Case Study-Performance Lawn Care – Episode 5

In reviewing the PLE data, Elizabeth Burke noticed that defects received from suppliers have decreased (worksheet Defects After Delivery). Upon investigation, she learned that in 2010, PLE experienced some quality problems due to an increasing number of defects in materials received from suppliers. The company instituted an initiative in August 2011 to work with suppliers to reduce these defects, to more closely coordinate deliveries, and to improve material quality through reengineering supplier production policies. Elizabeth noted that the program appeared to reverse an increasing trend in defects; she would like to predict what might have happened had the supplier initiative not been implemented and how the number of defects might further be reduced in the near future.

In meeting with PLE’s human resources director, Elizabeth also discovered a concern about the high rate of turnover in its field service staff. Senior managers have suggested that the department look closer at its recruiting policies, particularly to try to identify the characteristics of individuals that lead to greater retention. However, in a recent staff meeting, HR managers could not agree on these characteristics. Some argued that years of education and grade point averages were good predictors. Others argued that hiring more mature applicants would lead to greater retention. To study these factors, the staff agreed to conduct a statistical study to determine the effect that years of education, college grade point average, and age when hired have on retention. A sample of 40 field service engineers hired 10 years ago was selected to determine the influence of these variables on how long each individual stayed with the company. Data are compiled in the Employee Retention worksheet.

Finally, as part of its efforts to remain competitive, PLE tries to keep up with the latest in production technology. This is especially important in the highly competitive lawn mower line, in which competitors can gain a real advantage if they develop more cost-effective means of production. Therefore, the lawn mower division spends a great deal of effort in testing new technology. When new production technology is introduced, firms often experience learning, resulting in a gradual decrease in the time required to produce successive units. Generally, the rate of improvement declines until the production time levels off. One example is the production of a new design for lawn mower engines. To determine the time required to produce these engines, PLE produced 50 units on its production line; test results are given on the worksheet Engines in the database. Because PLE is continually developing new technology, understanding the rate of learning can be useful in estimating future production costs without having to run extensive prototype trials, and Elizabeth would like a better handle on this. Use techniques of regression analysis to assist her in evaluating the data in these three worksheets and reaching useful conclusions. Summarize your work in a formal report with all appropriate results and analyses and upload your Word document AND your Excel worksheet file

PerformanceLawnEquipmentDatabase_Episode4.xlsx

Dealer Satisfaction

Dealer Satisfaction tc={BC340A24-8BBA-491F-B401-F2D940BCB741}: [Threaded comment] Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924 Comment: 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.
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

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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 tc={4E1782D3-7E9F-4E7B-83FB-A93AAF2BD2E6}: [Threaded comment] Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924 Comment: This chart is showing End-User Satisfaction between North America, South America, Europe, Pacific Rim and China. The data that was selected was rated on a a survery scale from 0-5 and between the the years of 2010-2014, except for China who started later in 2012. North America, South America, Europe, and the Pacific Rim all have the same sample size of 100 for each year between 2010 through 2014. China has a smaller sample size of 50 between the years of 2012 through 2014. You cansee that the ratings of 5's, 4's, and 3's are the highest ratings. North America's rating of 4 decreases every year starting with 2010 while the 5 ratings increase through the years. The Pacfic Rim's 4 ratings are highest rated and is basically constant throughout the years while the 5 ratings are lower then 4 ratings the 5's are constant throughout the years.
							Sample
North America	0	1	2	3	4	5	Size
2010	1	3	6	15	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 tc={3A6BEBAD-C122-4573-AF72-C42391975593}: [Threaded comment] Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924 Comment: This chart is showing PLE's Complaoints from registered by all customers each month within PLE's 5 regions. From this data we can conclude that there is more use of the equipment in the summer months because of the higher number of complaints recieved. Based off the data shown form the region of China, their compaints are few and are steady throughout the months. This could be because they do not use this type of equipment in comparison to the other regions.
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 4185 2 41883 41913 41944 41974 52 55 61 67 73 82 80 76 73 62 59 54 59 62 66 70 75 86 81 79 73 68 66 61 66 71 76 79 85 91 89 86 82 76 73 70 74 79 83 88 91 95 92 90 87 83 77 74 80 82 85 89 95 98 95 93 89 85 80 76 Pac 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 3 4 6 7 5 9 7 3 4 2 0 1 3 5 6 11 8 12 10 8 7 5 4 3 4 6 9 11 14 15 18 15 13 12 10 7 8 10 13 11 15 19 17 15 14 12 10 7 8 10 12 13 12 15 14 13 11 8 7 7 China 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 3 4 3 2 5 6 5 4 4 3 3 4 5 4 6 5 7 8 7 7 6 6 5 4 5 5 5 6 8 11 10 9 7 6 5 5

