# Performance Lawn Service, Episode7

Part 1: The Performance Lawn Equipment database contains data needed to develop a pro forma income statement. Dealers selling PLE products all receive 18 percent of sales revenue for their part of doing business, and this is accounted for as the selling expense. The tax rate is 50 percent. Develop an Excel worksheet to extract and summarize the data needed to develop the income statement for 2014, and implement an Excel model in the form of a pro forma income statement for the company.

Part 2: The CFO of Performance Lawn Equipment, J. Kenneth Valentine, would like to have a model to predict the net income for the next three years. To do this, you need to determine how the variables in the pro forma income statement will likely change in the future. Using the calculations and worksheet that you developed along with other historical data in the database, estimate the annual rate of change in sales revenue, the cost of goods sold, the operating expense, and the interest expense. Use these rates to modify the pro forma income statement to predict the net income over the next three years.

Because the estimates you derived from the historical data may not hold in the future, conduct appropriate “what-if” scenarios and/or parametric sensitivity analyses to investigate how the projections might change if these assumptions don’t hold. In your model, construct a tornado chart to show how the assumptions impact the net income . Summarize your results and conclusions in a report to Mr. Valentine. Upload your Word document AND your Excel worksheet file

2

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Dealer Satisfaction

Dealer Satisfaction
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our version of E

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Comment:

This chart is showing Dealer Satisfaction between

N

orth America

,

South America

,

Eur

ope

,

Pac

ific

Rim

and

China

. The data that was selected was rated on a a survery scale from 0-

5

and between the the years of

20 10

-20

14

, except for China who started later in

20

12

. North America was leading in

sample

si

z

e and “in 5s” dealer satisfacion for “excelltence”. Although North America recieved the highest

total

numbers in dealer satisfactions for excellent rankings, in

201

4

, South America recieved

6

0

surverys and North America recieved

56

within the level 5 category.

Survey Scale:

0 1 2 3 4 5

Sample North America

Size 2010

1 0 2 14

22 11 50 2011

0 0 2 14 20 14 50
2012

1 1 1 8

34 15 60 20

13

1 2 6 12 34

45 100 2014

2 3 5 15

44

56

1

25 South America
2010 0 0 0 2 6 2 10
2011 0 0 0 2 6 2 10
2012 0 0 1 4 11 14

30 2013

0 1 1 3 12

33

50
2014 1 1 2 4 22 60

90 Europe 2010 0 0 1 3 7 4 15
2011 0 0 1 2 8 4 15
2012 0 0 1 2 15 7 25
2013 0 0 1 2

21

6 30
2014 0 0 1 4

17

8 30
Pacific

Rim 2010 0 0 1 2 2 0 5
2011 0 0 1 1 3 0 5
2012 0 0 1 1 3 1 6
2013 0 0 0 2 5 3 10
2014 0 0 1 2 7 2 12
China
2012 0 0 0 1 0 0 1
2013 0 0 1 4 2 0 7
2014 0 0 1 5 8 2

16

Dealer Satisfaction by

Region

and

Year

0 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014

Pacific Rim

2010 2011 2012 2013 2014 China 2012 2013 2014 1 0 1 1 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 0 0 1 2 3 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 2 2 1 6 5 0 0 1 1 2 1 1 1 1 1 1 1 1 0 1 0 1 1 3 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 14 14 8 12 15 2 2 4 3 4 3 2 2 2 4 2 1 1 2 2 1 4 5 4 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 22 20 34 34 44 6 6 11 12 22 7 8 15 21 17 2 3 3 5 7 0 2 8 5 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 11 14 15 45 56 2 2 14 33 60 4 4 7 6 8 0 0 1 3 2 0 0 2

This chart is showing Dealer Satisfaction between North America, South America, Europe,

Pacific Rim

and China. The data that was selected was rated on a a survery scale from 0-5 and between the the years of 2010-2014, except for China who started later in 2012. North America was leading in sample size and “in 5s” dealer satisfacion for “excelltence”. Although North America recieved the highest total numbers in dealer satisfactions for excellent rankings, in 2014, South America recieved 60 surverys and North America recieved 56 within the level 5 category.

## End-User Satisfaction

D3-7E9F-4E7B-

FB-A

AAF2BD2E6}: [Threaded comment]
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0924
Comment:
This chart is showing End-User Satisfaction between North America, South America, Europe, Pacific Rim and China. The data that was selected was rated on a a survery scale from 0-5 and between the the years of 2010-2014, except for China who started later in 2012. North America, South America, Europe, and the Pacific Rim all have the same sample size of 100 for each year between 2010 through 2014. China has a smaller sample size of 50 between the years of 2012 through 2014. You cansee that the ratings of 5’s, 4’s, and 3’s are the highest ratings. North America’s rating of 4 decreases every year starting with 2010 while the 5 ratings increase through the years. The Pacfic Rim’s 4 ratings are highest rated and is basically constant throughout the years while the 5 ratings are lower then 4 ratings the 5’s are constant throughout the years.

Sample
North America 0 1 2 3 4 5 Size
2010 1 3 6 15

100

2011 1 2 4

40 100

2012 1 2 5 17 34 41 100
2013 0 2 4 15 33

100

2014 0 2 3 15

100

South America

2010 1 2 5 18

38 100

2011 1 3 6 17 36 37 100
2012 0 2 6

37 36 100

2013 0 2 5 20 37 36 100
2014 0 2 5 19 37 37 100

Europe

2010 1 2 4 21 36 36 100
2011 1 2 5 21 34 37 100
2012 1 1 4

37 31 100

2013 1 1 3 17 41 37 100
2014 0 1 2 19 45 33 100

Pacific Rim

2010 2 3 5 15 41 34 100
2011 1 2 7 15 41 34 100
2012 1 2 5 16 40 36 100
2013 0 2 4 17 40 37 100
2014 0 1 3 19

35 100

China

2012 0 3 3 6

10 50

2013 1 2 2 4 30 11 50
2014 0 1 1 3 31 14 50
 End-User Satisfaction tc={4E17 82 83 93 87 37 38 18 35 46 31 49 36 19 26 42 28

End-User Satisfaction by Region and Year

0 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 1 1 1 0 0 1 1 0 0 0 1 1 1 1 0 2 1 1 0 0 0 1 0 1 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 3 2 2 2 2 2 3 2 2 2 2 2 1 1 1 3 2 2 2 1 3 2 1 2 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 6 4 5 4 3 5 6 6 5 5 4 5 4 3 2 5 7 5 4 3 3 2 1 3 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 15 18 17 15 15 18 17 19 20 19 21 21 26 17 19 15 15 16 17 19 6 4 3 4 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 37 35 34 33 31 36 36 37 37 37 36 34 37 41 45 41 41 40 40 42 28 30 31 5 2010 2011 2012 2013 2014 South America 2010 2011 2012 2013 2014 Europe 2010 2011 2012 2013 2014 Pacific Rim 2010 2011 2012 2013 2014 China 2012 2013 2014 38 40 41 46 49 38 37 36 36 37 36 37 31 37 33 34 34 36 37 35 10 11 14

This chart is showing End-User Satisfaction between North America, South America, Europe, Pacific Rim and China. The data that was selected was rated on a a survery scale from 0-5 and between the the years of 2010-2014, except for China who started later in 2012. North America, South America, Europe, and the Pacific Rim all have the same sample size of 100 for each year between 2010 through 2014. China has a smaller sample size of 50 between the years of 2012 through 2014. You can see that the ratings of 5’s, 4’s, and 3’s are the highest ratings. North America’s rating of 4 decreases every year starting with 2010 while the 5 ratings increase through the years. The Pacfic Rim’s 4 ratings are highest rated and is basically constant throughout the years while the 5 ratings are lower then 4 ratings the 5’s are constant throughout the years.

## Complaints

Complaints
tc={3A6BEBAD-C

122

-45

73

-AF

72

-C4

23 91 97 55

93}: [Threaded comment]
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Comment:
This chart is showing PLE’s Complaoints from registered by all customers each month within PLE’s 5 regions. From this data we can conclude that there is more use of the equipment in the summer months because of the higher number of complaints recieved. Based off the data shown form the region of China, their compaints are few and are steady throughout the months. This could be because they do not use this type of equipment in comparison to the other regions. M

onth World NA SA

Eur Pac China

Jan

-10 1

69 102

12

52

3
Feb

-10 187 115

13 55 4
Mar

-10 210 128

15

61

6
Apr

-10 226 136

16

67

7
May

-10 2

32 137

17 73 5
Jun

-10 261 1

51

19 82 9
Jul

-10 245 140

18

80

7
Aug

-10 223

128 16

76

3
Sep

-10 1

95 103

15 73 4
Oct

-10 1

74 96

14

62

2
Nov

-10 1

54 84

11

59

0
Dec

-10 1

63 99

9 54 1
Jan-11 195 123

10 59 3
Feb-11 221 141

13 62 5
Mar-11 240 152

16

66

6
Apr-11 2

64 163

20 70 11
May-11 283 1

78

22

75

8
Jun-11 29

6 170

28

86

12
Jul-11 269 1

53

25

81

10
Aug-11 256 146

23

79

8
Sep-11 231 131

20 73 7
Oct-11 214 125

16

68

5
Nov-11

201

118

13 66 4
Dec-11 1

71

96 11 61 3
Jan-12 200 112

15 66 4 3
Feb-12 216 117

18 71 6 4
Mar-12 234 126

20 76 9 3
Apr-12 253 138

23 79 11 2
May-12 282

152 26

85

14 5
Jun-12 305

163 30 91 15 6
Jul-12 296 156

28

89

18 5
Aug-12 27

9 1

48

26 86 15 4
Sep-12 266 1

43

24 82 13 4
Oct-12 243

131 21 76 12 3
Nov-12 232

128 18 73 10 3
Dec-12 203 107

15 70 7 4
Jan-13

216

110

19 74 8 5
Feb-13 2

39

123 23 79 10 4
Mar-13

266 138 26 83 13 6
Apr-13 284 150

30

88

11 5
May-13 315 169

33 91 15 7
Jun-13 340 181

37 95 19 8
Jul-13 319

169 34 92 17 7
Aug-13 304 160

32 90 15 7
Sep-13 2

77

141 29 87 14 6
Oct-13 250

123 26 83 12 6
Nov-13 228

112 24 77 10 5
Dec-13 213 105

23 74 7 4
Jan-14

240

121

26 80 8 5
Feb-14 251

126 28 82 10 5
Mar-14 281 148

31 85 12 5
Apr-14 2

98 155

35 89 13 6
May-14 322 168

39 95 12 8
Jun-14 350 183

43 98 15 11
Jul-14 330

170 41 95 14 10
Aug-14 311 1

58

38 93 13 9
Sep-14 289 149

33 89 11 7
Oct-14 2

65

136 30 85 8 6
Nov-14 239

121 26 80 7 5
Dec-14 219 108

23 76 7 5

Complaints by Month and Region

World 40

179

40210 40

238

40269 40

299

40330 40

360

40391 40422 40452 40483 40

513

40544 40

57

5

40

603

40634 40664 40

695 407

25 40

756

40787 40817 40

848

40

878

40909 40940 40969 41000 41030 4

106

1 4

109

1 41122 41

153

4

1183

4

1214

4

124

4

41

275

4

130

6

4

133

4 41365 4

139

5 4

142

6

4

145

6

41487 4

151

8 41

548

4

157

9 41609 41

640

41

671

41

699

41

730

41

760

41791 4

182

1 4

185

2

4

188

3 4

191

3 4

194

4

41

974

169 187 210 226 232 261 245 223 195

174 154

163 195 221 240

264

283 296 269 256 231 214 201

171

200 216 234 253 282 305 296

279

266 243 232 203 216 239 266 284 315 340 319 304

277

250 228 213 240 251 281

298

322 350 330 311 289

265

239 219 NA 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41

244

4

127

5 41

306

4

1

334

41365 4

1395

41426 41456 41487 41518 41548 41579 41609 4

164

0 4

167

1 41699 4

173

0 4

176

0 41791 41821 41852 41883 41913 41944 4

197

4 102 115 128 136 137 151 140 128 103 96 84 99 123 141 152 163

178

170 153 146 131 125 118 96 112 117 126 138 152 163 156 148

143

131 128 107 110 123 138 150 169 181 169 160 141 123 112 105 121 126 148 155 168 183 170

158

149 136 121 108 SA 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 12 13 15 16 17 19 18 16 15 14 11 9 10 13 16 20 22 28 25 23 20 16 13 11 15 18 20 23 26 30 28 26 24 21 18 15 19 23 26 30 33 37 34 32 29 26 24 23 26 28 31 35 39 43 41 38 33 30 26 23 Eur 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 4185 2 41883 41913 41944 41974 52 55 61 67 73 82 80 76 73 62 59 54 59 62 66 70 75 86 81 79 73 68 66 61 66 71 76 79 85 91 89 86 82 76 73 70 74 79 83 88 91 95 92 90 87 83 77 74 80 82 85 89 95 98 95 93 89 85 80 76 Pac 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 3 4 6 7 5 9 7 3 4 2 0 1 3 5 6 11 8 12 10 8 7 5 4 3 4 6 9 11 14 15 18 15 13 12 10 7 8 10 13 11 15 19 17 15 14 12 10 7 8 10 12 13 12 15 14 13 11 8 7 7 China 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 3 4 3 2 5 6 5 4 4 3 3 4 5 4 6 5 7 8 7 7 6 6 5 4 5 5 5 6 8 11 10 9 7 6 5 5

This chart is showing PLE’s Complaints from registered customers each month within PLE’s 5 regions. From this data we can conclude that there is more use of the equipment in the summer months because of the higher number of complaints recieved. China has the fewest number of compaints, this is due to the less customer usage. Based off the data, the Pacific Rim and South America do not have as many complaints as North America does due to less people using or purchasing PLE’s equipment. .

Mower

Unit Sales

## Mower Unit Sales

tc={6A814A1A-8E51-48A1-A543-AEC7E2B5497F}: [Threaded comment]
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Comment:
The chart identifies the unit sales PLE’s mower equipment. We can see that the highest peak for mower sales is in the summer months and then a decline in sales starting in early fall months. BAsed off this chart, North America is the region with the highest unit sales for PLE’s mowers.

Month NA SA Europe Pacific China World
Jan-10 600

0

200

720

100 0

7020 Feb-10 7950 220 990 120

0

9

280 Mar-10 810

0

250

1

320

110 0

9

780 Apr-10 9050

280

1

650

120 0

111

0

0 May-10 9

900 310 1

590

130 0

11

930 Jun-10 1020

0 300 1

620

120 0

1

224

0 Jul-10 8730

280

159

0 140 0

10

740 Aug-10 8140

250

1

560

130 0

10080 Sep-10 6

480 230

1590 130 0

8

430 Oct-10 599

0

220

132

0 120 0

7650 Nov-10 532

0

210 990 130 0

6650 Dec-10 4640 180 660

140 0

5620 Jan-11

5

980

210

690

140 0 7020
Feb-11

7620

240 1020 150 0

9030 Mar-11

837

0

250

1

290

140 0

10050 Apr-11

8

830

290

162

0 150 0

10

890 May-11

9310

330

165

0

130 0

11

420 Jun-11

10230

310 1590 140 0

12

270 Jul-11

8720

290

1560

150 0

10720 Aug-11

7

710

270

1

530

140 0

9650 Sep-11

6320

250 1590 150 0

831

0 Oct-11

5

840

250

1

260

160 0

7

510 Nov-11

4

960

240 900 150 0

625

0 Dec-11

4350

210 660 150 0

537

0 Jan-12

6020

220

570

160 0

6

970 Feb-12

792

0

250 840 150 0

9160 Mar-12 8430 270 1110 160 0

9970 Apr-12

9040

310

1

500

170 0

11020 May-12

9

820

360

1

440

160 0

11780 Jun-12

10370

330

1410

170 0

1

2280 Jul-12 9050 310

144

0 160 0

10960 Aug-12 7620 300 1410 170 0

9500 Sep-12

6420

280

135

0

180 0

823

0 Oct-12

5890

270

1080

180 0

742

0 Nov-12

5340

260 840

190

0

6

630 Dec-12

4430

230 510 180 0

5350 Jan-13

610

0

250 480 200 0

7030 Feb-13

8010

270

750

190 0

9220 Mar-13 8430 280

114

0

200 0 10050
Apr-13

9110

320 1410 210 0

11050 May-13

9730 380 134

0

190 0

116

40 Jun-13

101

20

360

1360

200 0

1

204

0 Jul-13

9080

320 1410 200 0

1101

0 Aug-13

7820

310

1

490

210 0

9830 Sep-13

6540

300

1310

220 0 8370
Oct-13

6010

290 980 210 0

7490 Nov-13

5270

270

770

220 0

6530 Dec-13

5380

260 430 230 0

6300 Jan-14

6210

270

400

200 0

7080 Feb-14

803

0

280 750 190 0

9250 Mar-14

8540

300 970 210 0

1002

0 Apr-14

9120

340 1310 220 5

10995 May-14

9570 390 1260

200 16

11

436 Jun-14 10230 380

1240

210 22

1

208

2 Jul-14

9

580

350

1300

230 26

11486 Aug-14

7

680

340

1250

220 14

9504 Sep-14

6870

320

1210

220 15

8635 Oct-14

5930

310 970 230 11

745

1 Nov-14

526

0

300 650 240 3

645

3 Dec-14

4830

290 300 230 1

5

651

Mower Unit Sales by Month and Region

NA 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 6000 7950 8100 9050 9900 10200 8730 8140 6480 5990 5320 4640 5980 7620 8370 8830 9310 10230 8720 7710 6320 5840

496

0 4350 6020 7

920

8430 9040 9820 10370 9050 7620 6420 5890 5340 4430 6100 8010 8430 9110 9730 10120 9080 7820 6540 6010 5270 5380 6210 8030 8540 9120 9570 10230 9580 7680 6870 5930 5260 4830 SA 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 200 220 250 280 310 300 280 250 230 220 210 180 210 240 250 290 330 310 290 270 250 250 240 210 220 250 270 310 360 330 310 300 280 270 260 230 250 270 280 320 380 360 320 310 300 290 270 260 270 280 300 340 390 380 350 34 0 320 310 300 290 Europe 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 720 990

1320

1650 1590 1620 1590 1560 1590 1320 990 660 690 1020

129

0 1620 1650 1590 1560 1530 1590 1260 900 660 570 840 1110 1500 1440 1410 1440 1410 1350 1080 840 510 480 750 1140 1410 1340 1360 1410

1490

1310 980 770 430 400 750 970 1310 1260 1240 1300 1250 1210 970 650 300 Pacific 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 100 120 110 120 130 120 140 130 130 120 130 140 140 150 140 150 130 140 150 140 150 160 150 150 160 150 160 170 160 170 160 170 180 180 190 180 200 190 200 210 190 200 200 210 220 210 220 230 200 190 210 220 200 210 230 220 220 230 240 230 China 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 16 22 26 14 15 11 3 1 World 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 7020 9280 9780 11100

119

30 12240 10740 10080 8430 7650 6650 5620 7020 9030 10050

1089

0 1

1420 1

227

0 10720 9650 8310 7510

6250

5370 6970 9160 9970 11020 11780 12280 10960 9500 8230 7420 6630 5350 7030 9220 10050 11050 11640 12040 1

1010

9830 8370 7490 6530 6300 7080 9250 10020 10995 1

1436

12082 11486 9504 8635 7451 6453 5651

The chart identifies the unit sales on PLE’s mower equipment. We can see that the highest peak for mower sales is in the summer months and then a decline in sales starting in early fall months. Looking at the chart, North America is the region with the highest unit sales for PLE’s mowers.

