One of PLE’s manufacturing plants supplies various engine components to manufacturers of motorcycles on a Just In Time basis. Planned production capacity for one component is 100 units per shift, and the plant operates one shift per day. Because of fluctuations in customers’ assembly operations, however, demand fluctuates and is historically between 80 and 130 units per day. To maintain sufficient inventory to meet its Just In Time commitments, PLE’s management is considering a policy to run a second shift the next day if inventory falls to 50 or below at the end of a day (after the daily demand is known). For the annual budget-planning process, managers need to know how many additional shifts will be needed. The fundamental equation that governs this process each day is:
ending inventory = beginning inventory + production − demand ending inventory = beginning inventory + production − demand
Develop a spreadsheet model to simulate 260 working days (one year), and count the number of additional shifts that are required. Assume that the initial inventory is 100 units. Use psi functions for all uncertain cells in building your model. Using the number of additional shifts required as the output cell for a Monte Carlo simulation, find the distribution of the number of shifts that the company can expect to need over the next year. Explain and summarize your findings in a report to the plant manager and make a recommendation as to how many shifts to plan in next year’s budget. Upload your Word document AND your Excel worksheet file

Dealer Satisfaction

Dealer Satisfaction

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

2013 1 2 6 12 34 45 100

2014 2 3 5 15 44 56 125

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

End-User Satisfaction

Sample

North America 0 1 2 3 4 5 Size

2010 1 3 6 15 37 38 100

2011 1 2 4 18 35 40 100

2012 1 2 5 17 34 41 100

2013 0 2 4 15 33 46 100

2014 0 2 3 15 31 49 100

South America

2010 1 2 5 18 36 38 100

2011 1 3 6 17 36 37 100

2012 0 2 6 19 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 26 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 42 35 100

China

2012 0 3 3 6 28 10 50

2013 1 2 2 4 30 11 50

2014 0 1 1 3 31 14 50

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

Month World NA SA Eur Pac China

Jan-10 169 102 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-10 232 137 17 73 5

Jun-10 261 151 19 82 9

Jul-10 245 140 18 80 7

Aug-10 223 128 16 76 3

Sep-10 195 103 15 73 4

Oct-10 174 96 14 62 2

Nov-10 154 84 11 59 0

Dec-10 163 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-11 264 163 20 70 11

May-11 283 178 22 75 8

Jun-11 296 170 28 86 12

Jul-11 269 153 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-11 171 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-12 279 148 26 86 15 4

Sep-12 266 143 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-13 239 123 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-13 277 141 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-14 298 155 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-14 311 158 38 93 13 9

Sep-14 289 149 33 89 11 7

Oct-14 265 136 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 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 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 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 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 41852 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

Month NA SA Europe Pacific China World

Jan-10 6000 200 720 100 0 7020

Feb-10 7950 220 990 120 0 9280

Mar-10 8100 250 1320 110 0 9780

Apr-10 9050 280 1650 120 0 11100

May-10 9900 310 1590 130 0 11930

Jun-10 10200 300 1620 120 0 12240

Jul-10 8730 280 1590 140 0 10740

Aug-10 8140 250 1560 130 0 10080

Sep-10 6480 230 1590 130 0 8430

Oct-10 5990 220 1320 120 0 7650

Nov-10 5320 210 990 130 0 6650

Dec-10 4640 180 660 140 0 5620

Jan-11 5980 210 690 140 0 7020

Feb-11 7620 240 1020 150 0 9030

Mar-11 8370 250 1290 140 0 10050

Apr-11 8830 290 1620 150 0 10890

May-11 9310 330 1650 130 0 11420

Jun-11 10230 310 1590 140 0 12270

Jul-11 8720 290 1560 150 0 10720

Aug-11 7710 270 1530 140 0 9650

Sep-11 6320 250 1590 150 0 8310

Oct-11 5840 250 1260 160 0 7510

Nov-11 4960 240 900 150 0 6250

Dec-11 4350 210 660 150 0 5370

Jan-12 6020 220 570 160 0 6970

Feb-12 7920 250 840 150 0 9160

Mar-12 8430 270 1110 160 0 9970

Apr-12 9040 310 1500 170 0 11020

May-12 9820 360 1440 160 0 11780

Jun-12 10370 330 1410 170 0 12280

Jul-12 9050 310 1440 160 0 10960

Aug-12 7620 300 1410 170 0 9500

Sep-12 6420 280 1350 180 0 8230

Oct-12 5890 270 1080 180 0 7420

Nov-12 5340 260 840 190 0 6630

Dec-12 4430 230 510 180 0 5350

Jan-13 6100 250 480 200 0 7030

Feb-13 8010 270 750 190 0 9220

Mar-13 8430 280 1140 200 0 10050

Apr-13 9110 320 1410 210 0 11050

May-13 9730 380 1340 190 0 11640

Jun-13 10120 360 1360 200 0 12040

Jul-13 9080 320 1410 200 0 11010

Aug-13 7820 310 1490 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 8030 280 750 190 0 9250

Mar-14 8540 300 970 210 0 10020

Apr-14 9120 340 1310 220 5 10995

May-14 9570 390 1260 200 16 11436

Jun-14 10230 380 1240 210 22 12082

Jul-14 9580 350 1300 230 26 11486

Aug-14 7680 340 1250 220 14 9504

Sep-14 6870 320 1210 220 15 8635

Oct-14 5930 310 970 230 11 7451

Nov-14 5260 300 650 240 3 6453

Dec-14 4830 290 300 230 1 5651

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 4960 4350 6020 7920 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 200 220 250 280 310 300 280 250 230 220 210 180 210 240 250 290 330 310 290 270 250 250 240 210 220 250 270 310 360 330 310 300 280 270 260 230 250 270 280 320 380 360 320 310 300 290 270 260 270 280 300 340 390 380 350 340 320 310 300 290 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 1290 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 416 99 41730 41760 41791 41821 41852 41883 41913 41944 41974 7020 9280 9780 11100 11930 12240 10740 10080 8430 7650 6650 5620 7020 9030 10050 10890 11420 12270 10720 9650 8310 7510 6250 5370 6970 9160 9970 11020 11780 12280 10960 9500 8230 7420 6630 5350 7030 9220 10050 11050 11640 12040 11010 9830 8370 7490 6530 6300 7080 9250 10020 10995 11436 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

Month NA SA Eur Pac China World

Jan-10 570 250 560 212 0 1592

Feb-10 611 270 600 230 0 1711

Mar-10 630 260 680 240 0 1810

Apr-10 684 270 650 263 0 1867

May-10 650 280 580 269 0 1779

Jun-10 600 270 590 280 0 1740

Jul-10 512 264 760 290 0 1826

Aug-10 500 280 645 270 0 1695

Sep-10 478 290 650 263 0 1681

Oct-10 455 280 670 258 0 1663

Nov-10 407 290 888 240 0 1825

Dec-10 360 280 850 230 0 1720

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 2271

Jul-11 681 481 680 312 0 2154

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 1936

Dec-11 533 420 657 240 0 1850

Jan-12 620 510 610 250 10 2000

Feb-12 792 590 680 250 12 2324

Mar-12 890 610 730 260 20 2510

Apr-12 960 600 820 270 22 2672

May-12 1040 620 810 290 20 2780

Jun-12 1032 640 807 310 24 2813

Jul-12 1006 590 760 340 20 2716

Aug-12 910 600 720 320 31 2581

Sep-12 803 670 660 313 30 2476

Oct-12 730 630 630 290 37 2317

Nov-12 699 710 603 280 32 2324

Dec-12 647 570 570 260 33 2080

Jan-13 730 650 500 287 35 2202

Feb-13 930 680 590 290 50 2540

Mar-13 1160 724 620 300 63 2867

Apr-13 1510 730 730 310 68 3348

May-13 1650 760 740 330 70 3550

Jun-13 1490 800 720 340 82 3432

Jul-13 1460 840 670 350 80 3400

Aug-13 1390 830 610 341 90 3261

Sep-13 1360 820 599 330 100 3209

Oct-13 1340 810 560 320 102 3132

Nov-13 1240 827 550 300 110 3027

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 3553

Apr-14 2010 890 570 230 120 3820

May-14 2230 930 590 253 130 4133

Jun-14 2490 980 600 270 136 4476

Jul-14 2440 1002 580 280 134 4436

Aug-14 2334 970 570 250 132 4256

Sep-14 2190 960 550 230 137 4067

Oct-14 2080 930 530 220 130 3890

Nov-14 2050 920 517 190 139 3816

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 124 0 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 40 848 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

Sum of Percent Year

2010 2011 2012 2013 2014 Anova: Single Factor

Month

Jan 98.43% 98.44% 98.67% 98.92% 99.21% SUMMARY

Feb 98.09% 98.63% 98.79% 98.82% 99.14% Groups Count Sum Average Variance

Mar 97.58% 98.38% 98.67% 98.91% 99.28% 2010 12 11.8191937544 98.49% 0.000012772

Apr 98.60% 98.73% 98.80% 98.97% 99.22% 2011 12 11.8337272701 98.61% 0.0000022009

May 98.73% 98.73% 98.84% 99.11% 99.22% 2012 12 11.8531797187 98.78% 0.000000506

Jun 98.64% 98.78% 98.81% 98.91% 99.08% 2013 12 11.8723090976 98.94% 0.0000034754

Jul 98.58% 98.71% 98.89% 98.99% 99.23% 2014 12 11.8882528563 99.07% 0.0000137813

Aug 98.67% 98.67% 98.77% 99.12% 99.23%

Sep 98.94% 98.58% 98.77% 98.93% 98.69%

Oct 98.76% 98.69% 98.67% 98.99% 99.23% ANOVA

Nov 98.50% 98.69% 98.83% 98.43% 99.29% Source of Variation SS df MS F P-value F crit

Dec 98.39% 98.33% 98.81% 99.12% 98.01% Between Groups 0.0002607821 4 0.0000651955 9.9579207275 0.0000039122 2.5396886349

