randomly chosen standard egg carton, the set of possible values are:{0,1,2,3,4,5,6,7,8,9,10,11,12}

The given variable is discrete as it has counts.

Page 95 Problem 5 Answer

Given -Y = the number of students on a class list for a particular course who are absent on the first day of classes.

We will find the set of possible values of students who were absent on the first day of classes and also check if the set is discrete or not.

We will check if the set is countable or not.

Clearly, the number of students will not be negative integer because the count of students cannot be a negative or decimal value.

Hence, the set of possible values will be: {0,1,2,3,4,5,6,7,8,9,……….}

We would have been able to reduce the set to that particular number if we had known the exact number of students.

We know that a variable is said to be discrete if it has a specific values and are restricted to separate values like integers or we can say if the variable has counts.

Also, a variable is continuous if it can have any value over a continuous range like decimals, real numbers, etc.

Hence, the variable is discrete because it has specific values .

It means it has countable number of possible values.

ForY = the number of students on a class list for a particular course who are absent on the first day of class, the set of possible values are:

{0,1,2,3,4,5,6,7,8,9,…………}

The variable is discrete because it has counts.

Page 95 Problem 6 Answer

Given -U =the number of times a duffer has to swing at a golf ball before hitting it.

We will find the set of possible values of the number of times a duffer has to swing at a golf ball before hitting it and also determine if the set is discrete or not.

We will use the definition of discrete random variables.

We note that the number of swings have to be positive integer.

This is due to the fact that a negative or decimal number of swings will not make sense and also 0 is not possible.

The set of possible values is:N={1,2,3,4,5,6,7,…..}

We note that a variable is said to be discrete if it has a specific values and are restricted to separate values like integers or we can say if the variable has counts.

However, a variable is continuous if it can have any value over a continuous range like decimals, real numbers, etc.

Thus, the variable is discrete as it has counts.

ForU= the number of times a duffer has to swing at a golf ball before hitting it, the set of possible values is: N={1,2,3,4,5,6,7,…..}

The variable is discrete because it has counts.

Page 95 Problem 7 Answer

Given that X=the length of a randomly selected rattle snake We will find the set of possible values for the variable.

We will also need to check if it is discrete or not.

Clearly, the length will be positive because negative length is meaningless.

Also the length of a snake is always non zero.

The length will be any integral value and it may take on decimal values .

Hence any non negative integer real number can be the length of a snake.

Therefore, the set of possible values is:{x is a real number∣x>0}.

Now, we will check for discrete.

Using the definition, we have:

Discrete random variable are restricted to defined separated values for example integers or count.

Continuous random variable are not restricted to defined separated values but can occupy any value over a continuous range for example decimal, real or rational numbers.

Hence,X is continuous but not discrete .

This is due to the fact that the length of a snake can take decimal values as well.

ForX =  the length of a randomly selected rattle snake, the set of possible values is:

{x is a real number∣x>0}

Here,x is continuous but not discrete as the length of a snake can take decimal values as well.

Page 95 Problem 8 Answer

Given that Z= the sales tax percentage for a randomly selected amazon.com purchase.

We will find the set of possible values for the variable and also need to check whether it is discrete or not.

We will use the definition of discrete random variables.

Clearly, the sales tax percentage has to be positive, because a negative sales tax percentage is pointless.

A sales tax= 0 can be possible when we do not have to pay any sales tax.

The sales tax percentage can be at most100%, hence the sales tax is between0−100 %.

The sales tax percentage can take on integer values ans decimal values .

Hence any real number between 0 and100 % is possible.

Therefore, the set of possible values are :

{x %∣ x is a real number and 0≤x≤100}

We will now check whether the set is discrete or not.

We know that a variable is said to be discrete if it has a specific values and are restricted to separate values like integers or we can say if the variable has counts.

However, a variable is continuous if it can have any value over a continuous range like decimals, real numbers, etc.

Thus,x is continuous and not discrete.

This is due to the fact that sales tax percentage can take on decimal values as well.

ForZ= the sales tax percentage for a randomly selected amazon.com purchase , the set of possible values is : {x %∣ x is a real number and 0≤x≤100}

Here,x is continuous as sales tax percentage can take on decimal values as well.

Page 95 Problem 9 Answer

Given – Y= pH of a randomly chosen soil sample.

We will find the set of possible values for the variable and whether it is discrete or not.

We will use the definition of discrete random variables.

We know that the pH can only take on values between 0 and 14  including both the numbers.

Hence, the pH can take on integer values and can also take decimal values as well like pH of 2.5,etc.

Thus, any real number as pH can take number between 0 and 14 is possible.

We obtain the set of possible values as : {x∣x is a real number and 0≤x<14}

To determine it is discrete or not :

Now, we have to check if the set is discrete or not.

As we know, a variable is said to be discrete if it has a specific values and are restricted to separate values like integers or we can say if the variable has counts.

But a variable is continuous if it can have any value over a continuous range like decimals, real numbers, etc.

Hence,x is continuous but not discrete as pH can take on decimal values as well.

ForY = the pH of a randomly chosen soil sample, the set of possible values are : {x∣x is a real number and 0≤x<14}

Here,x is continuous but not discrete as pH can take on decimal values as well.

Page 95 Problem 10 Answer

Given that X is the tension (psi) at which a randomly selected tennis racket has been strung.

We need to find the set of possible values for the variable and check whether it  is discrete or not.

We will use the definition of discrete random variables.

This will take values between the minimum possible tension, denoted as M1, and the maximum possible tension, denoted as M2.

Hence, all the possible values are: {x|M1≤x≤M2 , x ϵ R}

The tension can not be negative as a string cannot support negative tension which means, M1, M2 ≥ 0.

Now, we will check whether it is discrete or not.

We note that a variable is said to be discrete if it has a specific values and are restricted to separate values like integers or we can say if the variable has counts.

However, a variable is continuous if it can have any value over a continuous range like decimals, real numbers, etc.

We know that a random variable X that has a countable number of possible values either finite or countable infinite will be termed as discrete random variable.

As the given random variable has uncountable number of possible values,  so we can say that it is not discrete.

For x to be the tension (psi) at which a randomly selected tennis racket has been strung, the set of possible values is: {M1≤x≤M2 , x ϵ R}

As the given random variable has uncountable number of possible values, we can conclude that it is not discrete.

Page 95 Problem 11 Answer

Given – X=the total number of coin tosses required for three individuals to obtain a match (HHH or TTT).

We will describe the set of possible values for the variable, and state whether the variable is discrete.

We haveX= number of coin tosses required until three individuals obtain a match.

Here, don’t know how many coin tosses are required at most, the number of coin tosses required can be any positive integer .

Possible values ={1,2,3,….}=Z+

As the possible values are all integers, we can say that the variable is discrete.

We obtain : Possible values ={1,2,3,….}=Z+

The given variable is discrete.

Page 96 Problem 12 Answer

Given -T= the number of pumps at two pumps in use.

We need to give the possible values for given random variables.

We note that the possible values for T will begin from 0 up to the maximum number of pumps.

We know that the number of pumps at one station =6.

Also, the number of pumps at another station=4.

Hence, the total number of pumps=10.

Using the number of pumps, we can say that the possible values for Random variableT∈[0,10].

Total number of pumps in use or possible values for T are give as: 0,1,2,3,4,5,6,7,8,9,10

Page 96 Problem 13 Answer

Given – X denote the difference between pumps at each station.

We will give the possible values for the given random variable.

For station,  1. Number of pumps ∈{1,2,3,4,5,6}

We will construct a table for differences when the number of pumps are6

for the first stations and 4 for second station.

By taking differences of every pump at every station, we obtain table which is given below:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 14

Looking at the above table we can conclude that the possible values of X will be: {−4,−3,−2,−1,0,1,2,3,4,5,6}

The possible values of X are :{−4,−3,−2,−1,0,1,2,3,4,5,6}

Page 96 Problem 14 Answer

Given – U denote the maximum between pumps at each station.

For1 st station, Numbe of pumps ∈{1,2,3,4,5,6}

For 2nd station,  Number of pumps ∈{1,2,3,4}

We need to give the possible values for the given random variables.We will ta

We will construct a table for maximum pumps when the number of pumps are 6 for the first stations and 4 for the second station.

We obtain : The possible values for the maximum number of pumps at either station is U∈[0,6].

The possible values of U are given below: 0,1,2,3,4,5,6.

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 15

Page 96 Problem 15 Answer

Given – The number of pumps at two pump stations are 6 And 4.

Also,Z=number of stations having exactly two pumps in use.

We will find the possible values of Z.

We will assign the values for the two stations.

The number of stations which have exactly two pumps in use :=2

Hence, the possible values for Random Variable Z∈[0,2].

The possible values for stations having exactly two pumps in use are: 0,1,2.

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 3 Exercise 3.4 Discrete Random Variables and Probability Distributions

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 3 Discrete Random Variables and Probability Distributions

Page 120 Problem 1 Answer

Given -n=25, so X∼Bin(25,.05).We will determineP(X≤2).We will use binomial distribution.

Using the definition, we obtain:

=B(2;25,0.05)

2
=∑ b(y;25,0.05)
y=0

(​25/0​)p/0

P(X≤2) =(1−p)25−0+(​25/1​)p1

=(1−p)25−1+(​25/2​)p2

=(1−p)25−2

0.28+0.36+0.23/0.87.

​We obtain: P(X≤2)=0.87

Page 120 Problem 2 Answer

Given -n=25,X∼Bin(25,.05).We will determine P(X≥5).We will use Binomial distribution.

Using the definition, we get :

P(X≥5) =1−P(X<5)

1−B(4;25,0.05)=

4
∑  b(y;25,0.05)
y=0

1−[(​25/0​)p0

=(1−p)25−0+(​25/1​)p1

= (1−p)25−1+(​25/2​)p2

=(1−p)25−2+(​25/3​)p3

=(1−p)25−3+(​25/4​)p4

(1−p)25−4]

1−0.28−0.36−0.23−0.09−0.03

0.007.

​We obtain: P(X>5)=0.007

Page 120 Problem 3 Answer

Given -n=25, so X∼Bin(25,.05).We will find P(1≤X≤4).We will use the binomial distribution.

Using the data, we obtain:

P(1≤X≤4)=P(X=1,X=2,X=3,X=4)

=B(4;25,0.05)−P(X=0)

4
∑   b(y;25,0.05)−P(X=0)
y=0

=0.28+0.36+0.23+0.09+0.03−0.28

=0.72.

​We obtain: P(1≤X≤4)=0.72

Page 120 Problem 4 Answer

Given -n=25, so X∼Bin(25,.05).

We will find the probability that none of the 25 boards is defective.

We will use the binomial distribution.

Using the binomial probability table, we obtain:

P(X=0)=b(0;25,0.05)​

=0.28

​The required probability is 0.28.

Page 120 Problem 5 Answer

Given -n=25, X∼Bin(25,..05).

We will calculate the expected value and standard deviation of X.

We will use the binomial distribution.

We will calculate the expected value and standard deviation of the random variableX

as follows:

E(X)=np​

=25⋅0.05

=1.25

σX=√np(1−p)

=√25⋅0.05⋅(1−0.05)

=1.09

We obtain: E(X)=1.25

σX=1.09

Page 120 Problem 6 Answer

Given – A particular telephone number is used to receive both voice calls and fax messages.

We suppose that 25% of the incoming calls involve fax messages, and consider a sample of 25  incoming calls.

We will find the probability that at most 6 of the calls involve a fax message.

We will use​b(x;n,p)=​(​n/x​),x=0,1,2,…,n 0px(1−p)n−x, otherwise

​We suppose X= number of incoming calls that involve fax messages.

Here,​X∼Bin(25,0,25)

​So, according to the question, we calculate:

B(6;25,0.25)=P(X≤6)

=0.5611

​The probability that at most 6 of the calls involve a fax message is 0.5611

Page 120 Problem 7 Answer

We supposeX= number of incoming calls that involve fax messages.

As 25% of the incoming calls involve fax messages, so p=0.25.

Also, there are n=25 incoming calls. Hence,X∼Bin(25,0,25)

(Binomial Distribution).We will find the probability that exactly 6 of the calls involve a fax message.

