Performance Lawn Service, Episode8

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

PerformanceLawnEquipmentDatabase_Episode7.xlsx

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

Having Trouble Meeting Your Deadline?

Get your assignment on Performance Lawn Service, Episode8 completed on time. avoid delay and – ORDER NOW

Get a 20% Discount

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

Mower

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

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

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

Fail

Pass

100

Pass

Fail

Pass

question 1

bernoulli distribution

question 2 (fraction of mowers that fail)

number of mowers that fail

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

P(X=x)

0.1626105724

0.2980641858

0.2704431665

0.1619354195

0.0719804589

0.0253324303

0.0073520801

0.001809677

0.000385616

0.0000722539

0.0000120521

0.0000018075

0.0000002457

0.0000000305

0.0000000035

0.0000000004

for blade weight questions, check the blade weight tab

Employee Retention

Employee Retention
								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=-0.06705YrsEducation+0679981College 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

Dealer Satisfaction

Having Trouble Meeting Your Deadline?

End-User Satisfaction

Complaints

Mower Unit Sales

Tractor Unit Sales

Q2

On-Time Delivery

Response Time

Part 2 – Shipping Cost

Fixed Cost

Part 3 – Regions and Averages

part 3

Q1

Part 3 – 2014 Customer Survey

Unit Production Costs

Items for 2014 Income Statement

Operating & Interest Expenses

Growth Prediction

Industry Mower Total Sales

Industry Tractor Total Sales

Q3

Defects After Delivery

Time to Pay Suppliers

Employee Satisfaction

Engines

Q4

Transmission Costs

Blade Weight

Mower Test

Employee Retention

5a-Gender

5b-Col

5c-Local

Purchasing Survey

Order a similar assignment, and have writers from our team of experts write it for you, guaranteeing you an A

Similar Posts

Payments

QUICK LINKS

Discover

Help

Get in touch