Solution Manual for Statistics for Business and Economics 8th Edition by Newbold

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Statistics for Business Business & Economics, 8 edition

c. Categorical, nominal (The response is categorical categorical because the responses can be grouped into classes or categories, in this case yes/no. yes/no. The measurement levels are nominal because the responses are words that describe the categories.) d. Categorical, ordinal (The response is categorical because the responses can be grouped into classes or categories. The measurement levels are ordinal because these are rankings.) 1.6 a. b. c. d.

Categorical – Qualitative – nominal Numerical – Quantitative - discrete Categorical – Qualitative – nominal: yes/no response Categorical – Qualitative – ordinal

a. b. c.

An example of ordinal responses: Benefits (1 to 5) An example of nominal nomina l responses: Gender (male/female– 1/2) An example of a numerical variable: Time (in seconds)

1.7

1.8 a. Various answers – Categorical variable with ordinal ordinal responses: PIR_grp (1-5) b. Various answers – Categorical variable with nominal responses: responses: female (male/female – 0/1) c. Various answers – Numerical variable with continuous responses: responses: daily_cost d. Various answers – Numerical variable with discrete discrete responses: PIR_p 1.9 a. Pie chart Breakdow n o f Travel Expenses Expenses

Other 7% Fees 20%

Food 17%

Transportation 31%

Lodging 25%

Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall.

Chapter Chapter 1: Describing Data: Graphical

b. Bar chart Breakdown of Travel Expenses Expenses 35% 30% 25%

t s o C 20% f o t n 15% e c r e P 10%

5% 0% Tr ans por t

Lodging

Food

Fees

Other

1.10 Pareto diagram – possible defects for a product line Pareto Chart of Defect Code_Ex1.10 200

100

80

150 t n u o C

60 100 40 50

0 Defect Code_Ex1.10 Count Percent Cum %

20

D 90 45.0 45.0

B 70 35.0 80.0

C 15 7.5 87.5

A 10 5.0 92.5

E 8 4.0 96..5 96

Other 7 3.5 100 10 0.0


0

t n e c r e P

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1.11 a. Bar chart Bank Customer Customer Satisfaction 80 70

s r e 60 m o t 50 s u C f 40 o r 30 e b m20 u N10

0 Very Satisfied

Moderately Satisfied

No Opin Opinion ion

Moderatel oderately y Dissatisfied

Very Dissatisfied

b. Pie chart



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1.13 Pie chart – U.S. federal budget spending Pie Chart of % U. S. Federal F ederal Budget Spending Spending

Deposit insurance Other fed op erations erations 1.0% 6.0% Entitlements 46.0%

Interest on debt 14.0%

Category Entitlements Defense Grants to states states Interest on debt Other fed operations Deposit insurance

Grants to states states 15.0%

Defense 18.0%


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1.14 a. Bar chart of the number of endangered end angered wildlife species in the United States Endangered Wil dlife i n the United States States 80 70 60

s e i c 50 e p S f 40 o r e b 30 m u N

20 10 0 Mammals

Bir ds

Reptiles

A mphibians

Fis hes

b. Bar chart of the number of endangered wildlife species outside outside the United States Endangered Wildl ife outside the United States 300 250 s e 200 i c e p S f 150 o r e b m100 u N

50 0 Mammals

Birds

Reptiles

A mphibians


Fis hes


c. Bar chart to compare the number nu mber of endangered species in the United States to the number of endangered species outside the United States St ates Endangered Wild life 300 250 s e i c 200 e p S f 150 o r e b m100 u N

In the United States Outside the United States

50 0

l s m a a m M

s i t l e p R e

s r d i B

n s a i b i h p A m

e s h s i F

1.15 a. Pareto diagram of most serious errors by all players players Pareto Chart of Error 50 100 40 80 t n u o C