We will use the formula:​b(x;n,p)={​(​n/x​)px(1−p)n−x/0,x=0,1,2,…,n, otherwise

​Using the definition, we calculate:

P(X=6)=b(6;25,0.25)

=(​25/6​)0.256

(1−0.25)25−6

=0.1828

​We obtain the probability of the event that exactly 6 of the calls involve a fax message as:

P(X=6)= 0.1828

Page 120 Problem 8 Answer

Given – A particular telephone number is used to receive both voice calls and fax messages.

Suppose that​25% of the incoming calls involve fax messages, and consider a sample of 25 incoming calls.

We suppose X= number of incoming calls that involve fax message.

We will find the probability that least 6 of the calls involve a fax message is given.

Using the formula:

b(x;n,p)={​(​n/x​)px

(1−p)n−x/0,x=0,1,2,…,n, otherwise

​We get:

P(X≥6)=1−P(X<6)

=1−P(X≤5)

=1−B(5;25,0.25)

=1−0.3783

=0.6217

​Now, the complement of event {X≥6} is event {X<6} ;

So,X takes only non negative values.

We will get the value using Appendix Table A.1.(column 0.25 and row 5).

We obtain the probability that at least 6 of the calls involve a fax message as: 0.6217

Page 120 Problem 9 Answer

We assumeX= number of incoming calls that involve fax messages.

Now, given that 25% of the incoming calls involve fax messages, and consider a sample of 25 incoming calls.

We find more than 6 of the calls involve a fax message by,

B(x;n,p)=P(X≤x)

y=0
∑ b(y;n,p),
x

= ​x:0,1,…,n

​Applying the formula for binomial property:

b(x;n,p)={​(​n/x​)px(1−p)n−x/0,x=0,1,2,…,n, otherwise

​We get:

P(X>6)=1−P(X≤6)

=1−0.5611

=0.4389

​Its complement{X>6} is event {X≤6}

We obtain the probability of more than 6 of the calls involve a fax message :0.4389

Page 121 Problem 10 Answer

Given -90% of all batteries from a certain supplier have acceptable voltages.

A certain type of flashlight requires two type-D batteries, and the flashlight will work only if both its batteries have acceptable voltages.

We will find the probability that at least nine will work? What assumptions did you make in the course of answering the question posed.

We suppose X=the number of working flashlights.

There is 0.9 probability that a battery have acceptable voltage, which indicates that probability of event A={battery with acceptable voltage} is 0.9.

We have:

n=10

p=P(A)∩P(A)

=0.9⋅0.9

=0.81

X≈Bin(25,0.81)​

Using the formula:

b(x;n,p)={​(​n/x​)px

(1−p)n−x/0,x=0,1,2,…,n, otherwise

​We get:

P(X≥9)=0.407

​=P(X=9)+P(X=10)

=b(9;10,0.81)+b(9;10,0.81)

=(​10/9​)0.819

(1−0.81)10−9+(​10/10​)0.8110

(1−0.81)10−10

=0.285+0.122​

We assume that batteries voltages are independent.

We obtain the probability of the event that at least nine will work is: P(X≥9)=0.407

Page 121 Problem 11 Answer

We suppose p= proportion of defective components.

Also X=number of defective components in the sample. Here, n=10 and p is the actual proportion, which implies that X can binomial distribution.

X∼Bin(10,p).Now, we assume A= {the batch is accepted} .

We will find the probability that the batch will be accepted when the actual proportion of defectives is 0.01,0.05,0.10,0.20,0.25.

We know that the batch will be accepted if at most 2 out of 10 are defective.

Hence, the general formula for p is:

P(A)=P(X≤2)

=B(2;10,p)

y=0
∑ b(y;10,p)
2

= When p=0.01

we get: P(X≤2)=B(x;n,p)

=B(2,10,0.01)

=[= BINOM.DIST (2,10,0.01, TRUE )]

≈0.9999

​Whenp=0.05, we get:

P(X≤2)=B(x;n,p)

=B(2,10,0.05)

=[=BINOM⋅DIST(2,10,0.05,TRUE)]

=0.9885

​Whenp=0.1, we get:

P(X≤2)=B(x;n,p)

=B(2,10,0.10)

=[= BINOM.DIST (2,10,0.1, TRUE )]

=0.9298

​Whenp=0.2, we get:

P(X≤2)=B(x;n,p)

=B(2,10,0.20)

=[=BINOM⋅DIST(2,10,0.2,TRUE)]

=0.6778

​Whenp=0.25, we get:

P(X≤2)=B(x;n,p)

=B(2,10,0.25)

=[= BINOM.DIST (2,10,0.25, TRUE )]

=0.5256

​We obtain:

P(A)=

{0.99988,p=0.01

{0.9885,p=0.05

{0.92981,p=0.1

{0.6778,p=0.2

{0.52559,p=0.25​

Page 121 Problem 12 Answer

Given – A graph of P(batch is accepted) as a function of p, with p on the horizontal axis and P(batch is accepted) on the vertical axis, is called the operating characteristic curve for the acceptance sampling plan.

We will use the results of part a) to sketch this curve f.

We will use the graphical utility.

We know that P(A) is a function of p

P(A)=B(2;10,p)

2
∑ b(y;10,p)
y=0

Hence, the exact operating characteristic curve i

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 12 1

The graph of the dat when 0≤p≤1 is:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 12

Page 121 Problem 13 Answer

We need to calculate the probability at the different proportion of defective components such asp=0.01,0.05,0.10,0.20,0.25.

We will put different values of p to obtain the probability.

Now, for p=0.01, we get:

P(X≤1)=B(x;n,p)

=B(1,10,0.01)

=[=BINOM.DIST(1,10,0.01,TRUE)]

=0.9957

​Now, for p=0.05

we get:

P(X≤1)=B(x;n,p)

=B(1,10,0.05)

=[=BINOM⋅DIST(1,10,0.05,TRUE)]

=0.9139

​Now,p=0.10

we get:

P(X≤1)=B(x;n,p)

=B(1,10,0.10)

=[=BINOM⋅DIST(1,10,0.10,TRUE)]

=0.7361​

Now, for p=0.20, we get

P(X≤1)=B(x;n,p)

=B(1,10,0.20)

=[=BINOM⋅DIST(1,10,0.20,TRUE)]

=0.3758

​Now, ifp=0.25

we get:

P(X≤1)=B(x;n,p)

=B(1,10,0.25)

=[=BINOM.DIST(1,10,0.25,TRUE)]

=0.2440

​The probability in tabular form is given below :

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 13 1

The required operating characteristic curve obtained is

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 13

Thus, the given probabilities form a linear curve.

Page 121 Problem 14 Answer

We suppose X= the number of defective components.

We will use binomial distribution to calculate the probability at different proportion of defective components whenp=0.01,0.05,0.10,0.20,0.25.

We will put different values of p to obtain the probability.

Here,n=15.

Whenp=0.01, we get:

P(X≤2)=B(x;n,p)

=B(2,15,0.01)

=[=BINOM⋅DIST(2,15,0.01,TRUE)]

=0.9996

​Whenp=0.05, we get:

P(X≤2)=B(x;n,p)

=B(2,15,0.05)

=[=BINOM⋅DIST(2,15,0.05,TRUE)]

=0.9638

​Whenp=0.10, we get

P(X≤2)=B(x;n,p)

=B(2,15,0.10)

=[=BINOM⋅DIST(2,15,0.10,TRUE)]

=0.8159

​Whenp=0.20, we get

P(X≤2)=B(x;n,p)

=B(2,15,0.20)

=[=BNOM⋅DIST(2,15,0.20,TRUE)]

=0.3980

​Whenp=0.25, we get:

P(X≤2)=B(x;n,p)

=B(2,15,0.25)

=[=BINOM⋅DIST(2,15,0.25,TRUE)]

=0.2361

​The probability in tabular form is given below :

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 14

We obtain the operating characteristic curve as follows:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 14 1

Therefore, the given probability form a polynomial curve.

Page 121 Problem 15 Answer

We need to calculate the probability at different proportion of defective component such asp=0.01,0.05,0.10,0.20,0.25

We will substitute different values of p to obtain the probability and obtain the curve.

We will construct the tables to summarize the data.

We obtain the probability in tabular form of part d as given below:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 15

Using the graphical utility, we get:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 15 1

Now, for part c) we have:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 15 2

Using the graphical utility, we obtain:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 15 3

We observe that the sampling plan of part c is more satisfactory as it has a linear relationship between p and P(acceptance).

Page 121 Problem 16 Answer

We assumeX= number of homes with detectors among 25 samples.

We will find the probability that the claim is rejected when the actual value of p=0.8.It follows binomial distribution X∼Bin(25,p)

.Here we considered the decision rule.We reject the claim that p≥0.8 if x≤15.

We calculate the probability of rejecting claim when the actual valuep=0.8 as follows:

P(X≤15 when p=0.8)

P(X≤15 when p=0.8)

x=0
∑ b(x;25,0.8)
15

x=0
∑(​25/x​)(0.8)x(0.2)25−x
15

Use Excel formula= BINOMDIST (15,25,0.8,1)​

0.017 We obtain the probability that the claim is rejected when the actual value of p Is 0.8 =0.017.

Page 121 Problem 17 Answer

We define X= number of houses with a fire detector.

Here,n=25.

Here, we suppose N={ not rejecting claim when p=0.7} .

We will reject the claim p≥0.8 if x≤15.

We will find the probability of not rejecting the claim when p=0.7.

We will denote N={ not rejecting claim whenp:0.7}.

We will reject the claim when x≤15, so we actually need to calculate probability thatX>15

whenp=0.7.

Hence,P(N)

p=0.7)

=1−B(15;25,0.7)

=P(X>15

=1−0.189

=0.811

​In the same way, whenp=0.6, we have:

P(N) =1−B(15;25,0.6)

=P(X>15 and p:0.6)

=1−0.575

=0.425

​When p is 0.7,

we get P(N)=0.811.

Whenp=0.6, we getP(N)=0.425.

Page 121 Problem 18 Answer

If the value 15 in the decision rule is replaced by 14, the probability that the claim is not rejecting when X:14 and p:0.8 then F(14):0.006.

We will determine how the “error probabilities” of parts (a) and (b) will change if the value 15 in the decision rule is replaced by 14.

We will calculate the following:

P(X>14)=1−P(X≤14 when p=0.7)

=1−x=0

∑  b(x;25,0.7)

15

=1−0.098

=0.902

​We obtain the probability as 0.902.

Page 122 Problem 19 Answer

Given – A toll bridge charges $1.00 for passenger cars and $2.50 for other vehicles.

Suppose that during daytime hours 60% of all vehicles are passenger cars.

We also know 25 vehicles  cross the bridge during a particular daytime period.

We will find the resulting expected toll revenue.

We suppose X= the number of passenger cars; then the toll revenue h(X) is a linear function of X.

We suppose h(X) denote the total revenue.

We get:

h(X)= Revenue from Passenger Vehicles + Revenue from Other Vehicles  =(\) Number of Passenger Vehicles })+(\.50) { Number of Other Vehicles }

=(1)(X)+(2.5)(25−X)

=62.5−1.5⋅X

​By the properties of expected value, we will simplify the formula for the expected value of h(X).

So, E[h(X)]

=E(62.5−1.5⋅X)

=E(62.5)−E(1.5⋅X)

=62.5−1.5⋅E(X)

​Finally, we will use the general formula for the expected value of a binomial random variable to compute the desired expected value.

We get:

E[h(X)]=62.5−1.5⋅E(X)

=62.5−1.5⋅n⋅p

=62.5−1.5⋅(25)⋅(0.6)

=40

​We obtain resulting expected toll revenue as $40.

Page 122 Problem 20 Answer

Given – We have a fixed value of n.

We will determine the values of p for which the variance is zero.

We will use the fact that the variance of constant is zero.

We note that the variance is a quadratic equation in terms of p.

Hence, we determine the roots by factoring.

V(x)np(1−p)=0

=0

⇒p=0 or 1−p=0

⇒p=0 or p=1

​Hence, these values of p gives zero variance.

Reason : if p=0,1 then the outcome of the of every trial is the same.

Basically, a binomial random variable is supposed to have a positive number of trials, that is n>0.

But, when n is fixed as n=0, the variance will be zero for any value of p.

This makes sense because if n=0 there are no trials to measure the variance of, and hence no variance.

We conclude that the variance of a binomial random variable will be zero whenp=0,1

for fixed values of n>0.If n=0, then the variance is zero for all values of probability p.

Page 122 Problem 21 Answer

To find-value of p is V(X) maximized

The critical values of a function i.e. maximized or minimized values occur where the first derivative is equal to zero.

