David Game College: Statistics for Management Report

This report provides a comprehensive overview of statistics for management, focusing on its application in the business context. It begins with an introduction to statistics, covering key features and various methods used for data gathering, analysis, and interpretation. The report then delves into the sources and types of data relevant to organizations like Samsung, exploring the differences between samples and populations. It highlights the importance of statistical methods in achieving competitive advantages, particularly for a company like Samsung operating in the digital industry. Furthermore, the report distinguishes between inferential and descriptive statistics, illustrating their consequences for business intelligence and decision-making. The analysis includes data from companies like Apple, Microsoft, Amazon, and Netflix, with detailed statistical summaries and regression analysis to support the findings. The report concludes by emphasizing the significance of statistical techniques in enhancing business performance and achieving organizational goals.

Statistics for Management

Table of Contents
Contents
INTRODUCTION...........................................................................................................................1
TASK 1............................................................................................................................................1
PART A...........................................................................................................................................1
1. Overview of statistics, with important features and overview of methods........................1
2. Sources and types of data or data, that organisation can assess.........................................2
3. Difference between a sample and a population..................................................................3
4. Importance of statistical methods to attain the competitive advantages for Samsung.......4
5. Distinguish among inferential statistics and descriptive with consequences for business
intelligence.............................................................................................................................4
PART B............................................................................................................................................6
REFERENCES..............................................................................................................................25
APPENDICES...............................................................................................................................26

INTRODUCTION
Statistics applies to a data gathering, structure, study and understanding in numerical
research. It offers a range of methods for modelling and forecasting organizational data, which
helps to make decisions about accounting, advertising as well as other services (Mendenhall and
Sincich, 2016). The current document will gather in-depth quantitative management
understanding, such as its use to solve company difficulties. Samsung is selected as a developer,
supplier and retailer in the UK for such a research, which deals with electronics. This includes
various sources and information forms, allowing businesses to track rivals' successful and
unsuccessful results in order to develop methods for enhancing their own output. To this end, the
importance of using statistical methods is also addressed in this document in order to meet the
respective company's business goals. In addition, important distinctions among empirical and
inferential statistics are illustrated, with consequences for business goals.
TASK 1
PART A
1. Overview of statistics, with important features and overview of methods
Using a variety of methods, analytics could be described as a resource for gathering,
analysing and interpreting significant data. This includes table, comparison through inference,
pattern prediction and much more, which are categorized as empirical and concise through two
types of metrics (Rasmussen and Ulrich, 2015). Statistics could be described as a calculus
department which really primarily deals with registering and analysing mathematical data, as per
Prof Bowled.
Main features of statistics:
Stats are composite of truths: Information gathered by statistical techniques rarely contains
individual or independent numbers, since it is difficult to compare these data, to draw a particular
conclusion (Cremers and Pareek, 2016).
Statistical data should be obtained in a logical way: the data must always be compiled
routinely, with an acceptable method, to take financial judgments or make a sensible conclusion
(Siva and et. al., 2016).
Data should be represented in quantitative sort: even data presented mathematically can be
used as statistical techniques. Thus subjective information such as intellect, attractiveness,
1