30 60 20

40

10

0 Error Count Per cent Cum %

20

C 12 26.7 26.7

B 10 22.2 48.9

D 10 22.2 71.1

E

A

7 15.6 86.7

6 13.3 100.0

0


t n e c r e P

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b. Pareto diagram of most serious errors by male players Pareto Chart of Error by Gender Gender Gender = Male

t n u o C

25

100

20

80

15

60

10

40

5

20

0 Error Count Percent Cum %

C 10 40.0 40.0

E

B

6 24.0 64.0

4 16.0 80.0

A 3 12.0 92.0

D

t n e c r e P

0

2 8.0 100.0

c. Pareto diagram of most serious errors by female players players Pareto Chart of Error by Gender Gender = Female 25

20

t n u o C

100 80

15

60 10 40 5

0 Error Count Percent Cum %

20

D

B

8 40.0 40.0

6 30. 0 70.0

A 3 15.0 85.0

C 2 10.0 95.0

Other 1 5.0 100.0


0

t n e c r e P


d. Component (stacked) bar chart of errors errors and gender gender Chart of Gender, Error Error A B C D E

25

20

t n u o C

15

10

5

0 Gender

Female

Male

1.16 Describe the data graphically


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Statistics for for Business & Economics, 8 edition

1.17 a. Cluster bar chart of the respondents’ major and gender Student Majors 16 14 12

s t n e d 10 u t S f 8 o r e b 6 m u N

Male Female

4 2 0

Acc ounting

Financial

Management Management

Marketing

Undecided Undecided

Major

b. Pie chart of their majors


Chapter 1: Describing Data: Graphical

1.18 a. Pie chart of the percent percen t of males in each of the activity level categories.

b. Pie chart of the percent of females in each of the activity level categories.


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1.19 a. Pie chart for worldwide market shares for February 2011.

b. Answer will vary: Pie Pie chart for worldwide market shares for October 2011.



c. Answers will vary; vary; Pie chart for Asian market shares for October 2011.

1.20 Customers at a New Mall Mall 700 600 s r e 500 m o t s u 400 C f o r 300 e b m u 200 N

100 0 Monday

Tues day

W ed ednes day

Thurs day

Friday

Day


Sat urday

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1.21 Time-series plot of number of males and females females enrolled in colleges (undergraduate and postbaccalaureate) in the United States from 2000 through 2008.

1.22 a. Time-series plot of Gross domestic investment and Gross Gross private domestic



1.23 a. Time-series plot of the Gross Domestic Product in Manufacturing in cu rrent and real (2005) dollars by industry for durable goods from from 2000 to 2009.

b. Time-series plot of Gross Domestic Product in Manufacturing Manufacturing in chained dollars (2005) by industry for nondurable goods from 2000 to 2009.


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1.24 a. The Euro (EUR) compared to 1 U.S. Dollar (USD)

b. The GBP compared to 1 U.S. Dollar (USD)

c. Answers may vary.



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1.25 a. Time-series plot of worldwide market shares of IE, Firefox, Chrome, Safari, and Opera.

b. Time-series plot of European market shares of IE, Firefox, Chrome, Safari, and Opera.


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c. Time-series plot of North American market shares of IE, Firefox, Chrome, Safari, and Opera.

1.26 Time-series plot of a stock market index (Dow Jones J ones Industrial Average) Average) over 14 years from the internet



1.27 Time-series plot of Gold Price data Time Series Seri es Plot of PRICE ($) 600

500

) $ 400 ( E C I R P 300

200

100 1

2

3

4

5

6

7 8 YEAR

9

10

11

12

13

14

1.28 Time-series plot of Housing Starts data Time Time Series Seri es Plot of S TARTS 12 11 10 9 S T R A T S

8 7 6 5 4 2

4

6

8

10

12 14 YEAR

16

18

20

22

24


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1.29 Time-series plot of Earnings per Share Time Series P lot of EARNINGS 90 80 70 60

S G N I N 50 R A E

40 30 20 10 3

6

9

12

15 YEAR

18

21

24

27

1.30 a. b. c. d. e.

