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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSat, 10 Nov 2012 08:22:05 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/10/t1352554113zmg99bxqn9tcpwi.htm/, Retrieved Fri, 29 Mar 2024 12:24:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=187341, Retrieved Fri, 29 Mar 2024 12:24:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact75
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [WS 8 ] [2012-11-10 13:22:05] [5bcb27a14a37b739141501b3993fea08] [Current]
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Dataseries X:
9.676
8.642
9.402
9.610
9.294
9.448
10.319
9.548
9.801
9.596
8.923
9.746
9.829
9.125
9.782
9.441
9.162
9.915
10.444
10.209
9.985
9.842
9.429
10.132
9.849
9.172
10.313
9.819
9.955
10.048
10.082
10.541
10.208
10.233
9.439
9.963
10.158
9.225
10.474
9.757
10.490
10.281
10.444
10.640
10.695
10.786
9.832
9.747
10.411
9.511
10.402
9.701
10.540
10.112
10.915
11.183
10.384
10.834
9.886
10.216
10.943
9.867
10.203
10.837
10.573
10.647
11.502
10.656
10.866
10.835
9.945
10.331
10.718
9.462
10.579
10.633
10.346
10.757
11.207
11.013
11.015
10.765
10.042
10.661




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187341&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187341&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187341&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ yule.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
19.676NANA0.157306134259259NA
28.642NANA-0.783367476851852NA
39.402NANA0.0965283564814815NA
49.61NANA-0.180853587962963NA
59.294NANA-0.0504091435185181NA
69.448NANA0.0511325231481482NA
710.31910.05190335648159.506791666666670.5451116898148150.267096643518519
89.5489.910639467592599.533291666666670.377347800925926-0.362639467592594
99.8019.79306307870379.569250.2238130787037040.00793692129629697
109.5969.817743634259269.578041666666670.239701967592592-0.221743634259257
118.9239.01212557870379.5655-0.553374421296296-0.0891255787037029
129.7469.456521412037049.57945833333333-0.1229369212962970.289478587962964
139.8299.761431134259269.6041250.1573061342592590.0675688657407409
149.1258.853507523148159.636875-0.7833674768518520.271492476851853
159.7829.768611689814819.672083333333330.09652835648148150.0133883101851868
169.4419.509146412037049.69-0.180853587962963-0.0681464120370361
179.1629.670924189814819.72133333333333-0.0504091435185181-0.508924189814813
189.9159.809632523148159.75850.05113252314814820.105367476851853
1910.44410.32052835648159.775416666666660.5451116898148150.123471643518521
2010.20910.15555613425939.778208333333330.3773478009259260.053443865740741
219.98510.02610474537049.802291666666670.223813078703704-0.0411047453703688
229.84210.07986863425939.840166666666670.239701967592592-0.237868634259259
239.4299.335583912037049.88895833333333-0.5533744212962960.0934160879629626
2410.1329.804604745370379.92754166666666-0.1229369212962970.327395254629632
259.84910.07530613425939.9180.157306134259259-0.226306134259257
269.1729.133382523148159.91675-0.7833674768518520.0386174768518526
2710.31310.03640335648159.9398750.09652835648148150.276596643518518
289.8199.784604745370379.96545833333333-0.1808535879629630.0343952546296311
299.9559.931757523148159.98216666666667-0.05040914351851810.023242476851852
3010.04810.02667418981489.975541666666670.05113252314814820.0213258101851856
