Statistics Case Study

Need help with a Statistics Case Study that covers the following subtopics

Executive Summary

Table of contents

Case background

Goals

Body of analysis

Alternative solutions

Implementation Plan

Conclusion

Recommendation

References

Appendices.

I have attached the calculations and data needed to create the case study as well as the outline for the case. Thanks

OUTLINE

Case Background

Lenovo is a US$34 billion personal technology company and the world’s largest PC vendor. They have more than 33,000 employees in more than 60 countries serving customers in more than 160 countries. A global Fortune 500 company, Lenovo has headquarters is in Beijing, China and Morrisville, North Carolina, U.S.

Goal

You have been asked by CFO of Lenovo to develop the most accurate method for forecasting Lenovo sales on quarterly basis. You have been provided with sales figures of the past 6 years for each quarter that are publicly available on investor relations page of Lenovo website.

Body of the Analysis

1. Plot time series and comment on its pattern.

2. Develop time series decomposition. Start by using four quarters moving average and centered moving average to develop indexes for each quarter. Then deseasonalize revenue and develop linear and quadratic estimated regression equations.

3. Forecast sales for all past years and also the next four quarters using both regression equations

4. Plot forecasted sales using both methods with the actual sales on a graph and comment on it.

5. Calculate mean square error of linear and quadratic regression models and comment on the results.

Alternative Solutions

1. Instead of decomposing time series, use actual sales that are not seasonally adjusted and develop additional data using dummy variables to reflect seasonability of each quarter.

2. Develop estimated quadratic regression equation with dummy variables.

3. Use quadratic regression model equation with dummy variables to forecast past sales and sales for the next four quarters.

4. Plot forecasted sales on the same graph and compare it with the actual sales and results from time decomposition methods.

5. Calculate mean square error using dummy variables method and compare results with MSE of time decomposition models. Comment on the results.

Implementation plan

Develop plan for Lenovo to implement new forecasting method that you chose as the most accurate and discuss implementation through the entire corporation and its business units.

Recommendations

Recommend the most accurate method of forecasting for Lenovo based on your analysis. Comment on benefits but also mention shortcomings.

David Borovka | Milcent Taruwinga BUSN5011 Case Study – Forecasting for Lenovo

>time

series decomposition moving centered seasonal- average

moving

irregular Revenue US’

00 time period FY Quarter Revenue US’000 4

-quarter

average

inde

x deseasonalized forecast quadratic MSE

forec. Quad. forecast linear MSE forec. lin. 1 Q1 20

-0

8 For the quarter ended Jun

0, 2007 3,8

9 6 3,907,389 3,89

22 138,465,

0 2,7

,682 1,4

,914,7

19 2

2007-08 For the quarter ended Sept 30, 2007 4,3

23 4087875.75 4,013,5

21 3,848,799 27

,133,379,896 2,960,845 1,108,

7,664,392 3

2007-08 For the quarter ended Dec 31, 2007 4,493,881 4187760.

25 4137818 1.086050909 4,206,786 3,827,7

15 143,695,070,011 3,210,008 993,566,410,982 4

2007-08 For the quarter ended Mar 31, 2008 3,734,203 419

35.75 4189748 0.8912715037 4,

9,234 3,832,368 208,7

,522,182 3,459,171 689,004,404,474 5

Q1 2008-09 For the quarter ended Jun 30, 2008 4,212,634 3966087.25 4078911.5 1.0327838689 4,3

,807 3,862,759 206,159,537,808 3,708,334 370,239,293,760 6

Q2 2008-09 For the quarter ended Sept 30, 2008 4,326,225 3725232.75 3845660 1.1

9629452 4,028,328 3,918,888 11,977,239,878 3,957,497 5,017,093,489 7

Q3 2008-09 For the quarter ended Dec 31, 2008 3,591,287 3532268.75 3628750.75 0.9896758547 3,361,855 4,000,755 408,192,803,280 4,206,660 713,695,631,714 8

Q4 2008-09 For the quarter ended Mar 31, 2009 2,770,785 3472520.25 3502394.5 0.7911116238 3,182,619 4,108,359 856,995,087,993 4,455,823 1,621,048,758,764 9