Tractor

Unit Sales

## Tractor Unit Sales

tc={65A5E7B3-7884-4D7D-9EEA-FA36

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Comment:
The chart identifies the unit sales PLE’s tractor equipment. We can see that throughout the years with the World orange line shown in the graph increases total sales between the years of 2010 to 2014. The line is basically increase in a positive direction on this graph. And the increase in tractor sales increase in each region throughout the years as well. Overall there is a positive correlations between time and tractor unit sales over all of the country regions.

Month NA SA Eur Pac China World
Jan-10 570 250 560

212

0

1592 Feb-10

611

270 600 230 0

1

711 Mar-10 630 260 680 240 0

1810 Apr-10

684

270 650

263

0

186

7 May-10 650 280 580 269 0

177

9 Jun-10 600 270 590 280 0

1740 Jul-10

512

264 760 290 0

1

826 Aug-10 500 280 645 270 0

1695 Sep-10

47

8

290 650 263 0

1

681 Oct-10

455

280

670 258

0

166

3 Nov-10 407 290

888

240 0

1

825 Dec-10 360 280

850

230 0

172

0 Jan-11

571

320 620 250 0

1761 Feb-11 650 350 760 275 0

2035 Mar-11 740 390 742 270 0

2142 Apr-11 840 440 780 280 0

2340 May-11 830

470

690 290 0 2280
Jun-11 760 490

721

300 0

2

271 Jul-11 681

481

680

312

0

215

4 Aug-11 670

460

711 305 0

2146 Sep-11 640 460 695 290 0

2085 Oct-11 620 440 650 260 0

1970 Nov-11 570 436 680 250 0

193

6 Dec-11

533

420

657

240 0

1850 Jan-12 620 510 610 250 10

2000 Feb-12 792 590 680 250 12

2

324 Mar-12 890 610 730 260 20

2510 Apr-12 960 600 820 270 22

267

2 May-12

104

0

620 810 290 20

278

0 Jun-12

1032

640

807

310 24

2

813 Jul-12

1006

590 760 340 20

2716 Aug-12

910

600 720 320 31

2

581 Sep-12 803 670 660

313

30

247

6 Oct-12 730 630 630 290 37

2

317 Nov-12 699 710 603 280 32 2324
Dec-12

647

570 570 260 33

2080 Jan-13 730 650 500

287

35

2

202 Feb-13 930 680 590 290 50

254

0 Mar-13

1160 724

620 300 63

286

7 Apr-13

1510

730 730 310 68

3

348 May-13 1650 760 740 330 70

3

550 Jun-13 1490

800

720 340 82

343

2 Jul-13

1460

840 670 350 80

3400 Aug-13

1390

830 610

341

90

326

1 Sep-13 1360 820 599 330 100

3

209 Oct-13 1340 810 560 320 102

3132 Nov-13 1240

827

550 300 110

302

7 Dec-13

1103

750

520

290 114

2777 Jan-14 1250 780 480 200 111

2821 Feb-14

1550 805 523

210 121 3209
Mar-14

1820

830 560 220 123

3

553 Apr-14 2010 890 570 230 120

3820 May-14

2230

930 590 253 130 4133
Jun-14

249

0

980 600 270 136

4476 Jul-14

2440

1002 580 280 134

4436 Aug-14

233

4

970 570 250 132

4256 Sep-14

2190

960 550 230 137

4067 Oct-14 2080 930 530 220 130

3890 Nov-14

205

0

920

517

190 139

3

816 Dec-14

2004 902

490 190 131

3717

Tractor Unit Sales by Month and Region

NA 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 570 611 630 684 650 600 512 500 478 455 407 360 571 650 740 840 830 760 681 670 640 620 570 533 620 792 890 960 1040 1032 1006 910 803 730 699 647 730 930 1160 1510 1650 1490 1460 1390 1360 1340 1240 1103 1250 1550 1820 2010 2230 2490 2440 2334 2190 2080 2050 2004 SA 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 250 270 260 270 280 270 264 280 290 280 290 280 320 350 390 440 470 490 481 460 460 440 436 420 510 590 610 600 620 640 590 600 670 630 710 570 650 680 724 730 760 800 840 830 820 810 827 750 780 805 830 890 930 980 1002 970 960 930 920 902 Eur 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 560 600 680 650 580 590 760 645 650 670 888 850 620 760 742 780 690 721 680 711 695 650 680 657 610 680 730 820 810 807 760 720 660 630 603 570 500 590 620 730 740 720 670 610 599 560 550 520 480 523 560 570 590 600 580 570 550 530 517 490 Pac 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 212 230 240 263 269 280 290 270 263 258 240 230 250 275 270 280 290 300 312 305 290 260 250 240 250 250 260 270 290 310 340 320 313 290 280 260 287 290 300 310 330 340 350 341 330 320 300 290 200 210 220 230 253 270 280 250 230 220 190 190 China 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 12 20 22 20 24 20 31 30 37 32 33 35 50 63 68 70 82 80 90 100 102 110 114 111 121 123 120 130 136 134 132 137 130 139 131 World 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 1592 1711 1810 1867 1779 1740 1826 1695 1681 1663 1825 1720 1761 2035 2142 2340 2280 2271 2154 2146 2085 1970 1936 1850 2000 2324 2510 2672 2780 2813 2716 2581 2476 2317 2324 2080 2202 2540 2867 3348 3550 3432 3400 3261 3209 3132 3027 2777 2821 3209 3553 3820 4133 4476 4436 4256 4067 3890 3816 3717

The chart identifies the unit sales for PLE’s tractor equipment. We can see that throughout the years with the World orange line shown in the graph increases total sales between the years of 2010 to 2014. The line is basically increase in a positive direction on this graph. And the increase in tractor sales increase in each region throughout the years as well. Overall there is a positive correlations between time and tractor unit sales over all of the country regions.

## Q2

of

Year

2010 2011 2012 2013 2014

Month
Jan

3%

7%

2%

1%

Feb

9%

2

%

4%

Sum

Mar

8

%

8%

98.67%

2010 12

1937544

12772

Apr

2011 12

3

3

2701

May 98.73% 98.73%

99.22% 2012 12

Jun

98.78%

98.91%

2013 12

2

0976

54

Jul

8%

2014 12

8

8

13

Aug 98.67% 98.67%

99.23%

Sep 98.94% 98.58% 98.77%

%

Oct

98.69% 98.67% 98.99% 99.23%

Nov

98.69%

98.43%

F

Dec

%

98.81% 99.12%

4

579

2

75

3

96

55

1

59

 Sum Percent Anova: Single Factor 9 8.4 98.44% 9 8.6 9 8.9 9 9.2 SUMMARY 98.09% 98.63% 9 8.7 9 8.8 9 9.1 Groups Count Average Variance 9 7.5 9 8.3 98.91% 99.28% 11. 819 98.49% 0.0000 98.6 0% 98.73% 98.80% 98.97% 99.22% 1 1.8 727 98.61% 0.0000022009 98.84% 99.11% 11.8531797187 98.78% 0.000000506 98.64% 98.81% 99.08% 1 1.87 309 98.94% 0.00000 347 9 8.5 98.71% 98.89% 98.99% 99.23% 1 1.88 252 563 99.07% 0.0000 1378 98.77% 99.12% 9 8.93 98.69% 98.76% ANOVA 98. 50% 98.83% 99.29% Source of Variation SS df MS P-value F crit 98.39% 9 8.33 98.01% Between Groups 0.0002607821 0.0000651955 9.9 207 0.0000039122 2.5 886 349 Within Groups 0.0003600906 0.000006 547 Total 0.0006208727

## On-Time Delivery

o the customer. For example, for the month of

of 2010, PLE’s had a total of

deliveries but out of that number,

when delivered on-time. This chart makes is easy to compare those deliveries.

Percent

Jan-10 1086

98.4%

Feb-10 1101 1080

%

Mar-10

1089

%

Apr-10

May-10 1183

Jun-10

1160 98.6%

Jul-10

98.6%

Aug-10

98.7%

Sep-10

1210

Oct-10

Nov-10 1198

Dec-10

1223 98.4%

Jan-11

98.4%

Feb-11

1224 98.6%

Mar-11

98.4%

Apr-11

98.7%

May-11

98.7%

Jun-11 1227

98.8%

Jul-11 1243 1227 98.7%
Aug-11

98.7%

Sep-11

98.6%

Oct-11

98.7%

Nov-11

1281 98.7%

Dec-11

Jan-12 1281 1264 98.7%
Feb-12 1320

98.8%

Mar-12

1334 98.7%

Apr-12

1320 98.8%

May-12

98.8%

Jun-12

98.8%

Jul-12 1352

98.9%

Aug-12

1360 98.8%

Sep-12

98.8%

Oct-12

98.7%

Nov-12

98.8%

Dec-12

98.8%

Jan-13

98.9%

Feb-13

1342 98.8%

Mar-13 1371 1356 98.9% Q2
Apr-13 1362

May-13 1350 1338

Anova: Single Factor

Jun-13

98.9%

Jul-13

1378 99.0% SUMMARY

Aug-13 1371

99.1% Groups Count Sum Average Variance

Sep-13

98.9% 2010 12 11.8191937544 98.49% 0.000012772

Oct-13

99.0% 2011 12 11.

7272701 98.61% 0.0000022009

Nov-13

1377 98.4% 2012 12 11.8531797187 98.78% 0.000000506

Dec-13

99.1% 2013 12 11.8

090976 98.94% 0.0000034754

Jan-14

1390

2014 12 11.8

5

63 99.07%

Feb-14

99.1%

Mar-14 1395 1385

%

Apr-14

1401 99.2%

ANOVA
May-14

1392 99.2% Source of Variation SS df MS F P-value F crit

Jun-14

1402 99.1% Between Groups 0.0002607821 4 0.0000651955 9.9579207275 0.0000039122

6349

Jul-14 1426 1415 99.2% Within Groups 0.0003600906 55 0.0000065471
Aug-14

1420 99.2%

Sep-14

1426 98.7% Total 0.0006208727 59

Oct-14

99.2%

Nov-14

1403 99.3%

Dec-14 1456

 Month tc={378CB2D4-4814-4165-B17B-6903BF4AE16B}: [Threaded comment] Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924 Comment: We decided to use a clustered column chart to represent the On-Time deliveries for PLE’s unit deliveries. The darker backgorund makes it easier to see the difference in the deliveries and the ones that were delivered on time t January 1086 98.4% Number of deliveries Number On Time 1069 9 8.1 1116 9 7.6 1216 1 199 98.6% 1168 98.7% 1176 1 198 1181 1205 1 189 1223 98.9% 1209 1194 98.8% 1180 98.5% 1243 1220 1201 1 241 1 237 1 217 1258 1 242 1 262 1 246 1212 1281 1264 1 272 1254 1 295 1278 1298 1 318 1296 98.3% 1304 1352 1 336 1 291 1 276 1 342 1326 1 337 1377 1385 1368 1356 1 338 1362 1 346 1349 1 333 1386 1371 1358 1348 99.0% 99.1% 1381 1366 1392 1359 1402 1387 1384 1370 833 1399 1369 1357 723 1401 99.2% 882 285 0.0000137813 1388 1376 9 9.3 1412 1403 1415 2.539 688 1431 1 445 1425 1414 1413 1427 98.0%

On Time Delivery by Month

Number of deliveries 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 1086 1101 1116 1216 1183 1176 1198 1205 1223 1209 1198 1243 1220 1241 1237 1258 1262 1227 1243 1281 1272 1295 1298 1318 1281 1320 1352 1336 1291 1342 1352 1377 1385 1356 1362 1349 1386 1358 1371 1362 1350 1381 1392 1371 1402 1384 1399 1369 1401 1388 1395 1412 1403 1415 1426 1431 1445 1425 1413 1456 Number On Time 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 1069 1080 1089 1199 1168 1160 1181 1189 1210 1194 1180 1223 1201 1224 1217 1242 1246 1212 1227 1264 1254 1278 1281 1296 1264 1304 1334 1320 1276 1326 1337 1360 1368 1338 1346

1333

1371 1342 1356 1348 1338 1366 1378 1359 1387 1370 1377 1357 1390 1376 1385 1401 1392 1402 1415 1420 1426 1414 1403 1427 Percent 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974

0.9

8

43

462

2467771637 0.98092

643

051771122 0.975

806

45

161

2

90

325

0.986019736

842

10531 0.

987

3

2037

19357565 0.9863945578231

292

4 0.9858096

828

046744 0.98672199170124486 0.98937040065412918 0.98759305210918114 0.

984

9749582637729 0.9

839

0989541432017 0.9

8442 622

9508

196

69 0.9

8630

13

698

6

301

364 0.9

838

3185125

303

152 0.9872813990461049 0.987

321

71156893822 0.987775061124

694

38 0.9871279163

314

5613 0.986729117876

658

85 0.985

849

0566037

735

3 0.98687258687258683 0.98690292758089371 0.98330

804 248

8

619

14 0.98672911787665885 0.9878787878787

879

1 0.986

686

39053254437 0.98802395

2095

8084 0.98

8381

0999

225

4064 0.98

8077

496

274

2

175

9 0.98890532544378695 0.9876543209876

542

7 0.98772563176

8953

12 0.98672566371681414 0.98825256975036

713

0.98813936249073386 0.98917748917748916 0.98821796759941094 0.98905908096280093 0.989720998

5315

7125 0.99

1111

11111111116 0.98913830557566984 0.989942528735

6321

5 0.9912

472

6477024072 0.9893009985

734

6643 0.98988439306358378 0.984274481

7726

9483 0.9912

344

7772096418 0.99214

846

53

818

7007 0.9913544668

587

8958 0.992831541

218

63804 0.99220963

1728

04533 0.99215965787598004 0.990

812

72084805649 0.992286

1150

0701258 0.99231306

778

47

659

1 0.986851

211

07266

438

0.99228070175438599 0.9929228

591 6489

742 0.9800

824

1758241754

We decided to use a clustered column chart to represent the On-Time deliveries for PLE’s unit deliveries. The darker backgorund makes it easier to see the difference in the deliveries and the ones that were delivered on time to the customer. For example, for the month of January of 2010, PLE’s had a total of 1086 deliveries but out of that number, 98.4% when delivered on-time. This chart makes is easy to compare those deliveries.

## Response Time

Response times to customer service calls
tc={912794B6-EB87-

4831

-A2F0-71C2CACF797B}: [Threaded comment]
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Comment:

From the data in this line graph, on response time between quarters, we are able to determine that there is no correlation between response times and quarters from how the lines on the graph are random.