Within Groups 0.0003600906 55 0.0000065471

Total 0.0006208727 59

On-Time Delivery

Month Number of deliveries Number On Time Percent

Jan-10 1086 1069 98.4%

Feb-10 1101 1080 98.1%

Mar-10 1116 1089 97.6%

Apr-10 1216 1199 98.6%

May-10 1183 1168 98.7%

Jun-10 1176 1160 98.6%

Jul-10 1198 1181 98.6%

Aug-10 1205 1189 98.7%

Sep-10 1223 1210 98.9%

Oct-10 1209 1194 98.8%

Nov-10 1198 1180 98.5%

Dec-10 1243 1223 98.4%

Jan-11 1220 1201 98.4%

Feb-11 1241 1224 98.6%

Mar-11 1237 1217 98.4%

Apr-11 1258 1242 98.7%

May-11 1262 1246 98.7%

Jun-11 1227 1212 98.8%

Jul-11 1243 1227 98.7%

Aug-11 1281 1264 98.7%

Sep-11 1272 1254 98.6%

Oct-11 1295 1278 98.7%

Nov-11 1298 1281 98.7%

Dec-11 1318 1296 98.3%

Jan-12 1281 1264 98.7%

Feb-12 1320 1304 98.8%

Mar-12 1352 1334 98.7%

Apr-12 1336 1320 98.8%

May-12 1291 1276 98.8%

Jun-12 1342 1326 98.8%

Jul-12 1352 1337 98.9%

Aug-12 1377 1360 98.8%

Sep-12 1385 1368 98.8%

Oct-12 1356 1338 98.7%

Nov-12 1362 1346 98.8%

Dec-12 1349 1333 98.8%

Jan-13 1386 1371 98.9%

Feb-13 1358 1342 98.8%

Mar-13 1371 1356 98.9% Q2

Apr-13 1362 1348 99.0%

May-13 1350 1338 99.1% Anova: Single Factor

Jun-13 1381 1366 98.9%

Jul-13 1392 1378 99.0% SUMMARY

Aug-13 1371 1359 99.1% Groups Count Sum Average Variance

Sep-13 1402 1387 98.9% 2010 12 11.8191937544 98.49% 0.000012772

Oct-13 1384 1370 99.0% 2011 12 11.8337272701 98.61% 0.0000022009

Nov-13 1399 1377 98.4% 2012 12 11.8531797187 98.78% 0.000000506

Dec-13 1369 1357 99.1% 2013 12 11.8723090976 98.94% 0.0000034754

Jan-14 1401 1390 99.2% 2014 12 11.8882528563 99.07% 0.0000137813

Feb-14 1388 1376 99.1%

Mar-14 1395 1385 99.3%

Apr-14 1412 1401 99.2% ANOVA

May-14 1403 1392 99.2% Source of Variation SS df MS F P-value F crit

Jun-14 1415 1402 99.1% Between Groups 0.0002607821 4 0.0000651955 9.9579207275 0.0000039122 2.5396886349

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

Aug-14 1431 1420 99.2%

Sep-14 1445 1426 98.7% Total 0.0006208727 59

Oct-14 1425 1414 99.2%

Nov-14 1413 1403 99.3%

Dec-14 1456 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 0.98434622467771637 0.98092643051771122 0.97580645161290325 0.98601973684210531 0.9873203719357565 0.98639455782312924 0.9858096828046744 0.98672199170124486 0.98937040065412918 0.98759305210918114 0.9849749582637729 0.98390989541432017 0.98442622950819669 0.98630136986301364 0.98383185125303152 0.9872813990461049 0.98732171156893822 0.98777506112469438 0.98712791633145613 0.98672911787665885 0.98584905660377353 0.98687258687258683 0.98690292758089371 0.98330804248861914 0.98672911787665885 0.98787878787878791 0.98668639053254437 0.9880239520958084 0.98838109992254064 0.98807749627421759 0.98890532544378695 0.98765432098765427 0.98772563176895312 0.98672566371681414 0.98825256975036713 0.98813936249073386 0.98917748917748916 0.98821796759941094 0.98905908096280093 0.98972099853157125 0.99111111111111116 0.98913830557566984 0.98994252873563215 0.99124726477024072 0.98930099857346643 0.98988439306358378 0.98427448177269483 0.99123447772096418 0.99214846538187007 0.99135446685878958 0.99283154121863804 0.99220963172804533 0.99215965787598004 0.99081272084805649 0.99228611500701258 0.99231306778476591 0.98685121107266438 0.99228070175438599 0.99292285916489742 0.98008241758241754

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

Q1 2013 Q2 2013 Q3 2013 Q4 2013 Q1 2014 Q2 2014 Q3 2014 Q4 2014

4.36 4.33 3.71 4.44 2.75 3.45 1.67 2.55

5.42 4.73 2.52 4.07 3.24 1.95 2.58 2.30

5.50 1.63 2.69 5.11 4.35 2.77 3.47 1.04

2.79 4.21 3.47 3.49 5.58 1.83 3.12 1.59

5.55 6.89 5.12 4.69 2.89 3.72 1.00 3.11

3.65 0.92 1.00 6.36 5.09 4.59 5.40 4.05

8.02 5.27 3.44 8.26 2.33 1.17 3.90 3.38

4.00 0.90 6.04 1.91 1.69 1.46 4.49 1.26

3.34 3.85 2.53 8.93 3.88 1.90 2.06 0.90

4.92 5.00 2.39 6.85 3.39 2.95 4.49 2.31

3.55 3.52 3.26 5.69 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 5.91 2.34 3.59 3.31 3.58

2.18 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

2.46 2.18 4.44 3.74 2.40 1.63 4.28 2.87

2.07 4.55 4.87 6.11 1.59 2.40 4.47 0.90

2.90 2.13 6.76 4.78 3.05 4.44 1.94 4.87

2.58 5.24 2.84 4.13 1.50 4.96 3.90 3.11

5.50 4.08 1.25 7.17 5.58 4.41 3.32 0.90

2.47 4.04 3.43 5.70 3.11 3.40 2.20 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 1.35