Q1 2009-10 For the quarter ended Jun 30, 2009 3,440,778 3764552 3618536.125 0.9508756804 3,525,864 4,241,702 512,423,510,421 4,704,986 1,390,328,851,506 10

Q2 2009-10 For the quarter ended Sept 30, 2009 4,087,231 4151203.75 3957877.875 1.0326824448 3,805,791 4,400,782 354,013,846,098 4,954,149 1,318,725,710,544 11

Q3 2009-10 For the quarter ended Dec 31, 2009 4,759,414 4577677.25 4364440.5 1.0904980833 4,455,355 4,585,600 16,963,662,532 5,203,312 559,439,317,153 12

Q4 2009-10 For the quarter ended Mar 31, 2010 4,317,392 4995865.25 4786771.25 0.9019424106 4,959,105 4,796,156 26,552,392,908 5,452,475 243,414,135,896 13

Q1 2010-11 For the quarter ended Jun 30, 2010 5,146,672 5258094.5 5126979.875 1.0038408821 5,273,942 5,032,450 58,318,843,709 5,701,638 182,923,478,807 14

Q2 2010-11 For the quarter ended Sep 30, 2010 5,759,983 5398592.75 5328343.625 1.0810081717 5,363,360 5,294,481 4,744,344,819 5,950,801 345,086,591,444 15

Q3 2010-11 For the quarter ended Dec 31, 2010 5,808,331 5591903.75 5495248.25 1.0569733588 5,437,261 5,582,250 21,021,841,668 6,199,964 581,715,282,116 16

Q4 2010-11 For the quarter ended Mar 31, 2011 4,879,385 6098504.5 5845204.125 0.8347672546 5,604,629 5,895,758 84,755,771,350 6,449,127 713,176,379,971 17

Q1 2011-12 For the quarter ended Jun 30, 2011 5,919,916 6739386.25 6418945.375 0.9222567967 6,066,308 6,235,003 28,458,019,542 6,698,290 399,401,543,455 18

Q2 2011-12 For the quarter ended Sep 30, 2011 7,786,386 7393609.5 7066497.875 1.1018733944 7,250,229 6,599,986 422,815,783,085 6,947,453 91,673,060,645 19

Q3 2011-12 For the quarter ended Dec 31, 2011 8,371,858 7916054.5 7654832 1.0936697239 7,837,016 6,990,706 716,238,996,676 7,196,616 410,111,548,372 20

Q4 2011-12 For the quarter ended Mar 31, 2012 7,496,278 8137608.25 8026831.375 0.9339025139 8,610,483 7,407,165 1,447,972,902,754 7,445,779 1,356,534,291,317 21

Q1 2012-13 For the quarter ended Jun 30, 2012 8,009,696 8384330.25 8260969.25 0.9695830789 8,207,765 7,849,361 128,453,287,387 7,694,942 262,987,724,621 22

Q2 2012-13 For the quarter ended Sep 30, 2012 8,672,601 8468350.25 8426340.25 1.0292251135 8,075,420 8,317,296 58,503,715,322 7,944,105 17,243,714,461 23

Q3 2012-13 For the quarter ended Dec 31, 2012 9,358,746 8662700.75 8565525.5 1.0926061688 8,760,855 8,810,968 2,511,251,657 8,193,268 322,155,498,058 24

Q4 2012-13 For the quarter ended Mar31, 2013 7,832,358 8,996,516 9,330,378 111,463,963,870 8,442,431 307,009,697,621 25

Q1 2013-14 For the quarter ended Jun 30, 2013 8,787,098 9,004,391 9,875,526 758,874,602,187 8,691,594 97,842,226,477 26

Q2 2013-14 For the quarter ended Sep 30, 2013

forecast
for the
upcoming
quarters: 10,446,411 264,684,193,689 8,940,757 621,207,328,990 27