Firstly, we assume that nis fixed and positive.

Now, we note that variance as a function of p, is:V(p)=np(1−p)​(0≤p≤1)

Now, we calculate V′(p):V′(p)

=d/dp[np(1−p)]

=d/dp[np−np2]

=n−2np

​We will substitute V′(X)=0 to get p as follows:

V′(p)n−2np

2np/p=0

=0

=n

=0.5

​We need to verify that p=0.5 is point of maximum.

We need to find the second derivative V′′(X)-V′′(p)

=d/dp[V′(p)]

=d/dp[n−2np]

=−2n

​We will now substitute p=0.5 in  V′′(0.5)-V′′(0.5)=−2n<0( since n>0)

​Hence, by the second derivative test, p=0.5 is a maximum as the second derivative is negative at the critical point.

The value of V(X) is maximum when p=0.5.

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 3 Exercise 3.3 Discrete Random Variables and Probability Distributions

Probability and Statistics for Engineering and the Sciences 8th Edition Chapter 3 Discrete Random Variables and Probability Distributions

Page 113 Problem 1 Answer

Given –

​y ​0 ​1 ​2 ​3
P(y) 0.6 0.25 0.1 0.05

We will find E(Y).

We will use the formula E(Y)=∑yp(y).

Applying the formula of expectation for the given data, we get:

E(Y)=∑yp(y)

=(0×0.6)+(1×0.25)+(2×0.1)+(3×0.05)

=0+0.25+0.2+0.15

=0.6

​We obtain: E(Y)=0.6

Page 113 Problem 2 Answer

Given –

​y ​0 ​1 ​2 x
P(y) 0.6 0.25 0.1 0.05

​We will calculate E(100Y2)

We note from previous part that E(Y2)=57.25.

We have:

E(Y2)=57.25

E(100Y2)=100E(Y2)

=100⋅57.25

=5725

We obtain : E(100Y2)=5725

Page 113 Problem 3 Answer

We are given that P(X=0)=1−p

P(X=1)=p

​We will find E(X2).We will useE(X2)=∑x2p(x).

Applying the formula, we get:

E(X2)=∑x2p(x)=[02×p(0)]+[12×p(1)]=[02×(1−p)]+[12×p]=p

​We obtain: E(X2)=p

Page 113 Problem 4 Answer

Given -P(X:0)=1−p

P(X:1)=p

​We will show that V(X)=p(1−p).

We know the formula V(X)=E(X2)−[E(x)]2.

We know that V (X)

E(X)= E(X − [E(x)]2)2

= ∑xp(x)

Now, = [0 × p(0)] + [1 × p(1)]

= [0 × (1 − p)] + [1 × p]

= p

Putting these values in the above formula, we get:

V (X) = E(X − [E(x)]2)2

= p − [p]2

= p − p2

= p(1 − p)

We showed that V(X)=p(1−p).

Page 113 Problem 5 Answer

Given: If X be the Bernoulli random variable with P(X:0)=1−p

P(X:1)=p

​We will find E(X79)

Using the definition, we obtain:

E(X79)=[079×p(0)]+[179×p(1)]=[079×(1−p)]+[179×p]=p

​We obtain the value:

E(X79)=p

Page 113 Problem 6 Answer

Given – The data:

x           1          2            3            4           5           6

 

p(x)      1/15    2/15     3/15       4/15     5/15    6/15

We also know that owner bought copy for $2.00 and sells it for$4.00.

We will justify whether to buy 3 or 4 copies per week. We will find E(X),

If n=3, we note that owner bought 3copies.

E(x) is the expected value it is given by sum of the products ofx

value with p(x).

Hence, after x=3 multiply p(x) with 3.

Using E(x)=x=1

∑     x⋅p(x)    ∞, we get L E(x)=1(1/15)+2(1/15)+3(1/15)+3(4/15)+3(3/15)+3(2/15)

=41/15

=2.733

​The cost of 3 copies is given as 3(2)=6

We conclude that the owner sells those 3 copies at 4 each.

We suppose the net revenue, R=4(x)−6.

IT is in the form of W=a(x)+b.

Using the formula for expectation, we get:

​E(aX+b)=a(E(x))+b

Here E(x)=E[4(x)−6] here a=4 ,b=6

Hence E(W)=4

E(x)−6

​Using its value from the previous step , we get:

E(x)=41/15

=2.733

Thus,E(W)=4(41/15)−6

≈4.933

​Now, we will suppose n=4.

let us assume that owner bought 4 copies .

We note the formula:

E(x)=n=0

∑ x⋅p(x)

∞ So , the expectation is:

E(x)=n=0

∑     x⋅p(x) ∞

E(x)=1(1/15)+2⋅(1/15)+3(1/15)+4(4/15)+4(3/15)+4(2/15)

=50/15

≈3.33

The cost of 4 copies is given as 4(2.00)=8.00.

Hence, the owner sells those 4 copies at 8 each.

We know that the net revenue is R=4(x)−8.

It is in the form of W=a(x)+b.

Therefore, the expected value of this can be calculated as:

​E(x)=E[4(x)−8]

Here a=4 b=8

Hence E(W)=4

E(x)−8

​Using the previous step, we get:

​E(x)=10/3=3.733 approx

Hence E(W)=4(10/3)−8=5.33 3approx.

​If owner buys 3 copies he will get net revenue of 4.933.

If he if owner buys 4 copies he will get net revenue of 5.333

Clearly, it is better to buy 4 copies.

Page 114 Problem 7 Answer

Given – A random variable x  and x is the random variable for both the batches. so we need to calculate both mean and variance of x .

After computing mean and variance we also need to compute  expected number of pounds after the next customer product is shipped and variance of pound left

We also have that a company has 100 lb of certain chemical in stock The customer orders in 5 lb batches do we are left with100−5X lb’s in total

We will find mean and variance for this.

Using the definition, we get:

E(x)=x=1

∑   xp(x) ∞

=1(0.2)+2(0.4)+3(0.3)+4(0.1)

=2.3

Also, the variance is:

V(x)E(x2)=[E(x2)−E(x)2]

=1(0.2)+2(0.4)+3(0.3)+4(0.1)

=6.1​

Putting the obtained values, we get:

V(x)=[E(x2)−[E(x)]2]

=6.1−(2.3)2

=0.81

​We observe that  100−5X  is in the form of a(x)+b

So, applying the rule:

E(aX+b)=a(E(x))+b=(−5)E(x)+100

Here a=−5

​​b=100

So −5(E(x))+100=−5(2.3)+100

=88.5.

​We also have the rule V(ax)=a2

v(x) for constant v(x)  is zero.

Hence,

V(100−5x)=(−5)2

V(x)=20.25

​We obtain:

E(x)=2.3

V(x)=0.81

E(100−5X)=88.5

V(100−5X)=20.25

Page 113 Problem 8 Answer

We will plot a graph for pm f(x), We are given x and p(x) values.

For the data given , we obtain the graph as:

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 8

We note that for the given x values and p(x) values, in order to plot graph for negative values of x  we will multiply x with−1.

Clearly, the spread of both these graphs is same.

Therefore, we can say that V(X)=V(−X).

Using the spread in graphs, we showed that V (X) = V (−X).

Probability and Statistics for Engineering and the Sciences, 8th Edition, Chapter 3 Discrete Random Variables and Probability Distributions 8 1

Page 114 Problem 9 Answer

Using the formula we can prove that V(X)=V(−X)

We note that the variance is given as V(aX+b)=a{2}

(v(x)) for constant V(x) is zero

According to the formula V(aX+b)=a{2}

V(x) for constant V(x) is zero

Here, inV(−X)

we have: a=−1

b=0

Now, using the formula we can prove that V(x)=V(−x) , It is in the form of V(aX+b) here a=−1 and b=0

ThusV(−x)=(−1){2} V(x).

It is equal to V(x).

Hence,V(x)=V(−x)

With the help of proposition involving V(aX+b), we showed that V(x)=V(−x).

Page 114 Problem 10 Answer

We will prove that V(aX+b)=a2⋅σX2

​We note that:

h(X)=aX+bV(aX+b)

=a2⋅σy2

​We also note that

h(X)=aX+bE[h(X)]

=aμ+b

Here μ=E(X).

The variance of a X+b is given by:V(aX+b)=[E(aX+b)2−(E(aX+b))2]

Using the given data, we obtain:

V (aX + b) = ∑ (x − μ)2 p(x)V (aX + b)


=∑    [E ( (aX + b)2) − (E(aX + b))2]

V (aX + b) = [aX − aμ] ⋅ p(x)

We see that a is constant so taking a out of the summation it becomes a2

Thus, V(aX+b)=a2


∑          (x−(E(x))2)⋅p(x)

V(aX+b)=a{2}

V(x) We showed that V(aX+b)=a2 V(X).

Page 114 Problem 11 Answer

Given -a≤X≤b

We need to show that a≤E(X)≤b.

We will use: E(X)=μx

μx=∑x∈S x⋅p(x)

​Multiplying p(x) in the given inequality, we get:

a≤x≤b

So, a⋅p(x)≤x⋅p(x)≤b⋅p(x)

For all x∈S. This now implies that for the sum over all x∈S, the given inequality is satisfied.

x∈S

∑ a⋅p(x)≤x∈S

∑ x⋅p(x)≤x∈S

∑ b⋅p(x)

We obtain:

x∈S

∑ a⋅p(x) x∈S

∑ b⋅p(x) =a⋅x∈S

∑ p(x) =a⋅1=a;

=b⋅x∈S

∑ p(x) =b⋅1=b;

So, the sum over all x∈S of pmf p(x) is 1

We have that E(X)=x∈S

∑ x⋅p(x)

Hence, using the data obtained, we get: a≤E(X)≤b

Fora≤X≤b we showed that a≤E(X)≤b.

Ring Theory & Vector Calculus Notes

Ring Theory And Vector Calculus

Definition of Homomorphism – Homomorphic Image – Elementary Properties of Homomorphism -Kernel of a Homomorphism – Fundamental theorem of Homomorphism – Maximal Ideals – Prime Ideals.

2 Homomorphism of Rings, Maximal and Prime Ideals
3 Homomorphism of Rings, Maximal and Prime Ideals

In groups, we have learned that one way of knowing more information about a group is to examine its interaction with other groups by using homomorphism.

The concept of homomorphism in rings is analogous to that of homomorphism in groups. The homomorphism in rings is a mapping that preserves the relations a + b = c and ab = d, the addition and multiplication operations.

Definition. (Homomorphism). Let R, R0 be two rings. A mapping f: R → R0 is – said to be a homomorphism if (a)f(a + b) = f(a) + f(b) and (b)f(ab) = f(a)f(b) for all a, b ∈ R.

Note 1. The operations +, · on the left-hand side of the properties (a), (b) are that of the ring R, while the operations +, · on the right-hand side of the properties (a), (b) are that of the ring R0.

Note 2. Since R, R0 are commutative groups under addition clearly property (a) shows that a ring homomorphism is a group homomorphism from (R, +) to (R0, +).

Definition. If f: R → R0 is a homomorphism of a ring R into R0 then the image set f(R) = R¯ = {f(x) | x ∈ R} is called the f – homomorphic image of R.

Definition. Let R, R0 be two rings. A homomorphism f: R → R0 is called an epimorphism or onto homomorphism if f is onto mapping.

A homomorphism f: R → R0 is called a monomorphism if f is one-one mapping

A homomorphism f: R → R0 is called an isomorphism if f is both one-one and onto mapping.

A homomorphism f: R → R of a ring R into itself is called an endomorphism.

A homomorphism f: R → R which is both one-one and onto is called an automorphism.

Notation. 1. If f: R → R0 is an onto homomorphism or epimorphism then R 0 is the homomorphic image of R and we write R ‘ R0.

If f: R → R0 is an isomorphism then we say that R is isomorphic to R 0 or R, R 0 are isomorphic and we write R ∼= R0.

Note:

1. If f: R → R0 is an onto homomorphism then f(R) = R0.

2. If U is an ideal of the ring R, then R/U = {x + U | x ∈ R} is also a ring w.r.t. addition and multiplication of cosets. Then the mapping f: R → R/U defined by f(x) = x + U for all x ∈ R is called the natural homomorphism from R onto R/U.

3. A homomorphism is used to simplify a ring while retaining certain of its features. An isomorphism is used to show that two rings are algebraically identical.