including dismissive knowledge which could not be represented in quantitative type should not
be used on same (Kreps, 2019).
Overview of statistical methods:
As per the opinion of John Dillard, it is important to consider the adequate statistical
methods in the age of information to evaluate an information and infer the appropriate outcome.
It involves
Mean: this approach makes it easier to identify a general trend of a set of data in
evaluating the estimated value of broad data. But using this approach on information that has
distorted distribution can provide inaccurate information, which has a significant effect on the
result made to achieve business objectives (Martins and Isouard, 2015).
Hypothesis testing: This process, also recognized as probability testing that is used to
check if a theory is valid for a set of data, otherwise it will consider alternative hypothesis. In
probability theory we consider the null hypothesis unless the test's meaning value is higher than
that of the specified meaning point. Unless the meaning quality is lower than that of the
predicted meaning, otherwise the null hypothesis must be dismissed (Gerrish, 2016).
Standard deviations: It is applicable specifically to information that is widely distributed
for average, that allows to quickly determine the distribution of datasets. However, if results with
odd warbling such as large volumes of anomalies or anti-normal shrieked graph cannot be
collected by implementing standard approach of variance (Jiraporn, Leelalai and Tong, 2016).
Sample size determination: It is hard to obtain reliable results if calculating significant
population data such as business data about revenue, client base and much more. Thus, taking a
test of information with both the correct size is important in order to achieve an accurate number.
So if the survey doesn't include specified parameters, mistakes may also be created (Valenti and
et. al., 2017).
Correlation and Regression: All approaches were used to assess the interconnectedness or
partnership among any information which can be used to evaluate trends. In the regression study,
we calculate a coefficient of sample correlation, precisely the factor of similarity of Pearson
Consumer Moment. The coefficient of test association, marked r (Hubbard, 2015).
2. Sources and types of data or data, that organisation can assess
A company such as Samsung, that runs its digital company in different countries, could use
a number of sources to gain economic benefits. This includes statistical sources that are used to
2

gather data for official purposes such as census reporting and non-statistical sources that are used
to gather information for administrative purposes (Gerrish, 2016).
Methods of statistical information gathering:
Survey: This tool is also considered a survey sample used only to measure the features of
high population information. Using that same approach allows the company to easily determine
the present and past pattern, but it may also trigger mistake. Samsung could use this source to
gain input on both the product quality and products from consumers and other shareholders in
order to make more improvements (Wang and et. al., 2016).
Government and Semi-government sources: Such outlets include information on an
organization's local and national company performance, making it easy to accomplish a goal
through collection of data. Company executives could use this semi-statistical tool to gather data
on the strong position of their own company and other rivals operating in same industry.
Census: An evaluation of data is focused for each and every community object. The
benefit of using such an approach is that data becomes more relevant. It does, however, entail
high costs and time to process and process data (Jiraporn, Leelalai and Tong, 2016).
Types of statistical data:
Primary data: This contains qualitative raw business information, i.e. non-manufacturing
data that can be gathered from sources such as survey, questionnaire, and much more. Primary
information is 'genuine' in the context that they had not undergone quantitative activities and are
authentic (Hubbard, 2015).
Secondary data: This comprises of data that might be subject to empirical analysis and it
can be obtained by governmental and semi-governmental organization, newspapers as well as
further media sources. It indicates that - in the past certain authors or scholars have already
obtained it data and are available in either written or undisclosed type (Valenti and et. al., 2017).
3. Difference between a sample and a population
A community contains whole items from a specific information set about that measures
such as average, mean and method are used to infer desired outcome. But if a community
comprises of large amounts of data, then it is difficult to apply these techniques, as in such cases
the probability of failure is high. While test means larger data volume that is extracted from
either a larger population and contains each item that needs results to be obtained. Applying
statistical techniques to the test provides the advantage of measuring more reliable results to
3