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

Mower Unit Sales

Mower Unit Sales tc={6A814A1A-8E51-48A1-A543-AEC7E2B5497F}: [Threaded comment] Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924 Comment: The chart identifies the unit sales PLE's mower equipment. We can see that the highest peak for mower sales is in the summer months and then a decline in sales starting in early fall months. BAsed off this chart, North America is the region with the highest unit sales for PLE's mowers.
Month	NA	SA	Europe	Pacific	China	World
Jan-10	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 34 0 320 310 300 290 Europe 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 720 990 1320 1650 1590 1620 1590 1560 1590 1320 990 660 690 1020 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 41699 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
	tc={65A5E7B3-7884-4D7D-9EEA-FA365565A5C9}: [Threaded comment] Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924 Comment: The chart identifies the unit sales PLE's tractor equipment. We can see that throughout the years with the World orange line shown in the graph increases total sales between the years of 2010 to 2014. The line is basically increase in a positive direction on this graph. And the increase in tractor sales increase in each region throughout the years as well. Overall there is a positive correlations between time and tractor unit sales over all of the country regions.	Month	NA	SA	Eur	Pac	China	World
Jan-10	570	250	560	212	0	1592
Feb-10	611	270	600	230	0	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 1240 1103 1250 1550 1820 2010 2230 2490 2440 2334 2190 2080 2050 2004 SA 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 250 270 260 270 280 270 264 280 290 280 290 280 320 350 390 440 470 490 481 460 460 440 436 420 510 590 610 600 620 640 590 600 670 630 710 570 650 680 724 730 760 800 840 830 820 810 827 750 780 805 830 890 930 980 1002 970 960 930 920 902 Eur 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 560 600 680 650 580 590 760 645 650 670 888 850 620 760 742 780 690 721 680 711 695 650 680 657 610 680 730 820 810 807 760 720 660 630 603 570 500 590 620 730 740 720 670 610 599 560 550 520 480 523 560 570 590 600 580 570 550 530 517 490 Pac 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 212 230 240 263 269 280 290 270 263 258 240 230 250 275 270 280 290 300 312 305 290 260 250 240 250 250 260 270 290 310 340 320 313 290 280 260 287 290 300 310 330 340 350 341 330 320 300 290 200 210 220 230 253 270 280 250 230 220 190 190 China 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10 12 20 22 20 24 20 31 30 37 32 33 35 50 63 68 70 82 80 90 100 102 110 114 111 121 123 120 130 136 134 132 137 130 139 131 World 40179 40210 40238 40269 40299 40330 40360 40391 40422 40452 40483 40513 40544 40575 40603 40634 40664 40695 40725 40756 40787 40817 40848 40878 40909 40940 40969 41000 41030 41061 41091 41122 41153 41183 41214 41244 41275 41306 41334 41365 41395 41426 41456 41487 41518 41548 41579 41609 41640 41671 41699 41730 41760 41791 41821 41852 41883 41913 41944 41974 1592 1711 1810 1867 1779 1740 1826 1695 1681 1663 1825 1720 1761 2035 2142 2340 2280 2271 2154 2146 2085 1970 1936 1850 2000 2324 2510 2672 2780 2813 2716 2581 2476 2317 2324 2080 2202 2540 2867 3348 3550 3432 3400 3261 3209 3132 3027 2777 2821 3209 3553 3820 4133 4476 4436 4256 4067 3890 3816 3717