5 – 7 classes 7 – 8 classes 8 – 10 classes 8 – 10 classes 10 – 11 classes

1.31 a. w cl asswi dt h =

Lar Lar gest gest Obse Obserr vati vati on- S mall est est Obse Obserr vati vati on



Nu mber Of Cl ass es

85 20 8 (roun round d down down) 8 1 90 3 0 20 8 230 40 24 (round up ) 8 5 00 1 4 0 45 8 



b. w











c. w



d. w











1.32 a. frequency distribution Bin 10 20 30 40 50 60 70

Frequency 0 5 3 8 3 5 4

More

0

b. histogram and c. ogive Histogram

y c n e u q e r F

9 8 7 6 5 4 3 2 1 0

120.00% 100.00% 80.00% 60.00% 40.00% 20.00% 0.00% 10

20

30

40

50

60

70

Mo More

Bin

Frequency

Cumulative %

d. stem-and-leaf display Stem-and-Leaf Di spl ay: Data_Ex1.32 Data_Ex1.32 St em- andand- l eaf of Dat a_Ex1. a_Ex1. 32 Leaf Leaf Uni t = 1. 0 2 5 7 8 9 ( 6) 13 9 9 7 4 2

1 1 2 2 3 3 4 4 5 5 6 6

N

= 28

23 557 14 8 2 567 56779 799 9 0144 14 699 24 55


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1.33 Stem-and-leaf display Stem-and-Leaf Displ ay: StudyHrs _Ex1.33 _Ex1.33 St em- and and- l eaf eaf of of St St udyHr s_Ex1. s_Ex1. 33 Leaf Leaf Uni t = 0. 10 1 1 3 7 8 ( 4) 8 7 5 3 2 1

1 1 2 2 3 3 4 4 5 5 6 6

N

= 20

0 34 6889 0 566 5669 9 4 58 02 5 2 7

1.34 Classes 0<10 10<20 20<30 30<40 40<50 Total

Frequency 8 10 13 12 6 49

a. Relative Frequency 16.33% 20.41% 26.53% 24.49% 12.24% 1 2.24% 100.00%

b. Cumulative Frequency 8 18 31 43 49

c. Cumulative Relative Frequency 16.33% 36.74% 63.27% 87.76% 100.00%

1.35 Scatter plot



1.36 a. Construct a cumulative relative frequency distribution Age 18-24 25-34 35-44 45-54 55+ Total

b. c.

Relative Cumulative Frequency 11.30% 30.41% 54.05% 77.53% 100.01%

Percent 11.30% 19.11% 23.64% 23.48% 22.48%

54.05% 69.59%

1.37 For the file Water - construct a frequency distribution, cumulative frequency distribution, histogram, ogive and stem-and-leaf display. Various answers – one possibility is to use 8 classes classes with a width of .1. Bin Frequency Frequency Cum Freq 0 3.5 0 1 3.6 1 9 3.7 8 39 3.8 30 61 3.9 22 73 4 12 74 4.1 1 75 4.2 1 More

0

Histogram

y c n e u q e r F

35 30 25 20 15 10 5 0

120.00% 100.00% 80.00% 60.00% 40.00% 20.00% 0.00%

Bi n

Frequency

Cumulative %


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Stem-and-Leaf Display: Volume St em- andand- l eaf of Vol Vol umes Leaf Leaf Uni t = 0. 010 010 1 3 9 21 ( 17) 37 24 15 7 2 2 1

35 36 36 37 37 38 38 39 39 40 40 41

N

= 75

7 34 577799 111122344444 5556677777 555667777778899 7889999 99 0111112222244 556677899 01334444 56689 6 1

1.38 a. Histogram of the Returns data

Histogram 8 7 y 6 c n 5 e u 4 q e 3 r F 2 1 0

120.00% 100.00% 80.00% 60.00% 40.00% 20.00% 0.00% 100 100 150 150 200 200 250 250 300 300 350 350 400 400 More ore Bi n Freq Freque uenc ncy y