Example 1. Let R, R0 be two rings, and f: R → R0 be defined by f(x) = 00∀x ∈ R, where 00 ∈ R0 is the zero element.

Let a, b ∈ R. Then f(a) = 00, f(b) = 00 and hence f(a + b) = 00, f(ab) = 00.

Then a + b, ab ∈ R.

f(a + b) = 00 = 00 + 00 = f(a) + f(b) and f(ab) = 00 = 00.00 = f(a) · f(b).

∴ f is a homomorphism from R into R0. This is called Zero homomorphism.

Example 2. Let R be a ring and f: R → R be defined by f(x) = x∀x ∈ R.

Let a, b ∈ R so that a + b, ab ∈ R.

By definition, f(a + b) = a + b = f(a) + f(b) and f(ab) = ab = f(a)f(b).

Also for each y ∈ R (codomain), there exists y ∈ R (domain) so that f(y) = y

⇒ f is onto mapping.

Further for a, b ∈ R, f(a) = f(b) ⇒ a = b ⇒ f is one-one mapping.

Hence f is an automorphism. This is called Identity homomorphism.

Example 3. Let Z be the ring of integers and f: Z → 2Z be defined by f(n) = 2n∀n ∈ Z.

Let m, n ∈ Z. Then m+n, mn ∈ Z. Then f(m+n) = 2(m+n) = 2m+2n = f(m) + f(n). But f(mn) = 2(mn) 6= (2m)(2n) = f(m)f(n).

∴ f is not a ring homomorphism.

Although, the group (Z, +) is isomorphic to the group (2Z, +), the ring

(Z, +, ·) is not isomorphic to the ring (2Z, +, ·).

Theorem 1. Let f: R → R 0 be a homomorphism of a ring R into the ring R 0 and 0 ∈ R, 00 ∈ R0 be the zero elements. Then (1) f(0) = 0 f(−a) = −f(a)∀a ∈ R and (3) f(a − b) = f(a) − f(b) for all a,b ∈ R.

Proof. (1) For 0 ∈ R we have 0 + 0 = 0.

∴ f(0 + 0) = f(0) ⇒ f(0) + f(0) = f(0) + 00 ( ∵ f is homomorphism )

∴ f(0) = 00 ( by left cancellation law in R0 ) (2) For a ∈ R there exists −a ∈ R so that a + (−a) = 0.

∴ f(a + (−a)) = f(0) ⇒ f(a) + f(−a) = f(0) = 00. ( ∵ f is homomorphism)

∴ f(−a) = −f(a).

( ∵ f(a),f(−a) ∈ R0 , ring )

(3) For a,b ∈ R; f(a − b) = f(a + (−b)) = f(a) + f(−b) = f(a) − f(b) (f(−b) = −f(b)By(2))

Note 1. A homomorphism maps the zero element of R into the zero element of R0.

2. A homomorphism maps the negative ’ −a ’ of each element a ∈ R into the negative in R0 of the corresponding element a’.

Remark. If the rings R, R’ have unity elements 1, and 10 respectively then it does not necessarily follow that f(1) = 10 is true.

However, if R0 is an integral domain then f(1) = 10 is true.

Theorem 2. The homomorphic image of a ring is a ring.

Proof. Let R, R’ be two rings and f: R → R0 be a homomorphism.

By definition, homomorphic image of R = R¯ = f(R) = {f(x) ∈ R0 | x ∈ R}.

To prove that f(R) is a ring, we show that f(R) = R¯ is a subring of R0.

For 0 ∈ R, f(0) = 00 ∈ R0.

∴ f(0) = 00 ∈ R¯ and hence R¯ ⊂ R0.

Let a0, b0 ∈ R¯.

∴ There exists a,b ∈ R so that f(a) = a’,f(b) = b’.

Since a, b ∈ R we have a − b, ab ∈ R and hence f(a − b), f(ab) ∈ R¯.

Now a’ − b’ = f(a) − f(b) = f(a − b) ∈ R¯ (Theorem (1))

a0b0 = f(a)f(b) = f(ab) ∈ R.¯ (Homomorphism property (2))

Thus a’,b; ∈ R¯ ⇒ a’ − b’,a’b’ ∈ R¯.

∴ R¯ is a subring of R0 and hence R¯ is a ring.

Corollary. The homomorphic image of a commutative ring is a commutative ring.

Proof. Let R be a commutative ring and R¯ = f(R) be its holomorphic image.

a0,b0 ∈ R¯ ⇒ a0b0 = f(a)f(b) where a,b ∈ R

= f(ab) = f(ba) = f(b)f(a) = b0a0.

( ∵ R is commutative )

∴ f(R) = R¯ is commutative.

Theorem 3. If f: R → R0 be an isomorphism from the ring R to the ring R0 then

  1. f(0) = 00 where 0, 00 are the zero elements of R, R’.
  2. For each a ∈ R, f(−a) = −f(a).
  3. R0 is a commutative ring if R is a commutative ring,
  4. R0 is an integral domain if R is an integral domain. and
  5. R0 is a field if R is a field.

Proof. For (1), (2), and (3) see the proof of Theorem (1) of Art 3.1 and its corollary.

(4) Since f(0) = 0′ and f is one-one we have that 0 ∈ R is the only element whose image is 0′ ∈ R’.

∴ Let a’, b’ ∈ R’ and a’ ≠ 0′, b’ ≠0′.

Then there exists a, b ∈ R and a≠0, b 6= 0 so that f(a) = a’, f(b) = b0.

a, b ∈ R, a ≠ 0, b ≠ 0 and R has no zero divisors ⇒ ab ≠ 0.

⇒ f(ab) ≠ f(0) ( ∵ f is one-one )

⇒ f(a)f(b) ≠ 00 ⇒ a0b0 6= 00.

∴ R0 is without zero divisors.

Let 1 ∈ R be the unity element.

Then f(1) ∈ R0 and say f(1) = 10.

For a0 ∈ R0 there exists unique a ∈ R so that f(a) = a0.

For each a0 ∈ R0, a010 = f(a)f(1) = f(a1) = f(a) = a0.

∴ a010 = 10a0 = a0 ⇒ f(1) = 10 is the unity element of R0.

Hence R0 is an integral domain.

(5) If R is a field then

  1. R is commutative,
  2. R has a unity element and
  3. Every non-zero element of R has a multiplicative inverse.

By (3) and (5) R0 is commutative and has unity element 10 = f(1) for 1 ∈ R.

Let a’ ∈ R’ and a’ ≠ 0′. There exists a ∈ R so that f(a) = a0.

a = 0 ⇒ f(a) = f(0) ⇒ a0 = 00 and hence a ≠ 0.

Since R is a field, there exists a− 1 ∈ R so that aa− 1 = 1 = a− 1a.

∴ f a’− 1 = f(1) ⇒ f(a)f a− 1 = 10 = f a− 1 f(a).

Hence f a− 1 = f(a)− 1 is the multiplicative inverse of f(a) = a0.

∴ R’ is a field.

Theorem 4. Let R, R0 be two rings and f: R → R0 be a homomorphism. For every ideal U’ in the ring R’, f1 (U’) is an ideal in R.

Proof. Let U = f1 (U’) = {x ∈ R | f(x) ∈ U0}.

f(0) = 0′ ∈ U’ ⇒ 0 ∈ f1(U’) = U.

∴ U ≠ φ and U ⊂ R.

Let a, b ∈ U. By the def. of U = f− 1 (U’); f(a), f(b) ∈ U’.

U’ is an ideal, f(a), f(b) ∈ U0 ⇒ f(a) − f(b) ∈ U’

⇒ f(a − b) ∈ U’ ⇒ a − b ∈ f− 1 (U’) = U

∴ a, b ∈ U ⇒ a − b ∈ U

Let a ∈ U, r ∈ R. Then f(a) ∈ U’ and f(r) ∈ R’.

Since U0 is an ideal in R’; f(a)f(r), f(r)f(a) ∈ U’

⇒ f(ar), f(ra) ∈ U ⇒ ar, ra ∈ U, ∴ a ∈ U, r ∈ R ⇒ ar, ra ∈ U

Hence U = f1 (U) is an ideal in R.

Note. If S0 is a subring of R0 then f1 (S’) is a subring of R.

Theorem. 5. Let R, R0 be two rings and f: R → R’  be a homomorphism. For every ideal U in R,f(U) is an ideal in R¯ = f(R)

Proof. f(U) = {f(x) | x ∈ U}.

0 ∈ U ⇒ f(0) = 0′ ∈ f(U) ⇒ f(U) 6= φ and f(U) ⊂ f(R).

Let a’,b’ ∈ f(U). ’There exist a,b ∈ U such that f(a) = a0,f(b) = b0.

∴ a’ − b’ = f(a) − f(b) = f(a − b) ∈ f(U) ( ∵ a,b ∈ U and U is an ideal )

Let a’ ∈ f(U) and r’ ∈ f(R) = R¯.

There exist a ∈ U,r ∈ R such that f(a) = a’,f(r) = r’

a ∈ U,r ∈ R and U is an ideal ⇒ ar,ra ∈ U ⇒ f(ar),f(ra) ∈ f(U)

⇒ f(a) · f(r),f(r) · f(a) ∈ f(U) ( ∵ f is homomorphism )

⇒ a’r’,r ‘a’ ∈ f(U)

From (1) and (2): f(U) is an ideal in f(R) = R¯.

Note 1. If f: R → R’ is onto homomorphism then for every ideal U in R,f(U) is an ideal in R’.

2. The above theorem is true for a subring.

Ring Theory And Vector Calculus Kernel Of A Homomorphism

Definition. (Kernel). Let R, R’ be two rings and f: R → R’ be a homomorphism. The set {x ∈ R | f(x) = 0′} where 00 ∈ R’ is the zero element, is defined as the Kernel of the homomorphism f. The kernel of the homomorphism f: R → R’ is denoted by Ker for I(f).

Note 1. If f: R → R’ is a homomorphism then Ker f = f− 1 {00} ⊂ R.

2. For 0 ∈ R we have f(0) = 0′. Therefore 0 ∈ Ker f and hence Ker f 6= φ.

Example 1. Consider the Zero homomorphism f: R → R0 defined by f(x) = 0’∀x ∈ R.

Ker f = {x ∈ R | f(x) = 0′} = {x ∈ R | ∀x ∈ R} = R.

Example 2. Consider the identity homomorphism f: R → R defined by f(x) = x∀x ∈ R.

Ker f = {x ∈ R | f(x) = 0} = {x ∈ R | x = 0 only }( ∵ f(0) = 0) = {0}.

Theorem 1. If f is a homomorphism of a ring R into a ring R’ then Ker f is an ideal of R.

Proof. If 0 ∈ R is the zero element of R then f(0) = 0′ the zero element of R’.

∴ 0 ∈ Ker f and hence Ker f 6= φ, Ker f ⊂ R.

Let a,b ∈ Ker f and r ∈ R. Then f(a) = 0’f(b) = 0′.

f(a − b) = f(a) − f(b) = 0′ − 0′ = 0′ ⇒ a − b ∈ Ker f

f(ar) = f(a)f(r) = 0’f(r) = 0′ and f(ra) = f(r)f(a) = f(r)00 = 00 ⇒ ar,ra ∈ Ker f.

∴ a,b ∈ Ker f,r ∈ R ⇒ a − b ∈ Ker f and ar,ra ∈ Ker f.

Hence Ker f is an ideal of R.

Theorem 2. If f is a homomorphism of a ring R into the ring R 0 then f is an isomorphism if and only if Ker f = {0}.

Proof. Let f be an into isomorphism. That is, f is one-one homomorphism.

We prove that Ker f = {0}.

a ∈ R, f(a) = 0′ ⇒ f(a) = f(0) ⇒ a = 0 ( ∵ f is one-one )

∴ 0 ∈ R is the only element in R so that f(0) = 0′.

∴ By definition, Ker f = {0}.

Conversely, let Ker f = {0}. We now prove that f is one-one.

a, b ∈ R and f(a) = f(b) ⇒ f(a) − .f(b) = 0′ ⇒ f(a − b) = 00

⇒ a − b ∈ Ker f = {0} ⇒ a − b = 0 ⇒ a = b. ∴ f is one-one.

Note. Ker f = {0} ⇔ f is one-one.

Theorem 3. If U is an ideal of a ring R then the quotient ring R/U is a homomorphic image of R. or Every quotient ring of a ring is a homomorphic image of the ring.
Proof.