3110.08210.52648668981489.9813750.545111689814815-0.444486689814815
3210.54110.37380613425939.996458333333330.3773478009259260.167193865740742
3310.20810.229188078703710.0053750.223813078703704-0.0211880787037035
3410.23310.249201967592610.00950.239701967592592-0.0162019675925897
359.4399.4758339120370410.0292083333333-0.553374421296296-0.0368339120370358
369.9639.9382714120370410.0612083333333-0.1229369212962970.0247285879629633
3710.15810.243306134259310.0860.157306134259259-0.0853061342592571
389.2259.3218408564814810.1052083333333-0.783367476851852-0.0968408564814798
3910.47410.226153356481510.1296250.09652835648148150.247846643518519
409.7579.9921047453703710.1729583333333-0.180853587962963-0.23510474537037
4110.4910.161965856481510.212375-0.05040914351851810.328034143518519
4210.28110.270882523148110.219750.05113252314814820.0101174768518533
4310.44410.766403356481510.22129166666670.545111689814815-0.32240335648148
4410.6410.621097800925910.243750.3773478009259260.0189021990740752
4510.69510.476479745370410.25266666666670.2238130787037040.218520254629631
4610.78610.487035300925910.24733333333330.2397019675925920.298964699074071
479.8329.6937089120370410.2470833333333-0.5533744212962960.138291087962964
489.74710.119188078703710.242125-0.122936921296297-0.372188078703703
4910.41110.412014467592610.25470833333330.157306134259259-0.00101446759259183
509.5119.5135908564814810.2969583333333-0.783367476851852-0.00259085648148094
5110.40210.403153356481510.3066250.0965283564814815-0.00115335648148118
529.70110.114813078703710.2956666666667-0.180853587962963-0.413813078703702
5310.5410.249507523148110.2999166666667-0.05040914351851810.290492476851851
5410.11210.372840856481510.32170833333330.0511325231481482-0.260840856481479
5510.91510.908528356481510.36341666666670.5451116898148150.00647164351851792
5611.18310.777764467592610.40041666666670.3773478009259260.405235532407406
5710.38410.63077141203710.40695833333330.223813078703704-0.246771412037035
5810.83410.685701967592610.4460.2397019675925920.148298032407409
599.8869.9413339120370410.4947083333333-0.553374421296296-0.0553339120370371
6010.21610.395438078703710.518375-0.122936921296297-0.179438078703702
6110.94310.722431134259310.5651250.1573061342592590.220568865740741
629.8679.7842575231481510.567625-0.7833674768518520.0827424768518537
6310.20310.662278356481510.565750.0965283564814815-0.459278356481482
6410.83710.40502141203710.585875-0.1808535879629630.431978587962963
6510.57310.537965856481510.588375-0.05040914351851810.0350341435185193
6610.64710.646757523148110.5956250.05113252314814820.000242476851852302
6711.50211.136153356481510.59104166666670.5451116898148150.36584664351852
6810.65610.942139467592610.56479166666670.377347800925926-0.286139467592591
6910.86610.78739641203710.56358333333330.2238130787037040.0786035879629647
7010.83510.810451967592610.570750.2397019675925920.0245480324074094
719.9459.9994172453703710.5527916666667-0.553374421296296-0.054417245370372
7210.33110.424979745370410.5479166666667-0.122936921296297-0.0939797453703708
7310.71810.697514467592610.54020833333330.1573061342592590.0204855324074096
749.4629.7594241898148110.5427916666667-0.783367476851852-0.297424189814814
7510.57910.660403356481510.5638750.0965283564814815-0.0814033564814807
7610.63310.386313078703710.5671666666667-0.1808535879629630.246686921296297