slight or zero errors. One of the key difference between the population and sample is gathering of
data, as in population complete data form the data set is recognised whereas in sample only one
or more observations are considered to evaluate the key measures of research or survey
(Hubbard, 2015).
4. Importance of statistical methods to attain the competitive advantages for Samsung
In order to accomplish highly competitive strengths in digital industry, where rivalry is
strong, it is important that Samsung gather data about opponents ' last five years of success, their
sales, and much more. Notifying a director focused on teacher performance improvement is one
of the functions of metrics in industry. Most businesses often obtain around - the-job information
on employee satisfaction and morale, which can be monitored not just to keep workers engaged
but to also insure that they do not move elsewhere certain roles. Executives gathers key
information on the performance of workers, like the variety of tasks performed or the quantity
generated (Hubbard, 2015). In order to accomplish productivity, the information is analysed
parallel to organisational objective. Through implementing statistical techniques such as
comparison, variation analysis (ANNOVA), hypothesis testing, and much more, the
corresponding company's executives will take effective methods to enhance and boost financial
performance.
5. Distinguish among inferential statistics and descriptive with consequences for business
intelligence
Inferential and descriptive statistics are seen as two distinct sections of statistics which serve
to collect data, evaluate to analyse outcomes, but also with diverse goals. if the study depends on
inferential statistics and descriptive statistics, as usual people, lacks understanding of such two
types of statistics, is very hard to identify. Inferential statistics, as the aspects, are someone who
defines the community. Descriptive statistics identify the community through which information
could be obtained, while inferential helps by comparing the results out of the same population to
achieve the generalization. Depending on the surveys, descriptive statistics were used to
generalize the population. thus, among quantitative and linear regressions, i.e. what you do with
your data, there's a big difference. The fundamental distinction between offensive sections can
be clarified as follows:
It describes the probability of performance or business loss in an enterprise This approach
depicts visual data such as maps, graphs and charts.
4

Basis of comparison Descriptive statistics Inferential statistics
Meaning This section of the data is
primarily concerned to
describing the population in
which it is possible to achieve
information.
This involves making the
conclusions based on the
analysis of the test.
Usage in business This helps to describe history
and to predict a business'
condition.
It explains the chance of
getting success or meeting
failure of business in an
organisation
Forms of result It describes the probability of
performance or business loss
in an enterprise This approach
depicts visual data such as
maps, graphs and charts.
The results provides a
possibility of happening
events.
How it works The assessment of data mainly
used by using secondary
information.
This helps in tending the curve
towards possibility of events
and aspects.
5

PART B
Descriptive and Inferential statistics:
Net profit Apple MSFT Amazon Netflix
2015 53,394 12,193 123 596
2016 45,687 20,539 187 2371
2017 48,351 25,489 559 3033
2018 59,531 16,571 1211 10073
Apple
Mean 51189.67
Standard Error 4240.97
Median 48351
Standard Deviation 7345.58
Sample Variance 53957605.33
Skewness 1.48
Range 13844
Minimum 45687
Maximum 59531
Sum 153569
Count 3
Largest(1) 59531
Smallest(1) 45687
Confidence Level(95.0%) 18247.44
Microsoft
Mean 18698
Standard Error 2833.52130749
6

Median 18555
Standard Deviation 5667.0426149801
Sample Variance 32115372
Kurtosis -0.7362231163
Skewness 0.1265308912
Range 13296
Minimum 12193
Maximum 25489
Sum 74792
Count 4
Largest(1) 25489
Smallest(1) 12193
Confidence
Level(95.0%) 9017.5294159643
7

Amazon
Mean 520
Standard Error 249.5829855312
Median 373
Standard Deviation 499.1659710624
Sample Variance 249166.666666667
Kurtosis 0.7348682097
Skewness 1.2355157881
Range 1088
Minimum 123
Maximum 1211
Sum 2080
Count 4
Largest(1) 1211
Smallest(1) 123
Confidence Level(95.0%) 794.2844501654
Netflix
Mean 4018.25
Standard Error 2082.7854496243
Median 2702
Mode #N/A
Standard Deviation 4165.5708992486
Sample Variance 17351980.9166667
Kurtosis 2.9892051233
Skewness 1.6275421016
Range 9477
8

Minimum 596
Maximum 10073
Sum 16073
Count 4
Largest(1) 10073
Smallest(1) 596
Confidence Level(95.0%) 6628.3528588555
Mean of four year profits of companies named: Apple, MSFT, Amazon and Netflix are
51189.67, 18698, 520 and 4018.25 respectively. While risk or standard deviation of these
companies are 4240.97, 2833.52130749, 249.5829855312 and 2082.7854496243. Which shows
that in case of Amazon standard deviation in lower (Five important methods for Statistical Data
analysis. 2017).
9