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

Q2

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 tc={378CB2D4-4814-4165-B17B-6903BF4AE16B}: [Threaded comment] Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924 Comment: We decided to use a clustered column chart to represent the On-Time deliveries for PLE's unit deliveries. The darker backgorund makes it easier to see the difference in the deliveries and the ones that were delivered on time 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.	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 tc={912794B6-EB87-4831-A2F0-71C2CACF797B}: [Threaded comment] Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924 Comment: 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.
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 3.3353070575118 182 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 4.729422703646124 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 2.9756362972722856 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 Use Price Service 0 0 2 1 2 Quality Ease of Use Price Service 1 0 1 2 3 Quality Ease of Use Price Service 6 3 4 5 4 Quality Ease of Use Price Service 12 14 14 14 5 Quality Ease of Use Price Service 11 13 9 8

Pacific Rim

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

China

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

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

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

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

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

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

Unit Production Costs

Unit Production Costs
Month	Tractor	Mower
Jan-10	$1,750	$50
Feb-10	$1,755	$50
Mar-10	$1,763	$51
Apr-10	$1,770	$51
May-10	$1,778	$51
Jun-10	$1,785	$51
Jul-10	$1,792	$51
Aug-10	$1,795	$51
Sep-10	$1,801	$52
Oct-10	$1,804	$52
Nov-10	$1,810	$52
Dec-10	$1,813	$52
Jan-11	$1,835	$55
Feb-11	$1,841	$55
Mar-11	$1,848	$55
Apr-11	$1,854	$55
May-11	$1,860	$56
Jun-11	$1,866	$56
Jul-11	$1,872	$56
Aug-11	$1,878	$56
Sep-11	$1,885	$56
Oct-11	$1,892	$57
Nov-11	$1,897	$57
Dec-11	$1,903	$57
Jan-12	$1,925	$59
Feb-12	$1,931	$59
Mar-12	$1,938	$59
Apr-12	$1,944	$59
May-12	$1,950	$59
Jun-12	$1,956	$60
Jul-12	$1,963	$60
Aug-12	$1,969	$60
Sep-12	$1,976	$60
Oct-12	$1,983	$60
Nov-12	$1,990	$61
Dec-12	$1,996	$61
Jan-13	$1,940	$59
Feb-13	$1,946	$59
Mar-13	$1,952	$59
Apr-13	$1,958	$59
May-13	$1,964	$60
Jun-13	$1,970	$60
Jul-13	$1,976	$60
Aug-13	$1,983	$60
Sep-13	$1,990	$60
Oct-13	$1,996	$60
Nov-13	$2,012	$61
Dec-13	$2,008	$61
Jan-14	$2,073	$63
Feb-14	$2,077	$63
Mar-14	$2,081	$63
Apr-14	$2,086	$63
May-14	$2,092	$63
Jun-14	$2,098	$63
Jul-14	$2,104	$64
Aug-14	$2,110	$64
Sep-14	$2,116	$64
Oct-14	$2,122	$64
Nov-14	$2,129	$64
Dec-14	$2,135	$64

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
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
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
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
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