2013

Q2 2013

2013

## Q4

2013

Q1 2014 Q2 2014 Q3 2014 Q4 2014 4.3

6 4.33 3.7

1 4.4

4 2.7

5 3.4

5 1.6

7 2.55 5.4

2 4.7

3 2.52 4.07 3.2

4 1.9

5 2.58 2.3

0 5.5

0 1.63 2.6

9 5.1

1 4.35 2.77 3.47 1.04 2.79 4.2

1

3.47

3.49 5.58

1.83

3.1

2 1.5

9 5.55 6.8

9 5.12 4.6

9 2.8

9 3.72 1.00 3.11 3.6

5 0.92

1.00

6.3

6 5.09 4.5

9 5.40 4.05 8.02 5.2

7 3.44 8.2

6 2.33 1.1

7 3.9

0 3.3

8 4.00 0.90 6.04 1.91 1.69 1.4

6 4.49 1.2

6 3.34 3.8

5

2.53 8.93

3.88 1.90 2.06

0.90
4.9

2 5.00 2.39 6.85 3.39 2.9

5

4.49

2.31 3.5

5 3.52 3.26 5.6

9 5.14

4.69

3.57 2.71 3.52

5.20 4.68 3.05

0.98 3.34

3.41 1.65 1.25 5.13 3.59 5.9

1 2.34

3.59

3.31 3.58 2.1

8 5.29 1.07

1.00

2.80 4.03

2.79

2.96 4.35 1.00

2.86 1.82 3.06

2.39

2.09 3.78 2.4

6

2.18 4.44

3.74 2.40

1.63

4.28 2.87 2.07 4.55 4.8

7 6.1

1

1.59 2.40

4.47

0.90
2.90 2.13 6.7

6 4.78

3.05 4.44

1.94

4.87
2.58

5.24 2.84 4.1

3 1.50 4.96

3.90 3.11
5.50

4.08

1.25

7.1

7

5.58

4.41 3.32

0.90
2.47 4.04 3.43 5.7

0

3.11

3.40 2.2

0

3.52
4.24

5.09

2.98

1.00

1.08 3.15

3.52

3.18 1.88

7.66 4.65

3.40

3.63

4.87 2.31 0.90
4.25

4.65

2.66 2.04 1.86 3.97

1.00

1.3

5 5.08

0.90

4.99 4.37

1.90 3.85

5.90 1.62 4.40 2.01 3.76

2.47

6.07 2.81 1.09

1.87
1.64 1.34

3.12

3.20

1.00

1.7

6 4.60 1.03 6.4

0 8.05 2.12 5.8

3

1.00 5.58 3.52 2.31
3.68 4.91 4.32 3.94 1.19 4.92 4.14 1.99 3.92 5.06 3.61

2.47

3.79 2.63

4.13 3.97
4.13 3.26

4.02 3.89 5.86 3.27 2.43

1.00
3.34

4.26

2.63

6.88

0.90 2.86 2.34

3.51 3.28 1.70

4.47

1.71 2.24 3.83

2.53

2.41 3.24 2.30

4.18 6.39

0.90

1.79

4.14 2.47
3.25 5.3

5

4.73

6.5

7 3.87 2.70 2.65

4.02
5.20 2.33 2.65 4.18 2.46 3.61

3.21 2.03 5.28 3.67 2.36

8.82

3.84

0.90 3.85

3.62 4.33 4.73

3.64 3.35

2.43 3.38 2.20

4.12 4.64 1.05 5.62

5.50

1.54 4.38 4.57 1.40 2.65

2.67

0.90

6.51

0.90 2.87

2.99 2.49 3.42 4.16

6.40 0.90

3.69 2.11 4.19

2.67
3.97 0.90 3.21 2.87

1.73

2.86

3.03

4.33
1.26 3.51 3.55

7.4

5

3.52 3.12 1.90 1.95
6.16 5.95 5.93

3.49

2.23

1.86 2.09 2.70
6.40

2.05 5.52

3.03 5.35 2.41 1.03 1.76
1.00

8.21

4.96

7.46 5.11

2.98 2.95

2.64 3.63 2.52

4.85 4.84 6.46

0.90

7.42

4.49
5.34 3.99 5.57 2.88 5.61 1.01

3.79 1.62
3.74

2.59 4.82 0.95

3.63

4.56 2.48 1.10 5.63

1.34 3.18 3.05 3.87

5.67

2.71

4.50

Response Time by

Quarter

and Year

Q1 2013

4.356805690747569 5.4

1564

5561640849 5.50

147

957886802

2.78

66492627596018 5.54956842910

323

72 3.6535666521900567 8.019138264842

331

1

4.004536792

2517

467 3.3431904438999482 4.915911533

2600

773 3.5546503494

857

462 3.52

316

51

2083

9

257

8 1.25

339

53549223953 2.1813659868144897 4.3525

1128

4

1394

726 2.45888

2833

6505686 2.0693403411656619 2.9026272313

2182

15 2.5

783

995324105491 5.4993536350026258 2.473652

345

4863346 4.2446331

617

044049 1.87643219481

979

04 4.2502707

7830

01821 5.0840524

335

741062 4.4030024509425854 1.6400465637503658 6.4004

832

59

255

9975 3.679108901394

6476

3.9

1981

2

1311

870637 4.1274743

2795

87707 3.335

307

0575118 182 3.2786815

763 1892

25 3.2

441

311231537839 3.2535645158874105

5.19

940228

235

7914 5.281745886

293

356 4.

329

6535

222

340022 4.6425480076664822 2.651

5938

470198

308

3.4188237959257095 3.972181

8592

966884 1.2641333041

1887

74 6.1579749098542376 6.40

259

37417114616 1 3.63381

6633

680

5444

5.3400354017299829 3.737601347

836

6077 5.6347801245807201 Q2 2013 4.

332

5643203628719 4.7253575742855904 1.6261836647812742 4.205002

23147

1008 6.88708437185

268

88 0.92

273

817092645904 5.2

676

703929377258 0.9 3.849

696

30279

229

01 5.003429

667

6371017 3.5156336

692

365584 5.

19655

92759428549 5.1282537227292782 5.2

8528

1393

595

5059 1 2.1758940859639551 4.55459

8807

159346 2.13347707

206

26692 5.24136439555

732

1 4.0773214535205629 4.03920998753

7470

1 5.086

1743

587360255

7.65

92344

597

214836 4.6470289347111251 0.9 2.0076011863478924 1.3415140968631021 8.0482562664896253 4.913553401207901

5.05

73001756914895 3.257

615

9340591402 4.263339950

1268

29 1.699210

1776

180788 2.2969732966215815 5.3534252841258425 2.331

2703

418254386

3.66

66470790136372 4.7275287655123979 1.0453071

3390

55895 2.6700355177366872 4.15733834

2635 1942

0.9

3.50

7

673

3

1685

92908 5.95057

4494

2056484 2.0504684001265558 8.2124891817569736 2.516807

9431

08

1423

3.9860188720

25306

2 2.5933316904469392 1.33

90093

484544194

Q3 2013

3.

714

6412572171541 2.5241054166387

769

2.689668013160

1172

3.4734687281586232 5.121887857355178 1 3.4443303369032221 6.0388986233435578 2.5292204148415478

2.38

820

1442

3422517 3.2575

328

580848875 4.6841771612223244 3.5920

977

600896733 1.0686919770948591 2.8610331858787688 4.4406181180663413

4.86

67564036138362 6.7562134566530592

2.83

61203070078047 1.2506345731951298 3.4268334778305145 2.9840077834948899 4.6549896572530276 2.658026692485437

4.98

87814887613064

3.75

90027707908304 3.1200700098695235 2.1182925186865034

4.31

61

646

820651374 3.6110

86190

473

288

5 4.020589817925357 2.6307855071779342 4.4749861038569367 4.

184

2934072762734 4.7

294

22703646124 2.646999978721142 2.363

2449

077256026 3.639

784

3862930315 5.618093

614

7272593 0.9 6.4001208150573081 3.2102573234867307 3.

5474

379322538154 5.9302431103121496 5.5190132619161165 4.9623

297

448549426 4.8508693501632667 5.5698431018088019

4.81

7243512049318 3.1770789567660542

Q4 2013

4.4392094297145377 4.0731587306290749 5.11226802346

2093 3.48

56877947313478 4.6882091838633642 6.3605414298799587 8.2577867134241387 1.911404

5345

340855 8.9296140787191689 6.8537

1106

65638465 5.687837084318744

3.04

70982

9934

29061 5.9130352484353352 1 1.8187038323085289 3.7439

606

431726133 6.1054524950159248

4.77

54579200991429 4.

1273

587031391799 7.174651283188723 5.7005295376293361 1 3.3979271266653086 2.0414006586215692 4.3706494453581399 2.46602

327

1248

5595

3.2023929280549055 5.8332041

236

13541

3.93

61662048613653 2.4685073286527768 3.8865800989733543 6.8755

1029

0

3232

91 1.7119800860236865 6.3871489247540012 6.5707099

6667 60769

4.1814614734030329 8.8249639803543687 3.3480947750867927 5.499761538070743 6.5071526579267811 0.9 2.8718966505985009 7.4505069379520137 3.48786512504

739

22 3.0321399536696845 7.4588620110298507 4.844769601826556 2.8833146744582336 0.95167707614018582 3.0501850106738857 Q1 2014 2.74

5604

0207704064 3.2393556203765912 4.35392261907

1090

2 5.5

83725

438651

1628

2.894123937135737 5.0948083718190897 2.3263553849625169 1.68635

192

14035478 3.87925

847

10841767 3.391531

705

4430489 5.1440984371816736 0.98274408274446623 2.3405503235204379 2.8036

798

04

9521168 3.0573333298030776 2.4015251220640494 1.588542587438

1327

3.050259

7347

600386 1.5024861987563782 5.58

1679

0755721737 3.1106598463389674 1.0826270646299236 3.

6316

638862495894 1.8572

6075

51555849 1.8951628099835944 6.0711554816458371 1 1 1.1885672812291888 3.78

6145

5403850415 5.8584701456362378 0.9 2.2395776532954188 0.9 3.8749611086182996 2.464285372394079 3.8408806368403021 2.429744468923309 1.5390717600035715 0.9 3.6867980235052529 1.7277737207274186 3.5219481297695894 2.2330224702323904 5.35140183

8293

5316 5.1112406673433721 6.4554624678799879 5.6095641831285317 3.63205098

99320

315

3.86

95416570641101 Q2 2014 3.4465603756718339 1.95467528909212

2.76

9119381

7037

858 1.830401933041867 3.7153588062967176 4.

5882

04054819653 1.1652720867306927 1.4585909492627254 1.8973007253254766 2.954022

1556

84652 4.68

7944

24

6036

9321 3.3438613708160121 3.5946013293898433 4.0304668881464751 2.38

5789

8749003654 1.6263281476160047 2.3982745086716024 4.4406580935930835

4.95

79172890691554 4.4146033441240435 3.3970261109818241 3.1488661615032472 4.872832695476

2453

3.969714915804798 3.8509883405669827 2.8099522832082586 1.761472239089

1986

5.5786442397977227 4.9162933545478156 2.6285494722134901 3.2720810930943118 2.8562667092803169 3.83486

68648

570312 1.7931613082357218 2.7003026924678126 3. 61359089664

1835

7 0.9 3.3844030066422421 4.3807401

2789

29321 2.872878402634524 2.

113

6076692375356 2.8578058016893921 3.1247515916067643 1.85992958

8029

6269 2.4143211784423331 2.9756362972

7228

56 0.9 1.0139794620801696 4.5589501577371268 5.6660748749738561 Q3 2014 1.6701319585336023 2.5849427136818122 3.4712812824436696 3.1168675112239725 1 5.3960551516211126 3.895330913408543 4.4883640915286378 2.0577209700859385 4.4860002011118922

3.56

69281790687819 3.4085343334736535 3.3083

6571

34084206 2.7882290472261957 2.0893796280033712

4.27

85482113031321 4.4665714616057812 1.9354151921361336 3.8966397899712319 3.3183290004926675 2.1960299894344644 3.5221082233

21993

1 2.3136046896324842 1 5.8955778361705597 1.0873686808990897 4.5958403309923597 3.5192415528654237 4.14

1574

4438636466 4.133797013

608

2731 2.4295045553371892 2.3373820

6436

82848 2.5318425476398261 4.1416370853112312 2.64

5699

97246

1443

4 3.211

1527

80593693 3.85011697592563 2.202989783952944 4.5730

1576

5643504 2.9913637225290586 4.1850706869154237

3.02

59632315646741 1.9018393762307824 2.0914913041706313 1.0339421199460048 2.9528837406614912 7.4192420318722725 3.7933836059237365 2.4752080851867504 2.7

1286

47919453215 Q4 2014 2.5510757

682

699476 2.30313841

7619

6297 1.043

2483

764365315 1.5865764185495208 3.1144282689187093 4.0469112450868128

3.37

78203219757414 1.25575681572

6635

9 0.9 2.3109832641

6977

21 2.7098836613280581 1.6538044479151721 3.5820508815508219 2.9565219124837312 3.

7752

575695325503 2.8747584524811827 0.90147952555562361 4.8724379853869326

3.10

8204

7103

613148 0.9 3.51625

7921

1377305 3.1823331897161551 0.9 1.3526853040733839 1.6183518896927125 1.8669454407703596 1.0325304361234884 2.31182863949507 1.9896637882542563 3.9

6894

45844036526 1 3.5086081612011184 2.410366592403443 2.4695753796098869

4.01

89783890586117 2.0281505344886681 3.6200026175269158 4.1219250038469912 1.4048089001793413 2.4852340362034737 2.667

6015

937031479 4.3273157376010207 1.9502917626

1450

62 2.7026329421918489

1.75

8633944109897 2.6436946159723447 4.4879045349720403 1.6248547768103889 1.

10000

00000000001 4.4970204003679104

From the data in this line graph, on response time between quarters, we are able to determine that there is no correlation between response times and quarters from how the lines on the graph are random.

## Part 2 – Shipping Cost

/

Plant Existing /Proposed

Existing

.36

Kansas City Existing

Existing Toronto

Santiago Existing

Kansas City Existing

\$2.13

Proposed

Santiago Existing Shanghai

Proposed

Kansas City Existing

Proposed

Santiago Existing Mexico City

\$1.58

Proposed

Kansas City Existing

Proposed

Santiago Existing Melbourne \$1.49

Kansas City Existing

\$1.49

Santiago Existing London \$1.58

Kansas City Existing

Santiago Existing Caracas

Kansas City Existing

Santiago Existing Atlanta \$1.31 \$1.76
Singapore Proposed Toronto

\$2.03

Birmingham Proposed Toronto

Mowers

Frankfurt Proposed Toronto

Existing Proposed Existing Proposed

Mumbai Proposed Toronto

\$2.14 1

Auckland Proposed Toronto \$1.86

2 50%

Singapore Proposed Shanghai

\$1.78 3

Birmingham Proposed Shanghai

4

Frankfurt Proposed Shanghai

Mumbai Proposed Shanghai

\$1.47

Auckland Proposed Shanghai

Singapore Proposed Mexico City \$1.72

Birmingham Proposed Mexico City

\$1.79

Frankfurt Proposed Mexico City \$1.54

Mumbai Proposed Mexico City

Auckland Proposed Mexico City

Singapore Proposed Melbourne

Birmingham Proposed Melbourne \$1.52

Frankfurt Proposed Melbourne

Mumbai Proposed Melbourne

\$1.63

Auckland Proposed Melbourne

Singapore Proposed London

Birmingham Proposed London \$1.47

Frankfurt Proposed London

Mumbai Proposed London \$1.44 \$1.82
Auckland Proposed London

Singapore Proposed Caracas \$1.50

Birmingham Proposed Caracas \$1.37 \$1.86
Frankfurt Proposed Caracas

\$1.88

Mumbai Proposed Caracas

Auckland Proposed Caracas \$1.54 \$1.98
Singapore Proposed Atlanta \$1.73

Birmingham Proposed Atlanta

Frankfurt Proposed Atlanta

\$1.70

Mumbai Proposed Atlanta

Auckland Proposed Atlanta

 Unit Shipping Cost Plant Existing Proposed Customer Mowers Tractors Kansas City Toronto \$1 \$1.79 Santiago \$1.49 \$2.13 Shanghai \$1.58 Auckland \$1.47 \$2.03 Birmingham Mexico City \$1.32 \$1.76 Frankfurt \$1.22 Mumbai Melbourne \$1.72 \$2.34 Singapore \$1.80 London \$1.86 \$2.14 Caracas \$1.54 \$1.90 \$1.00 \$1.26 Atlanta \$1.31 \$1.82 \$1.71 \$1.34 \$1.78 Tactors \$1.52 \$1.87 Quartiles \$1.67 25% \$ 1.31 \$ 1.77 \$ 1.40 \$ 1.78 \$2.19 \$ 1.48 \$ 1.84 \$ 1.52 \$ 2.01 \$1.44 75% \$ 1.53 \$ 2.11 \$ 1.66 \$ 2.17 \$1.60 \$2.15 100% \$ 1.72 \$ 2.34 \$ 1.98 \$ 2.68 \$1.65 \$2.32 \$1.21 \$1.18 \$1.63 \$2.09 \$1.29 \$2.04 \$1.56 \$2.22 \$1.50 \$2.07 \$1.43 \$1.70 \$2.06 \$1.73 \$2.28 \$1.38 \$0.91 \$1.17 \$1.88 \$2.68 \$1.77 \$1.37 \$1.64 \$1.98 \$ 2.60 \$2.01 \$1.59 \$1.61 \$2.08 \$2.35 \$1.02 \$1.25 \$1.42 \$1.57 \$2.23 \$1.74 \$2.26

You can see in the table of quartiles with Mowers and Tactors in Existing and Proposed shipping cost locations that Mowers have a slight increase in shipping costs in the proposed then the existing. There is also an increase in shipping cost in Tactors in Proposed locations compared to Existing locations.