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 1.76 4.60 1.03

6.40 8.05 2.12 5.83 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 5.35 4.73 6.57 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 7.45 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 5.415645561640849 5.50147957886802 2.7866492627596018 5.5495684291032372 3.6535666521900567 8.0191382648423311 4.0045367922517467 3.3431904438999482 4.9159115332600773 3.5546503494857462 3.5231651208392578 1.2533953549223953 2.1813659868144897 4.3525112841394726 2.4588828336505686 2.0693403411656619 2.9026272313218215 2.5783995324105491 5.4993536350026258 2.4736523454863346 4.2446331617044049 1.8764321948197904 4.2502707783001821 5.0840524335741062 4.4030024509425854 1.6400465637503658 6.4004832592559975 3.6791089013946476 3.9198121311870637 4.1274743279587707 3.3353070575118182 3.2786815763189225 3.2441311231537839 3.2535645158874105 5.199402282357914 5.281745886293356 4.3296535222340022 4.6425480076664822 2.6515938470198308 3.4188237959257095 3.9721818592966884 1.2641333041188774 6.1579749098542376 6.4025937417114616 1 3.6338166336805444 5.3400354017299829 3.7376013478366077 5.6347801245807201 Q2 2013 4.3325643203628719 4.7253575742855904 1.6261836647812742 4.205002231471008 6.8870843718526888 0.92273817092645904 5.2676703929377258 0.9 3.8496963027922901 5.0034296676371017 3.5156336692365584 5.1965592759428549 5.1282537227292782 5.2852813935955059 1 2.1758940859639551 4.554598807159346 2.1334770720626692 5.241364395557321 4.0773214535205629 4.0392099875374701 5.0861743587360255 7.6592344597214836 4.6470289347111251 0.9 2.0076011863478924 1.3415140968631021 8.0482562664896253 4.913553401207901 5.0573001756914895 3.2576159340591402 4.263339950126829 1.6992101776180788 2.2969732966215815 5.3534252841258425 2.3312703418254386 3.6666470790136372 4.7275287655123979 1.0453071339055895 2.6700355177366872 4.1573383426351942 0.9 3.5076733168592908 5.9505744942056484 2.0504684001265558 8.2124891817569736 2.5168079431081423 3.9860188720253062 2.5933316904469392 1.3390093484544194 Q3 2013 3.7146412572171541 2.5241054166387769 2.6896680131601172 3.4734687281586232 5.121887857355178 1 3.4443303369032221 6.0388986233435578 2.5292204148415478 2.3882014423422517 3.2575328580848875 4.6841771612223244 3.5920977600896733 1.0686919770948591 2.8610331858787688 4.4406181180663413 4.8667564036138362 6.7562134566530592 2.8361203070078047 1.2506345731951298 3.4268334778305145 2.9840077834948899 4.6549896572530276 2.658026692485437 4.9887814887613064 3.7590027707908304 3.1200700098695235 2.1182925186865034 4.3161646820651374 3.6110861904732885 4.020589817925357 2.6307855071779342 4.4749861038569367 4.1842934072762734 4.72942270364612 4 2.646999978721142 2.3632449077256026 3.6397843862930315 5.6180936147272593 0.9 6.4001208150573081 3.2102573234867307 3.5474379322538154 5.9302431103121496 5.5190132619161165 4.9623297448549426 4.8508693501632667 5.5698431018088019 4.817243512049318 3.1770789567660542 Q4 2013 4.4392094297145377 4.0731587306290749 5.112268023462093 3.4856877947313478 4.6882091838633642 6.3605414298799587 8.2577867134241387 1.9114045345340855 8.9296140787191689 6.8537110665638465 5.687837084318744 3.0470982993429061 5.9130352484353352 1 1.8187038323085289 3.7439606431726133 6.1054524950159248 4.7754579200991429 4.1273587031391799 7.174651283188723 5.7005295376293361 1 3.3979271266653086 2.0414006586215692 4.3706494453581399 2.4660232712485595 3.2023929280549055 5.833204123613541 3.9361662048613653 2.4685073286527768 3.8865800989733543 6.875510290323291 1.7119800860236865 6.3871489247540012 6.5707099666760769 4.1814614734030329 8.8249639803543687 3.3480947750867927 5.499761538070743 6.5071526579267811 0.9 2.8718966505985009 7.4505069379520137 3.4878651250473922 3.0321399536696845 7.4588620110298507 4.844769601826556 2.8833146744582336 0.95167707614018582 3.0501850106738857 Q1 2014 2.7456040207704064 3.2393556203765912 4.3539226190710902 5.5837254386511628 2.894123937135737 5.0948083718190897 2.3263553849625169 1.6863519214035478 3.8792584710841767 3.3915317054430489 5.1440984371816736 0.98274408274446623 2.3405503235204379 2.8036798049521168 3.0573333298030776 2.4015251220640494 1.5885425874381327 3.0502597347600386 1.5024861987563782 5.5816790755721737 3.1106598463389674 1.0826270646299236 3.6316638862495894 1.8572607551555849 1.8951628099835944 6.0711554816458371 1 1 1.1885672812291888 3.7861455403850415 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 5.3514018382935316 5.1112406673433721 6.4554624678799879 5.6095641831285317 3.6320509899320315 3.8695416570641101 Q2 2014 3.4465603756718339 1.95467528909212 2.7691193817037858 1.830401933041867 3.7153588062967176 4.588204054819653 1.1652720867306927 1.4585909492627254 1.8973007253254766 2.954022155684652 4.6879442460369321 3.3438613708160121 3.5946013293898433 4.0304668881464751 2.3857898749003654 1.6263281476160047 2.3982745086716024 4.4406580935930835 4.9579172890691554 4.4146033441240435 3.3970261109818241 3.1488661615032472 4.8728326954762453 3.969714915804798 3.8509883405669827 2.8099522832082586 1.7614722390891986 5.5786442397977227 4.9162933545478156 2.6285494722134901 3.2720810930943118 2.8562667092803169 3.8348668648570312 1.7931613082357218 2.7003026924678126 3.6135908966418357 0.9 3.3844030066422421 4.3807401278929321 2.872878402634524 2.1136076692375356 2.8578058016893921 3.1247515916067643 1.8599295880296269 2.4143211784423331 2.97 56362972722856 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 3.5669281790687819 3.4085343334736535 3.3083657134084206 2.7882290472261957 2.0893796280033712 4.2785482113031321 4.4665714616057812 1.9354151921361336 3.8966397899712319 3.3183290004926675 2.1960299894344644 3.5221082233219931 2.3136046896324842 1 5.8955778361705597 1.0873686808990897 4.5958403309923597 3.5192415528654237 4.1415744438636466 4.1337970136082731 2.4295045553371892 2.3373820643682848 2.5318425476398261 4.1416370853112312 2.6456999724614434 3.211152780593693 3.85011697592563 2.202989783952944 4.573015765643504 2.9913637225290586 4.1850706869154237 3.0259632315646741 1.9018393762307824 2.0914913041706313 1.0339421199460048 2.9528837406614912 7.4192420318722725 3.7933836059237365 2.4752080851867504 2.7128647919453215 Q4 2014 2.5510757682699476 2.3031384176196297 1.0432483764365315 1.5865764185495208 3.1144282689187093 4.0469112450868128 3.3778203219757414 1.2557568157266359 0.9 2.3109832641697721 2.7098836613280581 1.6538044479151721 3.5820508815508219 2.9565219124837312 3.7752575695325503 2.8747584524811827 0.90147952555562361 4.8724379853869326 3.1082047103613148 0.9 3.5162579211377305 3.1823331897161551 0.9 1.3526853040733839 1.6183518896927125 1.8669454407703596 1.0325304361234884 2.31182863949507 1.9896637882542563 3.9689445844036526 1 3.5086081612011184 2.410366592403443 2.4695753796098869 4.0189783890586117 2.0281505344886681 3.6200026175269158 4.1219250038469912 1.4048089001793413 2.4852340362034737 2.6676015937031479 4.3273157376010207 1.9502917626145062 2.7026329421918489 1.758633944109897 2.6436946159723447 4.4879045349720403 1.6248547768103889 1.1000000000000001 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

Unit Shipping Cost

Plant Existing /Proposed Customer Mowers Tractors Plant Existing /Proposed

Kansas City Existing Toronto $1.36 $1.79 Kansas City Existing

Santiago Existing Toronto $1.49 $2.13 Santiago Existing

Kansas City Existing Shanghai $1.58 $2.13 Auckland Proposed

Santiago Existing Shanghai $1.47 $2.03 Birmingham Proposed

Kansas City Existing Mexico City $1.32 $1.76 Frankfurt Proposed

Santiago Existing Mexico City $1.22 $1.58 Mumbai Proposed

Kansas City Existing Melbourne $1.72 $2.34 Singapore Proposed

Santiago Existing Melbourne $1.49 $1.80

Kansas City Existing London $1.49 $1.86

Santiago Existing London $1.58 $2.14

Kansas City Existing Caracas $1.54 $1.90

Santiago Existing Caracas $1.00 $1.26

Kansas City Existing Atlanta $1.31 $1.82

Santiago Existing Atlanta $1.31 $1.76

Singapore Proposed Toronto $1.71 $2.03

Birmingham Proposed Toronto $1.34 $1.78 Mowers Tactors

Frankfurt Proposed Toronto $1.52 $1.87 Quartiles Existing Proposed Existing Proposed

Mumbai Proposed Toronto $1.67 $2.14 1 25% $ 1.31 $ 1.77 $ 1.40 $ 1.78

Auckland Proposed Toronto $1.86 $2.19 2 50% $ 1.48 $ 1.84 $ 1.52 $ 2.01

Singapore Proposed Shanghai $1.44 $1.78 3 75% $ 1.53 $ 2.11 $ 1.66 $ 2.17

Birmingham Proposed Shanghai $1.60 $2.15 4 100% $ 1.72 $ 2.34 $ 1.98 $ 2.68

Frankfurt Proposed Shanghai $1.65 $2.32

Mumbai Proposed Shanghai $1.21 $1.47

Auckland Proposed Shanghai $1.18 $1.63

Singapore Proposed Mexico City $1.72 $2.09

Birmingham Proposed Mexico City $1.29 $1.79

Frankfurt Proposed Mexico City $1.54 $2.04

Mumbai Proposed Mexico City $1.56 $2.22

Auckland Proposed Mexico City $1.50 $2.07

Singapore Proposed Melbourne $1.43 $1.70

Birmingham Proposed Melbourne $1.52 $2.06

Frankfurt Proposed Melbourne $1.73 $2.28

Mumbai Proposed Melbourne $1.38 $1.63

Auckland Proposed Melbourne $0.91 $1.17

Singapore Proposed London $1.88 $2.68

Birmingham Proposed London $1.47 $1.77

Frankfurt Proposed London $1.37 $1.64

Mumbai Proposed London $1.44 $1.82

Auckland Proposed London $1.98 $2.60

Singapore Proposed Caracas $1.50 $2.01

Birmingham Proposed Caracas $1.37 $1.86

Frankfurt Proposed Caracas $1.59 $1.88

Mumbai Proposed Caracas $1.61 $2.08

Auckland Proposed Caracas $1.54 $1.98

Singapore Proposed Atlanta $1.73 $2.35

Birmingham Proposed Atlanta $1.02 $1.25

Frankfurt Proposed Atlanta $1.42 $1.70

Mumbai Proposed Atlanta $1.57 $2.23

Auckland Proposed Atlanta $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

Fixed Costs of Capacity Increase or New Construction

Current Plants Additional Capacity Cost

Kansas City 10000 $605,000.00

Kansas City 20000 $985,000.00

Santiago 5000 $381,000.00

Santiago 10000 $680,000.00

Proposed Locations Maximum capacity Cost

Auckland 15,000 $917,000.00

Auckland 20,000 $1,136,000.00

Birmingham 15,000 $962,000.00

Birmingham 20,000 $1,180,000.00

Frankfurt 15,000 $874,000.00

Frankfurt 20,000 $1,093,000.00

Mumbai 15,000 $750,000.00

Mumbai 25,000 $959,000.00

Singapore 15,000 $839,000.00

Singapore 20,000 $1,058,000.00

Part 3 – Regions and Averages

Row Labels Average of Ease of Use Average of Quality Average of Price Average of Service

China 4.10 3.80 3.00 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 4.30

SA 3.92 4.28 3.50 4.24

Grand Total 4.17 4.40 3.67 4.14

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 3.8666666667 4.3333333333 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 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 0.5818844221

Ease of Use 200 833 4.165 0.6108291457

Price 200 734 3.67 1.1367839196

ANOVA

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

Between Groups 54.9033333333 2 27.4516666667 35.3531181914 0 3.0108152042

Within Groups 463.57 597 0.7764991625

Total 518.4733333333 599

Part 3 – 2014 Customer Survey

2014 Customer Survey

Quartiles

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 4.75 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 Frequency Distrbution

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

NA 4 5 4 4 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 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

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 Us e 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 Price 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 $1,750 $1 $50 $1