Cumu Cumullati ative %

b. stem-and-leaf display Stem-and-Le Stem-and-Leaf af Disp lay: Returns St em- and and- l eaf eaf of Ret Ret urns Leaf Uni t = 10 1 4 11 ( 3) 11 4 1 1 1

1 1 2 2 3 3 4 4 5

N

= 25

3 899 0014444 589 589 0000122 689 0



1.39 Describe the data in the Sun data file.

Histogram 40

120.00% 100.00% 80.00% 60.00% 40.00% 20.00% 0.00%

y 30 c n e u 20 q e r 10 F

0 224.5 224.5 229.5 229.5 234.5 234.5 239.5 239.5 244.5 244.5 249.5 249.5 More More Bi n Freq Frequ uency ency

Cumu umulativ lative e%

Stem-and-Le Stem-and-Leaf af Displ ay: weight s St em- andand- l eaf of wei ght ght s Leaf Leaf Uni t = 1. 0 1 1 5 16 25 38 ( 17) 45 32 20 9 4 2

22 22 22 23 23 23 23 23 24 24 24 24 24

N

= 100 100

4 8999 11111111111 222223333 4444444445555 6666666667 666666666777777 7777777 77 8888888888999 000000111111 22222222333 44455 67 99

1.40 Scatter plot


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1.41 Scatter plot

1.42 Scatter plot



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1.43 Scatter plot plot of price of orange juice juice vs. weekly quantities sold sold – Bishops file

data

Scatter plot – Bishop’s Supermarket

Weekly quantities sold - Orange Juice Bishops Supermarket Supermarket

3.0

e c i r P 2.5

2.0 200

300

400

500

Quantity

The scatter plot shows the expected negative n egative relationship between price and quantity demanded per unit of time. This is what economic theory suggests suggests for a typical demand curve.


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1.44 Acme Delivery – relation relation between shipping shipping cost and number of of delivery

days

Shipping Cost versus Number of Delivery Days Acme Deliv Delivery ery 10 9 8 7 6

s y a 5 D

4 3 2 1 0 3

4

5

6

7

8

9

10

Shipping Shipping Cost Cos t Maine to the west coast

The relationship appears to be negative; however there is significant variability in delivery time at each of the three shipping costs – regular, $3; fast, $5; and an d lightning, $10. 1.45 a. Row percentages are listed listed below Rows: Dayweek

1 2 3 4 5 6 Al l

Col umns: Sl sL evel

1

2

3

4

6 29 29 28 18 7 117

4 27 24 14 27 21 117

27 11 12 23 19 24 116

41 11 14 11 15 26 118

Al l 78 78 79 76 79 78 468

RowPct s . 1667 . 1667 . 1688 . 1624 . 1688 . 1667

b. Major differences in sales level by day of week indicate that sales levels are highest for days 1 and 6. This is where the largest number of Sales Level ‘4’ occur (the largest daily sales). Days 2 and 3 are similar with most of the sales in the lowest two levels. Days 4 and 5 have sales that are building towards the two largest days of 6 and 1.



1.46 Scatter plot of Citydat – taxbase versus comper Scatterplot of taxbase vs Comper Comper 1600000 1400000 1200000 e s a b x a t

1000000 800000 600000 400000 200000 0 0.00

0.05

0.10

0.15 Comper

0.20

0.25

0.30

No relationship between the two variables and hence no evidence that emphasis on attracting a larger percentage of commercial property increases the tax base. The two outlier points on the t he right side of the plot might be used to argue that a very high percentage of commercial commercial property will provide a larger larger tax base. That argument, however, is contrary to the overall pattern of the data. 1.47 a. Misleading histogram histogram of the data (Error: Unequal class widths) Histogram

y c n e u q e r F

35 30 25 20 15 10 5 0

Bi n

Frequency

b. Errors include unequal class widths. The first class intervals have widths of 10 whereas later class class intervals have widths of 20 and even 50. The classes also overlap. This graph does not accurately accurately reflect the data.