## Fixed Cost

Plants

Cost

Kansas City 10000

5,000.00

Kansas City

Santiago

Santiago 10000

Cost

Auckland

Auckland

Birmingham 15,000

Birmingham 20,000

Frankfurt 15,000

Frankfurt 20,000

Mumbai 15,000

Mumbai

Singapore 15,000

Singapore 20,000

 Fixed Costs of Capacity Increase or New Construction Current Additional Capacity \$60 20000 \$985,000.00 5000 \$381,000.00 \$680,000.00 Proposed Locations Maximum capacity 15,000 \$917,000.00 20,000 \$1,136,000.00 \$962,000.00 \$1,180,000.00 \$874,000.00 \$1,093,000.00 \$750,000.00 25,000 \$959,000.00 \$839,000.00 \$1,058,000.00

## Part 3 – Regions and Averages

China

2.60

Eur 4.33 4.10 3.90 3.87
NA 4.27 4.60 3.71 4.31
Pac 3.90 4.40 4.10

SA 3.92 4.28 3.50 4.24

4.40 3.67 4.14

 Row Labels Average of Ease of Use Average of Quality Average of Price Average of Service 4.10 3.80 3.00 4.30 Grand Total 4.17

## part 3

Row Labels Average of Price Average of Service Average of Ease of Use Average of Quality
China 3 2.6 4.1 3.8
Eur 3.9

4.1

NA 3.71 4.31 4.27 4.6
Pac 4.1 4.3 3.9 4.4
SA 3.5 4.24 3.92 4.28
Grand Total 3.67 4.14

3.8666666667 4.3333333333
4.165 4.395

Average of Price China Eur NA Pac SA 3 3.9 3.71 4.0999999999999996 3.5 Average of Service China Eur NA Pac SA 2.6 3.8666666666666667 4.3099999999999996 4.3 4.24 Average of Ease of Use China Eur NA Pac SA 4.0999999999999996 4.333333333333333 4.2699999999999996 3.9 3.92 Average of Quality China Eur NA Pac SA 3.8 4.0999999999999996 4.5999999999999996 4.4000000000000004 4.28

Q1

Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance

Quality 200 879 4.395

818844221

Ease of Use 200 833 4.165

108291457

Price 200 734 3.67

ANOVA
Source of Variation SS df MS F P-value F crit

Between Groups

33333333

2

0

08

42

Within Groups

597

764

25

Total

599

 0.5 0.6 1.1367839196 5 4.90 27.4516666667 35.3531181914 3.01 1520 463.57 0.7 9916 518.4733333333

## Part 3 – 2014 Customer Survey

2014 Customer Survey
Region Quality Ease of Use Price Service North America South America Europe Pacific Rim China
NA 4 1 3 4 Quality Ease of Use Price Service Quality Ease of Use Price Service Quality Ease of Use Price Service Quality Ease of Use Price Service Quality Ease of Use Price Service
NA 4 4 4 5 0 0% 1 1 1 2 0 0% 1 1 1 1 0 0% 2 3 1 1 0 0% 3 2 3 3 0 0% 2 3 2 1
NA 4 5 4 3 1 25% 4 4 3 4 1 25% 4 4 3 4 1 25% 4 4 4 3.25 1 25% 3 2 3 3 1 25% 3.25 4 3 2
NA 5 4 4 4 2 50% 5 4 4 4 2 50% 4 4 4 4 2 50% 4 4 4 4 2 50% 4 4 4 4 2 50% 4 4 3 3
NA 5 4 5 4 3 75% 5 5 4.25 5 3 75% 5 4 4 5 3 75% 5 5 5

3 75% 4.5 4 4 4 3 75% 4 4 3 3

NA 5 5 3 5 4 100% 5 5 5 5 4 100% 5 5 5 5 4 100% 5 5 5 5 4 100% 5 4 4 5 4 100% 5 5 4 4
NA 5 4 4 2
NA 5 5 4 5
NA 4 4 4 5
NA 4 5 4 5
NA 4 5 1 4
NA 5 5 4 4

NA 5 4 3 3 North America South America Europe Pacific Rim China
NA 4 5 4 4

Quality Ease of Use Price Service Value Quality Ease of Use Price Service Value Quality Ease of Use Price Service Value Quality Ease of Use Price Service Value Quality Ease of Use Price Service

NA 5 4 3 5 1 1 2 5 0 1 1 1 2 1 1 0 0 2 1 1 0 0 0 0 1 0 0 0 1
NA 5 5 2 5 2 0 2 10 3 2 0 1 8 0 2 1 0 1 2 2 0 1 0 0 2 1 0 2 3
NA 5 4 2 5 3 3 6 19 8 3 4 6 10 6 3 6 3 4 5 3 1 1 1 1 3 2 1 6 5
NA 5 4 2 5 4 30 47 41 44 4 24 35 23 22 4 12 14 14 14 4 4 6 7 5 4 5 7 2 1
NA 4 5 4 4 5 66 43 25 45 5 21 7 7 21 5 11 13 9 8 5 5 2 2 4 5 2 2 0 0
NA 4 4 5 4
NA 4 4 2 4
NA 4 3 3 4
NA 5 5 2 5
NA 5 3 4 3
NA 5 4 4 5

NA 5 5 2 5

NA 5 5 5 3

NA 4 4 5 4

NA 5 4 4 4
NA 5 1 5 5
NA 5 4 3 5

NA 4 5 1 4

NA 4 4 3 5
NA 5 3 4 4
NA 5 5 2 4

NA 5 4 4 4

NA 5 5 4 4

NA 5 5 4 5

NA 4 3 3 5
NA 5 4 4 3
NA 5 4 3 4
NA 5 5 1 5
NA 5 4 5 4
NA 3 4 3 4
NA 5 4 2 4

NA 5 5 4 5

NA 5 5 3 4

NA 5 4 4 4
NA 5 4 4 4
NA 5 4 4 5

NA 5 4 1 4
NA 5 4 5 5

NA 5 5 3 4
NA 5 4 4 5

NA 4 3 5 5
NA 5 4 4 4 Q1
NA 5 5 5 5
NA 5 5 4 5 Anova: Single Factor
NA 4 4 4 4
NA 5 4 5 5 SUMMARY
NA 4 5 5 4 Groups Count Sum Average Variance
NA 5 5 5 4 Quality 200 879 4.395 0.5818844221
NA 5 5 3 5 Ease of Use 200 833 4.165 0.6108291457
NA 5 4 4 4 Price 200 734 3.67 1.1367839196
NA 5 4 5 2
NA 4 4 5 5
NA 4 4 4 5 ANOVA
NA 5 4 4 4 Source of Variation SS df MS F P-value F crit
NA 5 4 3 5 Between Groups 54.9033333333 2 27.4516666667 35.3531181914 0 3.0108152042
NA 5 4 5 4 Within Groups 463.57 597 0.7764991625

NA 5 5 4 5

NA 5 4 4 4 Total 518.4733333333 599

NA 5 4 5 2

NA 5 3 4 5

NA 5 4 5 5

NA 5 4 1 5
NA 4 5 3 5
NA 3 5 2 5

NA 5 5 4 4
NA 4 4 3 5

NA 3 2 4 5
NA 1 4 3 4

NA 4 5 3 5
NA 5 5 4 4

NA 4 5 5 5

NA 5 5 4 5
NA 5 5 4 4

NA 4 2 4 5

NA 5 4 5 4
NA 5 4 5 4

NA 5 5 4 3

NA 5 5 5 5

NA 4 5 5 3

NA 5 5 4 5
NA 4 4 5 5
NA 5 5 3 4

NA 4 5 2 4
NA 5 5 5 4
NA 4 5 4 3
NA 4 5 5 4
SA 5 4 3 5
SA 5 4 2 4
SA 5 4 5 5
SA 4 2 4 5
SA 5 4 4 5
SA 4 5 2 5
SA 5 4 4 4
SA 4 5 3 5
SA 4 4 4 3
SA 4 4 2 4
SA 5 4 3 4
SA 3 3 5 5

SA 5 4 3 4

SA 5 4 2 5
SA 4 4 3 4
SA 4 4 3 5
SA 1 5 3 4

SA 5 4 2 4

SA 4 4 4 4
SA 4 4 5 5

SA 5 4 2 4
SA 4 4 5 5
SA 4 4 4 3

SA 3 3 4 5

SA 5 4 4 4

SA 4 4 4 1
SA 4 5 5 5
SA 4 1 4 5
SA 4 5 4 4
SA 4 4 4 5

SA 5 4 3 4
SA 4 4 4 5

SA 5 5 4 3
SA 5 5 4 4

SA 4 4 2 4
SA 4 4 4 5
SA 5 4 4 5
SA 5 4 4 4

SA 5 4 1 4
SA 3 4 4 5
SA 4 3 5 4
SA 4 4 2 3
SA 5 4 3 3
SA 4 3 4 5
SA 5 3 5 5

SA 5 4 4 4
SA 5 4 4 4

SA 3 4 3 4
SA 4 4 1 4
SA 4 3 4 3
Eur 4 5 5 3
Eur 4 4 4 2
Eur 3 4 5 4
Eur 3 4 1 3
Eur 4 4 5 5
Eur 5 5 5 5
Eur 5 5 5 1
Eur 4 5 5 4
Eur 3 4 4 4
Eur 3 5 3 3
Eur 4 4 5 4
Eur 5 4 5 5
Eur 5 3 4 4
Eur 5 5 4 5

Eur 3 4 4 4

Eur 4 5 4 5
Eur 4 5 4 4
Eur 5 4 4 5

Eur 4 5 4 4

Eur 3 5 3 4

Eur 4 4 4 2

Eur 5 5 3 4
Eur 5 3 4 5
Eur 4 5 2 4
Eur 4 3 4 4
Eur 5 4 3 3
Eur 2 4 4 4
Eur 5 4 5 4
Eur 4 5 4 3
Eur 5 4 1 5
Pac 5 4 4 5
Pac 5 5 5 5
Pac 4 4 4 4
Pac 4 3 4 4
Pac 5 4 5 4

Pac 4 4 4 4

Pac 5 5 4 5
Pac 4 2 3 3
Pac 3 4 4 4

Pac 5 4 4 5

China 5 5 4 4
China 5 5 4 3
China 4 4 3 3

China 4 4 3 3

China 4 4 3 2

China 4 4 3 3
China 4 4 3 2

China 3 4 3 3
China 3 4 2 2
China 2 3 2 1
Quartiles
4.75
Frequency Distrbution
Value

North America

1 Quality Ease of Use Price Service 1 2 5 0 2 Quality Ease of Use Price Service 0 2 10 3 3 Quality Ease of Use Price Service 3 6 19 8 4 Quality Ease of Use Price Service 30 47 41 44 5 Quality Ease of Use Price Service 66 43 25 45

South America

1 Quality Ease of Use Price Service 1 1 2 1 2 Quality Ease of Use Price Service 0 1 8 0 3 Quality Ease of Use Price Service 4 6 10 6 4 Quality Ease of Use Price Service 24 35 23 22 5 Quality Ease of Use Price Service 21 7 7 21

Europe

1 Quality Ease of Use Price Service 0 0 2 1 2 Quality Ease of Use Price Service 1 0 1 2 3 Quality Ease of Use Price Service 6 3 4 5 4 Quality Ease of Use Price Service 12 14 14 14 5 Quality Ease of Use Price Service 11 13 9 8

Pacific Rim

1 Quality Ease of Use Price Service 0 0 0 0 2 Quality Ease of Use Price Service 0 1 0 0 3 Quality Ease of Use Price Service 1 1 1 1 4 Quality Ease of Use Price Service 4 6 7 5 5 Quality Ease of Use Price Service 5 2 2 4

China

1 Quality Ease of Use Price Service 0 0 0 1 2 Quality Ease of Use Pric e Service 1 0 2 3 3 Quality Ease of Use Price Service 2 1 6 5 4 Quality Ease of Use Price Service 5 7 2 1 5 Quality Ease of Use Price Service 2 2 0 0

In this chart with the frequency distribution for North America, you can see that the quality, ease of use, and service production areas don’t need to really change anything. Those areas can do the same thing they are doing. The price section in this chart needs improvment in their pricing, by the wide variation in the distribution, you can reduce costs or use different materials.

In this chart with the frequency distribution for South America, you can see that quality and service areas don’t need to change anything they can keep on doing what they are doing. The ease of use can improve in turing all of those 4’s into 5’s for better ratings. Price again can change by reducing costs or changing materials to reduce the pricing.

In this chart with the frequency distribution shown in a historgram for Europe region, you can see all areas; quality, ease of use, price, and service all need improvments to get higher ratings from consumers. Price can reduce costs. Service can train their service workers to help customers better. Ease of use can improve the design of the product. Quality can improve on the procurment side to making better products.

In this chart with the frequency distribution shown in a histogram for Pacific Rim region, you can see most of the areas most rated number is 4’s. So, service, price, and ease of use can improve a little bit to make some of those 4’s into 5’s. Quality can improve the overall quality in products from the procurment side.

In this chart showning the China regions distribution between areas and ratings. All areas need improvment to make the customers want to get these products again. Quality needs to improve the quality of the product by changing the procument side of things. Ease of use comes from that if the quality is good and making it easy to use will follow a little. We need to train or hire more people to help with the companies customer service so our customers have a good experience with our company. Overall everything is connected so if you focus on some areas the others will some what follow.

## Unit Production Costs

Unit Production Costs
Month Tractor Mower
Jan-10

\$1

\$1

Feb-10

\$1 \$50 \$1

Mar-10

\$1

\$1

Apr-10

\$1 \$51 \$1

May-10

\$1 \$51 \$1

Jun-10

\$1 \$51 \$1

Jul-10

\$1 \$51 \$1

Aug-10

\$1 \$51 \$1

Sep-10

\$1

\$1

Oct-10

\$1 \$52 \$1

Nov-10

\$1 \$52 \$1

Dec-10

\$1 \$52 \$1

Jan-11

\$1

\$1

Feb-11

\$1 \$55 \$1

Mar-11

\$1 \$55 \$1

Apr-11

\$1 \$55 \$1

May-11

\$1

\$1

Jun-11

\$1 \$56 \$1

Jul-11

\$1 \$56 \$1

Aug-11

\$1 \$56 \$1

Sep-11

\$1 \$56 \$1

Oct-11

\$1

\$1

Nov-11

\$1 \$57 \$1

Dec-11

\$1 \$57 \$1

Jan-12

\$1

\$1

Feb-12

\$1 \$59 \$1

Mar-12

\$1 \$59 \$1

Apr-12

\$1 \$59 \$1

May-12

\$1 \$59 \$1

Jun-12

\$1 \$60 \$1

Jul-12

\$1 \$60 \$1

Aug-12

\$1 \$60 \$1

Sep-12

\$1 \$60 \$1

Oct-12

\$1 \$60 \$1

Nov-12

\$1

\$1

Dec-12

\$1 \$61 \$1

Jan-13

\$1 \$59 \$1

Feb-13

\$1 \$59 \$1

Mar-13

\$1 \$59 \$1

Apr-13

\$1 \$59 \$1

May-13

\$1 \$60 \$1

Jun-13

\$1 \$60 \$1

Jul-13 \$1,976 \$1 \$60 \$1
Aug-13 \$1,983 \$1 \$60 \$1
Sep-13 \$1,990 \$1 \$60 \$1
Oct-13 \$1,996 \$1 \$60 \$1
Nov-13

\$1 \$61 \$1

Dec-13

\$1 \$61 \$1

Jan-14

\$1

\$1

Feb-14

\$1 \$63 \$1

Mar-14

\$1 \$63 \$1

Apr-14

\$1 \$63 \$1

May-14

\$1 \$63 \$1

Jun-14

\$1 \$63 \$1

Jul-14

\$1

\$1

Aug-14

\$1 \$64 \$1

Sep-14

\$1 \$64 \$1

Oct-14

\$1 \$64 \$1

Nov-14

\$1 \$64 \$1

Dec-14

\$1 \$64 \$1

\$1,938

\$1,750 \$50
\$1,755
\$1,763 \$51
\$1,770
\$1,778
\$1,785
\$1,792
\$1,795
\$1,801 \$52
\$1,804
\$1,810
\$1,813
\$1,835 \$55
\$1,841
\$1,848
\$1,854
\$1,860 \$56
\$1,866
\$1,872
\$1,878
\$1,885
\$1,892 \$57
\$1,897
\$1,903
\$1,925 \$59
\$1,931
\$1,938
\$1,944
\$1,950
\$1,956
\$1,963
\$1,969
\$1,976
\$1,983
\$1,990 \$61
\$1,996
\$1,940
\$1,946
\$1,952
\$1,958
\$1,964
\$1,970
\$2,012
\$2,008
\$2,073 \$63
\$2,077
\$2,081
\$2,086
\$2,092
\$2,098
\$2,104 \$64
\$2,110
\$2,116
\$2,122
\$2,129
\$2,135
\$58

Tractor

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 1750 1755 1763 1770 1778 1785 1792 1795 1801 1804 1810 1813 1835 1841 1848 1854 1860 1866 1872 1878 1885 1892 1897 1903 1925 1931 1938 1944 1950 1956 1963 1969 1976 1983 1990 1996 1940 1946 1952 1958 1964 1970 1976 1983 1990 1996 2012 2008 2073 2077 2081 2086 2092 2098 2104 2110 2116 2122 2129 2135

Mower

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 50 50 51 51 51 51 51 51 52 52 52 52 55 55 55 55 56 56 56 56 56 57 57 57 59 59 59 59 59 60 60 60 60 60 61 61 59 59 59 59 60 60 60 60 60 60 61 61 63 63 63 63 63 63 64 64 64 64 64 64