Feb-10 $1,755 $1 $50 $1

Mar-10 $1,763 $1 $51 $1

Apr-10 $1,770 $1 $51 $1

May-10 $1,778 $1 $51 $1

Jun-10 $1,785 $1 $51 $1

Jul-10 $1,792 $1 $51 $1

Aug-10 $1,795 $1 $51 $1

Sep-10 $1,801 $1 $52 $1

Oct-10 $1,804 $1 $52 $1

Nov-10 $1,810 $1 $52 $1

Dec-10 $1,813 $1 $52 $1

Jan-11 $1,835 $1 $55 $1

Feb-11 $1,841 $1 $55 $1

Mar-11 $1,848 $1 $55 $1

Apr-11 $1,854 $1 $55 $1

May-11 $1,860 $1 $56 $1

Jun-11 $1,866 $1 $56 $1

Jul-11 $1,872 $1 $56 $1

Aug-11 $1,878 $1 $56 $1

Sep-11 $1,885 $1 $56 $1

Oct-11 $1,892 $1 $57 $1

Nov-11 $1,897 $1 $57 $1

Dec-11 $1,903 $1 $57 $1

Jan-12 $1,925 $1 $59 $1

Feb-12 $1,931 $1 $59 $1

Mar-12 $1,938 $1 $59 $1

Apr-12 $1,944 $1 $59 $1

May-12 $1,950 $1 $59 $1

Jun-12 $1,956 $1 $60 $1

Jul-12 $1,963 $1 $60 $1

Aug-12 $1,969 $1 $60 $1

Sep-12 $1,976 $1 $60 $1

Oct-12 $1,983 $1 $60 $1

Nov-12 $1,990 $1 $61 $1

Dec-12 $1,996 $1 $61 $1

Jan-13 $1,940 $1 $59 $1

Feb-13 $1,946 $1 $59 $1

Mar-13 $1,952 $1 $59 $1

Apr-13 $1,958 $1 $59 $1

May-13 $1,964 $1 $60 $1

Jun-13 $1,970 $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 $2,012 $1 $61 $1

Dec-13 $2,008 $1 $61 $1

Jan-14 $2,073 $1 $63 $1

Feb-14 $2,077 $1 $63 $1

Mar-14 $2,081 $1 $63 $1

Apr-14 $2,086 $1 $63 $1

May-14 $2,092 $1 $63 $1

Jun-14 $2,098 $1 $63 $1

Jul-14 $2,104 $1 $64 $1

Aug-14 $2,110 $1 $64 $1

Sep-14 $2,116 $1 $64 $1

Oct-14 $2,122 $1 $64 $1

Nov-14 $2,129 $1 $64 $1

Dec-14 $2,135 $1 $64 $1

$1,938 $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

Items for 2014 Income Statement

Revenue Performance Lawn Equipment

Mowers revenue 1,161,069.16 Income Statement for the year ending December 2014

Tractors Revenue 281,736.21 $ $

Total Revenue 1,442,805.38 Revenue

Mowers revenue 1,161,069.16

Tractors revenues 281,736.21

Total Revenue 1,442,805.38

Expenses

Administrative 7,738,943.00 Expenses

Depreciation 2,486,977.00 Administrative 7,738,943.00

Interest 117,855.00 Depreciation 2,486,977.00

Selling Expense 259,704.97 Interest 117,855.00

Tax Expense Selling Expense 259,704.97

Total Expenses 10,603,479.97

Gross profit/loss (9,160,674.59)

Tax (50%) (4,580,337.30)

Net loss (4,580,337.30)

Performance Lawn Equipment

Pro forma Income Statement for the year ending December 2014

$ $ $

As Reported Pro forma adjustments Pro forma

Revenue

Mowers revenue 1,161,069.16 – 0 1,161,069.16

Tractors revenues 281,736.21 – 0 281,736.21

Total Revenue 1,442,805.38 – 0 1,442,805.38 18.6434423505

Expenses

Administrative 7,738,943.00 – 0 7,738,943.00

Depreciation 2,486,977.00 – 0 2,486,977.00

Interest 117,855.00 – 0 117,855.00

Selling Expense 259,704.97 (259,704.97) 0

Total Expenses 10,603,479.97 259,704.97 10,343,775.00

Gross profit/loss (9,160,674.59) (8,900,969.62)

Tax (50%) (4,580,337.30) (4,450,484.81)

Net loss (4,580,337.30) (259,704.97) (4,450,484.81)

Operating & Interest Expenses

Operating and Interest Expenses

Month Administrative Depreciation Interest

Jan-10 $633,073 $140,467 $7,244

Feb-10 $607,904 $165,636 $7,679

Mar-10 $630,687 $142,853 $6,887

Apr-10 $613,401 $160,139 $6,917

May-10 $607,664 $165,876 $8,316

Jun-10 $632,967 $140,573 $7,428

Jul-10 $609,604 $163,936 $8,737

Aug-10 $607,749 $165,791 $7,054

Sep-10 $603,367 $170,173 $8,862

Oct-10 $629,083 $144,457 $8,488

Nov-10 $611,995 $161,545 $7,049

Dec-10 $625,712 $147,828 $8,807

$1,869,274

Jan-11 $656,123 $175,447 $7,430

Feb-11 $652,679 $178,891 $6,791

Mar-11 $655,521 $176,049 $8,013

Apr-11 $676,581 $154,989 $8,979

May-11 $676,581 $154,989 $7,484

Jun-11 $656,440 $175,130 $7,858

Jul-11 $661,969 $169,601 $7,424

Aug-11 $677,212 $154,358 $6,848

Sep-11 $653,545 $178,025 $6,751

Oct-11 $657,388 $174,182 $8,160

Nov-11 $672,475 $159,095 $7,898

Dec-11 $656,325 $175,245 $8,953

$2,026,001

Jan-12 $723,594 $226,526 $9,443

Feb-12 $759,042 $191,078 $8,464

Mar-12 $749,187 $200,933 $10,264

Apr-12 $751,499 $198,621 $8,547

May-12 $741,452 $208,668 $8,578

Jun-12 $729,122 $220,998 $9,519

Jul-12 $734,783 $215,337 $9,343

Aug-12 $748,208 $201,912 $8,448

Sep-12 $738,186 $211,934 $9,957

Oct-12 $759,403 $190,717 $9,738

Nov-12 $726,183 $223,937 $9,785

Dec-12 $757,037 $193,083 $8,191

$2,483,744

Jan-13 $672,232 $179,138 $9,914

Feb-13 $665,023 $186,347 $9,954

Mar-13 $667,657 $183,713 $10,859

Apr-13 $654,198 $197,172 $9,730

May-13 $659,435 $191,935 $10,430

Jun-13 $661,190 $190,180 $10,222

Jul-13 $647,321 $204,049 $10,102

Aug-13 $666,743 $184,627 $10,610

Sep-13 $678,705 $172,665 $9,374

Oct-13 $658,990 $192,380 $10,830

Nov-13 $656,221 $195,149 $9,017

Dec-13 $676,934 $174,436 $10,423

$2,251,791

Jan-14 $641,571 $210,589 $9,985

Feb-14 $634,973 $217,187 $9,766

Mar-14 $662,054 $190,106 $11,148

Apr-14 $654,962 $197,198 $9,339

May-14 $645,579 $206,581 $9,468

Jun-14 $658,112 $194,048 $10,324

Jul-14 $637,711 $214,449 $9,737

Aug-14 $638,317 $213,843 $9,290

Sep-14 $651,996 $200,164 $9,213

Oct-14 $630,766 $221,394 $10,143

Nov-14 $645,095 $207,065 $10,383

Dec-14 $637,807 $214,353 $9,059

$2,486,977

Jan-14 $641,571 $210,589 $9,985

Feb-14 $634,973 $217,187 $9,766

Mar-14 $662,054 $190,106 $11,148

Apr-14 $654,962 $197,198 $9,339

May-14 $645,579 $206,581 $9,468

Jun-14 $658,112 $194,048 $10,324

Jul-14 $637,711 $214,449 $9,737

Aug-14 $638,317 $213,843 $9,290

Sep-14 $651,996 $200,164 $9,213

Oct-14 $630,766 $221,394 $10,143

Nov-14 $645,095 $207,065 $10,383

Dec-14 $637,807 $214,353 $9,059

Total expenses $7,738,943 $2,486,977 $117,855

Growth Prediction

Industry Mower Total Sales

Performance Lawn Equipment

Month NA SA Eur Pac World change % change Income statement

Jan-10 60000 1 571 1 13091 1 1045 1 74,662.34 Historic Results Forecast Period

Feb-10 77184 1 611 1 17679 1 1111 1 96,585.26 2014 2015 2016 2017

Mar-10 77885 1 658 1 22759 1 1068 1 102,369.09 $ $ $ $

Apr-10 86190 1 778 1 27966 1 1237 1 116,171.47 Revenue

May-10 96117 1 886 1 27895 1 1313 1 126,210.09 Mowers revenue 1,161,069.16 1,164,610.42 1,200,023.03 1,236,623.74

Jun-10 97143 1 882 1 30566 1 1176 1 129,767.72 Tractors revenues 281,736.21 318,643.66 360,385.98 407,596.54

Jul-10 84757 1 848 1 29444 1 1359 1 116,409.43 Total Revenue 1,442,805.38 1,483,254.08 1,560,409.01 1,644,220.28

Aug-10 79804 1 735 1 28364 1 1238 1 110,140.95

Sep-10 64800 1 657 1 28393 1 1215 1 95,064.95 Expenses

Oct-10 59307 1 595 1 24444 1 1154 1 85,499.82 Administrative 7,738,943.00 8,512,837.30 9,364,121.03 10,300,533.13

Nov-10 52157 1 553 1 18000 1 1262 1 71,971.63 Depreciation 2,486,977.00 2,611,325.85 2,741,892.14 2,878,986.75

Dec-10 45049 1 462 1 12453 1 1386 1 59,349.05 Interest 117,855.00 117,855.00 117,855.00 117,855.00

1,184,201.80 Total Expenses 10,343,775.00 11,242,018.15 12,223,868.17 13,297,374.88

Jan-11 58627 1 553 1 12778 1 1443 1 73,401.16

Feb-11 76200 1 615 1 18214 1 1515 1 96,544.82 Gross profit/loss (8,900,969.62) (9,758,764.07) (10,663,459.16) (11,653,154.61)

Mar-11 82871 1 658 1 23889 1 1373 1 108,790.62 Tax (50%) (4,450,484.81) (4,879,382.03) (5,331,729.58) (5,826,577.30)

Apr-11 84904 1 784 1 29455 1 1442 1 116,584.48 Net loss (4,450,484.81) (4,879,382.03) (5,331,729.58) (5,826,577.30)

May-11 93100 1 846 1 29464 1 1215 1 124,625.39

Jun-11 93000 1 838 1 27414 1 1333 1 122,584.96

Jul-11 83048 1 763 1 27368 1 1415 1 112,594.29

Aug-11 74854 1 694 1 27321 1 1296 1 104,164.40 What if the administrative expenses are reduced by 80% and depreciation by 40% for the next three years?