Apple: SUMMARY OUTPUT
Regression Statistics
Multiple R 0.9675382273
R Square 0.9361302212
Adjusted R Square 0.9350290181
Standard Error 9.8209074442
Observations 60
ANOVA
df SS MS F Significance F
Regression 1
81992.113957
766
81992.113957
766
850.09771240
98
2.4308631321
5957E-036
Residual 58
5594.1129356
41
96.450223028
3
Total 59
87586.226893
407
Coefficie
nts
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
-
51.19886
1017
6.467997
5555
-
7.915720
5267
8.479955
78776999
E-011
-
64.14596
48113
-
38.25175
72227
-
64.14596
48113
-
38.25175
72227
X
Variable
1
0.031640
4012
0.001085
1944
29.15643
51801
2.430863
13215953
E-036
0.029468
1485
0.033812
6538
0.029468
1485
0.033812
6538
10

11
4000 4500 5000 5500 6000 6500 7000 7500 8000 8500
-30
-20
-10
0
10
20
30
40
50
X Variable 1 Residual Plot
X Variable 1
Residuals
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
50
100
150
200
250
X Variable 1 Line Fit Plot
Series1
Column B
X Variable 1
Y
0 20 40 60 80 100 120
0
100
200
300
Normal Probability Plot
Sample Percentile
Y

Microsoft: SUMMARY OUTPUT
Regression Statistics
Multiple R 0.9728245071
R Square 0.9463875217
Adjusted R Square 0.9454631686
Standard Error 6.0702620615
Observations 60
ANOVA
df SS MS F Significance F
Regression 1
37726.454829
5315
37726.454829
5315
1023.8376951
664
1.5087636372
7082E-038
Residual 58
2137.1887267
32
36.848081495
4
Total 59
39863.643556
2635
Coefficie
nts
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
-
59.04509
33347
3.997842
3988
-
14.76923
98661
2.598638
93582772
E-021
-
67.04764
43632
-
51.04254
23062
-
67.04764
43632
-
51.04254
23062
X
Variable
1
0.021462
4323
0.000670
7542
31.99746
38865
1.508763
63727082
E-038
0.020119
7719
0.022805
0926
0.020119
7719
0.022805
0926
12

13
4000 4500 5000 5500 6000 6500 7000 7500 8000 8500
-20
-10
0
10
20
X Variable 1 Residual Plot
X Variable 1
Residuals

Netflix: SUMMARY OUTPUT
Regression Statistics
Multiple R 0.9592354768
R Square 0.9201326999
Adjusted R Square 0.9187556775
Standard Error 30.3572196274
Observations 60
ANOVA
df SS MS F
Significanc
e F
Regression 1 615791.146 615791.146 668.204591 1.60141522
14
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
100
200
X Variable 1 Line Fit Plot
Series1
Column B
X Variable 1
Y
0 20 40 60 80 100 120
0
100
200
Normal Probability Plot
Sample Percentile
Y

661726 661726 2594
271931E-
033
Residual 58
53450.5254
432074
921.560783
5036
Total 59 669241.67
Coefficients
Standar
d Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
-
338.218945
8788
19.9931
038406
-
16.9167
803346
4.26279
098022
975E-
024
-
378.239
491399
-
298.198
400358
7
-
378.239
491399
-
298.198
400358
7
X Variable 1
0.08671069
35
0.00335
44238
25.8496
536004
1.60141
522271
94E-
033
0.07999
60847
0.09342
53023
0.07999
60847
0.09342
53023
15
4000 4500 5000 5500 6000 6500 7000 7500 8000 8500
-100
-50
0
50
100
X Variable 1 Residual Plot
X Variable 1
Residuals

16
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
500
X Variable 1 Line Fit Plot
Series1
Column B
X Variable 1
Y