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1.48 a. Time-series plot with vertical scale scale from 5000 to 5700. Time Series Plot of N um umber_Ex1.48 ber_Ex1.48 5700 5600 5500

8 4 . 1 5400 x E _ r e b 5300 m u N

5200 5100 5000 Month Jan Year 2006

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Nov

Dec

b. Time-series plot with vertical scale from 4000 to 7000. Time Tim e Ser ies Plot of Number_Ex1.48 Number_Ex1.48 7000

6500

8 6000 4 . 1 x E r_ 5500 e b m u N 5000

4500

4000 Month Jan Year 2006

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

c. Differences between the two graphs include the variability of the data series. One graph suggests greater variability in the data series while the other one suggests a relatively flat line with less variability. variability. Keep in mind the scale on which the measurements are made.



1.49 a. Time-series plot of Exchange Rates with vertical axis from 92 to 106. Time Time Seri es Plot of VALUE 106 104 102 E 100 U L A V 98

96 94 92 Jan

Feb

Mar

Apr

May

Jun Jul Month

Aug

Sep

Oct

Nov

Dec

b. Time-series plot of Exchange Rates with vertical axis from 75 to 120. Time Time Seri es Plot of VALUE 120

110

E 100 U L A V

90

80 Jan

Feb

Mar

Apr

May

Jun Jul Month

Aug

Sep

Oct

Nov

Dec

c. Differences between the two graphs include the variability variability of the the data series. One graph suggests greater variability in the data series while the other one suggests a relatively flat line with less variability. variability. Keep in mind the scale on which the measurements are made.


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1.50 Draw two time-series plots for Inventory Sales with different vertical ranges. Time Time Ser ies Pl ot of RATIO 1.60

1.55

1.50 O I T 1.45 A R

1.40

1.35

1.30 1

2

3

4

5

6 7 Index

8

9

10

9

10

11

12

Time Series Plot of RATIO 2.00

1.75

O I T A R

1.50

1.25

1.00

1

2

3

4

5

6 7 Index

8

11

12

Differences between the two graphs include the variability variability of the data series. One graph suggests greater variability in the data series while the other one suggests a relatively flat line with less less variability. Keep in mind the scale on which the measurements are made.



1.51 Describe graphically the time (in hours) that 20 students studied. Histogram of Hours_Ex1.5 Hours_Ex1.5 1 5

4 y c 3 n e u q e r F 2

1

0

2

3

4 5 Hours_Ex1.51

6

7

Stem-and-Leaf Displ ay: Hours _Ex1.51 _Ex1.51 St em- andand- l eaf of Hour our s_ Ex1. Ex1. 51 Leaf Leaf Uni t = 0. 10 1 3 4 7 9 ( 3) 8 6 4 3 1

2 2 3 3 4 4 5 5 6 6 7

N

= 20

4 89 0 669 04 568 568 02 58 2 57 3

1.52 a. Draw a histogram of 20 forecasted earnings per share. share.

Exercise 1.52 s t s 10 y l a 8 n A 6 f o 4 r e b 2 m 0 u N

100% 80% 60% 40% 20% 0% 10.44 10 .445 5 10 10.945 .945 11 11.44 .445 5 11 11.94 .945 5 12 12.44 .445 5

Forecast ($ per share)


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Answer to b., c. and d. are: (b) Relative Frequency Freq. 2 0.1 8 0.4 6 0.3 3 0.15 1 0.05