## Operating & Interest Expenses

Month

Interest

Jan-10

Feb-10

Mar-10

Apr-10

May-10

Jun-10

Jul-10

Aug-10

Sep-10

Oct-10

Nov-10

Dec-10

Jan-11

Feb-11

Mar-11

Apr-11

May-11 \$676,581 \$154,989

Jun-11

Jul-11

Aug-11

Sep-11

Oct-11

Nov-11

Dec-11

Jan-12

Feb-12

Mar-12

Apr-12

May-12

Jun-12

Jul-12

Aug-12

Sep-12

Oct-12

Nov-12

Dec-12

Jan-13

Feb-13

Mar-13

Apr-13

May-13

Jun-13

Jul-13

Aug-13

Sep-13

Oct-13

Nov-13

Dec-13

Jan-14

Feb-14

Mar-14

Apr-14

May-14

Jun-14

Jul-14

Aug-14

Sep-14

Oct-14

Nov-14

Dec-14

 Operating and Interest Expenses Administrative Depreciation \$633,073 \$140,467 \$7,244 \$607,904 \$165,636 \$7,679 \$630,687 \$142,853 \$6,887 \$613,401 \$160,139 \$6,917 \$607,664 \$165,876 \$8,316 \$632,967 \$140,573 \$7,428 \$609,604 \$163,936 \$8,737 \$607,749 \$165,791 \$7,054 \$603,367 \$170,173 \$8,862 \$629,083 \$144,457 \$8,488 \$611,995 \$161,545 \$7,049 \$625,712 \$147,828 \$8,807 \$656,123 \$175,447 \$7,430 \$652,679 \$178,891 \$6,791 \$655,521 \$176,049 \$8,013 \$676,581 \$154,989 \$8,979 \$7,484 \$656,440 \$175,130 \$7,858 \$661,969 \$169,601 \$7,424 \$677,212 \$154,358 \$6,848 \$653,545 \$178,025 \$6,751 \$657,388 \$174,182 \$8,160 \$672,475 \$159,095 \$7,898 \$656,325 \$175,245 \$8,953 \$723,594 \$226,526 \$9,443 \$759,042 \$191,078 \$8,464 \$749,187 \$200,933 \$10,264 \$751,499 \$198,621 \$8,547 \$741,452 \$208,668 \$8,578 \$729,122 \$220,998 \$9,519 \$734,783 \$215,337 \$9,343 \$748,208 \$201,912 \$8,448 \$738,186 \$211,934 \$9,957 \$759,403 \$190,717 \$9,738 \$726,183 \$223,937 \$9,785 \$757,037 \$193,083 \$8,191 \$672,232 \$179,138 \$9,914 \$665,023 \$186,347 \$9,954 \$667,657 \$183,713 \$10,859 \$654,198 \$197,172 \$9,730 \$659,435 \$191,935 \$10,430 \$661,190 \$190,180 \$10,222 \$647,321 \$204,049 \$10,102 \$666,743 \$184,627 \$10,610 \$678,705 \$172,665 \$9,374 \$658,990 \$192,380 \$10,830 \$656,221 \$195,149 \$9,017 \$676,934 \$174,436 \$10,423 \$641,571 \$210,589 \$9,985 \$634,973 \$217,187 \$9,766 \$662,054 \$190,106 \$11,148 \$654,962 \$197,198 \$9,339 \$645,579 \$206,581 \$9,468 \$658,112 \$194,048 \$10,324 \$637,711 \$214,449 \$9,737 \$638,317 \$213,843 \$9,290 \$651,996 \$200,164 \$9,213 \$630,766 \$221,394 \$10,143 \$645,095 \$207,065 \$10,383 \$637,807 \$214,353 \$9,059

## Industry Mower Total Sales

Industry Mower Total Sales
Month NA SA Eur Pac World
Jan-10

1 571 1

1

1

1

Feb-10

1 611 1

1 1111 1

1

Mar-10

1 658 1

1

1

1

Apr-10 86190 1 778 1

1 1237 1

1

May-10

1 886 1

1

1

1

Jun-10

1 882 1

1 1176 1

1

Jul-10

1 848 1

1 1359 1

1

Aug-10 79804 1 735 1

1

1

1

Sep-10

1 657 1

1

1

1

Oct-10

1 595 1

1

1

1

Nov-10

1 553 1

1 1262 1

1

Dec-10

1 462 1

1 1386 1

1

Jan-11

1 553 1

1 1443 1

1

Feb-11

1 615 1

1

1

1

Mar-11

1 658 1

1

1

1

Apr-11

1 784 1

1 1442 1

1

May-11

1 846 1

1 1215 1

1

Jun-11

1 838 1

1 1333 1

1

Jul-11

1 763 1

1 1415 1

1

Aug-11

1 694 1

1 1296 1

1

Sep-11 60769 1 625 1 29444 1 1402 1

1

Oct-11

1 610 1

1

1

1

Nov-11

1 571 1

1

1

1

Dec-11

1 512 1

1

1

1

Jan-12

1 537 1

1

1

1

Feb-12

1 595 1

1 1402 1

7

1

Mar-12

1 659 1

1

1

1

Apr-12

1 756 1

1 1574 1

1

May-12

1 878 1

1 1468 1

1

Jun-12

1 825 1

1 1560 1

1

Jul-12 86190 1 756 1 24828 1

1

1

Aug-12

1 714 1

1

1

1

Sep-12 60000 1 651 1

1

1

1

Oct-12

1 643 1

1

1

1

Nov-12

1 619 1 15273 1 1810 1

1

Dec-12

1 548 1

0

1

1

Jan-13

1 581 1

0 1887 1

1

Feb-13

1 614 1

1 1845 1

1

Mar-13

1 622 1 19655 1

1

1

Apr-13

1 727 1 25179 1 1981 1

1

May-13 90093 1 826 1

1 1810 1

1

Jun-13

1 783 1

1 1942 1

1

Jul-13

1 681 1 24737 1

1

1

Aug-13

1 646 1

1 2000 1

1

Sep-13

1 625 1

1

1

1

Oct-13

1 617 1

1

1

1

1

Nov-13

1 587 1

1 2095 1

1

Dec-13

1 591 1

0

1

1

Jan-14

1 563 1

0 1852 1

1

Feb-14

1 571 1

1 1743 1

1

Mar-14 83725 1 625 1

1 1892 1

1

Apr-14

1 723 1

1 2037 1

1

May-14

1 848 1

1 1887 1

83

1

Jun-14 99320 1 792 1 25306 1 1944 1

1

Jul-14

1 745 1

1

1

1

Aug-14

1 739 1

1 2037 1

61

1

Sep-14

1 667 1

1

1

1

Oct-14

1 660 1

1 2072 1

1

Nov-14

8

1 625 1

1 2182 1 68648 1

Dec-14

1 608 1 6977 0 2035 1

1

676

1628

60000 13091 1045 74662
77184 17679 96585
77885 22759 1068 102369
27966 116171
96117 27895 1313 126210
97143 30566 129768
84757 29444 116409
28364 1238 110141
64800 28393 1215 95065
59307 2

4444 1154 85500
52157 18000 71972
45049 12453 59349
58627 12778 73401
76200 18214 1515 96545
82871 23889 1373 108791
84904 29455 116584
93100 29464 124625
9

3000 27414 122585
83048 27368 112594
74854 27321 104164
92241
55619 23774 1468 81470
48155 17308 1351 67386
42647 12941 1389 57489
57885 10962 1509 70892
77647 1

5273 9491
8

1845 20556 1524 104583
86095 26786 115211
91776 24828 118949
100680 24737 127801
1441 113216
71887 25179 1545 99325
24545 1667 86863
55566 19286 1698 77193
50857 68558
42596 9107 1731 53982
58095 8571 69135
75566 13158 91182
80286 1923 102486
85140 113027
23103 115832
95472 24286 122482
87308 1961 114686
74476 26607 103729
61698 22982 2075 87381
57238 16897 2019 7677
50673 13750 67105
51238 7818 2150 61797
59712 7547 69673
77961 13889 94165
18302 104544
90297 25192 118250
91143 24706 1185
127363
93922 27083 2170 123919
73143 26042 1019
66699 2

6304 2018 9

5688
56476 22558 81766
5106 14773
46893 56510
72581 21120 96004

North America

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 4164 0 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 60000 77184.466019417479 77884.61538461539 8619

0.4

76190476198 96116.504854368934 97142.857142857145 8475

7.2

8

1553398061 79803.921568627455 64800 5930

6.9

30693069306 52156.862745098042 450

48.5

43689320388 58627.450980392161 76200 82871.287128712866 84903.846153846156 93100 93000 830

47.6

19047619053 748

54.3

68932038837 60769.230769230773 55619.047619047618 4815

5.33

9805825242 42647.058823529413 57884.61538461539 77647.058823529413 81844.660194174765 8609

5.23

8095238092 91775.700934579436 10067

9.6

1165048544 86190.476190476198 71886.792452830196 60000 55566.037735849059 5085

7.14

2857142862 42596.153846153851 58095.238095238099 75566.037735849066 80285.71428571429 85140.186915887854 90092.592592592599 95471.

6981

13207545 87307.692307692312 74476.190476190473 61698.113207547169 57238.095238095237 5067

3.07

6923076922 51238.095238095237 59711.538461538461 77961.165048543699 83725.490196078434 90297.029702970292 91142.857142857145 9932

0.3

88349514571 93921.568627450994 7

3142

.857142857145 66699.029126213602 56476.190476190481 5106

7.9

61165048546 46893.203883495145

South America

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 4164 0 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 571.42857142857144 611.11111111111109 657.

8947

368421052 77

7.7

7777777777783 885.71428571428578 882.35294117647049 848.4848484848485 735.29411764705878 657.14285714285722 594.59459459459458 552.63157894736844 461.53846153846155 552.63157894736844 615.38461538461536 65

7.8

9

47368421052 783.78378378378375 846.15384615384608 837.83783783783781 763.15789473684208 694 625 60

9.7

560975609756 571.42857142857144 512.19512195121956 536.58536585365857 595.2380952380953 658.53658536585374 756.09756097560978 878.04878048780495 825 756.09756097560978 714.28571428571433 651.16279069767438 642.85714285714289 619.04761904761904 547.61904761904759 581.39534883720933 613.63636363636363 622.22222222222217 72

7.27

272727272725 826.0869565217

3913

782.60869565217388 68

0.8

5

1063

82978722 6

45.8

3333333333337 625 617.021

2765

9574467 586.95652

1739

13038 590.90909090909088 562.5 571.42857142857144 625 723.404255319149 847.82608695652175 791.66666666666674 744.68085106382978 739.13043478260863 66

6.6

6666666666674 65

9.5

74468085

10645

625 608

Europe

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 13090.90909090909 17678.571428571428 22758.62068965517 27966.

1016

94915254 27894.736842105263 30566.037735849059 29444.444444444445 28363.636363636364 28392.857142857141 24444.444444444445 18000 124

52.8

30188679245 12777.777777777777 18214.285714285714 23888.888888888891 29454.545454545456 29464.285714285714 27413.793103448275 27368.421052631576 27321.428571428572 29444.444444444445 23773.584905660377 17307.692307692309 12941.176470588236 10961.538461538463 15272.727272727272 20555.555555555555 26785.714285714286 24827.586206896551 24736.842105263157 24827.586206896551 25178.571428571428 24545.454545454544 19285.714285714286 15272.727272727272 9107.1428571428569 8571.4285714285706 13157.894736842105 196

55.1

72413793101 25178.571428571428 23103.448275862069 24285.714285714286 24736.842105263157 26607.142857142855 22982.456140350878 16896.551724137931 13750 7818.181818181818 754

7.16

98113207549 13888.888888888889 18301.886792452831 25192.307692307695 24705.882352941178 25306.12244897959 27083.333333333332 26041.666666666668 26304.347826086956 225

58.1

39534883721 14772.727272727274 6976.7441860465124

Pacific

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 1045 1111.1111111111111 1067.9611650485438 123

7.11

34020618556 1313.1

3131

31313132 1176.4705882352941 1359.2233009708739 1238.0952380952381 1214.9532710280373 1153.8461538461538 1262.13

5922

3300972 1386.1386138613861 144

3.29

89690721649 1515.151515151515 1372.5490196078433 1442.3076923076924 1214.9532710280373 1333.3333333333335 1415.0943396226417 129

6.2

962962962963 1401.8691588785048 1467.8899082568807 13

51.3

513513513512 1388.8888888888889 150

9.4

33962264151 1401.8691588785048 1523.8095238095239 1574.0740740740741 1467.8899082568807 1559.6330275229359 1441.4414414414414 1545.4545454545455 1666.6666666666667 1698.1132075471698 1809.5238095238096 1730.7692307692309 1886.7924528301887 1844.6601941747574 1923.0769230769231 1981.1320754716983 1809.5238095238096 1941.7475728155341 1960.7843137

2549

04 2000 2075.4716981132078 2019.2307692307693 2095.2380952380954 21

49.5

32710280374 18

51.8

51851851852 1743.119266055046 1891.8918918918919 2037.037037037037 1886.7924528301887 1944.4444444444446 2169.8113207547171 2037.037037037037 201

8.34

86238532109 2072.0720720720719 2181.818181818182 2035.3982300884954

World

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 746

62.3

37662337668 96585.259670211133 102369.09197616122 116171.4690652311 126210.0872953198 129767.71840811797 116409.43414729823 110140.94728800612 95064.953271028033 85499.815885954507 71971.630246375498 593

49.0

50953399485 73401.159306189467 96544.821844821839 108790.61977405171 116584.48308448309 124625.39283146759 122584.964

2746

1944 112594.2923346101 104164.4014920714 92240.544372553719 8147

0.2

78530525859 67385.812036297473 57489.31930495776 70892.173174271666 94916.8933503

7327

104582.56185890571 115211.12401600207 118949.22583022068 127801.08678327154 113215.6013997898 9932

5.10

4141141885 86863.28400281888 77192.722371967655 68558.441558441569 53981.684981684986 69134.854468334073 91182.229030502291 102486.18584480653 113027.1631472037 115831.65163450023 122481.76866738955 114686.1

6979

051076 103729.16666666666 87381.041046011247 76770.899008059685 67105.271540054149 61796.718857466512 69673.060124711075 94164.601774916198 104544.26888042316 1182

49.7

7868763417 118583.35803558504 127362.62190960527 123919.39413260287 101960.69128134346 95688.392242820439 81765.976551231375 68647.506619594002 5

6509

.512966296817

## Industry Tractor Total Sales

Industry Tractor Total Sales
Month NA SA Eur Pac China World
Jan-10

1 984 0

1 987 1 278 0

1

Feb-10 8592 1

1

1 1090 1 283 0

1

Mar-10 8630 1 1016 0

1

1 285 0

1

Apr-10 8947 1

0

1 1209 1 288 0

1

1

May-10 8442 1

1 5273 1

1 286 0

1

Jun-10

1 1019 0 5315 1 1327 1 287 0

1

Jul-10 6145 1 977 0

1

1 289 0

1

Aug-10 5882 1 1057 1

1 1268 1 290 0

1

Sep-10 5595 1 1086 1 6075 1 1209 1 293 0

1

Oct-10

1 1045 0 6321 1 1168 1 295 0

1

Nov-10 4494 1

1 8381 1 1127 1 298 0

1

Dec-10 3913 1 1029 0 7944 1

1 301 0

1

Jan-11 5938 1 1172 1 5688 1 1185 1 306 0

1

Feb-11 6633 1 1273 1 7037 1 1286 1 302 0

1

Mar-11 7327 1 1423 1 6981 1 1286 1 303 0

1

Apr-11 8077 1 1612 1 7500 1 1346 1 307 0

1

May-11 7830 1 1728 1 6571 1 1388 1 309 0

1

Jun-11 7103 1

1

1

1 312 0

1

Jul-11

1 1776 1 6667 1 1490 1 315 0

1

Aug-11 6036 1 1685 1

1 1449 1 318 0

1

Sep-11

1 1679 1 6635 1 1394 1 321 0

1

Oct-11 5345 1

1

1

1 315 0

1

Nov-11 4831 1 1564 1 6476 1 1214 1 318 0

1

Dec-11

1

1 6250 1

1 320 0

1

Jan-12

1 1835 1 5922 1 1208 1 333 0

1

Feb-12

1

1 6667 1 1214 1 313 0

1

Mar-12

1 2202 1 7228 1 1256 1 606 1

1

Apr-12 7619 1

1

1 1311 1 571 1

1

May-12

1

1

1 1415 1 556 1

1

Jun-12

1

1 7921 1 1520 1 526 0

1

Jul-12 7752 1

1 7677 1

1 513 0

1

Aug-12 6894 1

1

1

1 769 1

1

Sep-12 6015 1

1

1 1527 1 750 1

1

Oct-12

1

1

1

1 732 1

1

Nov-12

1 2483 1

1 1366 1 714 1

1

Dec-12 4444 1 1986 1

1 1262 1 698 1

0

1

Jan-13 5000 1

1

1 1373 1 714 1

1

Feb-13

1

1

1 1436 1 1063 1

1

Mar-13

1

1

1

1 1264 1

1

Apr-13 9934 1 2517 1

1

1 1333 1

1

May-13 10645 1

1

1

1 1556 1

1

Jun-13 9491 1

1 7347 1 1667 1 1739 2

1

Jul-13

1

1 6979 1 1733 1

2

1

Aug-13 8528 1 2833 1 6489 1

1

2

1

Sep-13 8293 1 2789 1 6316 1 1642 1 2083 2

1

Oct-13

1 2765 1

1 1576 1 2128 2

1

Nov-13 7470 1 2746 1 5789 1

1

2

1

Dec-13 6509 1

1 5591 1 1450 1

2

1

Jan-14

1 2635 1 5106 1 1010 1 2292 2

1

Feb-14 8807 1 2703 1 5474 1 1045 1 2449 2

1

Mar-14

1 2795 1

1 1106 1

2

1

Apr-14

1

1

1 1150 1 2353 2

1

May-14

2 3131 1

1 1244 1 2600 2

1

Jun-14

2 3311 2

1 1357 1

2

1

Jul-14

2 3390 2 6304 1 1421 1 2600 2

1

Aug-14

2

2 6064 1

1 2549 2

1

Sep-14

1 3232 2 5789 1

1 2453 2

1

Oct-14

1 3131 1 5699 1 1128 1 2517 2 23147 1

Nov-14

1

1 5604 1 974 1

2

1

Dec-14

1

1 5444 1 979 1 2453 2 21993 1

7726 2093 6436

8143 5091 15486
1051 5310 16328
6071 1127 17132
1027 5856 1733
1057 1221 16281
7500 15451
7170 1324 15908
5926 14426
14262
5233 14064
1078 15381
1085 14275
14292
16534
17324
18846
17830
1815 6990 1449 17673
6239 16490
6762 16254
5664 15696
1618 6311 1256 14848
14405
4454 1522 1171 13719
5299 14600
6529 2115 16840
7120 18416
2151 8200 19856
8387 2214 7941 20517
8110 2278 20359
2100 1675 19721
2128 7200 1584 18579
2367 6735 17398
5368 2211 6495 1422 16230
4964 6061 1