Amazon: SUMMARY
OUTPUT
Regression Statistics
Multiple R 0.9703050133
R Square 0.9414918189
Adjusted R Square 0.9404830571
Standard Error 126.1845198932
Observations 60
ANOVA
df SS MS F Significance F
Regression 1
14860728.719
6021
14860728.719
6021
933.31435789
66
1.9066832032
0694E-037
Residual 58
923506.91751
8546
15922.533060
6646
Total 59
15784235.637
1206
17
0 20 40 60 80 100 120
0
500
Normal Probability Plot
Sample Percentile
Y

Coefficie
nts
Standard
Error t Stat P-value
Lower
95%
Upper
95%
Lower
95.0%
Upper
95.0%
Intercept
-
1560.748
5043558
83.10445
55553
-
18.78056
3496
2.495356
33947853
E-026
-
1727.100
1460513
-
1394.396
8626603
-
1727.100
1460513
-
1394.396
8626603
X
Variable
1
0.425967
0366
0.013943
1859
30.55019
4073
1.906683
20320694
E-037
0.398056
7175
0.453877
3557
0.398056
7175
0.453877
3557
18
4000 4500 5000 5500 6000 6500 7000 7500 8000 8500
-400
-200
0
200
400
X Variable 1 Residual Plot
X Variable 1
Residuals
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
0
2000
4000
X Variable 1 Line Fit Plot
Column E
Series1
X Variable 1
Y
0 20 40 60 80 100 120
0
2000
4000
Normal Probability Plot
Sample Percentile
Y

Correlation Amazon NASDAQ-IXIC
Amazon 1
NASDAQ-IXIC 0.9703050133 1
NASDAQ-IXIC Apple
NASDAQ-IXIC 1
Apple 0.9675382273 1
NASDAQ-IXIC Microsoft
NASDAQ-IXIC 1
Microsoft 0.9728245071 1
NASDAQ-IXIC Netflix
NASDAQ-IXIC 1
Netflix 0.9592354768 1
Standard error of share price data of companies Apple, MSFT, Amazon and Netflix are and ,
9.8209074442, 6.0702620615, 126.1845198932 and 30.3572196274 respectively which shows
that Microsoft is least risk as compare to other companies.
Histogram:
Apple:
Apple Frequency
80-100 13
101-120 19
121-140 4
141-160 5
161-180 10
181-200 6
19

201-220 1
221-240 2
Microsoft Frequency
30-50 23
51-70 15
71-90 6
91-110 10
111-130 5
20
80-100 101-120 121-140 141-160 161-180 181-200 201-220 221-240
0
2
4
6
8
10
12
14
16
18
20

Netflix Frequency
40-60 6
61-80 6
81-100 9
101-120 8
121-140 3
141-160 5
161-180 2
181-200 5
201-220 0
221-240 0
241-260 0
261-280 2
281-300 3
301-320 2
321-340 3
341-360 3
21
30-50 51-70 71-90 91-110 111-130
0
5
10
15
20
25

361-380 3
381-400 1
Amazon Frequency
300-400 11
401-500 3
501-600 6
601-700 4
701-800 7
801-900 4
901-1000 6
1001-1100 0
1100-1200 3
1201-1300 0
1301-1400 0
1401-1500 2
1501-1600 4
1601-1700 4
22
40-60
61-80
81-100
101
121
141
161
181
201
221
241
261
281
301
321
341
361
381
0
1
2
3
4
5
6
7
8
9
10