7710.34610.517882523148110.5682916666667-0.0504091435185181-0.171882523148145
7810.75710.637215856481510.58608333333330.05113252314814820.119784143518519
7911.207NANA0.545111689814815NA
8011.013NANA0.377347800925926NA
8111.015NANA0.223813078703704NA
8210.765NANA0.239701967592592NA
8310.042NANA-0.553374421296296NA
8410.661NANA-0.122936921296297NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 9.676 & NA & NA & 0.157306134259259 & NA \tabularnewline
2 & 8.642 & NA & NA & -0.783367476851852 & NA \tabularnewline
3 & 9.402 & NA & NA & 0.0965283564814815 & NA \tabularnewline
4 & 9.61 & NA & NA & -0.180853587962963 & NA \tabularnewline
5 & 9.294 & NA & NA & -0.0504091435185181 & NA \tabularnewline
6 & 9.448 & NA & NA & 0.0511325231481482 & NA \tabularnewline
7 & 10.319 & 10.0519033564815 & 9.50679166666667 & 0.545111689814815 & 0.267096643518519 \tabularnewline
8 & 9.548 & 9.91063946759259 & 9.53329166666667 & 0.377347800925926 & -0.362639467592594 \tabularnewline
9 & 9.801 & 9.7930630787037 & 9.56925 & 0.223813078703704 & 0.00793692129629697 \tabularnewline
10 & 9.596 & 9.81774363425926 & 9.57804166666667 & 0.239701967592592 & -0.221743634259257 \tabularnewline
11 & 8.923 & 9.0121255787037 & 9.5655 & -0.553374421296296 & -0.0891255787037029 \tabularnewline
12 & 9.746 & 9.45652141203704 & 9.57945833333333 & -0.122936921296297 & 0.289478587962964 \tabularnewline
13 & 9.829 & 9.76143113425926 & 9.604125 & 0.157306134259259 & 0.0675688657407409 \tabularnewline
14 & 9.125 & 8.85350752314815 & 9.636875 & -0.783367476851852 & 0.271492476851853 \tabularnewline
15 & 9.782 & 9.76861168981481 & 9.67208333333333 & 0.0965283564814815 & 0.0133883101851868 \tabularnewline
16 & 9.441 & 9.50914641203704 & 9.69 & -0.180853587962963 & -0.0681464120370361 \tabularnewline
17 & 9.162 & 9.67092418981481 & 9.72133333333333 & -0.0504091435185181 & -0.508924189814813 \tabularnewline
18 & 9.915 & 9.80963252314815 & 9.7585 & 0.0511325231481482 & 0.105367476851853 \tabularnewline
19 & 10.444 & 10.3205283564815 & 9.77541666666666 & 0.545111689814815 & 0.123471643518521 \tabularnewline
20 & 10.209 & 10.1555561342593 & 9.77820833333333 & 0.377347800925926 & 0.053443865740741 \tabularnewline
21 & 9.985 & 10.0261047453704 & 9.80229166666667 & 0.223813078703704 & -0.0411047453703688 \tabularnewline
22 & 9.842 & 10.0798686342593 & 9.84016666666667 & 0.239701967592592 & -0.237868634259259 \tabularnewline
23 & 9.429 & 9.33558391203704 & 9.88895833333333 & -0.553374421296296 & 0.0934160879629626 \tabularnewline
24 & 10.132 & 9.80460474537037 & 9.92754166666666 & -0.122936921296297 & 0.327395254629632 \tabularnewline
25 & 9.849 & 10.0753061342593 & 9.918 & 0.157306134259259 & -0.226306134259257 \tabularnewline
26 & 9.172 & 9.13338252314815 & 9.91675 & -0.783367476851852 & 0.0386174768518526 \tabularnewline
27 & 10.313 & 10.0364033564815 & 9.939875 & 0.0965283564814815 & 0.276596643518518 \tabularnewline
28 & 9.819 & 9.78460474537037 & 9.96545833333333 & -0.180853587962963 & 0.0343952546296311 \tabularnewline
29 & 9.955 & 9.93175752314815 & 9.98216666666667 & -0.0504091435185181 & 0.023242476851852 \tabularnewline
30 & 10.048 & 10.0266741898148 & 9.97554166666667 & 0.0511325231481482 & 0.0213258101851856 \tabularnewline
31 & 10.082 & 10.5264866898148 & 9.981375 & 0.545111689814815 & -0.444486689814815 \tabularnewline