Q3 2013-14 For the quarter ended Dec 31, 2013

11,043,035 total MSE 9,189,920

total MSE
28

Q4 2013-14 For the quarter ended Mar 31, 2014

11,665,396 9,439,083 29

Q1 2014-15 For the quarter ended Jun 30, 2014

12,313,495 9,688,246 LINEST (qudratic) 12868.9174834035 -85429.1981206864 3968182.02604002 Seasonal Indexes x^2

constant estimated quadratic regression equation:

Q1 Q2 Q3 Q4
ŷ = 3,968,182 + 12,869 x^2 – 8,542 x

1.086050909 0.8912715037
1.0327838689

1.1249629452

0.9896758547 0.7911116238
LINEST (linear)

0.9508756804 1.0326824448 1.0904980833 0.9019424106
249162.656447805 2462518.6804818

1.0038408821 1.0810081717 1.0569733588 0.8347672546
x constant 0.9222567967 1.1018733944 1.0936697239 0.9339025139
estimated linear regression equation:

0.9695830789 1.0292251135 1.0926061688
ŷ = 2,462,519 + 249,163 x 0.9758680614 1.0739504139 1.0682456831 0.8705990613

actual revenue

Q1 2007-08 Q2 2007-08 Q3 2007-08 Q4 2007-08

Q1 2008-09 Q2 2008-09 Q3 2008-09 Q4 2008-09 Q1 2009-10 Q2 2009-10 Q3 2009-10 Q4 2009-10 Q1 2010-11 Q2 2010-11 Q3 2010-11 Q4 2010-11 Q1 2011-12 Q2 2011-12 Q3 2011-12 Q4 2011-12 Q1 201 2-13 Q2 2012-13 Q3 2012-13 Q4 2012-13 Q1 2013-14 Q2 2013-14 Q3 2013-14 Q4 2013-14 Q1 2014-15 3813096 4310323 4493881 3734203 4212634 4326225 3591287 2770785 3440778 4087231 4759414 4317392 5146672 5759983 5808331 4879385 5919916 7786386 8371858 7496278 8009696 8672601 9358746 7832358 8787098 qudratic forecast Q1 2007-08 Q2 2007-08 Q3 2007-08 Q4 2007-08 Q1 2008-09 Q2 2008-09 Q3 2008-09 Q4 2008-09 Q1 2009-10 Q2 2009-10 Q3 2009-10 Q4 2009-10 Q1 2010-11 Q2 2010-11 Q3 2010-11 Q4 2010-11 Q1 2011-12 Q2 2011-12 Q3 2011-12 Q4 2011-12 Q1 2012-13 Q2 2012-13 Q3 2012-13 Q4 2012-13 Q1 2013-14 Q2 2013-14 Q3 2013-14 Q4 2013-14 Q1 2014-15 3895621.7453800002 3848799.2997599998 3827714.6891399999 3832367.9135200004 3862758.9729000004 3918887.8672799999 4000754.5966600003 4108359.1610400002 4241701.5604200009 4400781.7948000003 4585599.8641799996 4796155.7685599998 5032449.5079399999 5294481.082320001 5582250.4917000011 5895757.7360799992 6235002.8154599993 6599985.7298400002 6990706.4792200001 7407165.0636 7849361.4829799999 8317295.7373599987 8810967.8267400004 9330377.7511199992 9875525.5105000008 10446411.104880001 11043034.534260001 11665395.798640002 12313494.898019999 linear forecast Q1 2007-08 Q2 2007-08 Q3 2007-08 Q4 2007-08 Q1 2008-09 Q2 2008-09 Q3 2008-09 Q4 2008-09 Q1 2009-10 Q2 2009-10 Q3 2009-10 Q4 2009-10 Q1 2010-11 Q2 2010-11 Q3 2010-11 Q4 2010-11 Q1 2011-12 Q2 2011-12 Q3 2011-12 Q4 2011-12 Q1 2012-13 Q2 2012-13 Q3 2012-13 Q4 2012-13 Q1 2013-14 Q2 2013-14 Q3 2013-14 Q4 2013-14 Q1 2014-15 2711682 2960845 3210008 3459171 3708334 3957497 4206660 4455823 4704986 4954149 5203312 5452475 5701638 5950801 6199964 6449127 6698290 6947453 7196616 7445779 7694942 7944105 8193268 8442431 8691594 8940757 9189920 9439083 9688246