We know that R/U = {x + U | x ∈ R} is a ring with respect to the addition and multiplication of cosets defined as (a + U) + (b + U) = (a + b) + U and (a + U) : (b + U) = ab + U where a + U, b + U ∈ R/U.

Let f: R → R/U be a mapping defined by f(a) = a + U for all a ∈ R.

For a, b ∈ R, a = b ⇒ a + U = b + U ⇒ f(a) = f(b).

∴ the mapping f is well-defined.

For a, b ∈ R; f(a + b) = (a + b) + U = (a + U) + (b + U) = f(a) + f(b)

and f(ab) = ab + U = (a + U) · (b + U) = f(a) · f(b)

∴ f is a homomorphism.

Let x + U ∈ R/U. Then x ∈ R and for this x ∈ R we have f(x) = x + U.

∴ for each x + U ∈ R/U there exists x ∈ R so that f(x) = x + U.

∴ f is onto mapping.

Hence f: R → R/U is an onto homomorphism.

Note. Ker f = {x ∈ R | f(x) = 0 + U} = {x ∈ R | x + U = 0 + U} = {x ∈ R | x ∈ U} = U.

In view of this result, the above theorem can also be stated as follows: “Every ideal in a ring R is the Kernel of some homomorphism defined on R”.

f: R → R/U is called Canonical homomorphism.

Example  Z6  = {0, 1, 2, 3, 4, 5} under addition and multiplication modulo −6 is a ring.

U = {0, 3} is an ideal of Z6 and Z6/U = {0 + U, 1 + U, 2 + U} = set of 3 elements.

Take Z3 = {0, 1, 2}.

By the correspondence f(0) = 0 + U, f(1) = 1 + U and f(2) = 2 + U; Z 3 and Z6/U are isomorphic.

4 Theorem 4. (Fundamental theorem of homomorphism)

Let R, R’ be two rings and f: R → R ‘ be homomorphism with Kernel U. Then R is isomorphic to R/U.

Proof. a ∈ Ker f = U ⇒ f(a) = 00 where 00 is the zero element of R’.

Since U is an ideal of R, R/U = {x + U | x ∈ R} is the quotient ring of cosets under the addition and multiplication of cosets.

Since f: R → R’ is a homomorphism, f(R) = R¯ is a ring.

That is, for each f(x) ∈ R¯ we have x ∈ R.

Define φ = R/U → R¯ by φ(x + U) = f(x)∀x + U ∈ R/U a + U,b + U ∈ R/U and a + U = b + U ⇔ a − b ∈ U

⇔ f(a − b) = 00 ⇔ f(a) − f(b) = 00 = f(0) ⇔ f(a) = f(b) ⇔ φ(a + U) = φ(b + U).

∴ φ is well-defined and one-one mapping.

Let y ∈ R¯.

Since f: R → R¯ is onto, there exists x ∈ R so that f(x) = y. For this x ∈ R

we have x + U ∈ R/U.

∴ for each y ∈ R¯ there exists x + U ∈ R/U so that φ(x + U) = f(x) = y.

∴ φ is onto mapping.

Let a + U,b + U ∈ R/U. Then a,b ∈ R

φ[(a + U) + (b + U)] = φ[(a + b) + U] = f˙(a + b) (Definition of φ )

= f(a) + f(b) = φ(a + U) + φ(b + U) ( ∵ f is homomorphism )

φ[(a + U)(b + U)] = φ[a.b + U] = f(ab)

= f(a)f(b) = φ(a + U)φ(b + U) ( ∵ f is homomorphism )

∴ φ is a homomorphism.

Hence φ is an isomorphism from R/U to R¯ = f(R).

Note. 1. Every homomorphic image of a ring R is isomorphic to some quotient ring thereof.

2. If f: R → R0 is onto homomorphism from a ring R to the ring R’ and U is an ideal of R then R/U is isomorphic to R0. Then f(R) = R¯ = R0. In the above proof replace R¯ by R0.

3. R¯ = f(R) = the homomorphic image of R.

Let Ψ: R → R/U be the Canonical homomorphism. By fundamental theorem φ: R/U → R¯ is isomorphism.

For x ∈ R we have Ψ(x) = x + U ∈ R/U.

For this x + U ∈ R/U we have φ(x + U) = f(x) ∈ f(R) = R¯

Ring Theory & Vector Calculus Notes Fundamental theorem of homomorphism)

 

Also, for x ∈ R we have f(x) ∈ f(R) = R¯. Therefore, φ · Ψ = f.

Ring Theory And Vector Calculus Solved Problems

Example. 1. Is the ring 2Z isomorphic to ring 3Z?

Solution.

2Z = {2n | r ∈ Z} and 3Z = {3n | n ∈ Z}.

Define f : 2Z → 3Z by f(2x) = 3x∀2x ∈ 2Z. Let 2m, 2n ∈ 2Z.

f(2m + 2n) = f(2(m + n)) = 3(m + n) = 3m + 3n = f(2m) + f(2n)

f(2m.2n) = f(2(2mn)) = 3(2mn) 6= 3m.3n = f(2m)f(2n)

∴ The correspondence f does not preserve multiplication.

∴ Ring 2Z is not isomorphic to ring 3Z.

Example. 2. Let Z4, Z10 be modulo-4 and modulo – 10 rings. If f: Z4 → Z10 is defined by f(x) = 5x∀x ∈ Z4 then prove that f is a homomorphism.

Solution.

Given

Let Z4, Z10 be modulo-4 and modulo – 10 rings. If f: Z4 → Z10 is defined by f(x) = 5x∀x ∈ Z4

Let a, b ∈ Z4.

Let a+b = 4q1+r1 and a·b = 4q2+r2 where 0 ≤ r1 , r2 < 4.

f(a + b) =f (r1) = 5r1 = 5 (a + b − 4q1) =5a + 5b − 20q1 = 5a + 5b( modulo 10) = f(a) + f(b) in Z10.

f(a : b) =f (r2) = 5r2 = 5 (ab − 4q2) = 5ab − 20q2 =5ab(mod10) = 25ab(mod10) = 5a · 5b = f(a) · f(b) in Z10.

∴ Z4 is homomorphic to Z10.

Example. 3. Prove or disprove that f: M2(Z) → Z defined by 

⇒ \(f\left(\left[\begin{array}{ll}
a & b \\
c & d
\end{array}\right]\right)=a \forall\left[\begin{array}{ll}
a & b \\
c & d
\end{array}\right] \in \mathrm{M}_2(\mathrm{Z}) \text { is a ringhomomorphism. } \)

⇒ \(\text { Sol. Let } \mathrm{A}_1=\left[\begin{array}{ll}
a_1 & b_1 \\
c_1 & d_1
\end{array}\right], \mathrm{A}_2=\left[\begin{array}{ll}
a_2 & b_2 \\
c_2 & d_2
\end{array}\right] \in \mathrm{M}_2(\mathrm{Z})\)

where a1,b1,c1,d1,a2,b2,c2,d2

Z. By definition of f,f ( A1) = a1,f ( A2) = a2.

f ( A1 + A2) = f  \( \left(\begin{array}{ll}
a_1+a_2 & b_1+b_2 \\
c_1+c_2 & d_1+d_2
\end{array}\right)\) = a1 + a2 = f ( A1) + f ( A2)

∴ f preserves addition.

f ( A1 · A2) = f  \(\left(\begin{array}{cc}
a_1 a_2+b_1 c_2 & a_1 b_2+b_1 d_2 \\
c_1 a_2+d_1 c_2 & c_1 b_2+d_1 d_2
\end{array}\right)  \) = a1a2 + b1c2 6= a1a2 =

f ( A1) · f ( A2) f does not preserve multiplication.

∴ f is not a homomorphism.

Example. 4. Let Z(√2) = {m + n√2 | m,n ∈ Z} be a ring under addition and multiplication of numbers. Prove that f : Z(√2) → Z(√2) defined by f(m + n√2) = m − n√2∀m + n√2 ∈ Z(√2) is an automorphism. Also, find Ker f.

Solution.

Given

Let Z(√2) = {m + n√2 | m,n ∈ Z} be a ring under addition and multiplication of numbers.

Let a,b ∈ Z(√2) so that a = m1 + n1√2,b = m2 + n2√2 where m1,n1,m2,n2 ∈ Z.

Then we have a+b = (m1 + m2)+(n1 + n2) √2 and ab = (m1m2 + 2n1n2)+ (m1n2 + m2n1) √2. Clearly, f is well-defined.

By definition of f;

f(a + b) = (m1 + m2) − (n1 + n2) √2 = m1 − n1√2 + m2 − n2√2 = f(a) + f(b) and f(ab) = (m1m2 + 2n1n2)−(m1n2 + m2n1) √2 =(m1 − n1√2 m2 − n2√2 )= f(a)f(b)

∴ f is an endomorphism.

a,b ∈ Z(√2); f(a) = f(b) ⇒ m1 − n1√2 = m2 − n2√2

⇒ m1 = m2 and n1 = n2 ⇒ m1 + n1√2 = m2 + n2√2 ⇒ a = b

∴ f is one-one.

Let y = m + n√2 ∈ Z(√2), the co-domain of f.

Then x = m − n√2 ∈ Z(√2), the domain of f, exists so that

f˙(x) = f(m − n√2) = m − (−n√2) = m + n√2 = y.

∴ For each y ∈ Z(√2) there exists x ∈ Z(√2) so that f(x) = y.

∴ f is onto. Hence f is an automorphism.

f(m+n√2) = m−n√2 = 0, zero element of Z(√2) ⇒ m = 0,n = 0 ⇒ m+n√2 = 0, zero element.

∴ ker f = {0}.

Example. 5. Let Zb e is the ring of integers and Zn is the ring of residue classes modulo n. If a mapping f: Z → Zn is defined by f(x) = ¯r∀x ∈ Z where x ≡ r(modn) proves that f is a homomorphism. Also, find Ker f.

Solution.

Given

Let Zb e is the ring of integers and Zn is the ring of residue classes modulo n. If a mapping f: Z → Zn is defined by f(x) = ¯r∀x ∈ Z where x ≡ r(modn)

Let x, y ∈ Z.

By the definition of f; f(x) = ¯r, f(y) = ¯s where x ∼= r(modn) and y ≡ s(modn).

Clearly, f is well-defined.

We know that (1)x ≡ r(modn),

y ≡ s(modn) ⇒ x + y ≡ r + s(modn) and xy ≡ rs(modn) and (2)r + s = r¯ + ¯s, rs = ¯rs¯.

Now f(x+y) = r + s = ¯r+¯s = f(x)+f(y) and f(xy) = rs = ¯rs¯ = f(x)f(y).

∴ f is a homomorphism.

Hence Zn is a homomorphic image of Z.

This is called the natural homomorphism from Z to Zn.

Example. 6. Let C be the ring of Complex numbers and M2(R) be the ring of 2 × 2 matrices. If f : C → M2(R) is defined by f(a + ib) =\(\left(\begin{array}{cc}a & b \\ -b & a\end{array}\right)\) prove that f is an into isomorphism or monomorphism. Also, find Ker f

Solution.

Given

Let C be the ring of Complex numbers and M2(R) be the ring of 2 × 2 matrices. If f : C → M2(R) is defined by f(a + ib) =\(\left(\begin{array}{cc}a & b \\ -b & a\end{array}\right)\)

Let Z1, Z2 ∈ C and Z1 = x1 + iy 1, Z2 = x2 + iy2 where x1, y1, x2, y2 ∈ R.