(c) Cumulative Freq. 2 10 16 19 20

(d) Cumulative % 10.00% 50.00% 80.00% 95.00% 100.00%

d. Cumulative relative frequencies are are in the last column of the the table above. These numbers indicate the percent of analysts who forecast that level of earnings per share and all previous classes, classes, up to and including the current class. class. The third bin of 80% indicates indicates that 80% of the analysts have forecasted up to and including that level of earnings per share. 1.53 Use a pie chart or bar chart to plot internet usage and income data Chart of Internet_Ex1.53 Internet_Ex1.53 vs Income_Ex1.53 70 60 3 50 5 . 1 x E 40 _ t e n r e 30 t n I

20 10 0

<50

50<75 Income_Ex1.53

75+



1.54 Cluster bar chart for impact of SOX

1.55 Describe the familiarity with tax law data graphically Chart of Frequency_Ex1.55 vs Familiarity_Ex1.55 70 60

5 5 . 1 50 x E _ y 40 c n e 30 u q e 20 r F

10 0

l d i h C

t i d r e c x a t

e a g i r r M a

t y l n a e p

i n s a g l t a i p a C

d s e n d i i v D

e s t e s a n g r a h a x f c l t o a e i n a r r g w M a a U n

Familiarity_Ex1.55


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1.56 County Appraiser’s Office – Data Entry Process a. Pareto diagram

Pareto Chart for Defects 80

100 100

70 80

60 t 50 n u o 40 C

60 40

30 20

t n e c r e P

20

10 0

Defect Count Percent Cum %

e r l t e e e e r c c e l d e g a n s f d a f o l d N a m P a r a a e r r r r t v i o ro r L o o o o y S r l e r r r t r r r a c r E E E c e r e o p e n g t i n g t i n g t i n o o n g d s P r o W r P o s P o s P o s D e e 23 21 18 6 5 4

0 s e r s O t h 3

28.7

26.3

22.5

7.5

6.3

5.0

3.8

28.7

55.0

77.5

85.0

91.3

96.3

100.0

b. Recommendations should include a discussion of the data entry process. The data entry was being made by individuals with no knowledge of the data. Training of the data entry personnel should be a major recommendation. Increasing Increasing the size of the monitors used by the data entry staff would also reduce the number of errors.



1.57 a. Pie chart for Groupons purchased purchased during past week

b. Bar chart for Groupons purchased during past week


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1.58

1.59 Scatter plot of price of paint versus demand for paint. Scatterplot Scatterp lot of Price_Ex1.59 vs Qty_Ex1.59 10 9 9 8 5 . 1 x E 7 _ e c i r P 6

5 4 100

120

140 160 Qty_Ex1.59

180

200

Price and quantity demanded per unit of time are negatively related as predicted by the law of demand.



1.60 Plot the data for advertising expenditures and total sales Scatterplot of Sales_Ex1.60 vs Advert_Ex1.60 200 180 0 160 6 . 1 x E 140 _ s e l a S 120

100 80 6

7

8

9

10 11 12 Adv ert_Ex1 ert_Ex1.60 .60

13

14

1.61 Plot of quantity versus price, with price on the horizontal axis Scatterplot of Tons_Ex1.61 vs Price_Ex1.61 20.0 17.5 15.0

1 6 . 1 x 12.5 E _ s n o T 10.0

7.5 5.0

2

3

4 Price_Ex1.61

5

6


15

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1.62 Plot the batting averages vs. hours spent per week in a weight-training program. Scatterplot of BatChg_Ex1.62 BatChg_Ex1.62 vs Hours_Ex1.62 120 100 80

2 6 . 1 x 60 E _ g h C t 40 a B

20 0

5

10

15 Hours_Ex1.62

20

25

It appears that the number of hours spent per week in a special weighttraining program is positively related to the change in their batting averages from the previous season. 1.63 a. Describe the data with a cross table Type of Account Male Easy Checking 80 Intelligent Checking 12 Super Checking 27 Ultimate Checking 24 Subtotal 143

Female 100 24 27 6 157

Subtotal 180 36 54 30 300



b. Describe the data graphically Chart of Easy_Ex1.63, Intelli_Ex2., Super_Ex1.63, ... vs Gender_Ex1.6 200