Sep-11 60769 1 625 1 29444 1 1402 1 92,240.54

Oct-11 55619 1 610 1 23774 1 1468 1 81,470.28

Nov-11 48155 1 571 1 17308 1 1351 1 67,385.81 Performance Lawn Equipment

Dec-11 42647 1 512 1 12941 1 1389 1 57,489.32 Income statement

1,157,876.09 Historic Results Forecast Period

2014 2015 2016 2017

Jan-12 57885 1 537 1 10962 1 1509 1 70,892.17 $ $ $ $

Feb-12 77647 1 595 1 15273 1 1402 1 94,916.89 Revenue

Mar-12 81845 1 659 1 20556 1 1524 1 104,582.56 Mowers revenue 1,161,069.16 1,164,610.42 1,200,023.03 1,236,623.74

Apr-12 86095 1 756 1 26786 1 1574 1 115,211.12 Tractors revenues 281,736.21 318,643.66 360,385.98 407,596.54

May-12 91776 1 878 1 24828 1 1468 1 118,949.23 Total Revenue 1,442,805.38 1,483,254.08 1,560,409.01 1,644,220.28

Jun-12 100680 1 825 1 24737 1 1560 1 127,801.09

Jul-12 86190 1 756 1 24828 1 1441 1 113,215.60 Expenses

Aug-12 71887 1 714 1 25179 1 1545 1 99,325.10 Administrative 7,738,943.00 1,547,788.60 309,557.72 61,911.54

Sep-12 60000 1 651 1 24545 1 1667 1 86,863.28 Depreciation 2,486,977.00 1,492,186.20 895,311.72 537,187.03

Oct-12 55566 1 643 1 19286 1 1698 1 77,192.72 Interest 117,855.00 117,855.00 117,855.00 117,855.00

Nov-12 50857 1 619 1 15273 1 1810 1 68,558.44 Total Expenses 10,343,775.00 3,157,829.80 1,322,724.44 716,953.58

Dec-12 42596 1 548 1 9107 0 1731 1 53,981.68

1,131,489.90

Gross profit/loss (8,900,969.62) (1,674,575.72) 237,684.57 927,266.70

Jan-13 58095 1 581 1 8571 0 1887 1 69,134.85 Tax (50%) (4,450,484.81) (837,287.86) 118,842.29 463,633.35

Feb-13 75566 1 614 1 13158 1 1845 1 91,182.23 Net loss (4,450,484.81) (837,287.86) 118,842.29 463,633.35

Mar-13 80286 1 622 1 19655 1 1923 1 102,486.19

Apr-13 85140 1 727 1 25179 1 1981 1 113,027.16

May-13 90093 1 826 1 23103 1 1810 1 115,831.65

Jun-13 95472 1 783 1 24286 1 1942 1 122,481.77 Tornado Chart

Jul-13 87308 1 681 1 24737 1 1961 1 114,686.17

Aug-13 74476 1 646 1 26607 1 2000 1 103,729.17

Sep-13 61698 1 625 1 22982 1 2075 1 87,381.04

Oct-13 57238 1 617 1 16897 1 2019 1 76,770.90

Nov-13 50673 1 587 1 13750 1 2095 1 67,105.27

Dec-13 51238 1 591 1 7818 0 2150 1 61,796.72

1,125,613.12 (5,876.78) -0.5220962393

Jan-14 59712 1 563 1 7547 0 1852 1 69,673.06

Feb-14 77961 1 571 1 13889 1 1743 1 94,164.60

Mar-14 83725 1 625 1 18302 1 1892 1 104,544.27

Apr-14 90297 1 723 1 25192 1 2037 1 118,249.78

May-14 91143 1 848 1 24706 1 1887 1 118,583.36

Jun-14 99320 1 792 1 25306 1 1944 1 127,362.62

Jul-14 93922 1 745 1 27083 1 2170 1 123,919.39

Aug-14 73143 1 739 1 26042 1 2037 1 101,960.69

Sep-14 66699 1 667 1 26304 1 2018 1 95,688.39

Oct-14 56476 1 660 1 22558 1 2072 1 81,765.98

Nov-14 51068 1 625 1 14773 1 2182 1 68,647.51

Dec-14 46893 1 608 1 6977 0 2035 1 56,509.51

1,161,069.16 35,456.04 3.0537408648

Industry Tractor Total Sales

Month NA SA Eur Pac China World Change % Change

Jan-10 8143 1 984 0 5091 1 987 1 278 0 15,485.83

Feb-10 8592 1 1051 1 5310 1 1090 1 283 0 16,328.18

Mar-10 8630 1 1016 0 6071 1 1127 1 285 0 17,132.35

Apr-10 8947 1 1027 0 5856 1 1209 1 288 0 17,330.62

May-10 8442 1 1057 1 5273 1 1221 1 286 0 16,280.89

Jun-10 7500 1 1019 0 5315 1 1327 1 287 0 15,451.48

Jul-10 6145 1 977 0 7170 1 1324 1 289 0 15,908.41

Aug-10 5882 1 1057 1 5926 1 1268 1 290 0 14,425.63

Sep-10 5595 1 1086 1 6075 1 1209 1 293 0 14,261.55

Oct-10 5233 1 1045 0 6321 1 1168 1 295 0 14,064.35

Nov-10 4494 1 1078 1 8381 1 1127 1 298 0 15,381.41

Dec-10 3913 1 1029 0 7944 1 1085 1 301 0 14,275.34

186,326.04

Jan-11 5938 1 1172 1 5688 1 1185 1 306 0 14,291.68

Feb-11 6633 1 1273 1 7037 1 1286 1 302 0 16,533.66

Mar-11 7327 1 1423 1 6981 1 1286 1 303 0 17,323.62

Apr-11 8077 1 1612 1 7500 1 1346 1 307 0 18,845.80

May-11 7830 1 1728 1 6571 1 1388 1 309 0 17,830.03

Jun-11 7103 1 1815 1 6990 1 1449 1 312 0 17,673.15

Jul-11 6239 1 1776 1 6667 1 1490 1 315 0 16,490.40

Aug-11 6036 1 1685 1 6762 1 1449 1 318 0 16,253.93

Sep-11 5664 1 1679 1 6635 1 1394 1 321 0 15,696.01

Oct-11 5345 1 1618 1 6311 1 1256 1 315 0 14,847.59

Nov-11 4831 1 1564 1 6476 1 1214 1 318 0 14,405.22

Dec-11 4454 1 1522 1 6250 1 1171 1 320 0 13,719.41

193,910.51

Jan-12 5299 1 1835 1 5922 1 1208 1 333 0 14,600.47

Feb-12 6529 1 2115 1 6667 1 1214 1 313 0 16,840.19

Mar-12 7120 1 2202 1 7228 1 1256 1 606 1 18,416.03

Apr-12 7619 1 2151 1 8200 1 1311 1 571 1 19,855.97

May-12 8387 1 2214 1 7941 1 1415 1 556 1 20,517.20

Jun-12 8110 1 2278 1 7921 1 1520 1 526 0 20,359.05

Jul-12 7752 1 2100 1 7677 1 1675 1 513 0 19,720.51

Aug-12 6894 1 2128 1 7200 1 1584 1 769 1 18,579.21

Sep-12 6015 1 2367 1 6735 1 1527 1 750 1 17,398.43

Oct-12 5368 1 2211 1 6495 1 1422 1 732 1 16,230.13

Nov-12 4964 1 2483 1 6061 1 1366 1 714 1 15,591.09

Dec-12 4444 1 1986 1 5816 1 1262 1 698 1 14,210.03

212,318.31

Jan-13 5000 1 2257 1 5051 1 1373 1 714 1 14,397.83

Feb-13 6284 1 2353 1 6082 1 1436 1 1063 1 17,221.81

Mar-13 7785 1 2457 1 6327 1 1478 1 1264 1 19,314.63

Apr-13 9934 1 2517 1 7604 1 1512 1 1333 1 22,905.96

May-13 10645 1 2612 1 7789 1 1642 1 1556 1 24,248.74

Jun-13 9491 1 2749 1 7347 1 1667 1 1739 2 22,997.82

Jul-13 9182 1 2887 1 6979 1 1733 1 1702 2 22,487.92

Aug-13 8528 1 2833 1 6489 1 1700 1 1915 2 21,469.38

Sep-13 8293 1 2789 1 6316 1 1642 1 2083 2 21,127.34

Oct-13 8221 1 2765 1 5833 1 1576 1 2128 2 20,527.19

Nov-13 7470 1 2746 1 5789 1 1493 1 2292 2 19,793.63

Dec-13 6509 1 2534 1 5591 1 1450 1 2245 2 18,332.97

244,825.22

Jan-14 7267 1 2635 1 5106 1 1010 1 2292 2 18,314.48

Feb-14 8807 1 2703 1 5474 1 1045 1 2449 2 20,481.03

Mar-14 10168 1 2795 1 6022 1 1106 1 2400 2 22,493.67

Apr-14 11044 1 2997 1 6064 1 1150 1 2353 2 23,612.03

May-14 12120 2 3131 1 6344 1 1244 1 2600 2 25,443.74

Jun-14 13459 2 3311 2 6593 1 1357 1 2653 2 27,378.90

Jul-14 13048 2 3390 2 6304 1 1421 1 2600 2 26,768.99

Aug-14 12275 2 3277 2 6064 1 1263 1 2549 2 25,432.69

Sep-14 11347 1 3232 2 5789 1 1173 1 2453 2 24,000.05

Oct-14 10667 1 3131 1 5699 1 1128 1 2517 2 23,146.72

Nov-14 10459 1 3087 1 5604 1 974 1 2541 2 22,670.62

Dec-14 10082 1 3030 1 5444 1 979 1 2453 2 21,993.30

281,736.21 36,910.99 13.1012595342

Tornado Chart

-4450484.811629016 -4879382.0341685694 -5331729.5811128728 -5826577.3034424353
-4450484.811629016 -837287.85916856956 118842.28513712832 463633.34987006639