32 & 10.541 & 10.3738061342593 & 9.99645833333333 & 0.377347800925926 & 0.167193865740742 \tabularnewline
33 & 10.208 & 10.2291880787037 & 10.005375 & 0.223813078703704 & -0.0211880787037035 \tabularnewline
34 & 10.233 & 10.2492019675926 & 10.0095 & 0.239701967592592 & -0.0162019675925897 \tabularnewline
35 & 9.439 & 9.47583391203704 & 10.0292083333333 & -0.553374421296296 & -0.0368339120370358 \tabularnewline
36 & 9.963 & 9.93827141203704 & 10.0612083333333 & -0.122936921296297 & 0.0247285879629633 \tabularnewline
37 & 10.158 & 10.2433061342593 & 10.086 & 0.157306134259259 & -0.0853061342592571 \tabularnewline
38 & 9.225 & 9.32184085648148 & 10.1052083333333 & -0.783367476851852 & -0.0968408564814798 \tabularnewline
39 & 10.474 & 10.2261533564815 & 10.129625 & 0.0965283564814815 & 0.247846643518519 \tabularnewline
40 & 9.757 & 9.99210474537037 & 10.1729583333333 & -0.180853587962963 & -0.23510474537037 \tabularnewline
41 & 10.49 & 10.1619658564815 & 10.212375 & -0.0504091435185181 & 0.328034143518519 \tabularnewline
42 & 10.281 & 10.2708825231481 & 10.21975 & 0.0511325231481482 & 0.0101174768518533 \tabularnewline
43 & 10.444 & 10.7664033564815 & 10.2212916666667 & 0.545111689814815 & -0.32240335648148 \tabularnewline
44 & 10.64 & 10.6210978009259 & 10.24375 & 0.377347800925926 & 0.0189021990740752 \tabularnewline
45 & 10.695 & 10.4764797453704 & 10.2526666666667 & 0.223813078703704 & 0.218520254629631 \tabularnewline
46 & 10.786 & 10.4870353009259 & 10.2473333333333 & 0.239701967592592 & 0.298964699074071 \tabularnewline
47 & 9.832 & 9.69370891203704 & 10.2470833333333 & -0.553374421296296 & 0.138291087962964 \tabularnewline
48 & 9.747 & 10.1191880787037 & 10.242125 & -0.122936921296297 & -0.372188078703703 \tabularnewline
49 & 10.411 & 10.4120144675926 & 10.2547083333333 & 0.157306134259259 & -0.00101446759259183 \tabularnewline
50 & 9.511 & 9.51359085648148 & 10.2969583333333 & -0.783367476851852 & -0.00259085648148094 \tabularnewline
51 & 10.402 & 10.4031533564815 & 10.306625 & 0.0965283564814815 & -0.00115335648148118 \tabularnewline
52 & 9.701 & 10.1148130787037 & 10.2956666666667 & -0.180853587962963 & -0.413813078703702 \tabularnewline
53 & 10.54 & 10.2495075231481 & 10.2999166666667 & -0.0504091435185181 & 0.290492476851851 \tabularnewline
54 & 10.112 & 10.3728408564815 & 10.3217083333333 & 0.0511325231481482 & -0.260840856481479 \tabularnewline
55 & 10.915 & 10.9085283564815 & 10.3634166666667 & 0.545111689814815 & 0.00647164351851792 \tabularnewline
56 & 11.183 & 10.7777644675926 & 10.4004166666667 & 0.377347800925926 & 0.405235532407406 \tabularnewline
57 & 10.384 & 10.630771412037 & 10.4069583333333 & 0.223813078703704 & -0.246771412037035 \tabularnewline
58 & 10.834 & 10.6857019675926 & 10.446 & 0.239701967592592 & 0.148298032407409 \tabularnewline
59 & 9.886 & 9.94133391203704 & 10.4947083333333 & -0.553374421296296 & -0.0553339120370371 \tabularnewline
60 & 10.216 & 10.3954380787037 & 10.518375 & -0.122936921296297 & -0.179438078703702 \tabularnewline
61 & 10.943 & 10.7224311342593 & 10.565125 & 0.157306134259259 & 0.220568865740741 \tabularnewline
62 & 9.867 & 9.78425752314815 & 10.567625 & -0.783367476851852 & 0.0827424768518537 \tabularnewline
63 & 10.203 & 10.6622783564815 & 10.56575 & 0.0965283564814815 & -0.459278356481482 \tabularnewline