1701-1800 3
1801-1900 1
1901-2000 0
2001-2100 2
23

24

REFERENCES
Books and Journals
Mendenhall, W. M. and Sincich, T. L., 2016. Statistics for Engineering and the Sciences.
Chapman and Hall/CRC.
Rasmussen, T. and Ulrich, D., 2015. Learning from practice: how HR analytics avoids being a
management fad. Organizational Dynamics. 44(3). pp.236-242.
Cremers, M. and Pareek, A., 2016. Patient capital outperformance: The investment skill of high
active share managers who trade infrequently. Journal of Financial Economics. 122(2).
pp.288-306.
Siva, V. and et. al., 2016. The support of Quality Management to sustainable development: A
literature review. Journal of Cleaner Production. 138. pp.148-157.
Kreps, D. M., 2019. Microeconomics for managers. Princeton University Press.
Martins, J. and Isouard, G., 2015. An evidence-based framework: competencies and skills for
managers in Australian health services. Asia Pacific Journal of Health Management.
10(2). p.8.
Gerrish, E., 2016. The impact of performance management on performance in public
organizations: A meta‐analysis. Public Administration Review. 76(1). pp.48-66.
Jiraporn, P., Leelalai, V. and Tong, S., 2016. The effect of managerial ability on dividend policy:
how do talented managers view dividend payouts?. Applied Economics Letters. 23(12).
pp.857-862.
Valenti, M. W. and et. al., 2017. From Information to Action: Practical Strategies for Translating
Data and Statistics to Nonprofit Program Managers. The Journal of Nonprofit Education
and Leadership. 7(4).
Hubbard, R., 2015. Corrupt research: The case for reconceptualizing empirical management
and social science. Sage Publications.
Wang, Y. and et. al., 2016. An efficient and reliable statistical method for estimating functional
connectivity in large scale brain networks using partial correlation. Frontiers in
neuroscience. 10. p.123.
Online
Five important methods for Statistical Data analysis. 2017. [Online] Available
Through:<https://www.bigskyassociates.com/blog/bid/356764/5-Most-Important-
Methods-For-Statistical-Data-Analysis>.
25

APPENDICES
X-Xm X-Xm Y-Ym
X-Xm *
Y-Ym
X-Xm * Y-
Ym (X-Xm)2 (X-Xm)2 (Y-Ym)2
Microsoft Amazon
NASDAQ-
IXIC Microsoft Amazon Microsoft Amazon
NASDAQ-
IXIC
57.4 958.5 2170.7 124523.4 2080504.773 3290.86 918639.3 4711860
51.5 851.9 1884.7 97150.4 1605596.751 2656.992 725727.9 3552214
45.2 711.0 1687.9 76226.8 1200087.144 2039.39 505487.8 2849147
37.6 789.9 1437.2 54025.3 1135172.98 1413.153 623905.1 2065407
34.7 573.1 790.7 27471.9 453158.6114 1207.152 328462.7 625193.5
43.5 761.3 1486.0 64712.5 1131280.112 1896.558 579603.1 2208053
39.5 669.2 1461.3 57728.1 977846.61 1560.591 447770.7 2135432
47.0 1074.1 2201.8 103482.3 2365015.409 2208.98 1153791 4847756
44.6 1083.9 2265.0 100904.2 2454883.269 1984.726 1174745 5130008
38.4 848.6 1827.2 70123.2 1550539.172 1472.822 720099.2 3338667
31.0 770.9 1665.7 51636.3 1284175.039 960.9698 594358.8 2774596
30.8 700.8 1597.5 49212.6 1119497.243 948.9711 491073.9 2552109
25.6 637.3 1221.7 31242.9 778549.4456 654.013 406121.6 1492507
23.4 518.5 1218.9 28473.9 631963.7508 545.7378 268828.4 1485625
25.4 583.6 1428.4 36265.3 833622.1064 644.57 340585 2040389
26.6 522.0 1566.9 41682.6 817975.086 707.6716 272522.3 2455150
17.3 240.6 1058.8 18352.0 254765.409 300.4271 57896.37 1121062
15.6 247.9 1029.4 16038.0 255180.4185 242.7427 61452.76 1059628
14.6 176.4 883.1 12907.2 155799.2249 213.6283 31126.38 779833.7
6.2 32.5 651.4 4007.7 21167.40262 37.85614 1056.032 424285.4
6.0 51.7 584.1 3527.5 30223.77354 36.47545 2677.715 341140.3
4.0 58.9 503.5 2031.3 29671.48397 16.27462 3472.356 253544.6
0.4 39.1 295.8 113.0 11580.83368 0.145922 1532.462 87516.52
0.9 65.8 353.9 310.7 23276.92721 0.770815 4325.254 125267.9
-0.5 -3.9 203.0 -91.6 - 0.203665 14.92541 41217.87
26