Industry Mower Total Sales

Industry Mower Total Sales

Month NA SA Eur Pac World

Jan-10 60000 1 571 1 13091 1 1045 1 74662 1

Feb-10 77184 1 611 1 17679 1 1111 1 96585 1

Mar-10 77885 1 658 1 22759 1 1068 1 102369 1

Apr-10 86190 1 778 1 27966 1 1237 1 116171 1

May-10 96117 1 886 1 27895 1 1313 1 126210 1

Jun-10 97143 1 882 1 30566 1 1176 1 129768 1

Jul-10 84757 1 848 1 29444 1 1359 1 116409 1

Aug-10 79804 1 735 1 28364 1 1238 1 110141 1

Sep-10 64800 1 657 1 28393 1 1215 1 95065 1

Oct-10 59307 1 595 1 24444 1 1154 1 85500 1

Nov-10 52157 1 553 1 18000 1 1262 1 71972 1

Dec-10 45049 1 462 1 12453 1 1386 1 59349 1

Jan-11 58627 1 553 1 12778 1 1443 1 73401 1

Feb-11 76200 1 615 1 18214 1 1515 1 96545 1

Mar-11 82871 1 658 1 23889 1 1373 1 108791 1

Apr-11 84904 1 784 1 29455 1 1442 1 116584 1

May-11 93100 1 846 1 29464 1 1215 1 124625 1

Jun-11 93000 1 838 1 27414 1 1333 1 122585 1

Jul-11 83048 1 763 1 27368 1 1415 1 112594 1

Aug-11 74854 1 694 1 27321 1 1296 1 104164 1

Sep-11 60769 1 625 1 29444 1 1402 1 92241 1

Oct-11 55619 1 610 1 23774 1 1468 1 81470 1

Nov-11 48155 1 571 1 17308 1 1351 1 67386 1

Dec-11 42647 1 512 1 12941 1 1389 1 57489 1

Jan-12 57885 1 537 1 10962 1 1509 1 70892 1

Feb-12 77647 1 595 1 15273 1 1402 1 94917 1

Mar-12 81845 1 659 1 20556 1 1524 1 104583 1

Apr-12 86095 1 756 1 26786 1 1574 1 115211 1

May-12 91776 1 878 1 24828 1 1468 1 118949 1

Jun-12 100680 1 825 1 24737 1 1560 1 127801 1

Jul-12 86190 1 756 1 24828 1 1441 1 113216 1

Aug-12 71887 1 714 1 25179 1 1545 1 99325 1

Sep-12 60000 1 651 1 24545 1 1667 1 86863 1

Oct-12 55566 1 643 1 19286 1 1698 1 77193 1

Nov-12 50857 1 619 1 15273 1 1810 1 68558 1

Dec-12 42596 1 548 1 9107 0 1731 1 53982 1

Jan-13 58095 1 581 1 8571 0 1887 1 69135 1

Feb-13 75566 1 614 1 13158 1 1845 1 91182 1

Mar-13 80286 1 622 1 19655 1 1923 1 102486 1

Apr-13 85140 1 727 1 25179 1 1981 1 113027 1

May-13 90093 1 826 1 23103 1 1810 1 115832 1

Jun-13 95472 1 783 1 24286 1 1942 1 122482 1

Jul-13 87308 1 681 1 24737 1 1961 1 114686 1

Aug-13 74476 1 646 1 26607 1 2000 1 103729 1

Sep-13 61698 1 625 1 22982 1 2075 1 87381 1

Oct-13 57238 1 617 1 16897 1 2019 1 76771 1

Nov-13 50673 1 587 1 13750 1 2095 1 67105 1

Dec-13 51238 1 591 1 7818 0 2150 1 61797 1

Jan-14 59712 1 563 1 7547 0 1852 1 69673 1

Feb-14 77961 1 571 1 13889 1 1743 1 94165 1

Mar-14 83725 1 625 1 18302 1 1892 1 104544 1

Apr-14 90297 1 723 1 25192 1 2037 1 118250 1

May-14 91143 1 848 1 24706 1 1887 1 118583 1

Jun-14 99320 1 792 1 25306 1 1944 1 127363 1

Jul-14 93922 1 745 1 27083 1 2170 1 123919 1

Aug-14 73143 1 739 1 26042 1 2037 1 101961 1

Sep-14 66699 1 667 1 26304 1 2018 1 95688 1

Oct-14 56476 1 660 1 22558 1 2072 1 81766 1

Nov-14 51068 1 625 1 14773 1 2182 1 68648 1

Dec-14 46893 1 608 1 6977 0 2035 1 56510 1

72581 676 21120 1628 96004

Jan-14 59712 1 563 1 7547 0 1852 1 69673

Feb-14 77961 1 571 1 13889 1 1743 1 94165

Mar-14 83725 1 625 1 18302 1 1892 1 104544

Apr-14 90297 1 723 1 25192 1 2037 1 118250

May-14 91143 1 848 1 24706 1 1887 1 118583

Jun-14 99320 1 792 1 25306 1 1944 1 127363

Jul-14 93922 1 745 1 27083 1 2170 1 123919

Aug-14 73143 1 739 1 26042 1 2037 1 101961

Sep-14 66699 1 667 1 26304 1 2018 1 95688

Oct-14 56476 1 660 1 22558 1 2072 1 81766

Nov-14 51068 1 625 1 14773 1 2182 1 68648

Dec-14 46893 1 608 1 6977 0 2035 1 56510

Total Revenue 1,161,069.16

North America
40179 40210 40238 40269 40299 40330 40360 4039 1 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 60000 77184.466019417479 77884.61538461539 86190.476190476198 96116.504854368934 97142.857142857145 84757.281553398061 79803.921568627455 64800 59306.930693069306 52156.862745098042 45048.543689320388 58627.450980392161 76200 82871.287128712866 84903.846153846156 93100 93000 83047.619047619053 74854.368932038837 60769.230769230773 55619.047619047618 48155.339805825242 42647.058823529413 57884.61538461539 77647.058823529413 81844.660194174765 86095.238095238092 91775.700934579436 100679.61165048544 86190.476190476198 71886.792452830196 60000 55566.037735849059 50857.142857142862 42596.153846153851 58095.238095238099 75566.037735849066 80285.71428571429 85140.186915887854 90092.592592592599 95471.698113207545 87307.692307692312 74476.190476190473 61698.113207547169 57238.095238095237 50673.076923076922 51238.095238095237 59711.538461538461 77961.165048543699 83725.490196078434 90297.029702970292 91142.857142857145 99320.388349514571 93921.568627450994 73142.857142857145 66699.029126213602 56476.190476190481 51067.961165048546 46893.203883495145

South America
40179 40210 40238 40269 40299 40330 40360 4039 1 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 571.42857142857144 611.11111111111109 657.8947368421052 777.77777777777783 885.71428571428578 882.35294117647049 848.4848484848485 735.29411764705878 657.14285714285722 594.59459459459458 552.63157894736844 461.53846153846155 552.63157894736844 615.38461538461536 657.8947368421052 783.78378378378375 846.15384615384608 837.83783783783781 763.15789473684208 694 625 609.7560975609756 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 727.27272727272725 826.08695652173913 782.60869565217388 680.85106382978722 645.83333333333337 625 617.02127659574467 586.95652173913038 590.90909090909088 562.5 571.42857142857144 625 723.404255319149 847.82608695652175 791.66666666666674 744.68085106382978 739.13043478260863 666.66666666666674 659.57446808510645 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 27966.101694915254 27894.736842105263 30566.037735849059 29444.444444444445 28363.636363636364 28392.857142857141 24444.444444444445 18000 12452.830188679245 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 19655.172413793101 25178.571428571428 23103.448275862069 24285.714285714286 24736.842105263157 26607.142857142855 22982.456140350878 16896.551724137931 13750 7818.181818181818 7547.1698113207549 13888.888888888889 18301.886792452831 25192.307692307695 24705.882352941178 25306.12244897959 27083.333333333332 26041.666666666668 26304.347826086956 22558.139534883721 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 1237.1134020618556 1313.1313131313132 1176.4705882352941 1359.2233009708739 1238.0952380952381 1214.9532710280373 1153.8461538461538 1262.1359223300972 1386.1386138613861 1443.2989690721649 1515.151515151515 1372.5490196078433 1442.3076923076924 1214.9532710280373 1333.3333333333335 1415.0943396226417 1296.2962962962963 1401.8691588785048 1467.8899082568807 1351.3513513513512 1388.8888888888889 1509.433962264151 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 1960.7843137254904 2000 2075.4716981132078 2019.2307692307693 2095.2380952380954 2149.532710280374 1851.851851851852 1743.119266055046 1891.8918918918919 2037.037037037037 1886.7924528301887 1944.4444444444446 2169.8113207547171 2037.037037037037 2018.3486238532109 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 74662.337662337668 96585.259670211133 102369.09197616122 116171.4690652311 126210.0872953198 129767.71840811797 116409.43414729823 110140.94728800612 95064.953271028033 85499.815885954507 71971.630246375498 59349.050953399485 73401.159306189467 96544.821844821839 108790.61977405171 116584.48308448309 124625.39283146759 122584.96427461944 112594.2923346101 104164.4014920714 92240.544372553719 81470.278530525859 67385.812036297473 57489.31930495776 70892.173174271666 94916.89335037327 104582.56185890571 115211.12401600207 118949.22583022068 127801.08678327154 113215.6013997898 99325.104141141885 86863.28400281888 77192.722371967655 68558.441558441569 53981.684981684986 69134.854468334073 91182.229030502291 102486.1858448 0653 113027.1631472037 115831.65163450023 122481.76866738955 114686.16979051076 103729.16666666666 87381.041046011247 76770.899008059685 67105.271540054149 61796.718857466512 69673.060124711075 94164.601774916198 104544.26888042316 118249.77868763417 118583.35803558504 127362.62190960527 123919.39413260287 101960.69128134346 95688.392242820439 81765.976551231375 68647.506619594002 56509.512966296817