Gender_Ex1.63 Male Female

150

a t a 100 D

50

0 Easy_Ex1.63

Intelli_Ex1.63

Super_Ex1.63

Ultimate_Ex1.63

1.64 a. Describe the new new product data with with a cross table Age Friend Newspaper <21 years 30 20 21-35 60 30 35+ 18 42 Subtotal 108 92

Subtotal 50 90 60 200

b. Describe the data graphically Chart of <21_Ex1.64, 21-35_Ex1.64, 35+_Ex1.64 vs Intro_Ex1.64 Intro_Ex1.64 Friend Newspaper

90 80 70 60 a 50 t a D

40 30 20 10 0 <21_Ex1.64

21-35_Ex1.64

35+_Ex1.64


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1.65 a. Pie chart of soft drink preferences preferences Pie Chart of Prefer_Ex1.65 vs Brand_Ex1.65 Category A B C D E

A 30, 12.5%

E 14, 5.8%

B 50, 20.8%

D 100, 41.7% C 46, 19.2%

b. Construct a bar chart Chart of Prefer_Ex1.65 vs Brand_Ex1.65 100

80 5 6 . 1 60 x E _ r e f e r 40 P

20

0

A

B

C Brand_Ex1.65

D

E



1.66 a. Scatterplot

From the graph, it is evident that the temperature is positively related to the time it takes to mow. 1.67 Time-series plot of population growth for the state of New York Time Series Plot of State of NY Population Number of People 19300000 19200000

n o i t a l u p o P Y N

19100000 19000000 18900000 18800000 18700000 18600000 1997

1998

1999

2000 2001 Year

2002

2003

2004

Source: bea.doc.gov, regional economic account, interactive data tables

1.68 a. Cross table of method of payment and day day of purchase for Florin data Payment Am Ex MC Visa Cash Other Subtotal

M 7 1 6 3 2 19

T 0 4 6 1 0 11

W 3 4 4 0 4 15

Th 4 2 5 0 4 15

F 3 4 8 3 7 25

S 6 9 10 9 6 40

Tot 23 24 39 16 23 125


file.

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b. Pie chart of day of purchase

1.69 Cross table and bar chart of drink preference vs. type of vehicle Vehicle Pickup Car Subtotal

Beer 71 32 103

Wine 29 68 97

Subtotal 100 100 200

Chart of Beer_Ex1.69, Wine_Ex1.69 vs Vehicle_Ex1.69 Vehicle_Ex1.69 Vehicle_Ex1.69 Pickup Car

100

80

a t a D

60

40

20

0 Beer_Ex1.69

Wine_Ex1.69



1.70 a. Time-series plot of the closing costs

b. Answers may vary. vary. 1.71 Time-Series Plot of Facebook New Visitors


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1.72 a. Histogram of shopping times

b. Stem-and-leaf display Stem-and-Leaf Display: Time St em- andand- l eaf of Ti Ti me Leaf Leaf Uni t = 1. 0 9 19 26 44 52 52 41 35 27 20 15 8

1 2 2 3 3 4 4 5 5 6 6 7

N

= 104 104

888888899 0011111333 5555558 000011113344444444 77777788 00001222233 555678 00112222 5777799 00334 7889999 00001133

1.73 Stem-and-leaf display Stem-and-Leaf Display: GPA St em- andand- l eaf of GPA Leaf Leaf Uni t = 0. 010 010 4 6 9 11 23 25 28 35 40 45 60 74 ( 53) 29 6 6 4 1

21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

N

= 156 156

2689 25 013 56 113345555668 25 889 2788888 01289 15678 000488888888888 01112444568999 11111111111222223333444444444555566666666677777777999 111111111112222233334444444 44555566666666677777777999 00000022222222222222222 57 012 5



1.74 Scatter plot


1-47

Solution Manual for Statistics for Business and Economics 8th Edition by Newbold

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