5591
5816 1421
2257 5051 14398
6284 2353 6082 17222
7785 2457 6327 1478 19315
7604 1512 22906
2612 7789 1642 24249
2749 22998
9182 2887 1702 22488
1700 1915 21469
21127
8221 5833 20527
1493 2292 19794
2534 2245 18333
7267 18314
20481
10168 6022 2400 22494
11044 2997 6064 23612
12120 6344 25444
13459 6593 2653 27379
13048 26769
12275 3277 1263 25433
11347 1173 24000
10667
10459 3087 2541 22671
10082 3030
1323 1070 18652

North America

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41 640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 8142.8571428571422 8591.5492957746483 8630.1369863013697 894

7.3

6

84210526317 8441.5584415584417 7500 6144.5783132530123 5882.3529411764703 5595.2380952380954 5232.5581395348845 4494.3820224719102 3913.0434782608695 5937.5 6632.6530612244906 7326.7326732673273 8076.9230769230771 7830.1886792452833 7102.8037383177571 6238.5321100917436 6036.0360360360355 5663.716814159292 5344.8275862068958 4830.5084745762706 44

53.7

815126050418 5299.1452991452988 6528.9256198347121 7120 7619.0476190476193 838

7.09

67741935492 81 10.2362204724404 7751.937984496124 6893.939393939394 601

5.03

75939849619 5367.6470588235288 4964.0287769784172 4444.4444444444443 5000 6283.7837837837833 7785.2348993288579 9934.21052631579 1064

5.16

1290322581 9491 9182.3899371069183 8527.6073619631898 8292.6829268292677 8220.8588957055217 7469.8795180722891 6508.8757396449701 7267.4418604651155 8806.8181818181802 10167.597765363127 11043.956043956043 12119.565217391304 13459.45945945946 130

48.1

28342245989 12275.132275132275 1134

7.15

0259067357 10666.666666666666 10459.183673469388 10082

South America

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41 640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 984 10

50.5

836575875487 1015.625 1026.6159695817489 1056.6037735849056 1018.8679245283018 977.4436090225563 1056.6037735849056 1086.1423220973782 1044.7761194029849 1078.0669144981412 1029.4117647058822 1172.1611721611721 1272.7272727272725 1423.3576642335765 1611.7216117216117 1727.9411764705881 1814.8148148148148 1776 1684.9816849816848 1678.8321167883209 1617.6470588235293 1563.6363636363635 1521.7391304347825 1834.5323741007192 2114.6953405017921 2202.1660649819491 2150.5376344086021 2214.2857142857142 2277.5800711743768 2099.6441281138787 2127.6595744680853 2367.4911660777389 2210.5263157894738 2482.5174825174827 1986.0627177700351 22

56.9

444444444448 235

2.94

11764705883 2456.7474048442909 251

7.24

13793103451 2611.6838487972509 27

49.1

408934707906 2886.5979381443299 2832.764505119454 2789.1156462585036 2764.5051194539251 27

45.7

627118644068 2533.7837837837837 2635.1351351351354 2702.7027027027029 2794.6127946127949 2996.6329966329968 3131.3131313131316 3310.8108108108108 3389.8305084745766 3277.0270270270271 3232.3232323232323 3131.3131313131316 3087.2483221476509 3030.3030303030305

Europe

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 5090.909090909091 5309.7345132743358 6071.4285714285716 5855.8558558558561 5272.727272727273 5315.3153153153153 7169.8113207547176 5925.9259259259261 607

4.76

63551401874 6320.7547169811323 8380.9523809523816 7943.9252336448599 5688.0733944954127 7037.0370370370374 6981.132075471698 7500 6571.4285714285716 6990.2912621359228 6666.666666666667 6761.9047619047624 6634.6153846153848 6310.6796116504856 6476.1904761904761 6250 5922.3300970873788 6666.666666666667 7227.7227722772286 8200 7941.176470588236 7920.792079207 9212 7676.7676767676767 7200 6734.6938775510198 6494.8453608247419 6060.6060606060601 5816.3265306122448 5050.5050505050503 6082.4742268041236 6326.5306122448974 7604.1666666666661 7789.4736842105258 73

46.9

387755102034 6979.166666666667 6489.3617021276596 6315.7894736842109 5833.333333333333 5789.4736842105267 5591.3978494623652 5106.3829787234044 5473.6842105263158 6021.5053763440865 606

3.82

97872340427 6344.0860215053763 6593.4065934065939 6304.347826086957 6063.8297872340427 5789.4736842105267 5698.9247311827958 5604.3956043956041 5444.4444444444443

Pacific

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 987 1090.0473933649289 1126.7605633802816 1209.3023255813953 1220.6572769953052 1327.0142180094788 1324.2009132420092 1267.605633802817 1209.3023255813953 1168.2242990654206 1126.7605633802816 1084.9056603773586 1184.8341232227488 1285.7142857142858 1285.7142857142858 1346.1538461538462 1387.5598086124403 1449.2753623188407 1490.3846153846155 1449.2753623188407 1394.2307692307693 1256.0386473429953 1213.5922330097087 1170.7317073170732 1207.7294685990339 1213.5922330097087 1256.0386473429953 1310.6796116504854 141

4.63

41463414635 1519.607843137255 1674.8768472906402 1584.158415841584 1527.0935960591132 1421.5686274509806 1365.8536585365855 1262.1359223300972 1372.5490196078433 1435.6435643564355 1477.8325123152708 1512.1951219512196 1641.7910447761194 1666.6666666666667 1732.6732673267325 1700 1641.7910447761194 1576.3546798029556 1492.5373134328358 1450 1010.10101010101 1044.7761194029849 1105.5276381909548 1150 1243.7810945273632 1356.7839195979898 1421.3197969543146 1262.6262626262626 117

3.46

93877551019 1128.2051282051282 974.35897435897436 979.38144329896909

China

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 278 283 285 288 286 287 289 290 293 295 298 301 306 302 303 307 309 312 315 318 321 315 318 320 333.33333333333337 312.5 606.06060606060601 571.42857142857133 555.55555555555554 526.31578947368428 512.82051282051282 769.23076923076928 750 731.70731707317077 714.28571428571433 697.67441860465124 714.28571428571433 1063 1264 1333.3333333333335 1555.5555555555557 1739.1304347826087 1702.127659574468 191

4.89

36170212767 2083.3333333333335 2127.6595744680849 2291.6666666666665 2244.8979591836733 2291.6666666666665 2448.9795918367345 2400 2352.9411764705883 2600 2653.0612244897957 2600 2549.0196078431372 2452.8301886792451 2517 2541 2452.8301886792451

World

40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 15485.827364316103 16328.177768112695 17132.348420012986 17330.615077530954 16280.886107982882 15451.483910501687 15908.411398406914 14425.633349435642 14261.550181077717 14064.35482268759 15381.41255497461 14275.338779719703 14291.676593971913 16533.663433037509 17323.621594248918 18845.796830272586 17830.027142679752 17673.153172992101 16490.401766399569 16253.930247756805 15696.014975118776 14847.587500892381 1440

5.22

3401936522 13719.411885118465 14600.466025397956 16840.188427993337 18416.03421337191 19855.971854686119 2051

7.19

5307728711 20359.049234240487 19720.512435599452 18579.213122991761 17398.426601389649 16230.129230188584 15591.094371402309 14210.025912098807 14397.831903482002 17221.810489524978 19314.626910507486 22905.96004813246 24248.74233614793 22997.820014039953 22487.917191375578 21469.377637268572 21127.340647579105 20527.194347858862 19793.626314204532 18332.973158006946 18314.484223735177 20481.032187050154 22493.666036040744 23612.032608397036 25443.736066418991 27378.895949558133 26768.988797130431 25432.686015877025 24000.046319474648 231

46.7

24604316583 22670.622639347333 21993.298087858158

Q3

Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance

2010 12 9916

2011 12

2012 12 9431

2013 12 8029

2014 12

ANOVA
Source of Variation SS df MS F P-value F crit

Between Groups

4

83333333

0.0000

Within Groups

55

Total

59

 826.3333333333 135.3333333333 10049 837.4166666667 121.5378787879 785.9166666667 2749.7196969697 669.0833333333 959.3560606061 5955 496.25 2940.0227272727 984600.333333333 2461 50.0 178.215438334 2.5396886349 75965.6666666667 1381.1939393939 1060566

## Defects After Delivery

Defects After Delivery
tc={ADAA7B03-0CEE-47E5-A080-EAB2C7DB9812}: [Threaded comment]
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Comment:

We can conclude that Defects had a slight increase from 2010 to 2011 which can be attributed to an increase in unit sales. But over the years from the years of 2010 to 2014 the amount of defects decreased overall . This shows that the company is evolving and improving their manufacturing process.

Defects per million items received from suppliers Month 2010 2011 2012 2013 2014
January 812 828 824 682 571
February

810 832 836 695 575
March

813 847 818 692 547
April

823 839 825 686 542
May 832 832 804 673 532
June

848 840 812 681 496
July

837 849 806 696 472
August

831 857 798 688 460
September

827 839 804 671 441
October

838 842 713 645 445
November

826 828 705 617 438
December

819 816 686 603 436
Total 9916 10049 9431 8029 5955
Q3
Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance
2010 12 9916 826.3333333333 135.3333333333
2011 12 10049 837.4166666667 121.5378787879
2012 12 9431 785.9166666667 2749.7196969697
2013 12 8029 669.0833333333 959.3560606061
2014 12 5955 496.25 2940.0227272727
ANOVA
Source of Variation SS df MS F P-value F crit
Between Groups 984600.333333333 4 246150.083333333 178.215438334 0.0000 2.5396886349
Within Groups 75965.6666666667 55 1381.1939393939
Total 1060566 59
we conduct two regression analyses (i) what may have happened had the supplier initiative not been impelemented (

ii) how the number of defects might further be reduced in the future

. i) what might have happened had the supplier initiative not been implemented here the analysis is based on months from January 2010 to when the supplier initiative was done in august 2011. Let t be the number of months from December 2009; that is January 2010 be t=1, February 2010 be t=2 and so on Defects per million items received from suppliers is the dependent variabe while time is the independent variable Defects time t
812 1
810 2
813 3
823 4
832 5
848 6
837 7
831 8
827 9
838 10
826 11
819 12
828 13
832 14
847 15
839 16
832 17
840 18
849 19
857 20
The following is the regression equation SUMMARY OUTPUT Regression

Statistics Multiple R 0.6994187048 R Square 0.4891865246 Adjusted R Square 0.4608079981 Standard Error 9.4427395385 Observation

s

20

ANOVA

df SS MS F

Significance F Regression 1

1537.0240601504

1537.0240601504

1

7.23

79114202 0.0005989968 Residual

18

160

4.97

59398496 89.1653299916 Total 19 3142
Coefficients

Standard Error

t Stat

P-value

Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 816.0368421053 4.3864495472 186.0358436435 5.14111788361825E-31 806.8212535732 825.2524306373

806.8212535732 825.2524306373
X Variable 1 1.5203007519 0.3661737333 4.1518563824

0.0005989968

0.7509982849 2.2896032188

0.7509982849 2.2896032188
Regression Equation y=1.520301x + 816.0368 defects= 1.520301* t + 816.0368 This means had the supplier initiative not taken place, the number of defects would have increased with time where t is the number of months from the baseline. had the supplier initiative of August 2011 not taken place, this regression equation would have predicted what would have happened in subsequent months after august 2011 ii) how the number of defects might further be reduced in the future
here we analyze regression resuts from september 2011 when the supplier initiative was undertaken the new baseline is august 2011, so for september 2011, t=1, october 2011 t=2, and so on. Defects

Time t 839 1
842 2
828 3
816 4
824 5
836 6
818 7
825 8
804 9
812 10
806 11
798 12
804 13
713 14
705 15
686 16
682 17
695 18
692 19
686 20
673 21
681 22
696 23
688 24
671 25
645 26
617 27
603 28
571 29
575 30
547 31
542 32
532 33
496 34
472 35
460 36
441 37
445 38
438 39
436 40
The regression results are:

SUMMARY OUTPUT

Regression Statistics Multiple R

0.9750468977 R Square

0.9507164528 Adjusted R Square

0.9494195173 Standard Error

30.1520143865 Observations

40

ANOVA
df SS MS F Significance F

Regression 1

6664

46.5

29080675

666446.529080675

733.0483948942 1.90959818846179E-26 Residual 38

345

47.4

709193246 909.1439715612 Total 39

700994

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept

897.7307692308 9.716537693 92.3920430916 2.48445466444305E-46 878.0606670317 917.4008714299

878.0606670317 917.4008714299
X Variable 1

-11.181988743 0.4130025443 -27.0748664797 1.9095981884618E-26 -12.0180686833 -10.3459088026

-12.0180686833 -10.3459088026
The value of R-squared means the model is a good fit for the data. The p-values indicate statistical significance Regression Equation

y=-11.182X +897.7308 defects=897.7308-11.182*t here t is the number of months from august 2011

Defects After Delivery by Year

2010 2011 2012 2013 2014 9916 10049 9431 8029 5955 2010 2011 2012 2013 2014 812 828 824 682 571 2010 2011 2012 2013 2014 810 832 836 695 575 2010 2011 2012 2013 2014 813 847 818 692 547 2010 2011 2012 2013 2014 823 839 825 686 542 2010 2011 2012 2013 2014 832 832 804 673 532 2010 2011 2012 2013 2014 848 840 812 681 496 2010 2011 2012 2013 2014 837 849 806 696 472 2010 2011 2012 2013 2014 831 857 798 688 460 2010 2011 2012 2013 2014 827 839 804 671 441 2010 2011 2012 2013 2014 838 842 713 645 445 2010 2011 2012 2013 2014 826 828 705 617 438 2010 2011 2012 2013 2014 819 816 686 603 436

We can conclude that Defects had a slight increase from 2010 to 2011 which can be attributed to an increase in unit sales. But over the years from the years of 2010 to 2014 the amount of defects decreased overall . This shows that the company is evolving and improving their manufacturing process.

## Time to Pay Suppliers

Time to Pay Suppliers
Month

Jan-10

Feb-10

Mar-10

Apr-10 8.32
May-10

Jun-10

Jul-10 8.34
Aug-10 8.32
Sep-10 8.36
Oct-10 8.33
Nov-10 8.32
Dec-10 8.29
Jan-11 7.89
Feb-11 7.65
Mar-11 7.58
Apr-11

May-11

Jun-11 7.45
Jul-11 7.36
Aug-11

Sep-11

Oct-11 7.3
Nov-11 7.27
Dec-11

Jan-12

Feb-12

Mar-12 7.22
Apr-12

May-12 7.25
Jun-12 7.23
Jul-12 7.28
Aug-12 7.25
Sep-12 7.24
Oct-12

Nov-12 7.21
Dec-12 7.23
Jan-13 7.24
Feb-13 7.19
Mar-13 7.21
Apr-13 7.23
May-13 7.22
Jun-13 7.19
Jul-13 7.17
Aug-13 7.15
Sep-13 7.16
Oct-13 7.16
Nov-13 7.15
Dec-13 7.14
Jan-14

Feb-14 7.11
Mar-14 7.11
Apr-14 7.11
May-14 7.11
Jun-14 7.12
Jul-14

Aug-14 7.09
Sep-14 7.09
Oct-14

Nov-14

Dec-14 7.08
Working Days
8.32
8.28
8.29
8.36
8.35
7.53
7.48
7.35
7.32
7.25
7.22
7.21
7.29
7.26
7.12
7.08
7.04
7.06

## Employee Satisfaction

Quarter Production

Sample size

Sample size Total Sample size

2.86 100

10 3.51 30 3.07 140

100 3.76 10 3.38 30 3.07 140

2.84 100 3.86 10 3.45 30 3.04 140

2.83 100 3.48 10 3.61 30 3.04 140

2.91 100 3.75 20 3.37 30 3.11 150

2.94 100 3.92 20

30

150

2.86 100 3.89 20 3.47 30 3.12 150

2.83 100 3.58 20 3.66 30 3.10 150

2.95 100 3.82 20 3.71 40 3.25 160

3.01 100 4.01 20 3.53 40 3.27 160

3.03 100 3.92 20 3.62 40 3.29 160

2.96 100 3.84 20 3.48 40 3.20 160

3.05 100 3.92 20 3.52 40 3.28 160

3.12 100 4.00 20 3.37 40 3.29 160

3.06 100 3.93 20 3.46 40 3.27 160

3.02 100

20 3.59 40 3.25 160

 Employee Satisfaction Results Averages using a 5 point scale Design & Sales & Sample size Manager Administration 1st Q-11 3.81 2nd Q-11 2.91 3rd Q-11 4th Q-11 1st Q-12 2nd Q-12 3.53 3.19 3rd Q-12 4th Q-12 1st Q-13 2nd Q-13 3rd Q-13 4th Q-13 1st Q-14 2nd Q-14 3rd Q-14 4th Q-14 3.70