64 & 10.837 & 10.405021412037 & 10.585875 & -0.180853587962963 & 0.431978587962963 \tabularnewline
65 & 10.573 & 10.5379658564815 & 10.588375 & -0.0504091435185181 & 0.0350341435185193 \tabularnewline
66 & 10.647 & 10.6467575231481 & 10.595625 & 0.0511325231481482 & 0.000242476851852302 \tabularnewline
67 & 11.502 & 11.1361533564815 & 10.5910416666667 & 0.545111689814815 & 0.36584664351852 \tabularnewline
68 & 10.656 & 10.9421394675926 & 10.5647916666667 & 0.377347800925926 & -0.286139467592591 \tabularnewline
69 & 10.866 & 10.787396412037 & 10.5635833333333 & 0.223813078703704 & 0.0786035879629647 \tabularnewline
70 & 10.835 & 10.8104519675926 & 10.57075 & 0.239701967592592 & 0.0245480324074094 \tabularnewline
71 & 9.945 & 9.99941724537037 & 10.5527916666667 & -0.553374421296296 & -0.054417245370372 \tabularnewline
72 & 10.331 & 10.4249797453704 & 10.5479166666667 & -0.122936921296297 & -0.0939797453703708 \tabularnewline
73 & 10.718 & 10.6975144675926 & 10.5402083333333 & 0.157306134259259 & 0.0204855324074096 \tabularnewline
74 & 9.462 & 9.75942418981481 & 10.5427916666667 & -0.783367476851852 & -0.297424189814814 \tabularnewline
75 & 10.579 & 10.6604033564815 & 10.563875 & 0.0965283564814815 & -0.0814033564814807 \tabularnewline
76 & 10.633 & 10.3863130787037 & 10.5671666666667 & -0.180853587962963 & 0.246686921296297 \tabularnewline
77 & 10.346 & 10.5178825231481 & 10.5682916666667 & -0.0504091435185181 & -0.171882523148145 \tabularnewline
78 & 10.757 & 10.6372158564815 & 10.5860833333333 & 0.0511325231481482 & 0.119784143518519 \tabularnewline
79 & 11.207 & NA & NA & 0.545111689814815 & NA \tabularnewline
80 & 11.013 & NA & NA & 0.377347800925926 & NA \tabularnewline
81 & 11.015 & NA & NA & 0.223813078703704 & NA \tabularnewline
82 & 10.765 & NA & NA & 0.239701967592592 & NA \tabularnewline
83 & 10.042 & NA & NA & -0.553374421296296 & NA \tabularnewline
84 & 10.661 & NA & NA & -0.122936921296297 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=187341&T=1

[TABLE]
[ROW][C]Classical Decomposition by Moving Averages[/C][/ROW]
[ROW][C]t[/C][C]Observations[/C][C]Fit[/C][C]Trend[/C][C]Seasonal[/C][C]Random[/C][/ROW]
[ROW][C]1[/C][C]9.676[/C][C]NA[/C][C]NA[/C][C]0.157306134259259[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]8.642[/C][C]NA[/C][C]NA[/C][C]-0.783367476851852[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]9.402[/C][C]NA[/C][C]NA[/C][C]0.0965283564814815[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]9.61[/C][C]NA[/C][C]NA[/C][C]-0.180853587962963[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]9.294[/C][C]NA[/C][C]NA[/C][C]-0.0504091435185181[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]9.448[/C][C]NA[/C][C]NA[/C][C]0.0511325231481482[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]10.319[/C][C]10.0519033564815[/C][C]9.50679166666667[/C][C]0.545111689814815[/C][C]0.267096643518519[/C][/ROW]
[ROW][C]8[/C][C]9.548[/C][C]9.91063946759259[/C][C]9.53329166666667[/C][C]0.377347800925926[/C][C]-0.362639467592594[/C][/ROW]
[ROW][C]9[/C][C]9.801[/C][C]9.7930630787037[/C][C]9.56925[/C][C]0.223813078703704[/C][C]0.00793692129629697[/C][/ROW]
[ROW][C]10[/C][C]9.596[/C][C]9.81774363425926[/C][C]9.57804166666667[/C][C]0.239701967592592[/C][C]-0.221743634259257[/C][/ROW]
[ROW][C]11[/C][C]8.923[/C][C]9.0121255787037[/C][C]9.5655[/C][C]-0.553374421296296[/C][C]-0.0891255787037029[/C][/ROW]