dummy variables

method 3656400.29 4271731.8899999997 4262650.18 3121254.4699999997 3645892.8899999997 4360908.41 4451510.620000001 3409798.83 4034121.17 4848820.6100000003 5039106.74 4097078.87 4821085.13 5735468.4900000002 6025438.540000001 5183094.59 6006784.7699999996 7020852.0499999998 7410506.0199999996 6667845.9900000002 7591220.0899999999 8704971.290000001 9194309.1799999997 8551333.0700000003 9574391.0900000017 10787826.210000001 11376848.02 10833555.83 11956297.770000001
dummy variables

dummy variables dummy variables dummy variables
time period FY Quarter Revenue US’000

MSE

1 Q1 2007-08

1 0 0

2 Q2 2007-08 For the quarter ended Sept 30, 2007

0 1 0

3 Q3 2007-08 For the quarter ended Dec 31, 2007 4,493,881 0 0 1

4 Q4 2007-08 For the quarter ended Mar 31, 2008 3,734,203 0 0 0

5 Q1 2008-09 For the quarter ended Jun 30, 2008 4,212,634 1 0 0

6 Q2 2008-09 For the quarter ended Sept 30, 2008 4,326,225 0 1 0

7 Q3 2008-09 For the quarter ended Dec 31, 2008 3,591,287 0 0 1

8 Q4 2008-09 For the quarter ended Mar 31, 2009 2,770,785 0 0 0

9 Q1 2009-10 For the quarter ended Jun 30, 2009 3,440,778 1 0 0

10 Q2 2009-10 For the quarter ended Sept 30, 2009 4,087,231 0 1 0

11 Q3 2009-10 For the quarter ended Dec 31, 2009 4,759,414 0 0 1

12 Q4 2009-10 For the quarter ended Mar 31, 2010 4,317,392 0 0 0

13 Q1 2010-11 For the quarter ended Jun 30, 2010 5,146,672 1 0 0

14 Q2 2010-11 For the quarter ended Sep 30, 2010 5,759,983 0 1 0

15 Q3 2010-11 For the quarter ended Dec 31, 2010 5,808,331 0 0 1

16 Q4 2010-11 For the quarter ended Mar 31, 2011 4,879,385 0 0 0

17 Q1 2011-12 For the quarter ended Jun 30, 2011 5,919,916 1 0 0

18 Q2 2011-12 For the quarter ended Sep 30, 2011 7,786,386 0 1 0

19 Q3 2011-12 For the quarter ended Dec 31, 2011 8,371,858 0 0 1

20 Q4 2011-12 For the quarter ended Mar 31, 2012 7,496,278 0 0 0

21 Q1 2012-13 For the quarter ended Jun 30, 2012 8,009,696 1 0 0

22 Q2 2012-13 For the quarter ended Sep 30, 2012 8,672,601 0 1 0

23 Q3 2012-13 For the quarter ended Dec 31, 2012 9,358,746 0 0 1

24 Q4 2012-13 For the quarter ended Mar31, 2013 7,832,358 0 0 0

25 Q1 2013-14 For the quarter ended Jun 30, 2013 8,787,098 1 0 0

26 Q2 2013-14 For the quarter ended Sep 30, 2013 x 0 1 0

27 Q3 2013-14 For the quarter ended Dec 31, 2013 x 0 0 1

28 Q4 2013-14 For the quarter ended Mar 31, 2014 x 0 0 0

29 Q1 2014-15 For the quarter ended Jun 30, 2014 x 1 0 0

– 77,389.79*time period +

*time period^2 + 489,883.8*q1 +

*q2 +

*q3

constant x x^2 q1 q2 q3

3,231,445.79

12,460.49

1,145,223.72 1,151,229.35

					q1	q2	q3	quadratic forecast	Forecast error
For the quarter ended Jun 30, 2007	3,813,096	3,656,400	156,695.71	24,553,545,532.40
4,310,323	4,271,732	38,591.11	1,489,273,771.03
4,262,650	231,230.82	53,467,692,117.87
3,121,254	612,948.53	375,705,900,429.16
3,645,893	566,741.11	321,195,485,764.03
4,360,908	– 34,683.41	1,202,938,929.23
4,451,511	– 860,223.62	739,984,676,405.91
3,409,799	– 639,013.83	408,338,674,931.27
4,034,121	– 593,343.17	352,056,117,385.65
4,848,821	– 761,589.61	580,018,734,059.95
5,039,107	– 279,692.74	78,228,028,808.71
4,097,079	220,313.13	48,537,875,250.40
4,821,085	325,586.87	106,006,809,916.40