784.3427464
-3.0 -42.3 67.2 -198.5 -2841.43568 8.736157 1790.42 4509.421
-5.1 -83.8 -19.1 98.4 1604.864103 26.39825 7024.678 366.6486
-4.5 -105.4 -229.8 1033.3 24214.58266 20.21804 11103.54 52807.11
-6.9 -160.2 -400.1 2760.4 64091.44246 47.60437 25661.91 160070.4
-9.1 -178.3 -520.9 4733.3 92869.1809 82.5679 31784.94 271345
-9.4 -139.0 -655.4 6167.8 91129.09722 88.54859 19330.27 429611.9
-11.6 -91.5 -532.6 6186.7 48754.88208 134.9395 8380.182 283650
-12.1 -159.7 -631.4 7631.9 100825.2441 146.1187 25501.97 398625.3
-12.8 -170.0 -682.5 8752.6 116047.4554 164.4844 28914.74 465749.1
-18.0 -213.2 -1001.9 18067.3 213642.3374 325.1783 45468.46 1003840
-16.7 -206.1 -896.5 14928.3 184743.6696 277.2583 42462.11 803780.8
-19.6 -269.3 -1069.2 20944.8 287903.9049 383.7181 72502.73 1143249
-14.6 -335.2 -974.7 14190.3 326745.1291 211.9386 112368 950114
-19.0 -376.3 -1286.6 24421.9 484204.6707 360.2864 141626.8 1655437
-15.1 -341.9 -1230.6 18531.1 420697.6379 226.7463 116863.7 1514469
-14.7 -253.0 -837.2 12302.1 211775.2872 215.9334 63990.44 700866.8
-16.1 -264.1 -735.9 11838.5 194321.6324 258.784 69724.17 541575.4
-17.7 -303.0 -790.8 13973.8 239586.9917 312.2134 91780.71 625424.8
-25.4 -417.0 -1224.4 31132.6 510541.5 646.4932 173858.4 1499224
-26.4 -416.0 -1031.9 27215.9 429223.4166 695.6448 173025.5 1064772
-23.5 -392.7 -716.3 16798.1 281296.6102 549.9447 154215.9 513097.5
-25.8 -494.8 -857.7 22125.5 424367.367 665.4214 244790.8 735680
-23.6 -499.6 -774.6 18265.6 386987.3537 556.1111 249623.5 599940.4
-22.0 -507.1 -903.2 19829.8 457972.4536 482.0559 257123.4 815712.6
-29.2 -556.8 -943.7 27600.1 525412.6395 855.3529 309974.3 890585
-26.6 -548.7 -881.1 23449.4 483430.7707 708.3623 301064.4 776263.6
-29.7 -574.3 -1209.3 35971.8 694556.6465 884.7513 329847.3 1462522
-24.3 -618.5 -1108.5 26889.2 685634.5679 588.3759 382546.4 1228857
-23.3 -590.2 -1053.0 24528.2 621469.9099 542.6365 348351.8 1108721
27

-24.1 -623.4 -1213.8 29217.1 756704.6217 579.357 388619.3 1473426
-24.6 -606.4 -1351.2 33241.8 819384.4027 605.2438 367737.1 1825736
-25.7 -589.8 -1264.3 32485.7 745711.596 660.1957 347879.8 1598500
-27.7 -615.9 -1474.8 40893.6 908286.3371 768.8365 379287.7 2175088
-29.0 -604.1 -1436.4 41707.4 867695.6688 843.0834 364904.6 2063267
-30.0 -616.3 -1602.0 48016.9 987298.1704 898.4223 379829.8 2566301
28