[ROW][C]12[/C][C]9.746[/C][C]9.45652141203704[/C][C]9.57945833333333[/C][C]-0.122936921296297[/C][C]0.289478587962964[/C][/ROW]
[ROW][C]13[/C][C]9.829[/C][C]9.76143113425926[/C][C]9.604125[/C][C]0.157306134259259[/C][C]0.0675688657407409[/C][/ROW]
[ROW][C]14[/C][C]9.125[/C][C]8.85350752314815[/C][C]9.636875[/C][C]-0.783367476851852[/C][C]0.271492476851853[/C][/ROW]
[ROW][C]15[/C][C]9.782[/C][C]9.76861168981481[/C][C]9.67208333333333[/C][C]0.0965283564814815[/C][C]0.0133883101851868[/C][/ROW]
[ROW][C]16[/C][C]9.441[/C][C]9.50914641203704[/C][C]9.69[/C][C]-0.180853587962963[/C][C]-0.0681464120370361[/C][/ROW]
[ROW][C]17[/C][C]9.162[/C][C]9.67092418981481[/C][C]9.72133333333333[/C][C]-0.0504091435185181[/C][C]-0.508924189814813[/C][/ROW]
[ROW][C]18[/C][C]9.915[/C][C]9.80963252314815[/C][C]9.7585[/C][C]0.0511325231481482[/C][C]0.105367476851853[/C][/ROW]
[ROW][C]19[/C][C]10.444[/C][C]10.3205283564815[/C][C]9.77541666666666[/C][C]0.545111689814815[/C][C]0.123471643518521[/C][/ROW]
[ROW][C]20[/C][C]10.209[/C][C]10.1555561342593[/C][C]9.77820833333333[/C][C]0.377347800925926[/C][C]0.053443865740741[/C][/ROW]
[ROW][C]21[/C][C]9.985[/C][C]10.0261047453704[/C][C]9.80229166666667[/C][C]0.223813078703704[/C][C]-0.0411047453703688[/C][/ROW]
[ROW][C]22[/C][C]9.842[/C][C]10.0798686342593[/C][C]9.84016666666667[/C][C]0.239701967592592[/C][C]-0.237868634259259[/C][/ROW]
[ROW][C]23[/C][C]9.429[/C][C]9.33558391203704[/C][C]9.88895833333333[/C][C]-0.553374421296296[/C][C]0.0934160879629626[/C][/ROW]
[ROW][C]24[/C][C]10.132[/C][C]9.80460474537037[/C][C]9.92754166666666[/C][C]-0.122936921296297[/C][C]0.327395254629632[/C][/ROW]
[ROW][C]25[/C][C]9.849[/C][C]10.0753061342593[/C][C]9.918[/C][C]0.157306134259259[/C][C]-0.226306134259257[/C][/ROW]
[ROW][C]26[/C][C]9.172[/C][C]9.13338252314815[/C][C]9.91675[/C][C]-0.783367476851852[/C][C]0.0386174768518526[/C][/ROW]
[ROW][C]27[/C][C]10.313[/C][C]10.0364033564815[/C][C]9.939875[/C][C]0.0965283564814815[/C][C]0.276596643518518[/C][/ROW]
[ROW][C]28[/C][C]9.819[/C][C]9.78460474537037[/C][C]9.96545833333333[/C][C]-0.180853587962963[/C][C]0.0343952546296311[/C][/ROW]
[ROW][C]29[/C][C]9.955[/C][C]9.93175752314815[/C][C]9.98216666666667[/C][C]-0.0504091435185181[/C][C]0.023242476851852[/C][/ROW]
[ROW][C]30[/C][C]10.048[/C][C]10.0266741898148[/C][C]9.97554166666667[/C][C]0.0511325231481482[/C][C]0.0213258101851856[/C][/ROW]
[ROW][C]31[/C][C]10.082[/C][C]10.5264866898148[/C][C]9.981375[/C][C]0.545111689814815[/C][C]-0.444486689814815[/C][/ROW]
[ROW][C]32[/C][C]10.541[/C][C]10.3738061342593[/C][C]9.99645833333333[/C][C]0.377347800925926[/C][C]0.167193865740742[/C][/ROW]
[ROW][C]33[/C][C]10.208[/C][C]10.2291880787037[/C][C]10.005375[/C][C]0.223813078703704[/C][C]-0.0211880787037035[/C][/ROW]
[ROW][C]34[/C][C]10.233[/C][C]10.2492019675926[/C][C]10.0095[/C][C]0.239701967592592[/C][C]-0.0162019675925897[/C][/ROW]
[ROW][C]35[/C][C]9.439[/C][C]9.47583391203704[/C][C]10.0292083333333[/C][C]-0.553374421296296[/C][C]-0.0368339120370358[/C][/ROW]
[ROW][C]36[/C][C]9.963[/C][C]9.93827141203704[/C][C]10.0612083333333[/C][C]-0.122936921296297[/C][C]0.0247285879629633[/C][/ROW]
[ROW][C]37[/C][C]10.158[/C][C]10.2433061342593[/C][C]10.086[/C][C]0.157306134259259[/C][C]-0.0853061342592571[/C][/ROW]
[ROW][C]38[/C][C]9.225[/C][C]9.32184085648148[/C][C]10.1052083333333[/C][C]-0.783367476851852[/C][C]-0.0968408564814798[/C][/ROW]