5,735,468	24,514.51	600,961,200.54
6,025,439	– 217,107.54	47,135,683,924.85
5,183,095	– 303,709.59	92,239,515,057.97
6,006,785	– 86,868.77	7,546,183,201.31
7,020,852	765,533.95	586,042,228,602.60
7,410,506	961,351.98	924,197,629,449.92
6,667,846	828,432.01	686,299,595,192.64
7,591,220	418,475.91	175,122,087,250.33
8,704,971	– 32,370.29	1,047,835,674.68
9,194,309	164,436.82	27,039,467,771.71
8,551,333	– 718,975.07	516,925,151,281.51
9,574,391	– 787,293.09	619,830,409,561.75
10,787,826	– 1	270,992,500,059
11,376,848
10,833,556
11,956,298
Equation of the model with dummy variables:
Revenue US’000 =	3,231,445.79	12,460.49	1,145,223.72	1,151,229.35
coeficients	-77,389.79	489,883.80

actual revenue Q1 2007-08 Q2 2007-08 Q3 2007-08 Q4 2007-08 Q1 2008-09 Q2 2008-09 Q3 2008-09 Q4 2008-09 Q1 2009-10 Q2 2009-10 Q3 2009-10 Q4 2009-10 Q1 2010-11 Q2 2010-11 Q3 2010-11 Q4 2010-11 Q1 2011-12 Q2 2011-12 Q3 2011-12 Q4 2011-12 Q1 201 2-13 Q2 2012-13 Q3 2012-13 Q4 2012-13 Q1 2013-14 Q2 2013-14 Q3 2013-14 Q4 2013-14 Q1 2014-15 3813096 4310323 4493881 3734203 4212634 4326225 3591287 2770785 3440778 4087231 4759414 4317392 5146672 5759983 5808331 4879385 5919916 7786386 8371858 7496278 8009696 8672601 9358746 7832358 8787098 qudratic forecast Q1 2007-08 Q2 2007-08 Q3 2007-08 Q4 2007-08 Q1 2008-09 Q2 2008-09 Q3 2008-09 Q4 2008-09 Q1 2009-10 Q2 2009-10 Q3 2009-10 Q4 2009-10 Q1 2010-11 Q2 2010-11 Q3 2010-11 Q4 2010-11 Q1 2011-12 Q2 2011-12 Q3 2011-12 Q4 2011-12 Q1 2012-13 Q2 2012-13 Q3 2012-13 Q4 2012-13 Q1 2013-14 Q2 2013-14 Q3 2013-14 Q4 2013-14 Q1 2014-15 3895621.7453800002 3848799.2997599998 3827714.6891399999 3832367.9135200004 3862758.9729000004 3918887.8672799999 4000754.5966600003 4108359.1610400002 4241701.5604200009 4400781.7948000003 4585599.8641799996 4796155.7685599998 5032449.5079399999 5294481.082320001 5582250.4917000011 5895757.7360799992 6235002.8154599993 6599985.7298400002 6990706.4792200001 7407165.0636 7849361.4829799999 8317295.7373599987 8810967.8267400004 9330377.7511199992 9875525.5105000008 10446411.104880001 11043034.534260001 11665395.798640002 12313494.898019999 linear forecast Q1 2007-08 Q2 2007-08 Q3 2007-08 Q4 2007-08 Q1 2008-09 Q2 2008-09 Q3 2008-09 Q4 2008-09 Q1 2009-10 Q2 2009-10 Q3 2009-10 Q4 2009-10 Q1 2010-11 Q2 2010-11 Q3 2010-11 Q4 2010-11 Q1 2011-12 Q2 2011-12 Q3 2011-12 Q4 2011-12 Q1 2012-13 Q2 2012-13 Q3 2012-13 Q4 2012-13 Q1 2013-14 Q2 2013-14 Q3 2013-14 Q4 2013-14 Q1 2014-15 2711682 2960845 3210008 3459171 3708334 3957497 4206660 4455823 4704986 4954149 5203312 5452475 5701638 5950801 6199964 6449127 6698290 6947453 7196616 7445779 7694942 7944105 8193268 8442431 8691594 8940757 9189920 9439083 9688246 dummy variables method 3656400.29 4271731.8899999997 4262650.18 3121254.4699999997 3645892.8899999997 4360908.41 4451510.620000001 3409798.83 4034121.17 4848820.6100000003 5039106.74 4097078.87 4821085.13 5735468.4900000002 6025438.540000001 5183094.59 6006784.7699999996 7020852.0499999998 7410506.0199999996 6667845.9900000002 7591220.0899999999 8704971.290000001 9194309.1799999997 8551333.0700000003 9574391.0900000017 10787826.210000001 11376848.02 10833555.83 11956297.770000001