## Engines

Sample

1

2 62.3
3

SUMMARY OUTPUT

4

5 58.1 Regression Statistics
6 56.9 Multiple R

7

R Square

8

Adjusted R Square

9 55.1 Standard Error

10 54.3 Observations 50
11 53.7
12

ANOVA

13 52.8 df SS MS F Significance F
14

Regression 1

891.5529337335

15

Residual 48

16 51.8 Total 49

458

17

18 51.3 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
19

Intercept

57.1339282346 59.2334187042

20 50.5 X Variable 1

-0.3284419196 -0.2567873721

21 50.2
22 50.0 The value of R-squared means the model is a good fit for the data.
23 49.7 The p-values indicate statistical significance
24 49.5
25

26

27 49.1

28 49.0
29

30 48.5
31

32

33 48.1
34

35

36 47.6
37 47.4
38

39 46.9
40

41 46.7
42

43 46.5
44 46.5
45

46

47

48 45.8
49 45.7
50

 Engine Production Time Production Time (min) 65.1 time is the dependent variable and sample is the independent variable 60.4 58.7 0.9213573188 57.0 0.8488993088 56.5 0.8457513778 1.8182687867 53.2 52.5 891.5529337335 269.6689638672 2.48594348198823E-21 52.1 158.6928662665 3.3061013806 10 50.2 51.5 50.9 58.1836734694 0.5220964329 111.4423884214 1.29129366690705E-59 57.1339282346 59.2334187042 -0.2926146459 0.0178188871 -16.421600527 2.48594348198821E-21 -0.3284419196 -0.2567873721 49.3 The regression equation is : y=58.18367-0.29261x 49.4 Production Time=58.18367-0.29261*x This means that as the number of units produced increase, the production time reduces and therefore creating a more cost-effective means of production 48.8 48.3 48.2 47.9 47.7 47.1 46.8 46.6 46.2 46.3 46.0 45.6

Q4

Anova: Single Factor
SUMMARY
Groups Count Sum Average Variance

Current 30

30

30 8953

ANOVA
Source of Variation SS df MS F P-value F crit

Between Groups

2

Within Groups

87

Total

89

 8688 289.6 2061.1448275862 Process A 8565 285.5 4217.6379310345 Process B 298.4333333333 435.3574712644 2621.0888888889 1310.5444444444 0.5855750995 0.5589648105 3.1012957567 194710.066666667 2238.046743295 197331.155555556

## Transmission Costs

Q4
Current Process A Process B

\$242.00

Anova: Single Factor

SUMMARY

\$242.00 Groups Count Sum Average Variance

Current 30 8688 289.6 2061.1448275862

Process A 30 8565 285.5 4217.6379310345

Process B 30 8953 298.4333333333 435.3574712644

\$286.00

\$242.00

\$300.00 ANOVA

\$314.00

Source of Variation SS df MS F P-value F crit

\$300.00 Between Groups 2621.0888888889 2 1310.5444444444 0.5855750995 0.5589648105 3.1012957567

Within Groups 194710.066666667 87 2238.046743295

\$242.00

Total 197331.155555556 89

\$273.00

\$281.00

\$304.00

\$391.00

\$312.00

\$306.00

\$299.00 \$301.00 \$312.00
\$300.00 \$277.00

\$278.00

\$288.00

\$303.00

\$315.00

\$286.00

\$321.00

 Unit Tractor Transmission Costs \$242.00 \$292.00 \$176.00 \$275.00 \$321.00 \$286.00 \$199.00 \$314.00 \$269.00 \$219.00 \$327.00 \$273.00 \$278.00 \$264.00 \$265.00 \$300.00 \$296.00 \$435.00 \$301.00 \$333.00 \$285.00 \$384.00 \$315.00 \$288.00 \$387.00 \$299.00 \$304.00 \$302.00 \$145.00 \$335.00 \$266.00 \$351.00 \$216.00 \$277.00 \$281.00 \$331.00 \$284.00 \$289.00 \$247.00 \$276.00 \$259.00 \$280.00 \$312.00 \$322.00 \$267.00 \$209.00 \$210.00 \$282.00 \$391.00 \$303.00 \$297.00 \$306.00 \$346.00 \$236.00 \$230.00 \$287.00 \$383.00 \$332.00 \$295.00 \$336.00 \$217.00 \$313.00 \$274.00 \$339.00 \$338.00

## Blade Weight

Blade Weight Sample Weight

1

4.88

Question 4( Average blade

weight

)

2 4.92

we use the average function in Excel 3

5.02 average blade weight 4.9908 4 4.97
5 5.00

for variability, we use the sample standard deviation 6 4.99

s.d. 0.10928756 7 4.86
8

5.07 9

5.04 QUESTION 5 (probability blade weights will exceed 5.20) 10 4.87

we calculate the z-score associated with 5.20 11 4.77 z

1.9142160368 12 5.14

probability (Z. Z>1.914216) 0.027796 13 5.04
14 5.00
15 4.88

QUESTION 6 (probability blade weights will be

less than 4.80

) 16 4.91
17 5.09

we calculate the z-score associated with 4.80 18 4.97 z

-1.7458528672 19 4.98

probability (Z<-1.74585) 0.0404182609 20 5.07
21 5.03

QUESTION 7 (actual pecentage less than 4.80 or greater than 5.20) 22 5.12
23 5.08 less than 4.80 8
24 4.86

more than 5.20

7
25 5.11 total 15
26 4.92
27

5.18 actaul percentage <4.80 or > 5.20 4.2857% 28

4.93 29 5.12
30 5.08

QUESTION 8 (is the process stable over time) 31 4.75

we can make a scatter plot to investigate the stability of the process 32 4.99
33 5.00
34 4.91
35 5.18
36 4.95
37 4.63
38 4.89
39 5.11
40 5.05
41 5.03
42 5.02
43 4.96
44 5.04
45 4.93
46 5.06
47 5.07
48 5.00
49 5.03
50 5.00
51 4.95

from the scatter plot, we can observe that the process is quite stable because most values are close to each other 52 4.99
53 5.02
54 4.90

Question 9 (are there any outliers) 55 5.10

5.87 56

5.01 yes, there are possible outliers. For example,the 171st blade with a weight of 5.87 is an outlier because it is far from the other values. 57 4.84
58 5.01
59 4.88

QUESTION 10 (Is the distribution normal) 60 4.97

beloe mean

180
61 4.97

above mean

170
62 5.06
63 5.06

since the number of values below the mean is close to the number of values above the mean, the distribution is pretty normal 64 5.04
65 4.87
66 5.00
67 5.03
68 5.02
69 5.02
70 5.06
71

5.21 72 5.09
73 4.97
74 5.01
75 4.90
76 4.89
77 4.93
78 5.16
79 5.02
80 5.01
81 5.10
82 5.03
83 5.07
84 4.92
85 5.08
86 4.96
87

4.74 88 4.91
89 5.12
90 5.00
91 4.93
92 4.88
93 4.88
94 4.81
95 5.16
96 5.03
97 4.87
98 5.09
99

4.94 100 5.08
101 4.97
102 5.23
103 5.12
104 5.09
105 5.12
106 4.93
107

4.79 108 5.10
109 5.12
110 4.86
111 5.00
112 4.94
113 4.95
114 4.95
115 4.87
116 5.09
117 4.94
118 5.01
119 5.04
120 5.05
121 5.05
122 4.97
123 4.96
124 4.96
125 4.99
126 5.04
127 4.91
128 5.19
129 5.03
130 4.99
131 5.12
132 4.97
133 4.88
134 5.07
135 5.01
136 4.89
137 4.95
138 5.09
139 5.09
140 4.89
141 4.93
142 4.85
143 5.03
144 4.92
145 5.09
146 4.99
147 4.92
148 4.87
149 4.90
150 5.02
151 5.21
152 5.02
153 4.9
154 5
155 5.16
156 5.03
157 4.96
158 5.04
159 4.98
160 5.07
161 5.02
162 5.08
163 4.85
164 4.9
165 4.97
166 5.09
167 4.89
168 4.87
169 5.01
170 4.97
171 5.87
172 5.33
173 5.11
174 5.07
175 4.93
176 4.99
177 5.04
178 5.14
179 5.09
180 5.06
181 4.85
182 4.93
183 5.04
184 5.09
185 5.07
186 4.99
187 5.01
188 4.88
189 4.93
190 5.1
191 4.94
192 4.88
193 4.89
194 4.89
195 4.85
196 4.82
197 5.02
198 4.9
199 4.73
200 5.04
201 5.07
202 4.81
203 5.04
204 5.03
205 5.01
206 5.14
207 5.12
208 4.89
209 4.91
210 4.97
211 4.98
212 5.01
213 5.01
214 5.09
215 4.93
216 5.04
217 5.11
218 5.07
219 4.95
220 4.86
221 5.13
222 4.95
223 5.22
224 4.81
225 4.91
226 4.95
227 4.94
228 4.81
229 5.11
230 4.81
231 4.97
232 5.07
233 5.03
234 4.81
235 4.95
236 4.89
237 5.08
238 4.93
239 4.99
240 4.94
241 5.13
242 5.02
243 5.07
244 4.82
245 5.03
246 4.85
247 4.89
248 4.82
249 5.18
250 5.02
251 5.05
252 4.88
253 5.08
254 4.98
255 5.02
256 4.99
257 5.02
258 5.03
259 5.02
260 5.07
261 4.95
262 4.95
263 4.94
264 5.12
265 5.08
266 4.91
267 4.96
268 4.96
269 4.94
270 5.19
271 4.91
272 5.01
273 4.93
274 5.05
275 4.96
276 4.92
277 4.95
278 5.08
279 4.97
280 5.04
281 4.94
282 4.98
283 5.03
284 5.05
285 4.91
286 5.09
287 5.21
288 4.87
289 5.02
290 4.81
291 4.96
292 5.06
293 4.86
294 4.96
295 4.99
296 4.94
297 5.06
298 4.95
299 5.02
300 5.01
301 5.04
302 5.01
303 5.02
304 5.03
305 5.18
306 5.08
307 5.14
308 4.92
309 4.97
310 4.92
311 5.14
312 4.92
313 5.03
314 4.98
315 4.76
316 4.94
317 4.92
318 4.91
319 4.96
320 5.02
321 5.13
322 5.13
323 4.92
324 4.98
325 4.89
326 4.88
327 5.11
328 5.11
329 5.08
330 5.03
331 4.94
332 4.88
333 4.91
334 4.86
335 4.89
336 4.91
337 4.87
338 4.93
339 5.14
340 4.87
341 4.98
342 4.88
343 4.88
344 5.01
345 4.93
346 4.93
347 4.99
348 4.91
349 4.96
350 4.78

Blade Weights

Weight 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 15 9 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 4.88 4.92 5.0199999999999996 4.97 5 4.99 4.8600000000000003 5.07 5.04 4.87 4.7699999999999996 5.14 5.04 5 4.88 4.91 5.09 4.97 4.9800000000000004 5.07 5.03 5.12 5.08 4.8600000000000003 5.1100000000000003 4.92 5.18 4.93 5.12 5.08 4.75 4.99 5 4.91 5.18 4.95 4.63 4.8899999999999997 5.1100000000000003 5.05 5.03 5.0199999999999996 4.96 5.04 4.93 5.0599999999999996 5.07 5 5.03 5 4.95 4.99 5.0199999999999996 4.9000000000000004 5.0999999999999996 5.01 4.84 5.01 4.88 4.97 4.97 5.0599999999999996 5.0599999999999996 5.04 4.87 5 5.03 5.0199999999999996 5.0199999999999996 5.0599999999999996 5.21 5.09 4.97 5.01 4.9000000000000004 4.8899999999999997 4.93 5.16 5.0199999999999996 5.01 5.0999999999999996 5.03 5.07 4.92 5.08 4.96 4.74 4.91 5.12 5 4.93 4.88 4.88 4.8099999999999996 5.16 5.03 4.87 5.09 4.9400000000000004 5.08 4.97 5.23 5.12 5.09 5.12 4.93 4.79 5.0999999999999996 5.12 4.8600000000000003 5 4.9400000000000004 4.95 4.95 4.87 5.09 4.9400000000000004 5.01 5.04 5.05 5.05 4.97 4.96 4.96 4.99 5.04 4.91 5.19 5.03 4.99 5.12 4.97 4.88 5.07 5.01 4.8899999999999997 4.95 5.09 5.09 4.8899999999999997 4.93 4.8499999999999996 5.03 4.92 5.09 4.99 4.92 4.87 4.9000000000000004 5.0199999999999996 5.21 5.0199999999999996 4.9000000000000004 5 5.16 5.03 4.96 5.04 4.9800000000000004 5.07 5.0199999999999996 5.08 4.8499999999999996 4.9000000000000004 4.97 5.09 4.8899999999999997 4.87 5.01 4.97 5.87 5.33 5.1100000000000003 5.07 4.93 4.99 5.04 5.14 5.09 5.0599999999999996 4.8499999999999996 4.93 5.04 5.09 5.07 4.99 5.01 4.88 4.93 5.0999999999999996 4.9400000000000004 4.88 4.8899999999999997 4.8899999999999997 4.8499999999999996 4.82 5.0199999999999996 4.9000000000000004 4.7300 000000000004 5.04 5.07 4.8099999999999996 5.04 5.03 5.01 5.14 5.12 4.8899999999999997 4.91 4.97 4.9800000000000004 5.01 5.01 5.09 4.93 5.04 5.1100000000000003 5.07 4.95 4.8600000000000003 5.13 4.95 5.22 4.8099999999999996 4.91 4.95 4.9400000000000004 4.8099999999999996 5.1100000000000003 4.8099999999999996 4.97 5.07 5.03 4.8099999999999996 4.95 4.8899999999999997 5.08 4.93 4.99 4.9400000000000004 5.13 5.0199999999999996 5.07 4.82 5.03 4.8499999999999996 4.8899999999999997 4.82 5.18 5.0199999999999996 5.05 4.88 5.08 4.9800000000000004 5.0199999999999996 4.99 5.0199999999999996 5.03 5.0199999999999996 5.07 4.95 4.95 4.9400000000000004 5.12 5.08 4.91 4.96 4.96 4.9400000000000004 5.19 4.91 5.01 4.93 5.05 4.96 4.92 4.95 5.08 4.97 5.04 4.9400000000000004 4.9800000000000004 5.03 5.05 4.91 5.09 5.21 4.87 5.0199999999999996 4.8099999999999996 4.96 5.0599999999999996 4.8600000000000003 4.96 4.99 4.94000 00000000004 5.0599999999999996 4.95 5.0199999999999996 5.01 5.04 5.01 5.0199999999999996 5.03 5.18 5.08 5.14 4.92 4.97 4.92 5.14 4.92 5.03 4.9800000000000004 4.76 4.9400000000000004 4.92 4.91 4.96 5.0199999999999996 5.13 5.13 4.92 4.9800000000000004 4.8899999999999997 4.88 5.1100000000000003 5.1100000000000003 5.08 5.03 4.9400000000000004 4.88 4.91 4.8600000000000003 4.8899999999999997 4.91 4.87 4.93 5.14 4.87 4.9800000000000004 4.88 4.88 5.01 4.93 4.93 4.99 4.91 4.96 4.78

sample

weight

## Mower Test

Sample
Observation 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
1

Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass

2 Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass
3 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass
4 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
5 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
6 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
7 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
8 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass
9 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
10 Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
11 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
12 Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
13 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail
14 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
15 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
16 Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
17 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
18 Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
19 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
20 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
21 Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass
22 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
23 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
24 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
25 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
26 Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
27 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
28 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
29 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
30 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
31 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
32 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
33 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
34 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
35 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
36 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
37 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
38 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
39 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
40 Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
41 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
42 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
43 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
44 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
45 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
46 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
47 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
48 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
49 Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
50 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
51 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
52 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
53 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
54 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
55 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass
56 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
57 Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
58 Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
59 Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
60 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
61 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
62 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
63 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail
64 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
65 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
66 Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
67 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
68 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
69 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
70 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass
71 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
72 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
73 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
74 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
75 Pass Pass Fail Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass
76 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
77 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
78 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
79 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
80 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
81 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
82 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
83 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
84 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
85 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
86 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass
87 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
88 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass
89 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
90 Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
91 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
92 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
93 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
94 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
95 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
96 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
97 Pass Pass Pass Pass Pass Fail Pass Pass Pass Fail Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
98 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
99 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
100 Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fail Pass Pass

)

54

3000

fraction of mowers that fail

be the associated probability per failure

x P(X=x)
0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16 0
17 0
18 0
19 0
20 0
 Mower Test Functional Performance Pass Fail question 1 bernoulli distribution question 2 ( fraction of mowers that fail number of mowers that fail total number of mowers 0.018 QUESTION 3 (Probability of having x failures) Let x be the number of failures and P(X=x) x is from 0 to 20 0.1626105724 0.2980641858 0.2704431665 0.1619354195 0.0719804589 0.0253324303 0.0073520801 0.001809677 0.000385616 0.0000722539 0.0000120521 0.0000018075 0.0000002457 0.0000000305 0.0000000035 0.0000000004 for blade weight questions, check the blade weight tab

## Employee Retention

Employee Retention

Differences

ity Status

Gender

Local

t-Test: Two-Sample Assuming Equal Variances

10 18 3.01 33 F Y Y
10 16 2.78 25 M Y Y

Local

10 18 3.15 26 M Y N

Mean

10 18 3.86 24 F Y Y Variance

Variance

9.6 16 2.58 25 F Y Y Observations 13 27 Observations 22
8.5 16 2.96 23 M Y Y

Pooled Variance

8.4 17 3.56 35 M Y Y

0 Hypothesized Mean Difference 0

8.4 16 2.64 23 M Y Y df 38 df 37
8.2 18 3.43 32 F Y Y t Stat

t Stat

7.9 15 2.75 34 M N Y

P(T<=t) one-tail

7.6 13 2.95 28 M N Y

t Critical one-tail

7.5 13

23 M N Y

P(T<=t) two-tail

7.5 16 2.86 24 M Y Y

t Critical two-tail

7.2 15 2.38 23 F N Y
6.8 16 3.47 27 F Y Y
6.5 16 3.10 26 M Y Y
6.3 13 2.98 21 M N Y

6.2 16 2.71 23 M Y N
5.9 13 2.95 20 F N Y t-Test: Two-Sample Assuming Equal Variances
5.8 18