Then f (Z1) = f (x1 + iy1) = \(\left(\begin{array}{cc}
x_1 & y_1 \\
-y_1 & x_1
\end{array}\right) \) and f (Z2) = f (x2 + iy2) = \( \left(\begin{array}{cc}
x_2 & y_2 \\
-y_2 & x_2
\end{array}\right)\)

f (Z1 + Z2) = f ((x1 + x2) + i (y1 + y2))  = \( \left(\begin{array}{cc}
x_1+x_2 & y_1+y_2 \\
-\left(y_1+y_2\right) & x_1+x_2
\end{array}\right) \)=

⇒ \(\left(\begin{array}{cc}
x_1 & y_1 \\
-y_1 & x_1
\end{array}\right)+\left(\begin{array}{cc}
x_2 & y_2 \\
-y_2 & x_2
\end{array}\right) \) =  f (Z1) + f (Z2)

f (Z1 · Z2) = f ((x1x2 − y1y2) + i (x1y2 + x2y1)) =\( \left(\begin{array}{cc}
x_1 x_2-y_1 y_2 & x_1 y_2+x_2 y_1 \\
-\left(x_1 y_2+x_2 y_1\right) & x_1 x_2-y_1 y_2
\end{array}\right)\)

⇒ \(\left(\begin{array}{cc}
x_1 & y_1 \\
-y_1 & x_1
\end{array}\right) \cdot\left(\begin{array}{cc}
x_2 & y_2 \\
-y_2 & x_2
\end{array}\right)\) = f (Z1) · f (Z2)

∴ f is a homomorphism from C to M2(R).

f (Z1) = f (Z2) ⇒ \(\left(\begin{array}{cc}
x_1 & y_1 \\
-y_1 & x_1
\end{array}\right)=\left(\begin{array}{cc}
x_2 & y_2 \\
-y_2 & x_2
\end{array}\right)\) ⇒ x1 = x2, y1 = y2

⇒ x1 + iy1 = x2+ iy2⇒ Z1 = Z2.

∴ f is one-one.

⇒ \(\text { For }\left(\begin{array}{ll}
a & b \\
c & d
\end{array}\right) \in \mathrm{M}_2(\mathrm{Z}) \text { and }\left(\begin{array}{ll}
a & b \\
c & d
\end{array}\right) \neq\left(\begin{array}{cc}
a & b \\
-b & a
\end{array}\right)\)

there is no complex number a + ib ∈ C satisfying the correspondence.

∴ f is not onto. Hence f is a monomorphism or an isomorphism.

Note. Instead of M2(R) if we take ring of 2×2 matrices = S =\(\left\{\left(\begin{array}{cc}a & b \\ -b & a\end{array}\right) \mid a, b \in \mathrm{R}\right\}\)

then f: C → S will be isomorphism onto. 1 = 1 + 0i ∈ C is the unity in C, and

f(1) = f(1 + 0i) = \(\left(\begin{array}{cc}1 & 0 \\ -0 & 1\end{array}\right)=\left(\begin{array}{ll}1 & 0 \\ 0 & 1\end{array}\right)\) = unit matrix in M2 (R)

f(a + ib) =\(\left(\begin{array}{cc}a & b \\ -b & a\end{array}\right)=\left(\begin{array}{ll}0 & 0 \\ 0 & 0\end{array}\right)\) = zero element in M2(R) ⇒ a = 0, b = 0

⇒ a + ib = 0 + i0 = 0 = Zero element in C.

∴ Ker f = {0}

Example. 7. Let R be the ring of integers and R 0 be the set of even integers in which addition is the same as that of integers and multiplication (∗) is defined by a ∗ b = ab/2∀a, b ∈ R 0. Prove that R is isomorphic to R’.

Solution.

Given

Let R be the ring of integers and R 0 be the set of even integers in which addition is the same as that of integers and multiplication (∗) is defined by a ∗ b = ab/2∀a, b ∈ R 0

We know that R0 = {2n | n ∈ Z} is a commutative group under addition.

Let a, b, c ∈ R0 so that a = 2m, b = 2n, c = 2p where m, n, p ∈ Z.

a, b ∈ R0 ⇒ a ∗ b = (ab/2) = (2m)(2n)/2 = 2(mn) = 2q where q ∈ Z.

∴ ∗ is a binary operation in R0.

∴ ∗ is a binary operation in R0. a, b, c ∈ R0 ⇒ (a ∗ b) ∗ c = ab2 ∗ c = (ab/22)c = ABC 4 = a(bc/2)2 = a ∗ bc2 = a ∗ (b ∗ c)

∴ ∗ is associative in R’.

a, b, c ∈ R’ ⇒ a ∗ (b + c) = a(b2+c) = ab2 + ac2 = a ∗ b + a ∗ c.

Similarly, (b + c) ∗ a = b ∗ a + c ∗ a. ∴ ∗ is distributive over addition.

a, b ∈ R 0 and a ∗ b = ab2 = ba2 = b ∗ a ⇒ ∗ is commutative in R 0.

Hence (R0, +, ∗) is a commutative ring.

Define f: R → R0 by f(x) = 2x∀x ∈ R.. Obviously f is well defined.

Let x, y ∈ R so that x + y, xy ∈ R. Then f(x) = 2x, f(y) = 2y.

Now f(x + y) = 2(x + y) = 2x + 2y = f(x) + f(y) and

f(xy) = 2(xy) = (2x)(22 y) = 2x ∗ 2y = f(x) ∗ f(y).

∴ f is a homomorphism from R into R 0.

x, y ∈ R, f(x) = f(y) ⇒ 2x = 2y ⇒ x = y ⇒ f is one-one.

Let b ∈ R0. Then b = 2a where a ∈ R and for a ∈ R we have f(a) = 2a = b.

∴ for each b ∈ R 0 there exists a ∈ R so that b = f(a) ⇒ f is onto .

Hence f is an isomorphism from R to R 0.

Example. 8. Prove that any homomorphism defined on a field is either isomorphism or zero homomorphism. Or, prove that a field has no proper homomorphic image.

Solution. Let F be a field R be a ring and f: F → R be a homomorphism.

Then we know that Ker f is an ideal of the field F.

Since a field has no proper ideals either Ker f = F or Ker f = {0} where ’ 0 ’ is the zero element of F.

Let Ker f = F.

By definition of Ker f., we have f(x) = 00∀x ∈ F where 00 ∈ R is the zero element.

∴ homomorphic image of F = f(F) = {0′}.

Hence, in this case, f is a zero homomorphism. Let Ker f = {0}.

∴ By theorem (2) Art. 3.2, f is an isomorphism from F to R.

Hence, in this case, a homomorphic image of F = f(F) is also a field.

Example. 9. Prove that Z/hnior Z/nZ is isomorphic to Zn:

Solution. Define f: Z → Zn as f(x) = ¯r∀x ∈ Z.

Then f is a homomorphism

∀r¯ ∈ Zn we have r ∈ Z and for this r ∈ Z, f(r) = ¯r ( ∵ r ≡ r mod n).

∴ f: Z → Zn is onto homomorphism. But Ker f = nZ = hni.

By fundamental theorem; Z/ Ker f ∼= Zn(i.e.,) Z/hni ∼= Zn.

Further, if n is a prime = p then Zp is a field.

Z/hpi ∼= Zp ⇒ that a quotient ring of an integral domain is isomorphic to the field Zp.

Hence a quotient ring of an integral domain may be a field.

Note. Z6= {0, 1, 2, 3, 4, 5} is a ring and U = {0, 3} is an ideal of Z6.

Z6/U = {0 + U, 1 + U, 2 + U} contains only 3 elements.

If we define φ : Z3 → Z6/N as φ(0) = 0 + U, φ(1) = 1 + U, φ(2) = 2 + U, then φ is an isomorphism.

Ring Theory And Vector Calculus Maximal Ideals

The concept of a maximal ideal of a ring is analogous to the idea of the maximum normal subgroup in Group Theory.

Definition. (Maximal Ideal). A maximal ideal M of a ring R is an ideal different from R such that there is no proper ideal U of R properly containing M.

(or)

Let R be a ring and M be an ideal of R so that M 6= R. M is said to be a maximal ideal of R if whenever U is an ideal of R such that M ⊂ U ⊂ R then either R = U or U = M.

Note 1. M is a maximal ideal of R if any ideal U of R containing M is either R or M.

2. An ideal M of a ring R is called a maximal ideal if M is not included in any other ideal of R except R itself. That is, the only ideal that properly contains a maximal ideal is the entire ring.

3. If M is a maximal ideal of the ring R then there exists no ideal U of R so that M ⊂ U ⊂ R

Theorem. 1. In the ring Z of integers the ideal generated by a prime integer is a maximal ideal.

Proof. Let p be the prime integer and M = HPI = pZ = {pn | n ∈ Z} be the ideal generated by p.

Let U be any ideal so that M ⊂ U ⊂ Z

Since every ideal of Z is a principal ideal, U is a principal ideal so U = hqi where q is an integer.

M ⊂ U ⊂ Z ⇒ hpi ⊂ hqi ⊂ Z ⇒ p ∈ hqi ⇒ p = qm, m ∈ Z.

Since p is a prime, either q = 1 or m = 1

m = 1 ⇒ p = q ⇒ hpi = hqi ⇒ M = U; q = 1 ⇒ hqi = Z ⇒ U = Z

∴ M is a maximal ideal.

Note. An ideal generated by a composite integer is not a maximal ideal.

Consider M = h6i = {. . . , −12, −6, 0, 6, 12, . . .}, the ideal generated by composite integer 6.

There exists ideal U = h3i = {. . . , −12, −9, −6, −3, 0, 3, 6, . . .} so that M ⊂ U ⊂ Z

Theorem. 2. If M is a maximal ideal of the ring of integers Z then M is generated by prime integer.

Proof. Let M = hni where n ∈ Z be a maximal ideal of Z.

We prove that n is a prime integer.

If possible, suppose that n = ab where a, and b are prime integers.

Then U = hai is an ideal of Z and U ⊃ M so that M ⊂ U ⊂ Z

Since M is the maximal ideal of Z, by the definition either U = Z or M = U.

Case (1). Let U = Z. Then U = hai = h1i so that a = 1

∴ n = ab = b ⇒ n is a prime integer.

Case (2). Let M = U

Then U = hai = M ⇒ a ∈ M ⇒ a ∈ hni ⇒ a = rn for some r ∈ Z.

∴ n = ab = (rn)b = n(rb) ⇒ 1 = rb ⇒ r = 1, b = 1.

∴ n = a(1) = a ⇒ n is a prime integer.

From cases (1) and (2) we have that n is a prime integer.

Note. 1. For the ring of integers Z, any ideal generated by a prime integer is a maximal ideal.

2. A ring may have more than one maximal ideal. For example, the ring Z has h2i, h3i, h5i, . . . as maximal ideals.

Theorem. 3. An ideal in Z is a maximal ideal if and only if it is generated by a prime integer.

Proof. Write the proofs of Theorems (1) and (2).

Theorem. 4. An ideal U of a commutative ring R with unity is maximal if and only if the quotient ring RU is a field.

Proof. R is a commutative ring with unity and U is an ideal ⇒ the quotient ring R/U = {x + U | x ∈ R} is commutative and has a unity element.

Zero element of R/U = 0 + U = U where 0 ∈ R is the zero element in R.

Unity element of R/U = 1 + U where 1 ∈ R is the unity element in R. It is to be noted that a + U = U zero element of R/U ⇔ a ∈ U

(1) Suppose that U is a maximal ideal of R. We prove that R/U is a field.

To prove that R/U is a field we have to show that every non-zero element of R/U has a multiplicative inverse.

Let x + U ∈ R/U and x + U be non-zero elements. Then x /∈ U

If hxi is the principal ideal of R then hxi + U is also an ideal of R.

( ∵ The sum of two ideals is also an ideal. Ex. 4)

x /∈ U ⇒ U ⊂ hxi + U

Now we have, U ⊂ hxi + U ⊆ R and U is maximal ideal ⇒ hxi + U = R = h1i

⇒ there exists a ∈ U and α ∈ R such that a + xα = 1

∴ 1 + U = (a + xα) + U = (a + U) + (xα + U) (sum of cosets)

= U + (xα + U) = (0 + U) + (xα + U)

( ∵ a ∈ U ⇒ a + U = U)

= xα + U = (x + U)(α + U) (Product of cosets)

∴ for non-zero element x + U ∈ R/U there exists α + U ∈ R/U such that (x + U)(α + U) = 1 + U.

Hence every non-zero element of R/U is invertible.

∴ R/U is a field.

(2) suppose that R/U is a field. We prove that U is the maximal ideal.

Let U0 be an ideal of R so that U 0 ⊃ U and U 0 6= U

Now we show that U0 = R

Since U0 ⊃ U and U0 6= U, there exists α ∈ U0 such that α /∈ U

α /∈ U ⇒ α + U is non-zero element of R/U

R/U is a field of α + U is a non-zero element of R/U

⇒ α + U has multiplicative inverse, say x + U.

∴ (α + U)(x + U) = 1 + U

⇒ αx + U = 1 + U ⇒ 1 − αx ∈ U ⊂ U 0 ( ∵ a + U = b + U ⇒ a − b ∈ U)

x ∈ R, α ∈ U0 and U0 is an ideal ⇒ αx ∈ U0.