Industry Tractor Total Sales

Industry Tractor Total Sales

Month NA SA Eur Pac China World

Jan-10 8143 1 984 0 5091 1 987 1 278 0 15486 1

Feb-10 8592 1 1051 1 5310 1 1090 1 283 0 16328 1

Mar-10 8630 1 1016 0 6071 1 1127 1 285 0 17132 1

Apr-10 8947 1 1027 0 5856 1 1209 1 288 0 17331 1

May-10 8442 1 1057 1 5273 1 1221 1 286 0 16281 1

Jun-10 7500 1 1019 0 5315 1 1327 1 287 0 15451 1

Jul-10 6145 1 977 0 7170 1 1324 1 289 0 15908 1

Aug-10 5882 1 1057 1 5926 1 1268 1 290 0 14426 1

Sep-10 5595 1 1086 1 6075 1 1209 1 293 0 14262 1

Oct-10 5233 1 1045 0 6321 1 1168 1 295 0 14064 1

Nov-10 4494 1 1078 1 8381 1 1127 1 298 0 15381 1

Dec-10 3913 1 1029 0 7944 1 1085 1 301 0 14275 1

Jan-11 5938 1 1172 1 5688 1 1185 1 306 0 14292 1

Feb-11 6633 1 1273 1 7037 1 1286 1 302 0 16534 1

Mar-11 7327 1 1423 1 6981 1 1286 1 303 0 17324 1

Apr-11 8077 1 1612 1 7500 1 1346 1 307 0 18846 1

May-11 7830 1 1728 1 6571 1 1388 1 309 0 17830 1

Jun-11 7103 1 1815 1 6990 1 1449 1 312 0 17673 1

Jul-11 6239 1 1776 1 6667 1 1490 1 315 0 16490 1

Aug-11 6036 1 1685 1 6762 1 1449 1 318 0 16254 1

Sep-11 5664 1 1679 1 6635 1 1394 1 321 0 15696 1

Oct-11 5345 1 1618 1 6311 1 1256 1 315 0 14848 1

Nov-11 4831 1 1564 1 6476 1 1214 1 318 0 14405 1

Dec-11 4454 1 1522 1 6250 1 1171 1 320 0 13719 1

Jan-12 5299 1 1835 1 5922 1 1208 1 333 0 14600 1

Feb-12 6529 1 2115 1 6667 1 1214 1 313 0 16840 1

Mar-12 7120 1 2202 1 7228 1 1256 1 606 1 18416 1

Apr-12 7619 1 2151 1 8200 1 1311 1 571 1 19856 1

May-12 8387 1 2214 1 7941 1 1415 1 556 1 20517 1

Jun-12 8110 1 2278 1 7921 1 1520 1 526 0 20359 1

Jul-12 7752 1 2100 1 7677 1 1675 1 513 0 19721 1

Aug-12 6894 1 2128 1 7200 1 1584 1 769 1 18579 1

Sep-12 6015 1 2367 1 6735 1 1527 1 750 1 17398 1

Oct-12 5368 1 2211 1 6495 1 1422 1 732 1 16230 1

Nov-12 4964 1 2483 1 6061 1 1366 1 714 1 15591 1

Dec-12 4444 1 1986 1 5816 1 1262 1 698 1 14210 1

Jan-13 5000 1 2257 1 5051 1 1373 1 714 1 14398 1

Feb-13 6284 1 2353 1 6082 1 1436 1 1063 1 17222 1

Mar-13 7785 1 2457 1 6327 1 1478 1 1264 1 19315 1

Apr-13 9934 1 2517 1 7604 1 1512 1 1333 1 22906 1

May-13 10645 1 2612 1 7789 1 1642 1 1556 1 24249 1

Jun-13 9491 1 2749 1 7347 1 1667 1 1739 2 22998 1

Jul-13 9182 1 2887 1 6979 1 1733 1 1702 2 22488 1

Aug-13 8528 1 2833 1 6489 1 1700 1 1915 2 21469 1

Sep-13 8293 1 2789 1 6316 1 1642 1 2083 2 21127 1

Oct-13 8221 1 2765 1 5833 1 1576 1 2128 2 20527 1

Nov-13 7470 1 2746 1 5789 1 1493 1 2292 2 19794 1

Dec-13 6509 1 2534 1 5591 1 1450 1 2245 2 18333 1

Jan-14 7267 1 2635 1 5106 1 1010 1 2292 2 18314 1

Feb-14 8807 1 2703 1 5474 1 1045 1 2449 2 20481 1

Mar-14 10168 1 2795 1 6022 1 1106 1 2400 2 22494 1

Apr-14 11044 1 2997 1 6064 1 1150 1 2353 2 23612 1

May-14 12120 2 3131 1 6344 1 1244 1 2600 2 25444 1

Jun-14 13459 2 3311 2 6593 1 1357 1 2653 2 27379 1

Jul-14 13048 2 3390 2 6304 1 1421 1 2600 2 26769 1

Aug-14 12275 2 3277 2 6064 1 1263 1 2549 2 25433 1

Sep-14 11347 1 3232 2 5789 1 1173 1 2453 2 24000 1

Oct-14 10667 1 3131 1 5699 1 1128 1 2517 2 23147 1

Nov-14 10459 1 3087 1 5604 1 974 1 2541 2 22671 1

Dec-14 10082 1 3030 1 5444 1 979 1 2453 2 21993 1

7726 2093 6436 1323 1070 18652

Jan-14 7267 1 2635 1 5106 1 1010 1 2292 2 18314

Feb-14 8807 1 2703 1 5474 1 1045 1 2449 2 20481

Mar-14 10168 1 2795 1 6022 1 1106 1 2400 2 22494

Apr-14 11044 1 2997 1 6064 1 1150 1 2353 2 23612

May-14 12120 2 3131 1 6344 1 1244 1 2600 2 25444

Jun-14 13459 2 3311 2 6593 1 1357 1 2653 2 27379

Jul-14 13048 2 3390 2 6304 1 1421 1 2600 2 26769

Aug-14 12275 2 3277 2 6064 1 1263 1 2549 2 25433

Sep-14 11347 1 3232 2 5789 1 1173 1 2453 2 24000

Oct-14 10667 1 3131 1 5699 1 1128 1 2517 2 23147

Nov-14 10459 1 3087 1 5604 1 974 1 2541 2 22671

Dec-14 10082 1 3030 1 5444 1 979 1 2453 2 21993

281,736.21

North America
40179 40210 40238 40269 40299 40330 40360 40 391 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 8142.8571428571422 8591.5492957746483 8630.1369863013697 8947.3684210526317 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 4453.7815126050418 5299.1452991452988 6528.9256198347121 7120 7619.0476190476193 8387.0967741935492 8110.2362204724404 7751.937984496124 6893.939393939394 6015.0375939849619 5367.6470588235288 4964.0287769784172 4444.4444444444443 5000 6283.7837837837833 7785.2348993288579 9934.21052631579 10645.161290322581 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 13048.128342245989 12275.132275132275 11347.150259067357 10666.666666666666 10459.183673469388 10082

South America
40179 40210 40238 40269 40299 40330 40360 40 391 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 984 1050.5836575875487 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 2256.9444444444448 2352.9411764705883 2456.7474048442909 2517.2413793103451 2611.6838487972509 2749.1408934707906 2886.5979381443299 2832.764505119454 2789.1156462585036 2764.5051194539251 2745.7627118644068 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 6074.7663551401874 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 7920.7920792079212 7676.7676767676767 7200 6734.6938775510198 6494.8453608247419 6060.6060606060601 5816.3265306122448 5050.5050505050503 6082.4742268041236 6326.5306122448974 7604.16666 66666661 7789.4736842105258 7346.9387755102034 6979.166666666667 6489.3617021276596 6315.7894736842109 5833.333333333333 5789.4736842105267 5591.3978494623652 5106.3829787234044 5473.6842105263158 6021.5053763440865 6063.8297872340427 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 1414.6341463414635 1519.607843137255 1674.8768472906402 1584.158415841584 1527.0935960591132 1421.5686274509806 1365.8536585365855 1262.1359223300972 1372.5490196078433 1435.6435643564355 1477.832512315270 8 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 1173.4693877551019 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 1914.8936170212767 2083.3333333333335 2127.6595744680849 2291.666666 6666665 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 14405.223401936522 13719.411885118465 14600.466025397956 16840.188427993337 18416.03421337191 19855.971854686119 20517.195307728711 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 23146.724604316583 22670.622639347333 21993.298087858158

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

Defects After Delivery

Defects After Delivery

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

Observations 20

ANOVA

df SS MS F Significance F

Regression 1 1537.0240601504 1537.0240601504 17.2379114202 0.0005989968

Residual 18 1604.9759398496 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 666446.529080675 666446.529080675 733.0483948942 1.90959818846179E-26

Residual 38 34547.4709193246 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 Working Days

Jan-10 8.32

Feb-10 8.28

Mar-10 8.29

Apr-10 8.32

May-10 8.36

Jun-10 8.35

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 7.53

May-11 7.48

Jun-11 7.45

Jul-11 7.36

Aug-11 7.35

Sep-11 7.32

Oct-11 7.3

Nov-11 7.27

Dec-11 7.25

Jan-12 7.22

Feb-12 7.21

Mar-12 7.22

Apr-12 7.29

May-12 7.25

Jun-12 7.23

Jul-12 7.28

Aug-12 7.25

Sep-12 7.24

Oct-12 7.26

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 7.12

Feb-14 7.11

Mar-14 7.11

Apr-14 7.11

May-14 7.11

Jun-14 7.12

Jul-14 7.08

Aug-14 7.09

Sep-14 7.09

Oct-14 7.04

Nov-14 7.06

Dec-14 7.08

Employee Satisfaction

Employee Satisfaction Results

Averages using a 5 point scale

Design & Sales &

Quarter Production Sample size Manager Sample size Administration Sample size Total Sample size