[ROW][C]39[/C][C]10.474[/C][C]10.2261533564815[/C][C]10.129625[/C][C]0.0965283564814815[/C][C]0.247846643518519[/C][/ROW]
[ROW][C]40[/C][C]9.757[/C][C]9.99210474537037[/C][C]10.1729583333333[/C][C]-0.180853587962963[/C][C]-0.23510474537037[/C][/ROW]
[ROW][C]41[/C][C]10.49[/C][C]10.1619658564815[/C][C]10.212375[/C][C]-0.0504091435185181[/C][C]0.328034143518519[/C][/ROW]
[ROW][C]42[/C][C]10.281[/C][C]10.2708825231481[/C][C]10.21975[/C][C]0.0511325231481482[/C][C]0.0101174768518533[/C][/ROW]
[ROW][C]43[/C][C]10.444[/C][C]10.7664033564815[/C][C]10.2212916666667[/C][C]0.545111689814815[/C][C]-0.32240335648148[/C][/ROW]
[ROW][C]44[/C][C]10.64[/C][C]10.6210978009259[/C][C]10.24375[/C][C]0.377347800925926[/C][C]0.0189021990740752[/C][/ROW]
[ROW][C]45[/C][C]10.695[/C][C]10.4764797453704[/C][C]10.2526666666667[/C][C]0.223813078703704[/C][C]0.218520254629631[/C][/ROW]
[ROW][C]46[/C][C]10.786[/C][C]10.4870353009259[/C][C]10.2473333333333[/C][C]0.239701967592592[/C][C]0.298964699074071[/C][/ROW]
[ROW][C]47[/C][C]9.832[/C][C]9.69370891203704[/C][C]10.2470833333333[/C][C]-0.553374421296296[/C][C]0.138291087962964[/C][/ROW]
[ROW][C]48[/C][C]9.747[/C][C]10.1191880787037[/C][C]10.242125[/C][C]-0.122936921296297[/C][C]-0.372188078703703[/C][/ROW]
[ROW][C]49[/C][C]10.411[/C][C]10.4120144675926[/C][C]10.2547083333333[/C][C]0.157306134259259[/C][C]-0.00101446759259183[/C][/ROW]
[ROW][C]50[/C][C]9.511[/C][C]9.51359085648148[/C][C]10.2969583333333[/C][C]-0.783367476851852[/C][C]-0.00259085648148094[/C][/ROW]
[ROW][C]51[/C][C]10.402[/C][C]10.4031533564815[/C][C]10.306625[/C][C]0.0965283564814815[/C][C]-0.00115335648148118[/C][/ROW]
[ROW][C]52[/C][C]9.701[/C][C]10.1148130787037[/C][C]10.2956666666667[/C][C]-0.180853587962963[/C][C]-0.413813078703702[/C][/ROW]
[ROW][C]53[/C][C]10.54[/C][C]10.2495075231481[/C][C]10.2999166666667[/C][C]-0.0504091435185181[/C][C]0.290492476851851[/C][/ROW]
[ROW][C]54[/C][C]10.112[/C][C]10.3728408564815[/C][C]10.3217083333333[/C][C]0.0511325231481482[/C][C]-0.260840856481479[/C][/ROW]
[ROW][C]55[/C][C]10.915[/C][C]10.9085283564815[/C][C]10.3634166666667[/C][C]0.545111689814815[/C][C]0.00647164351851792[/C][/ROW]
[ROW][C]56[/C][C]11.183[/C][C]10.7777644675926[/C][C]10.4004166666667[/C][C]0.377347800925926[/C][C]0.405235532407406[/C][/ROW]
[ROW][C]57[/C][C]10.384[/C][C]10.630771412037[/C][C]10.4069583333333[/C][C]0.223813078703704[/C][C]-0.246771412037035[/C][/ROW]
[ROW][C]58[/C][C]10.834[/C][C]10.6857019675926[/C][C]10.446[/C][C]0.239701967592592[/C][C]0.148298032407409[/C][/ROW]
[ROW][C]59[/C][C]9.886[/C][C]9.94133391203704[/C][C]10.4947083333333[/C][C]-0.553374421296296[/C][C]-0.0553339120370371[/C][/ROW]
[ROW][C]60[/C][C]10.216[/C][C]10.3954380787037[/C][C]10.518375[/C][C]-0.122936921296297[/C][C]-0.179438078703702[/C][/ROW]
[ROW][C]61[/C][C]10.943[/C][C]10.7224311342593[/C][C]10.565125[/C][C]0.157306134259259[/C][C]0.220568865740741[/C][/ROW]
[ROW][C]62[/C][C]9.867[/C][C]9.78425752314815[/C][C]10.567625[/C][C]-0.783367476851852[/C][C]0.0827424768518537[/C][/ROW]
[ROW][C]63[/C][C]10.203[/C][C]10.6622783564815[/C][C]10.56575[/C][C]0.0965283564814815[/C][C]-0.459278356481482[/C][/ROW]
[ROW][C]64[/C][C]10.837[/C][C]10.405021412037[/C][C]10.585875[/C][C]-0.180853587962963[/C][C]0.431978587962963[/C][/ROW]