Industry Mower Total Sales

Industry Mower Total Sales
Month	NA	SA	Eur	Pac	World
Jan-10	60000	571	13091	1045	74662
Feb-10	77184	611	17679	1111	96585
Mar-10	77885	658	22759	1068	102369
Apr-10	86190	778	27966	1237	116171
May-10	96117	886	27895	1313	126210
Jun-10	97143	882	30566	1176	129768
Jul-10	84757	848	29444	1359	116409
Aug-10	79804	735	28364	1238	110141
Sep-10	64800	657	28393	1215	95065
Oct-10	59307	595	24444	1154	85500
Nov-10	52157	553	18000	1262	71972
Dec-10	45049	462	12453	1386	59349
Jan-11	58627	553	12778	1443	73401
Feb-11	76200	615	18214	1515	96545
Mar-11	82871	658	23889	1373	108791
Apr-11	84904	784	29455	1442	116584
May-11	93100	846	29464	1215	124625
Jun-11	93000	838	27414	1333	122585
Jul-11	83048	763	27368	1415	112594
Aug-11	74854	694	27321	1296	104164
Sep-11	60769	625	29444	1402	92241
Oct-11	55619	610	23774	1468	81470
Nov-11	48155	571	17308	1351	67386
Dec-11	42647	512	12941	1389	57489
Jan-12	57885	537	10962	1509	70892
Feb-12	77647	595	15273	1402	94917
Mar-12	81845	659	20556	1524	104583
Apr-12	86095	756	26786	1574	115211
May-12	91776	878	24828	1468	118949
Jun-12	100680	825	24737	1560	127801
Jul-12	86190	756	24828	1441	113216
Aug-12	71887	714	25179	1545	99325
Sep-12	60000	651	24545	1667	86863
Oct-12	55566	643	19286	1698	77193
Nov-12	50857	619	15273	1810	68558
Dec-12	42596	548	9107	1731	53982
Jan-13	58095	581	8571	1887	69135
Feb-13	75566	614	13158	1845	91182
Mar-13	80286	622	19655	1923	102486
Apr-13	85140	727	25179	1981	113027
May-13	90093	826	23103	1810	115832
Jun-13	95472	783	24286	1942	122482
Jul-13	87308	681	24737	1961	114686
Aug-13	74476	646	26607	2000	103729
Sep-13	61698	625	22982	2075	87381
Oct-13	57238	617	16897	2019	76771
Nov-13	50673	587	13750	2095	67105
Dec-13	51238	591	7818	2150	61797
Jan-14	59712	563	7547	1852	69673
Feb-14	77961	571	13889	1743	94165
Mar-14	83725	625	18302	1892	104544
Apr-14	90297	723	25192	2037	118250
May-14	91143	848	24706	1887	118583
Jun-14	99320	792	25306	1944	127363
Jul-14	93922	745	27083	2170	123919
Aug-14	73143	739	26042	2037	101961
Sep-14	66699	667	26304	2018	95688
Oct-14	56476	660	22558	2072	81766
Nov-14	51068	625	14773	2182	68648
Dec-14	46893	608	6977	2035	56510

Industry Tractor Total Sales

Industry Tractor Total Sales
Month	NA	SA	Eur	Pac	China	World
Jan-10	8143	984	5091	987	278	15483
Feb-10	8592	1051	5310	1090	283	16325
Mar-10	8630	1016	6071	1127	285	17129
Apr-10	8947	1027	5856	1209	288	17327
May-10	8442	1057	5273	1221	286	16278
Jun-10	7500	1019	5315	1327	287	15448
Jul-10	6145	977	7170	1324	289	15905
Aug-10	5882	1057	5926	1268	290	14422
Sep-10	5595	1086	6075	1209	293	14258
Oct-10	5233	1045	6321	1168	295	14061
Nov-10	4494	1078	8381	1127	298	15378
Dec-10	3913	1029	7944	1085	301	14272
Jan-11	5938	1172	5688	1185	306	14289
Feb-11	6633	1273	7037	1286	302	16530
Mar-11	7327	1423	6981	1286	303	17320
Apr-11	8077	1612	7500	1346	307	18842
May-11	7830	1728	6571	1388	309	17826
Jun-11	7103	1815	6990	1449	312	17669
Jul-11	6239	1776	6667	1490	315	16487
Aug-11	6036	1685	6762	1449	318	16250
Sep-11	5664	1679	6635	1394	321	15692
Oct-11	5345	1618	6311	1256	315	14844
Nov-11	4831	1564	6476	1214	318	14402
Dec-11	4454	1522	6250	1171	320	13716
Jan-12	5299	1835	5922	1208	333	14597
Feb-12	6529	2115	6667	1214	313	16836
Mar-12	7120	2202	7228	1256	606	18412
Apr-12	7619	2151	8200	1311	571	19852
May-12	8387	2214	7941	1415	556	20513
Jun-12	8110	2278	7921	1520	526	20355
Jul-12	7752	2100	7677	1675	513	19716
Aug-12	6894	2128	7200	1584	769	18575
Sep-12	6015	2367	6735	1527	750	17394
Oct-12	5368	2211	6495	1422	732	16226
Nov-12	4964	2483	6061	1366	714	15587
Dec-12	4444	1986	5816	1262	698	14207
Jan-13	5000	2257	5051	1373	714	14394
Feb-13	6284	2353	6082	1436	1063	17218
Mar-13	7785	2457	6327	1478	1264	19310
Apr-13	9934	2517	7604	1512	1333	22901
May-13	10645	2612	7789	1642	1556	24244
Jun-13	9491	2749	7347	1667	1739	22993
Jul-13	9182	2887	6979	1733	1702	22483
Aug-13	8528	2833	6489	1700	1915	21465
Sep-13	8293	2789	6316	1642	2083	21123
Oct-13	8221	2765	5833	1576	2128	20523
Nov-13	7470	2746	5789	1493	2292	19789
Dec-13	6509	2534	5591	1450	2245	18329
Jan-14	7267	2635	5106	1010	2292	18311
Feb-14	8807	2703	5474	1045	2449	20477
Mar-14	10168	2795	6022	1106	2400	22489
Apr-14	11044	2997	6064	1150	2353	23607
May-14	12120	3131	6344	1244	2600	25439
Jun-14	13459	3311	6593	1357	2653	27374
Jul-14	13048	3390	6304	1421	2600	26764
Aug-14	12275	3277	6064	1263	2549	25428
Sep-14	11347	3232	5789	1173	2453	23995
Oct-14	10667	3131	5699	1128	2517	23142
Nov-14	10459	3087	5604	974	2541	22666
Dec-14	10082	3030	5444	979	2453	21989