1st Q-11 2.86 100 3.81 10 3.51 30 3.07 140

2nd Q-11 2.91 100 3.76 10 3.38 30 3.07 140

3rd Q-11 2.84 100 3.86 10 3.45 30 3.04 140

4th Q-11 2.83 100 3.48 10 3.61 30 3.04 140

1st Q-12 2.91 100 3.75 20 3.37 30 3.11 150

2nd Q-12 2.94 100 3.92 20 3.53 30 3.19 150

3rd Q-12 2.86 100 3.89 20 3.47 30 3.12 150

4th Q-12 2.83 100 3.58 20 3.66 30 3.10 150

1st Q-13 2.95 100 3.82 20 3.71 40 3.25 160

2nd Q-13 3.01 100 4.01 20 3.53 40 3.27 160

3rd Q-13 3.03 100 3.92 20 3.62 40 3.29 160

4th Q-13 2.96 100 3.84 20 3.48 40 3.20 160

1st Q-14 3.05 100 3.92 20 3.52 40 3.28 160

2nd Q-14 3.12 100 4.00 20 3.37 40 3.29 160

3rd Q-14 3.06 100 3.93 20 3.46 40 3.27 160

4th Q-14 3.02 100 3.70 20 3.59 40 3.25 160

Engines

Engine Production Time

Sample Production Time (min)

1 65.1 time is the dependent variable and sample is the independent variable

2 62.3

3 60.4 SUMMARY OUTPUT

4 58.7

5 58.1 Regression Statistics

6 56.9 Multiple R 0.9213573188

7 57.0 R Square 0.8488993088

8 56.5 Adjusted R Square 0.8457513778

9 55.1 Standard Error 1.8182687867

10 54.3 Observations 50

11 53.7

12 53.2 ANOVA

13 52.8 df SS MS F Significance F

14 52.5 Regression 1 891.5529337335 891.5529337335 269.6689638672 2.48594348198823E-21

15 52.1 Residual 48 158.6928662665 3.3061013806

16 51.8 Total 49 1050.2458

17 51.5

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

19 50.9 Intercept 58.1836734694 0.5220964329 111.4423884214 1.29129366690705E-59 57.1339282346 59.2334187042 57.1339282346 59.2334187042

20 50.5 X Variable 1 -0.2926146459 0.0178188871 -16.421600527 2.48594348198821E-21 -0.3284419196 -0.2567873721 -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 49.3 The regression equation is : y=58.18367-0.29261x

26 49.4 Production Time=58.18367-0.29261*x

27 49.1 This means that as the number of units produced increase, the production time reduces and therefore creating a more cost-effective means of production

28 49.0

29 48.8

30 48.5

31 48.3

32 48.2

33 48.1

34 47.9

35 47.7

36 47.6

37 47.4

38 47.1

39 46.9

40 46.8

41 46.7

42 46.6

43 46.5

44 46.5

45 46.2

46 46.3

47 46.0

48 45.8

49 45.7

50 45.6

Q4

Anova: Single Factor

SUMMARY

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

ANOVA

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

Between Groups 2621.0888888889 2 1310.5444444444 0.5855750995 0.5589648105 3.1012957567

Within Groups 194710.066666667 87 2238.046743295

Total 197331.155555556 89

Transmission Costs

Unit Tractor Transmission Costs

Q4

Current Process A Process B

$242.00 $242.00 $292.00 Anova: Single Factor

$176.00 $275.00 $321.00

$286.00 $199.00 $314.00 SUMMARY

$269.00 $219.00 $242.00 Groups Count Sum Average Variance

$327.00 $273.00 $278.00 Current 30 8688 289.6 2061.1448275862

$264.00 $265.00 $300.00 Process A 30 8565 285.5 4217.6379310345

$296.00 $435.00 $301.00 Process B 30 8953 298.4333333333 435.3574712644

$333.00 $285.00 $286.00

$242.00 $384.00 $315.00

$288.00 $387.00 $300.00 ANOVA

$314.00 $299.00 $304.00 Source of Variation SS df MS F P-value F crit

$302.00 $145.00 $300.00 Between Groups 2621.0888888889 2 1310.5444444444 0.5855750995 0.5589648105 3.1012957567

$335.00 $266.00 $351.00 Within Groups 194710.066666667 87 2238.046743295

$242.00 $216.00 $277.00

$281.00 $331.00 $284.00 Total 197331.155555556 89

$289.00 $247.00 $276.00

$259.00 $280.00 $312.00

$322.00 $267.00 $273.00

$209.00 $210.00 $281.00

$282.00 $391.00 $303.00

$304.00 $297.00 $306.00

$391.00 $346.00 $312.00

$236.00 $230.00 $287.00

$383.00 $332.00 $306.00

$299.00 $301.00 $312.00

$300.00 $277.00 $295.00

$278.00 $336.00 $288.00

$303.00 $217.00 $313.00

$315.00 $274.00 $286.00

$321.00 $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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 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 4.7300000000000004 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 4.9400000000000004 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

Mower Test Functional Performance

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 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

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

question 1

bernoulli distribution

question 2 (fraction of mowers that fail)

number of mowers that fail 54

total number of mowers 3000

fraction of mowers that fail 0.018

QUESTION 3 (Probability of having x failures)

Let x be the number of failures and P(X=x) be the associated probability per failure x is from 0 to 20

x P(X=x)

0 0.1626105724

1 0.2980641858

2 0.2704431665

3 0.1619354195

4 0.0719804589

5 0.0253324303

6 0.0073520801

7 0.001809677

8 0.000385616

9 0.0000722539

10 0.0000120521

11 0.0000018075

12 0.0000002457

13 0.0000000305

14 0.0000000035

15 0.0000000004

16 0

17 0

18 0

19 0

20 0

for blade weight questions, check the blade weight tab

Employee Retention

Employee Retention

Gender Differences Locality Status

YearsPLE YrsEducation College GPA Age Gender College Grad Local t-Test: Two-Sample Assuming Equal Variances t-Test: Two-Sample Assuming Equal Variances

10 18 3.01 33 F Y Y

10 16 2.78 25 M Y Y Female Male Local

10 18 3.15 26 M Y N Mean 5.5307692308 5.5407407407 Mean 7.2227272727

10 18 3.86 24 F Y Y Variance 12.2506410256 6.4494301994 Variance 3.7027922078

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 8.281391513 Pooled Variance 4.5625386617

8.4 17 3.56 35 M Y Y Hypothesized Mean Difference 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 -0.0102643826 t Stat 5.2094943403

7.9 15 2.75 34 M N Y P(T<=t) one-tail 0.4959320257 P(T<=t) one-tail 0.0000036859 7.6 13 2.95 28 M N Y t Critical one-tail 1.6859544602 t Critical one-tail 1.6870936196 7.5 13 2.50 23 M N Y P(T<=t) two-tail 0.9918640514 P(T<=t) two-tail 0.0000073717 7.5 16 2.86 24 M Y Y t Critical two-tail 2.0243941639 t Critical two-tail 2.026192463 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 College Graduation 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 3.36 25 M Y Y 5.4 16 2.75 24 M Y N Non-College Grad College Grad 5.1 17 2.48 32 M Y N Mean 4.8923076923 5.8481481481 4.8 14 2.76 28 M N Y Variance 5.8191025641 9.1095156695 4.7 16 3.12 25 F Y N Observations 13 27 4.5 13 2.96 23 M N Y Pooled Variance 8.0704378468 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 -0.9966907369 3.7 16 2.86 23 M Y N P(T<=t) one-tail 0.162609673 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 0.325219346 3.5 14 2.84 21 M N Y t Critical two-tail 2.0243941639 3.4 16 3.13 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 0.3875599015 0.3 18 3.79 24 F Y N R Square 0.1502026772 Adjusted R Square 0.0793862337 Standard Error 2.7255269941 Observations 40 ANOVA df SS MS F Significance F Regression 3 47.2678437532 15.7559479177 2.1210141269 0.1146353121 Residual 36 267.4259062468 7.4284973957 Total 39 314.69375 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -2.7371084598 4.504149393 -0.6076859848 0.5472103219 -11.8719468233 6.3977299037 -11.8719468233 6.3977299037 X Variable 1 -0.0670542938 0.3551646907 -0.188797748 0.851311676 -0.7873616722 0.6532530847 -0.7873616722 0.6532530847 X Variable 2 0.6799813193 1.1835513772 0.5745262372 0.5691848142 -1.7203721287 3.0803347674 -1.7203721287 3.0803347674 X Variable 3 0.2915358125 0.1350439268 2.1588220923 0.0376058426 0.0176540348 0.5654175903 0.0176540348 0.5654175903 The value of R-Squared is low, meaning the model is not a good fit for the data. Regression Equation 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 SUMMARY OUTPUT Regression Statistics Multiple R 0.3766581987 R Square 0.1418713987 Adjusted R Square 0.1192890671 Standard Error 2.6658054354 Observations 40 ANOVA df SS MS F Significance F Regression 1 44.6460424662 44.6460424662 6.2824070206 0.0165919207 Residual 38 270.0477075338 7.1065186193 Total 39 314.69375 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept -2.0148656837 3.0424830991 -0.6622438377 0.5118115929 -8.1740507134 4.144319346 -8.1740507134 4.144319346 X Variable 1 0.3002928701 0.1198069428 2.5064730241 0.0165919207 0.0577563944 0.5428293458 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 Non- Local t-Test: Two-Sample Assuming Equal Variances 10 10 10 6.2 Local Non- Local 10 5.4 Mean 7.2227272727 3.6294117647 9.6 5.1 Variance 3.7027922078 5.6909558824 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 Purchasing Survey Purchasing Survey Delivery speed Price level Price flexibility Manufacturing image Overall service Salesforce image Product quality Usage Level Satisfaction Level Size of firm Purchasing Structure Industry Buying Type 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