Nonlinear regression

XLSTAT 2013.5.05 – Nonlinear regression – on 12/11/2013 at 7:06:16 PM Y / Quantitative: Workbook = Book1.xlsx / Sheet = dummy variables / Range = ‘dummy variables’!$D$3:$D$28 / 25 rows and 1 column X / Quantitative: Workbook = Book1.xlsx / Sheet = dummy variables / Range = ‘dummy variables’!$A$3:$B$30,’dummy variables’!$E$3:$H$30 / 25 rows and 4 columns Stop conditions: Iterations = 200 / Convergence =

0.000

01 Function: Y =

pr1

pr2

*X1+

pr3

*X2+

pr4

*X3+

pr5

*X4+

pr6

*X1^2+

pr7

*X2^2+

pr8

*X3^2+

pr9

*X4^2 Summary statistics: Variable Observations Obs. with missing data Obs. without missing data Minimum Maximum Mean Std. deviation Revenue US’000 25 0 25

277078

5.000 935874

6.000 5667462.280 1998003.655 time period 25 0 25

1.000 25.000 1

3.000 7.360 q1 25 0 25 0.000 1.000

0.280 0.458 q2 25 0 25 0.000 1.000

0.240 0.436 q3 25 0 25 0.000 1.000 0.240 0.436
Correlation matrix: Variables

time period q1 q2 q3 Revenue US’000
time period 1.000

-0.000 -0.078

0.000

0.889 q1 -0.000 1.000

-0.350

-0.016 q2 -0.078 -0.350 1.000

-0.316 0.045 q3 0.000 -0.350 -0.316 1.000

0.114 Revenue US’000 0.889 -0.016 0.045 0.114 1.000
Nonlinear regression of variable Revenue US’000: Goodness of fit statistics: Observations 25.000
DF 16.000 R² 0.929 SSE 6774812501447.660 MSE

423425781340.479 RMSE 650711.750 Model parameters: Parameter Value Standard error pr1

3231445.787 503915.136 pr2

-77389.785 76089.389 pr3

489883.798 365436.980 pr4

1145223.724 377434.649 pr5

1151229.348 376136.614 pr6

12460.486 2842.214 pr7 0.000 0.000
pr8 0.000 0.000
pr9 0.000 0.000
Equation of the model: Revenue US’000 = 3231445.78663446-77389.7850241539*time period+489883.797953054*q1+1145223.72360249*q2+1151229.3482176*q3+12460.4864163614*time period^2 Predictions and residuals:

Observations time period q1 q2 q3 Revenue US’000

Pred(Revenue US’000)