αx ∈ U0, 1 − αx ∈ U0 ⇒ αx + (1 − αx) = 1 ∈ U0

∴ 1 ∈ U0 and U 0 is an ideal ⇒ U0 = R.

Hence U is a maximal ideal.

Ring Theory And Vector Calculus Prime Ideals

Definition. (Prime Ideal) An ideal U6= R of a commutative ring R is said to be prime ideal if for all a, b ∈ R and a, b ∈ U ⇒ a ∈ U or b ∈ U.

Example. For an integral domain R, the null ideal is a prime ideal.

∵ a, b ∈ R, ab ∈ h0i ⇒ ab = 0 ⇒ a = 0 or b = 0

Theorem. An ideal U≠ R of a commutative ring R, is a prime idea if and only if R/U is an integral domain.

Proof. Let R/U be an integral domain.

We now prove that U is a prime ideal of R.

∀a, b ∈ R and a, b ∈ U ⇒ ab + U = U ⇒ (a + U).(b + U) = 0 + U

⇒ a + U = 0 + U or b + U = 0 + U (0 + U is the zero element of R/U)

⇒ a ∈ U or b ∈ U.

∴ U is a prime ideal of R.

Conversely, let U be a prime ideal of R.

We now prove that R/U is an integral domain.

a + U, b + U ∈ R/U and (a + U) · (b + U) = 0 + U

⇒ ab + U = 0 + U ⇒ ab ∈ U ⇒ a ∈ U or b ∈ U ( ∵ U is prime ideal)

⇒ a + U = 0 +. U or b + U = 0 + U.

∴ R/U has no zero divisors and hence is an integral domain.

Corollary. Every maximal ideal of a commutative ring R with unity is a prime ideal.

Proof. Let U be a maximal ideal of a ring R.

By Theorem (3) of Art. 3.3; R/U is a field. ⇒ R/U is an integral domain.

By Theorem (4) of Art. 3.3; U is a prime ideal.

Thus every maximal ideal of R is a prime ideal.

Note. 1. The converse of the above corollary is not true.

That is, a prime ideal of a commutative ring with unity need not be a maximal ideal.

Consider the integral domain Z of integers.

The null ideal = h0i of Z is a prime ideal. But the h0i ideal is not the maximal ideal.

There exists ideal = h2i of Z such that h0i ⊂ h2i ⊂ Z and h2i 6= h0i, h2i 6= Z.

Example. If R = {0, 2, 4, 6} is a ring with respect to addition and multiplication modulo 8, then show that M = {0, 4} is a maximal ideal of R but not a prime ideal.

Solution.

Given

R = {0, 2, 4, 6} is a ring with respect to addition and multiplication modulo 8

For 0, 0 ∈ M, 0 − 0 = 0 ∈ M, For 0,4 or 4, 0 ∈ M,

0 − 4 = 8 − 4 = 4 ∈ M, 4 − 0 = 4 ∈ M. For 4, 4 ∈ M, 4 − 4 = 0 ∈ M.

For 0 ∈ M, 0, 2, 4, 6 ∈ R we have 0.0 = 0, 0.2 = 0, 0.4 = 0, 0.6 = 0 ∈ M For

4 ∈ M, 0, 2, 4, 6 ∈ R we have 4.0 = 0 ∈ M, 4.2 = 8 = 0 ∈ M, 4.4 = 16 = 0 ∈ M,

4.6 = 24 = 0 ∈ M.

∴ M is an ideal of R.

U1 = {0, 2, 4} is not an ideal, for, 2, 4 ∈ U1 we have 2 − 4 = −2 = 6 ∈/ U1

U2 = {0, 4, 6} is not ideal, for, 6, 4 ∈ U 2 we have 6 − 4 = 2 ∈/ U2.

∴ There is no ideal U of R such that M ⊂ U ⊂ R.

∴ M = {0, 4} is a maximal ideal.

For 2, 6 ∈ R and 2.6 = 12 = 4 ∈ M does not imply either 2 ∈ M or 6 ∈ M

∴ M is not a prime ideal.

Ring Theory And Vector Calculus Field Of Quotients Of An Integral  Domain

If an integral domain is such that every non-zero element of it has a multiplicative inverse the n it is a field. However many integral domains do not form fields.

Though the integral domain of integers is not a field, it is such that it can be embedded in the field of rational numbers. In this section, we show that every integral domain can be regarded as being contained in a certain field. The minimal field containing an integral domain is called a field of quotients of an integral domain.

Theorem. Every integral domain can be embedded in a field. (or) An integral domain D can be embedded in a field F such that every element of F can be regarded a quotient of two elements of D.

Proof. Let D be an integral domain with at least two elements.

Consider S = {(a, b) | a, b ∈ D, b 6= 0}. Then S 6= φ and S ⊂ D × D.

For all (a, b), (c, d) ∈ S define a relation ∼ on S as (a, b) ∼ (c, d) if and only if ad = bc. We now prove that ∼ is an equivalence relation on S.

(1) For each (a, b) ∈ S we have ab = ba which implies that (a, b) ∼ (a, b).

(2) (a, b), (c, d) ∈ S and (a, b) ∼ (c, d) ⇒ ad = bc ⇒ cb = da ⇒ (c, d) ∼ (a, b).

(3) (a, b), (c, d), (e, f) ∈ S and (a, b) ∼ (c, d), (c, d) ∼ (e, f) ⇒ ad = bc, cf = de.

⇒ (ad)f = (bc)f; cf = de ⇒ (af)d = b(de) = d(be) ⇒ af = be ( ∵ d 6= 0)

⇒ (a, b) ∼ (e, f)

∴ ’ 0 ’ is an equivalence relation on S.

The equivalence relation partitions the set S into equivalence classes which are either identical or disjoint.

For (a, b) ∈ S let a/b denote the equivalence class of (a, b). Then a/b = {(x, y) ∈ S | (x, y) ∼ (a, b)}. If a/b, c/d are the equivalence classes of (a, b), (c, d) ∈ S then either a/b = c/d or a/b ∩ c/d = φ. It is evident that a/b = c/d if and only if ad = bc.

Let F denote the set of all the equivalence classes or the set of quotients.

Then F = {n a/b | (a, b)} ∈ S˙ o.

Since D has at least two elements, say, 0, a ∈ D

we have quotients  \(\frac{0}{a}, \frac{a}{a} \in F and \frac{0}{a} \neq \frac{a}{a}\)

∴ the set F has at least two elements.

For a/b, c/d ∈ F define addition (+) and multiplication ( · ) as

⇒ \(\frac{a}{b}+\frac{c}{d}=\frac{a d+b c}{b d} and \frac{a}{b}, \frac{c}{d}=\frac{a c}{b d}\)

Since D is without zero divisors, b ≠ 0, d ≠ 0 ∈ D ⇒ bd≠ 0

So \(\frac{a d+b c}{b d}, \frac{a c}{b d} \in F\)

Now we prove that the addition and multiplication defined above are well defined.

Let \(\frac{a}{b}=\frac{a^{\prime}}{b^{\prime}} and \frac{c}{d}=\frac{c^{\prime}}{d^{\prime}}. Then a b^{\prime}=a^{\prime} b$ and c d^{\prime}=c^{\prime} d\)

⇒ \(Now (I) \Rightarrow a b^{\prime} d d^{\prime}=a^{\prime} b d d^{\prime} and b b^{\prime} c d^{\prime}=b b^{\prime} c^{\prime} d\)

⇒ \(\text { (2) For } \frac{a}{b}, \frac{c}{d} \in F ; \frac{a}{b}+\frac{c}{d}=\frac{a d+b c}{b d}=\frac{b c+a d}{d b}=\frac{c}{d}+\frac{a}{b} \Rightarrow\)

⇒ \((3) For $u \neq 0 \in D$ we have $\frac{0}{u} \in F$ such that $\frac{0}{u}+\frac{a}{b}=\frac{0 b+u a}{u b}=\frac{u a}{u b}=\frac{a}{b} \forall \frac{a}{b} \in F. \therefore \frac{0}{u} \\)in F is the zero element.

⇒ \( \frac{a d+b c}{b d}=\frac{a^d d+b c}{b^{\prime} d^{\prime}} \)

Also (I) ⇒\( a b^{\prime} c d^{\prime}=a^{\prime} b c^{\prime} d \Rightarrow(a c)\left(b^{\prime} d^{\prime}\right)=\left(a^{\prime} c^{\prime}\right)(b d) \Rightarrow \frac{a c}{b d}=\frac{a^{\prime} c^{\prime}}{b^{\prime} d^{\prime}} \)

∴ The addition and multiplication of quotients are well-defined binary operations on F.

We now prove that (F, +, ·) is a field.

⇒ \(\text { (1) For } \frac{a}{b}, \frac{c}{d}, \frac{c}{f} \in F ;\left(\frac{a}{b}+\frac{c}{d}\right)+\frac{c}{f}=\frac{a d+b c}{b d}+\frac{c}{b}=\frac{(a d+b c) f+(b d) e}{(b d) f}=\frac{a(d f)+(c f+d e) b}{b(d f)}= \)

⇒ \(\frac{a}{b}+\frac{c f+d c}{d f}=\frac{a}{b}+\left(\frac{c}{d}+\frac{c}{f}\right) \Rightarrow \) addition is assocative.

⇒ \(\text { (2) For } \frac{a}{b}, \frac{c}{d} \in F ; \frac{a}{b}+\frac{c}{d}=\frac{a d+b c}{b d}=\frac{b c+a d}{d h}=\frac{c}{d}+\frac{a}{b} \Rightarrow \text { addition is commutative. } \)

(3) For \(u \neq 0 \in D we have \frac{0}{u} \in F such that \frac{0}{u}+\frac{a}{b}=\frac{0 b+u a}{u b}=\frac{u a}{u b}=\frac{a}{b} \forall \frac{a}{b} \in F. therefore \frac{0}{u} \in F\)is the zero element.

(4) Let\(\frac{a}{b} \in F. Then \frac{-a}{b} \in F such that \frac{a}{b}+\frac{-a}{b}=\frac{a b+(-a) b}{b^2}=\frac{0}{b^2}=\frac{0}{u}\left(because 0 u=0 b^2\right)\) therefore every element in F has additive inverse.

(5) For \(\frac{a}{b}, \frac{c}{d}, \frac{c}{f} \in F ;\left(\frac{a}{b}, \frac{c}{d}\right) \cdot \frac{c}{f}=\)

⇒ \(\frac{a c}{b d} \cdot \frac{c}{f}=\frac{(a c) c}{(b d) f}=\frac{a(c)}{b(d f)}=\frac{a}{b} \cdot \frac{c c}{d f}=\frac{a}{b} \cdot\left(\frac{c}{d} \cdot \frac{c}{f}\right)\)

∴ multiplication is associative.

⇒ \(\text { (6) For } \frac{a}{b}, \frac{c}{d} \in F ; \frac{a}{b} \cdot \frac{c}{d}=\frac{a c}{b d}=\frac{c a}{d b}=\frac{c}{d} \cdot \frac{a}{b}\)

∴ multiplication is commutative

(7) for \(u \neq 0 \in D we have frac{u}{u} \in Fsuch that \frac{a}{b} \cdot \frac{u}{u}=\frac{a u}{b u}=\frac{a}{b} \forall \frac{a}{b} \in Ftherefore frac{u}{u} \in F\) is the unity element.

(8) Let\(\frac{a}{b} \in F and \frac{a}{b} \neq \frac{\underline{0}}{u}.\)

⇒ \(Then  a u \neq 0 which implies that a \neq 0 asu \neq 0. therefore b \neq 0 and a \neq 0 \Rightarrow \frac{b}{a} \in F\)

⇒ \(therefore for \frac{a}{b}\left(\neq \frac{a}{u}\right) \in F there exists\frac{b}{a} \in F such that\)

⇒ \( \frac{a}{b} \cdot \frac{b}{a}=\frac{a b}{b a}=\frac{u}{u} \quad(because(a b) u=(b a) u)\)

∴ every non-zero element in F has multiplicative inverse

⇒ \(\text { (9) For } \frac{a}{b}, \frac{c}{d}, \frac{c}{f} \in F ; \frac{a}{b} \cdot\left(\frac{c}{d}+\frac{c}{f}\right)=\frac{a}{b} \cdot \frac{c f+d c}{d f}=\frac{a(c f+d c)}{b(d f)}=\frac{(a c f+a d c)(b d f)}{(b d f) b d f)}\)

⇒ \(\frac{a c f \text { bdf+adebdf }}{(b a d) b d f)}=\frac{a c f}{b d f}+\frac{a d e}{b d f}=\frac{a c}{b d}+\frac{a c}{b f}=\frac{a}{b} \cdot \frac{c}{d}+\frac{a}{b} \cdot \frac{c}{f}\)

Similarly we can prove that \( \left(\frac{c}{d}+\frac{c}{f}\right) \cdot \frac{a}{b}=\frac{c}{d} \cdot \frac{a}{b}+\frac{e}{f} \cdot \frac{a}{b}\)

∴ Multiplication is distributive over addition.