25 M Y Y

5.4 16 2.75 24 M Y N

College Grad

5.1 17 2.48 32 M Y N Mean

4.8 14 2.76 28 M N Y Variance

4.7 16 3.12 25 F Y N Observations 13 27
4.5 13 2.96 23 M N Y Pooled Variance

4.3 16 2.80 25 M Y N Hypothesized Mean Difference 0
4 17 3.57 24 M Y Y df 38
3.9 16 3.00 26 F Y N t Stat

3.7 16 2.86 23 M Y N P(T<=t) one-tail 3.7 15 3.19 24 M N N t Critical one-tail 1.6859544602
3.7 16 3.50 23 F Y N P(T<=t) two-tail 3.5 14 2.84 21 M N Y t Critical two-tail 2.0243941639
3.4 16

24 M Y N

2.5 13 1.75 22 M N N
1.8 16 2.98 25 M Y N
1.5 15 2.13 22 M N N SUMMARY OUTPUT
0.9 16 2.79 23 F Y Y
0.8 18 3.15 26 M Y N Regression Statistics
0.7 13 1.84 22 F N N Multiple R

0.3 18 3.79 24 F Y N R Square

Adjusted R Square

Standard Error

Observations 40
ANOVA
df SS MS F Significance F
Regression 3

Residual 36

Total 39

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept

-11.8719468233 6.3977299037

X Variable 1

-0.7873616722 0.6532530847

-1.7203721287 3.0803347674

0.0176540348 0.5654175903

Regression Equation

SUMMARY OUTPUT
Regression Statistics
Multiple R

R Square

Adjusted R Square

Standard Error

Observations 40

ANOVA
df SS MS F Significance F

Regression 1

44.6460424662

Residual 38

Total 39 314.69375

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept

-8.1740507134 4.144319346

X Variable 1

0.0165919207

0.0577563944 0.5428293458

Gender Local
YearsPLE YrsEducation College GPA Age College Grad t-Test: Two-Sample Assuming Equal Variances
Female Male
Mean 5.5307692308 5.5407407407 7.2227272727
12.2506410256 6.4494301994 3.7027922078
Pooled Variance 8.281391513 4.5625386617
Hypothesized Mean Difference
-0.0102643826 5.2094943403
P(T<=t) one-tail 0.4959320257 0.0000036859
t Critical one-tail 1.6859544602 1.6870936196
2.50 P(T<=t) two-tail 0.9918640514 0.0000073717
t Critical two-tail 2.0243941639 2.026192463
College Graduation
3.36
Non-College Grad
4.8923076923 5.8481481481
5.8191025641 9.1095156695
8.0704378468
-0.9966907369
0.162609673
0.325219346
3.13
0.3875599015
0.1502026772
0.0793862337
2.7255269941
47.2678437532 15.7559479177 2.1210141269 0.1146353121
267.4259062468 7.4284973957
314.69375
-2.7371084598 4.504149393 -0.6076859848 0.5472103219 -11.8719468233 6.3977299037
-0.0670542938 0.3551646907 -0.188797748 0.851311676 -0.7873616722 0.6532530847
X Variable 2 0.6799813193 1.1835513772 0.5745262372 0.5691848142 -1.7203721287 3.0803347674
X Variable 3 0.2915358125 0.1350439268 2.1588220923 0.0376058426 0.0176540348 0.5654175903
The value of R-Squared is low, meaning the model is not a good fit for the data.
y=-0.06705X1+ 0.679981X2+ 0.291536X3 -2.73711
YearsPLE=-0.06705*YrsEducation+0679981*College GPA +0.291536*Age -2.73711
From the p-values of the multiple regression equation above, at a significance level of 0.05, only the age variable is statistically significant
There is sufficient evidence that the age variable has a non-zero correlation with the years of employee retention
There is insufficient evidence that the variables years of education, college GPA, are correlated with the years of employee retention therefore we fail to reject the null hypothesis because they have p-values greater than 005. They are statistically insignificant. The intercept is als statistically insignificant.
Therefore, the age variable seems to be a good predictor of employee retention while years of education and college GPA are not good predictors of years of retention.
The best regression equation is the one with the age as the independent variable
The following is the regression equation with only age as the independent variable
0.3766581987
0.1418713987
0.1192890671
2.6658054354
44.6460424662 6.2824070206 0.0165919207
270.0477075338 7.1065186193
-2.0148656837 3.0424830991 -0.6622438377 0.5118115929 -8.1740507134 4.144319346
0.3002928701 0.1198069428 2.5064730241 0.0577563944 0.5428293458
YearsPLE=0.300293*Age-2.01487
The low value of R-squared may indicate that this is not a good model

## 5a-Gender

Female Male
10 10 t-Test: Two-Sample Assuming Equal Variances
10 10
9.6 8.5 Female Male
8.2 8.4 Mean 5.5307692308 5.5407407407
7.2 8.4 Variance 12.2506410256 6.4494301994
6.8 7.9 Observations 13 27
5.9 7.6 Pooled Variance 8.281391513
4.7 7.5 Hypothesized Mean Difference 0
3.9 7.5 df 38
3.7 6.5 t Stat -0.0102643826
0.9 6.3 P(T<=t) one-tail 0.4959320257 0.7 6.2 t Critical one-tail 1.6859544602
0.3 5.8 P(T<=t) two-tail 0.9918640514 5.4 t Critical two-tail 2.0243941639
5.1
4.8
4.5
4.3
4
3.7

3.7

3.5
3.4
2.5
1.8
1.5
0.8

## 5b-Col

Non-College Grad College Grad t-Test: Two-Sample Assuming Equal Variances
7.9 10
7.6 10 Non-College Grad College Grad
7.5 10 Mean 4.8923076923 5.8481481481
7.2 10 Variance 5.8191025641 9.1095156695
6.3 9.6 Observations 13 27
5.9 8.5 Pooled Variance 8.0704378468
4.8 8.4 Hypothesized Mean Difference 0
4.5 8.4 df 38
3.7 8.2 t Stat -0.9966907369
3.5 7.5 P(T<=t) one-tail 0.162609673 2.5 6.8 t Critical one-tail 1.6859544602
1.5 6.5 P(T<=t) two-tail 0.325219346 0.7 6.2 t Critical two-tail 2.0243941639
5.8
5.4

5.1

4.7

4.3
4

3.9

3.7
3.7
3.4
1.8

0.9

0.8

0.3

## 5c-Local

Local

t-Test: Two-Sample Assuming Equal Variances

10 10

10 6.2 Local Non- Local
10 5.4 Mean 7.2227272727

9.6 5.1 Variance 3.7027922078

8.5 4.7 Observations 22 17
8.4 4.3 Pooled Variance 4.5625386617
8.4 3.9 Hypothesized Mean Difference 0
8.2 3.7 df 37
7.9 3.7 t Stat 5.2094943403
7.6 3.7 P(T<=t) one-tail 0.0000036859 7.5 3.4 t Critical one-tail 1.6870936196
7.5 2.5 P(T<=t) two-tail 0.0000073717 7.2 1.8 t Critical two-tail 2.026192463
6.8 1.5
6.5 0.8
6.3 0.7
5.9 0.3

5.8
4.8
4.5
4
3.5
0.9

 Non- Local 3.6294117647 5.6909558824

## Purchasing Survey

Purchasing Survey

Industry

4.1 0.6 6.9 4.7 2.4 2.3 5.2 32 4.2 0 0 1 1
1.8 3 6.3 6.6 2.5 4 8.4 43 4.3 1 1 0 1
3.4 5.2 5.7 6 4.3 2.7 8.2 48 5.2 1 1 1 2
2.7 1 7.1 5.9 1.8 2.3 7.8 32 3.9 1 1 1 1
6 0.9 9.6 7.8 3.4 4.6 4.5 58 6.8 0 0 1 3
1.9 3.3 7.9 4.8 2.6 1.9 9.7 45 4.4 1 1 1 2
4.6 2.4 9.5 6.6 3.5 4.5 7.6 46 5.8 0 0 1 1
1.3 4.2 6.2 5.1 2.8 2.2 6.9 44 4.3 1 1 0 2
5.5 1.6 9.4 4.7 3.5 3 7.6 63 5.4 0 0 1 3
4 3.5 6.5 6 3.7 3.2 8.7 54 5.4 1 1 0 2
2.4 1.6 8.8 4.8 2 2.8 5.8 32 4.3 0 0 0 1
3.9 2.2 9.1 4.6 3 2.5 8.3 47 5 0 0 1 2
2.8 1.4 8.1 3.8 2.1 1.4 6.6 39 4.4 1 1 0 1
3.7 1.5 8.6 5.7 2.7 3.7 6.7 38 5 0 0 1 1
4.7 1.3 9.9 6.7 3 2.6 6.8 54 5.9 0 0 0 3
3.4 2 9.7 4.7 2.7 1.7 4.8 49 4.7 0 0 0 3
3.2 4.1 5.7 5.1 3.6 2.9 6.2 38 4.4 0 1 1 2
4.9 1.8 7.7 4.3 3.4 1.5 5.9 40 5.6 0 0 0 2
5.3 1.4 9.7 6.1 3.3 3.9 6.8 54 5.9 0 0 1 3
4.7 1.3 9.9 6.7 3 2.6 6.8 55 6 0 0 0 3
3.3 0.9 8.6 4 2.1 1.8 6.3 41 4.5 0 0 0 2
3.4 0.4 8.3 2.5 1.2 1.7 5.2 35 3.3 0 0 0 1
3 4 9.1 7.1 3.5 3.4 8.4 55 5.2 0 1 0 3
2.4 1.5 6.7 4.8 1.9 2.5 7.2 36 3.7 1 1 0 1
5.1 1.4 8.7 4.8 3.3 2.6 3.8 49 4.9 0 0 0 2
4.6 2.1 7.9 5.8 3.4 2.8 4.7 49 5.9 0 0 1 3
2.4 1.5 6.6 4.8 1.9 2.5 7.2 36 3.7 1 1 0 1
5.2 1.3 9.7 6.1 3.2 3.9 6.7 54 5.8 0 0 1 3
3.5 2.8 9.9 3.5 3.1 1.7 5.4 49 5.4 0 0 1 3
4.1 3.7 5.9 5.5 3.9 3 8.4 46 5.1 1 1 0 2
3 3.2 6 5.3 3.1 3 8 43 3.3 1 1 0 1
2.8 3.8 8.9 6.9 3.3 3.2 8.2 53 5 0 1 0 3
5.2 2 9.3 5.9 3.7 2.4 4.6 60 6.1 0 0 0 3
3.4 3.7 6.4 5.7 3.5 3.4 8.4 47 3.8 1 1 0 1
2.4 1 7.7 3.4 1.7 1.1 6.2 35 4.1 1 1 0 1
1.8 3.3 7.5 4.5 2.5 2.4 7.6 39 3.6 1 1 1 1
3.6 4 5.8 5.8 3.7 2.5 9.3 44 4.8 1 1 1 2
4 0.9 9.1 5.4 2.4 2.6 7.3 46 5.1 0 0 1 3
0 2.1 6.9 5.4 1.1 2.6 8.9 29 3.9 1 1 1 1
2.4 2 6.4 4.5 2.1 2.2 8.8 28 3.3 1 1 1 1
1.9 3.4 7.6 4.6 2.6 2.5 7.7 40 3.7 1 1 1 1
5.9 0.9 9.6 7.8 3.4 4.6 4.5 58 6.7 0 0 1 3
4.9 2.3 9.3 4.5 3.6 1.3 6.2 53 5.9 0 0 0 3
5 1.3 8.6 4.7 3.1 2.5 3.7 48 4.8 0 0 0 2
2 2.6 6.5 3.7 2.4 1.7 8.5 38 3.2 1 1 1 1
5 2.5 9.4 4.6 3.7 1.4 6.3 54 6 0 0 0 3
3.1 1.9 10 4.5 2.6 3.2 3.8 55 4.9 0 0 1 3
3.4 3.9 5.6 5.6 3.6 2.3 9.1 43 4.7 1 1 1 2
5.8 0.2 8.8 4.5 3 2.4 6.7 57 4.9 0 0 1 3
5.4 2.1 8 3 3.8 1.4 5.2 53 3.8 0 0 1 3
3.7 0.7 8.2 6 2.1 2.5 5.2 41 5 0 0 0 2
2.6 4.8 8.2 5 3.6 2.5 9 53 5.2 1 1 1 2
4.5 4.1 6.3 5.9 4.3 3.4 8.8 50 5.5 1 1 0 2
2.8 2.4 6.7 4.9 2.5 2.6 9.2 32 3.7 1 1 1 1
3.8 0.8 8.7 2.9 1.6 2.1 5.6 39 3.7 0 0 0 1
2.9 2.6 7.7 7 2.8 3.6 7.7 47 4.2 0 1 1 2
4.9 4.4 7.4 6.9 4.6 4 9.6 62 6.2 1 1 0 2
5.4 2.5 9.6 5.5 4 3 7.7 65 6 0 0 0 3
4.3 1.8 7.6 5.4 3.1 2.5 4.4 46 5.6 0 0 1 3
2.3 4.5 8 4.7 3.3 2.2 8.7 50 5 1 1 1 2
3.1 1.9 9.9 4.5 2.6 3.1 3.8 54 4.8 0 0 1 3
5.1 1.9 9.2 5.8 3.6 2.3 4.5 60 6.1 0 0 0 3
4.1 1.1 9.3 5.5 2.5 2.7 7.4 47 5.3 0 0 1 3
3 3.8 5.5 4.9 3.4 2.6 6 36 4.2 0 1 1 2
1.1 2 7.2 4.7 1.6 3.2 10 40 3.4 1 1 1 1
3.7 1.4 9 4.5 2.6 2.3 6.8 45 4.9 0 0 0 2
4.2 2.5 9.2 6.2 3.3 3.9 7.3 59 6 0 0 0 3
1.6 4.5 6.4 5.3 3 2.5 7.1 46 4.5 1 1 0 2
5.3 1.7 8.5 3.7 3.5 1.9 4.8 58 4.3 0 0 0 3
2.3 3.7 8.3 5.2 3 2.3 9.1 49 4.8 1 1 1 2
3.6 5.4 5.9 6.2 4.5 2.9 8.4 50 5.4 1 1 1 2
5.6 2.2 8.2 3.1 4 1.6 5.3 55 3.9 0 0 1 3
3.6 2.2 9.9 4.8 2.9 1.9 4.9 51 4.9 0 0 0 3
5.2 1.3 9.1 4.5 3.3 2.7 7.3 60 5.1 0 0 1 3
3 2 6.6 6.6 2.4 2.7 8.2 41 4.1 1 1 0 1
4.2 2.4 9.4 4.9 3.2 2.7 8.5 49 5.2 0 0 1 2
3.8 0.8 8.3 6.1 2.2 2.6 5.3 42 5.1 0 0 0 2
3.3 2.6 9.7 3.3 2.9 1.5 5.2 47 5.1 0 0 1 3
1 1.9 7.1 4.5 1.5 3.1 9.9 39 3.3 1 1 1 1
4.5 1.6 8.7 4.6 3.1 2.1 6.8 56 5.1 0 0 0 3
5.5 1.8 8.7 3.8 3.6 2.1 4.9 59 4.5 0 0 0 3
3.4 4.6 5.5 8.2 4 4.4 6.3 47 5.6 0 1 1 2
1.6 2.8 6.1 6.4 2.3 3.8 8.2 41 4.1 1 1 0 1
2.3 3.7 7.6 5 3 2.5 7.4 37 4.4 0 1 0 1
2.6 3 8.5 6 2.8 2.8 6.8 53 5.6 1 1 0 2
2.5 3.1 7 4.2 2.8 2.2 9 43 3.7 1 1 1 1
2.4 2.9 8.4 5.9 2.7 2.7 6.7 51 5.5 1 1 0 2
2.1 3.5 7.4 4.8 2.8 2.3 7.2 36 4.3 0 1 0 1
2.9 1.2 7.3 6.1 2 2.5 8 34 4 1 1 1 1
4.3 2.5 9.3 6.3 3.4 4 7.4 60 6.1 0 0 0 3
3 2.8 7.8 7.1 3 3.8 7.9 49 4.4 0 1 1 2
4.8 1.7 7.6 4.2 3.3 1.4 5.8 39 5.5 0 0 0 2
3.1 4.2 5.1 7.8 3.6 4 5.9 43 5.2 0 1 1 2
1.9 2.7 5 4.9 2.2 2.5 8.2 36 3.6 1 1 0 1
4 0.5 6.7 4.5 2.2 2.1 5 31 4 0 0 1 1
0.6 1.6 6.4 5 0.7 2.1 8.4 25 3.4 1 1 1 1
6.1 0.5 9.2 4.8 3.3 2.8 7.1 60 5.2 0 0 1 3
2 2.8 5.2 5 2.4 2.7 8.4 38 3.7 1 1 0 1
3.1 2.2 6.7 6.8 2.6 2.9 8.4 42 4.3 1 1 0 1
2.5 1.8 9 5 2.2 3 6 33 4.4 0 0 0 1
Delivery speed Price level Price flexibility Manufacturing image Overall service Salesforce image Product quality Usage Level Satisfaction Level Size of firm Purchasing Structure Buying Type
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