Residual

Obs1

1.000 1.000 0.000 0.000

3813096.000 3656400.286 156695.714 Obs2 2.000

0.000 1.000 0.000

43103

23.000 4271731.886 38591.114 Obs3

3.000 0.000 0.000 1.000

4493881.000 4262650.158 231230.842 Obs4 4.000

0.000 0.000 0.000

3734203.000 3121254.429 612948.571 Obs5

5.000 1.000 0.000 0.000

4212634.000 3645892.820 566741.180 Obs6

6.000 0.000 1.000 0.000

4326225.000 4360908.311 -34683.311 Obs7 7.000

0.000 0.000 1.000

3591287.000 4451510.474 -860223.474 Obs8 8.000

0.000 0.000 0.000

2770785.000 3409798.637 -639013.637 Obs9 9.000

1.000 0.000 0.000

3440778.000 4034120.919 -593342.919 Obs10 10.000

0.000 1.000 0.000

4087231.000 4848820.302 -761589.302 Obs11 11.000

0.000 0.000 1.000

47594

14.000 5039106.356 -279692.356 Obs12 12.000

0.000 0.000 0.000

4317392.000 4097078.410 220313.590 Obs13 13.000

1.000 0.000 0.000

5146672.000 4821084.584 325587.416 Obs14

14.000 0.000 1.000 0.000

5759983.000 5735467.858 24515.142 Obs15 15.000

0.000 0.000 1.000

5808331.000 6025437.803 -217106.803 Obs16

16.000 0.000 0.000 0.000

4879385.000 5183093.749 -303708.749 Obs17 17.000

1.000 0.000 0.000

5919916.000 6006783.814 -86867.814 Obs18 18.000

0.000 1.000 0.000

7786386.000 7020850.979 765535.021 Obs19 19.000

0.000 0.000 1.000

8371858.000 7410504.816 961353.184 Obs20 20.000

0.000 0.000 0.000

7496278.000 6667844.653 828433.347 Obs21 21.000

1.000 0.000 0.000

8009696.000 7591218.609 418477.391 Obs22 22.000

0.000 1.000 0.000

8672601.000 8704969.665 -32368.665 Obs23

23.000 0.000 0.000 1.000

9358746.000 9194307.394 164438.606 Obs24 24.000

0.000 0.000 0.000

7832358.000 8551331.122 -718973.122 Obs25

25.000 1.000 0.000 0.000

8787098.000 9574388.969 -787290.969

Pred(Revenue US’000) / Revenue US’000

Active 3656400.285979717 4271731.8858540785 4262650.1575268516 3121254.4291996225 3645892.8198757749 4360908.3110810276 4451510.4740846911 3409798.6370883537 4034120.919095397 4848820.3016315401 5039106.3559660958 4097078.4103006497 4821084.5836385842 5735467.8575056195 6025437.8031710647 5183093.7488365099 6006783.8135053348 7020850.9787032614 7410504.8156995988 6667844.6526959343 7591218.6086956523 8704969.6652244683 9194307.3935516961 8551331.1218789238 9574388.9692095332 3813096 4310323 4493881 3734203 4212634 4326225 3591287 2770785 3440778 4087231 4759414 4317392 5146672 5759983 5808331 4879385 5919916 7786386 8371858 7496278 8009696 8672601 9358746 7832358 8787098 2000000 10000 000 2000000 10000000 Pred(Revenue US’000)
Revenue US’000

Residuals

Active Obs1 Obs2 Obs3 Obs4 Obs5 Obs6 Obs7 Obs8 Obs9 Obs10 Obs11 Obs12 Obs13 Obs14 Obs15 Obs16 Obs17 Obs18 Obs19 Obs20 Obs21 Obs22 Obs23 Obs24 Obs25 156695.71402028296 38591.114145921543 231230.84247314837 612948.57080037752 566741.18012422509 -34683.311081027612 -860223.47408469114 -639013.63708835375 -593342.919095397 -761589.30163154006 -279692.35596609581 220313.5896993503 325587.41636141576 24515.142494380474 -217106.8031710647 -303708.74883650988 -86867.81350533478 765535.02129673865 961353.18430040125 828433.34730406571 418477.39130434766 -32368.665224468336 164438.60644830391 -718973.12187892385 -787290.96920953318 Observations
Residual

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