In view of (1), (2), (3), (4), (5), (6), (7), (8) and (9) (F, +, ·) is a field.

Now we have to prove that D is embedded in the field F, that is, we have to show that there exists an isomorphism of D into F.

Define the mapping φ : D → F by φ(a) = ax/x ∀a ∈ D and x(6= 0) ∈ D.

⇒ \(a, b \in D \text { and } \phi(a)=\phi(b) \Rightarrow \frac{a x}{x}=\frac{b x}{\tau} \Rightarrow(a x)^x x=(b x) x\)

⇒ \(\Rightarrow(a-b) x^2=0 \Rightarrow a-b=0 \text { since } x^2 \neq 0 \Rightarrow a=b . \quad therefore \phi \text { is one – one. }\)

⇒ \(\text { For } a, b \in D ; \phi(a+b)=\frac{(a+b) x}{x}=\frac{(a+b) x x}{x x}=\frac{a x x+b x x}{x x}=\frac{a x}{x}+\frac{b x}{x}=\phi(a)+\phi(b)\)

⇒ \(\phi(a b)=\frac{(a b) x}{x}=\frac{(a b) x x}{x x}=\frac{a x}{x}, \frac{b x}{x}=\phi(a) \phi(b)\)

∴ φ is a homomorphism. Hence φ is an isomorphism of D into F.

∴ the integral domain D is embedded in the field F.

Note. 1. Every element in the field F is in the form of a quotient of two elements in D. So, the field F is called the “field of quotients of D ”

2. The equivalence class of (a, b) ∈ S is also denoted as [(a, b)] or [a, b] or (a, b) Then [(a, b)] = [(c, d)] ⇔ ad = bc, [(a, b)] + [(c, d)] = [(ad + bc, bd)], [(a, b)] · [(c, d)] = [(ac, bd)], the zero element of F = [(0, 1)] and the unity element of F = [(1, 1)]

3. If D is the ring of integers then the field F, constructed in the above theorem, would be the field Q of rational numbers.

Ring Theory And Vector Calculus Prime Fields

Definition. A field is said to be prime if it has no subfield other than itself.

The field Zp = {0, 1, 2, . . . , p − 1} where p is a prime and the field Q, of the set of all rational numbers, are prime fields. The field of real numbers R is not a prime field.
We now establish that any field F contains a subfield isomorphic to Zp or contains a subfield isomorphic to Q.

Theorem. 1. If R is a ring with unity element ’ 1 ’ then f: Z → R defined by f(x) = n · 1∀n ∈ Z is a homomorphism.

Proof. Let m, n ∈ Z. Then f(m) = m.1, f(n) = n.1.

f(m + n) = (m + n) · 1 = m · 1 + n · 1 = f(m) + f(n)

Let m > 0, n > 0.

(mn) · 1 = 1 + 1 + . . . + 1(mn times ) = {(1 + 1 + . . . + 1)m times } {(1 +1 + . . . + 1)n times } = (m.1)(n.1) (using Distributivity in the ring R )

Similarly, ∀m, n ∈ Z we can prove that (mn).1 = (m.1)(n.1) using Distributivity.

∴ f(mn) = (mn) · 1 = (m · 1)(n · 1) = f(m)f(n).

Hence f: Z → R is a homomorphism.

Theorem. 2. If R is a ring with unity element ’ I ’ and characteristic of R = n > 0 Then R contains a subring isomorphic to Zn.

Proof. Consider the homomorphism f: Z → R defined by f(m) = m.1∀m ∈ Z.

∴ Ker f is an ideal of Z.

But every ideal in Z is the form hsi = sZ where s ∈ Z.

Characteristic of ring R = n > 0 ⇒ n is the least positive integer such that n.1 = 0.

∴ Ker f = nZ = hni By fundamental Theorem, f(Z) ⊆ R is isomorphic to Z/nz = Z/hni.

But Z/nz = Z/hni is isomorphic to Zn.

∴ f(Z) ⊆ R is isomorphic to Zn.

Theorem.3. If R is a ring with unity element ’ 1 ’ and characteristic of R = 0 then R contains a subring isomorphic to Z.

Proof. Consider the homomorphism f: Z → R defined by f(m) = m: 1∀m ∈ Z.

Characteristic of R = 0 ⇒ m.1 ≠ 0∀m ∈ Z and m≠ 0.

Ker f = {m ∈ Z | f(m) = m.1 = 0} = {0}.

∴ f(Z) ⊆ R is isomorphic to Z.

Corollary. A field F of prime characteristic = p contains a subfield isomorphic to Zp and a field F of characteristic zero contains a subfield isomorphic to Q, the field of rational numbers.

Proof. Let F be the field of characteristic = p, a prime.

Then p is the least positive integer such that p.1 = 0.

∴ Ker f = pZ = hpi,

Hence, by the above theorem; F contains a subfield isomorphic to Zp.

Let F be the field of characteristic = 0.

By the above Theorem; F contains a subring isomorphic to Z.

But the field F contains a field of quotients of Z which is the field Q of rational numbers. Thus we have established that apart from isomorphism the only prime fields are Q and Zp.

Ring Theory And Vector Calculus Multiple Choice Questions:

1. For any two elements a,b in a ring, a(−b) =

(1) −(ab)

(2) ab

(3) −(ba)

(4) None

Answer: 1. −(ab)

2. If (R, +, ·) is a ring then (R, +) is

(1) A group

(2) An abelian group

(3) A finite group

(4) Semigroup

Answer: (2) An abelian group

3. The residue classes modulo 11 with respect to addition and multiplication modulo 11 is

(1) Commutative ring

(2) An integral domain

(3) A field

(4) Skew field

Answer: (3) A field

4. The characteristic of the residue classes mod 8 is

(1) 0

(2) 2

(3) 8

(4) none

Answer: (3) 8

5. If F is a field then the number of ideals in F is

(1) 0

(2) 1

(3) 2

(4) Infinite

Answer: (3) 2

6. If a, b are nilpotent elements in a commutative ring then ab is

(1) Nilpotent

(2) Not nilpotent

(3) Idempotent

(4) Zero

Answer: (1) nilpotent

7. The characteristic of the field of rational numbers is.

(1) 0

(2) ∞

(3) A prime

(4) None

Answer: (1) 0

8. With the usual addition and multiplication, the set of all even integers is

(1) A ring

(2) A field

(3) An integral domain

(4) None

Answer: (1) a ring

9. The number of proper ideals of a field is

(1) 0

(2) 1

(3) 2

(4) None of these

Answer: (3) 2

10. The set a + bi | a, b ∈ Z, i 2 = −1 of Gaussian integers is

(1) Ring

(2) Integral do ain

(3) Field

(4) None

Answer: (1) Ring

11. A commutative ring satisfying cancellation laws is a

(1) Field

(2) Skew field

(3) Integral domain

(4) None

Answer: (3) integral domain

12. In the ring Z of integers the ideal generated by 7 is

(1) Prime ideal

(2) Maximal ideal

(3) Not maximal

(4) None

Answer: (4) none

13. For the homomorphism f : R → R defined by f(x) = x∀x ∈ R Ker f =

(1) {0}

(2) R

(3){0, 1}

(4) none

Answer: (1) {0}

14. In the ring of integers Z, the units are (1) 0,1 (2).1 only (3) 1, −1 only (4) none 17. If a, b are two non-zero elements of a Euclidean ring R and b is a unit in R, then

(1) d(ab) = d(1)

(2) d(ab) > d(1)

(3) d(ab) < d(1)

(4) None

Answer: (3) d(ab) < d(1)

15. If F is a field and f: F → R is a homomorphism so that Ker f = {0} then f is

(1) Isomorphism

(2) Monomorphism

(3) Zero homomorphism

(4) None

Answer: (3) zero homomorphism

16. In the ring Z[i] of Gaussian integers 1 + i is

(1) Unit

(2) Unity element

(3) Prime element

(4) None

Answer: (3) prime element

17. If U is an ideal of ring R with unity 1 such that 1 ∈ U then U is

(1) U

(2) R

(3) ⊆ R

(4) none

Answer: (1) U

18. Let R be a commutative ring with unity and a ∈ R, then U = {ra | r ∈ R} is

(1) left ideal only

(2) ideal only

(3) prime ideal

(4) smallest ideal containing ’ a ’

Answer: (3) prime ideal

19. Every ring of numbers with unity is

(1) integral domain

(2) division ring

(3) field

(4) none

Answer: (1) integral domain

20. The ring R = {a + b√2 | a, b ∈ Q} is

(1) Integral domain

(2) Skew field

(3) Field

(4) None

Answer: (2) Skew field

21. If S1, S2 are two subrings of a ring R then S1 + S2 is

(1) Subring

(2) Ideal

(3) Need not be a subring

Answer: (3) need not be a subring

22. A subring S of a ring R is called ideal if

(1) α ∈ S, a ∈ R ⇒ αa ∈ S

(2) α ∈ S, a ∈ R ⇒ aα ∈ S

(3) α ∈ S, a ∈ R ⇒ αa, aα ∈ S

(4) none

Answer: (1) α ∈ S, a ∈ R ⇒ αa ∈ S

23. The set Q of rational numbers is

(1) subring

(2) ideal

(3) not subring

(4) not ideal, for the ring of real numbers

Answer: (1) subring

24. A homomorphic image of an integral domain is

(1) a ring

(2) integral domain

(3) need not be an integral domain

(4) none

Answer: (1) a ring

25. If R is a non-zero ring so that a2 = a∀∈ R then the characteristic of R =

(1) 0

(2) 1

(3) 2

(4) Prime

Answer: (3) 2

26. If the characteristic of a ring R is 2 and a, b ∈ R then (a + b) 2 =

(1) a2 + 2ab + b2

(2) a2 + ab + ba + b2

(3) a2 + b2

(4) none

Answer: (1) a2 + 2ab + b2

27. If p is a prime, the ring of integers modulo p is.

(1) Field

(2) Integral domain

(3) Skew field

(4) None

Answer: (1) Field

Ring Theory & Vector Calculus Fill In The Blanks:

1. If R is a ring without zero divisors then hold in R.

Answer: Cancellation laws

2. A ring R has no zero divisors if

Answer: There exist a, b ∈ R and ab = 0 ⇒ a = 0 or b = 0

3. A division ring has divisors.

Answer: No zero-divisors

4. A finite integral domain is a

Answer: Field

5. In a ring R if a2 = a for a ∈ R0 a0 is called w. r. t. multiplication.

Answer: Idempotent element

6. a ∈ 0 ∈ R, R is a ring, is called nilpotent element if there exists

Answer: There exist a, b ∈ R and ab = 0 ⇒ a = 0 or b = 0

7. If characteristic of a ring R = 2 and a, b ∈ R commute then (a − b)2 =

Answer:  n ∈ N so that an = 0 46. a2 + b2

8. A subring of (Z6, +6, × 6) is

Answer: {0, 3}

9. A field has ideals.

Answer: Need not be an ideal

10. The union of two ideals of a ring R, of R.

Answer: No proper

11. For a field every ideal is

Answer: Need not be an ideal

12. A subring of (R, +, ·) which is not an ideal is

Answer: A principal ideal

13. In the quotient ring R/U the zero element is and the unity element is

Answer: (Q, +, ·) 52. U, 1 + U

14. An ideal U of a ring R is a prime ideal if.

Answer: U ⊂ U 0 ⊂ R

15. For the ring of integers any ideal generated by a prime integer is a

Answer: an ideal of R

16. For a commutative ring R, with unity if U is a maximal ideal then R/U is a

Answer: Maximal ideal

17. If f: R → R’ is a ring isomorphism and R is an integral domain then R’ is

Answer:  associates