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 tc={ADAA7B03-0CEE-47E5-A080-EAB2C7DB9812}: [Threaded comment] Your version of Excel allows you to read this threaded comment; however, any edits to it will get removed if the file is opened in a newer version of Excel. Learn more: https://go.microsoft.com/fwlink/?linkid=870924 Comment: We can conclude that Defects had a slight increase from 2010 to 2011 which can be attributed to an increase in unit sales. But over the years from the years of 2010 to 2014 the amount of defects decreased overall . This shows that the company is evolving and improving their manufacturing process.
Defects per million items received from suppliers
Month	2010	2011	2012	2013	2014
January	812	828	824	682	571
February	810	832	836	695	575
March	813	847	818	692	547
April	823	839	825	686	542
May	832	832	804	673	532
June	848	840	812	681	496
July	837	849	806	696	472
August	831	857	798	688	460
September	827	839	804	671	441
October	838	842	713	645	445
November	826	828	705	617	438
December	819	816	686	603	436
Total	9916	10049	9431	8029	5955
				Q3
				Anova: Single Factor
				SUMMARY
				Groups	Count	Sum	Average	Variance
				2010	12	9916	826.3333333333	135.3333333333
				2011	12	10049	837.4166666667	121.5378787879
				2012	12	9431	785.9166666667	2749.7196969697
				2013	12	8029	669.0833333333	959.3560606061
				2014	12	5955	496.25	2940.0227272727
				ANOVA
				Source of Variation	SS	df	MS	F	P-value	F crit
				Between Groups	984600.333333333	4	246150.083333333	178.215438334	0.0000	2.5396886349
				Within Groups	75965.6666666667	55	1381.1939393939
				Total	1060566	59

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
2	62.3
3	60.4
4	58.7
5	58.1
6	56.9
7	57.0
8	56.5
9	55.1
10	54.3
11	53.7
12	53.2
13	52.8
14	52.5
15	52.1
16	51.8
17	51.5
18	51.3
19	50.9
20	50.5
21	50.2
22	50.0
23	49.7
24	49.5
25	49.3
26	49.4
27	49.1
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 15 9 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 4.88 4.92 5.0199999999999996 4.97 5 4.99 4.8600000000000003 5.07 5.04 4.87 4.7699999999999996 5.14 5.04 5 4.88 4.91 5.09 4.97 4.9800000000000004 5.07 5.03 5.12 5.08 4.8600000000000003 5.1100000000000003 4.92 5.18 4.93 5.12 5.08 4.75 4.99 5 4.91 5.18 4.95 4.63 4.8899999999999997 5.1100000000000003 5.05 5.03 5.0199999999999996 4.96 5.04 4.93 5.0599999999999996 5.07 5 5.03 5 4.95 4.99 5.0199999999999996 4.9000000000000004 5.0999999999999996 5.01 4.84 5.01 4.88 4.97 4.97 5.0599999999999996 5.0599999999999996 5.04 4.87 5 5.03 5.0199999999999996 5.0199999999999996 5.0599999999999996 5.21 5.09 4.97 5.01 4.9000000000000004 4.8899999999999997 4.93 5.16 5.0199999999999996 5.01 5.0999999999999996 5.03 5.07 4.92 5.08 4.96 4.74 4.91 5.12 5 4.93 4.88 4.88 4.8099999999999996 5.16 5.03 4.87 5.09 4.9400000000000004 5.08 4.97 5.23 5.12 5.09 5.12 4.93 4.79 5.0999999999999996 5.12 4.8600000000000003 5 4.9400000000000004 4.95 4.95 4.87 5.09 4.9400000000000004 5.01 5.04 5.05 5.05 4.97 4.96 4.96 4.99 5.04 4.91 5.19 5.03 4.99 5.12 4.97 4.88 5.07 5.01 4.8899999999999997 4.95 5.09 5.09 4.8899999999999997 4.93 4.8499999999999996 5.03 4.92 5.09 4.99 4.92 4.87 4.9000000000000004 5.0199999999999996 5.21 5.0199999999999996 4.9000000000000004 5 5.16 5.03 4.96 5.04 4.9800000000000004 5.07 5.0199999999999996 5.08 4.8499999999999996 4.9000000000000004 4.97 5.09 4.8899999999999997 4.87 5.01 4.97 5.87 5.33 5.1100000000000003 5.07 4.93 4.99 5.04 5.14 5.09 5.0599999999999996 4.8499999999999996 4.93 5.04 5.09 5.07 4.99 5.01 4.88 4.93 5.0999999999999996 4.9400000000000004 4.88 4.8899999999999997 4.8899999999999997 4.8499999999999996 4.82 5.0199999999999996 4.9000000000000004 4.7300 000000000004 5.04 5.07 4.8099999999999996 5.04 5.03 5.01 5.14 5.12 4.8899999999999997 4.91 4.97 4.9800000000000004 5.01 5.01 5.09 4.93 5.04 5.1100000000000003 5.07 4.95 4.8600000000000003 5.13 4.95 5.22 4.8099999999999996 4.91 4.95 4.9400000000000004 4.8099999999999996 5.1100000000000003 4.8099999999999996 4.97 5.07 5.03 4.8099999999999996 4.95 4.8899999999999997 5.08 4.93 4.99 4.9400000000000004 5.13 5.0199999999999996 5.07 4.82 5.03 4.8499999999999996 4.8899999999999997 4.82 5.18 5.0199999999999996 5.05 4.88 5.08 4.9800000000000004 5.0199999999999996 4.99 5.0199999999999996 5.03 5.0199999999999996 5.07 4.95 4.95 4.9400000000000004 5.12 5.08 4.91 4.96 4.96 4.9400000000000004 5.19 4.91 5.01 4.93 5.05 4.96 4.92 4.95 5.08 4.97 5.04 4.9400000000000004 4.9800000000000004 5.03 5.05 4.91 5.09 5.21 4.87 5.0199999999999996 4.8099999999999996 4.96 5.0599999999999996 4.8600000000000003 4.96 4.99 4.94000 00000000004 5.0599999999999996 4.95 5.0199999999999996 5.01 5.04 5.01 5.0199999999999996 5.03 5.18 5.08 5.14 4.92 4.97 4.92 5.14 4.92 5.03 4.9800000000000004 4.76 4.9400000000000004 4.92 4.91 4.96 5.0199999999999996 5.13 5.13 4.92 4.9800000000000004 4.8899999999999997 4.88 5.1100000000000003 5.1100000000000003 5.08 5.03 4.9400000000000004 4.88 4.91 4.8600000000000003 4.8899999999999997 4.91 4.87 4.93 5.14 4.87 4.9800000000000004 4.88 4.88 5.01 4.93 4.93 4.99 4.91 4.96 4.78

sample

weight

Mower Test

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
0.9	16	2.79	23	F	Y	Y
0.8	18	3.15	26	M	Y	N
0.7	13	1.84	22	F	N	N
0.3	18	3.79	24	F	Y	N

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

Operating & Interest Expenses

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

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