[ROW][C]65[/C][C]10.573[/C][C]10.5379658564815[/C][C]10.588375[/C][C]-0.0504091435185181[/C][C]0.0350341435185193[/C][/ROW]
[ROW][C]66[/C][C]10.647[/C][C]10.6467575231481[/C][C]10.595625[/C][C]0.0511325231481482[/C][C]0.000242476851852302[/C][/ROW]
[ROW][C]67[/C][C]11.502[/C][C]11.1361533564815[/C][C]10.5910416666667[/C][C]0.545111689814815[/C][C]0.36584664351852[/C][/ROW]
[ROW][C]68[/C][C]10.656[/C][C]10.9421394675926[/C][C]10.5647916666667[/C][C]0.377347800925926[/C][C]-0.286139467592591[/C][/ROW]
[ROW][C]69[/C][C]10.866[/C][C]10.787396412037[/C][C]10.5635833333333[/C][C]0.223813078703704[/C][C]0.0786035879629647[/C][/ROW]
[ROW][C]70[/C][C]10.835[/C][C]10.8104519675926[/C][C]10.57075[/C][C]0.239701967592592[/C][C]0.0245480324074094[/C][/ROW]
[ROW][C]71[/C][C]9.945[/C][C]9.99941724537037[/C][C]10.5527916666667[/C][C]-0.553374421296296[/C][C]-0.054417245370372[/C][/ROW]
[ROW][C]72[/C][C]10.331[/C][C]10.4249797453704[/C][C]10.5479166666667[/C][C]-0.122936921296297[/C][C]-0.0939797453703708[/C][/ROW]
[ROW][C]73[/C][C]10.718[/C][C]10.6975144675926[/C][C]10.5402083333333[/C][C]0.157306134259259[/C][C]0.0204855324074096[/C][/ROW]
[ROW][C]74[/C][C]9.462[/C][C]9.75942418981481[/C][C]10.5427916666667[/C][C]-0.783367476851852[/C][C]-0.297424189814814[/C][/ROW]
[ROW][C]75[/C][C]10.579[/C][C]10.6604033564815[/C][C]10.563875[/C][C]0.0965283564814815[/C][C]-0.0814033564814807[/C][/ROW]
[ROW][C]76[/C][C]10.633[/C][C]10.3863130787037[/C][C]10.5671666666667[/C][C]-0.180853587962963[/C][C]0.246686921296297[/C][/ROW]
[ROW][C]77[/C][C]10.346[/C][C]10.5178825231481[/C][C]10.5682916666667[/C][C]-0.0504091435185181[/C][C]-0.171882523148145[/C][/ROW]
[ROW][C]78[/C][C]10.757[/C][C]10.6372158564815[/C][C]10.5860833333333[/C][C]0.0511325231481482[/C][C]0.119784143518519[/C][/ROW]
[ROW][C]79[/C][C]11.207[/C][C]NA[/C][C]NA[/C][C]0.545111689814815[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]11.013[/C][C]NA[/C][C]NA[/C][C]0.377347800925926[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]11.015[/C][C]NA[/C][C]NA[/C][C]0.223813078703704[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]10.765[/C][C]NA[/C][C]NA[/C][C]0.239701967592592[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]10.042[/C][C]NA[/C][C]NA[/C][C]-0.553374421296296[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]10.661[/C][C]NA[/C][C]NA[/C][C]-0.122936921296297[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=187341&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=187341&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
19.676NANA0.157306134259259NA
28.642NANA-0.783367476851852NA
39.402NANA0.0965283564814815NA
49.61NANA-0.180853587962963NA
59.294NANA-0.0504091435185181NA
69.448NANA0.0511325231481482NA
710.31910.05190335648159.506791666666670.5451116898148150.267096643518519
89.5489.910639467592599.533291666666670.377347800925926-0.362639467592594
99.8019.79306307870379.569250.2238130787037040.00793692129629697
109.5969.817743634259269.578041666666670.239701967592592-0.221743634259257
118.9239.01212557870379.5655-0.553374421296296-0.0891255787037029
129.7469.456521412037049.57945833333333-0.1229369212962970.289478587962964
139.8299.761431134259269.6041250.1573061342592590.0675688657407409
149.1258.853507523148159.636875-0.7833674768518520.271492476851853
159.7829.768611689814819.672083333333330.09652835648148150.0133883101851868
169.4419.509146412037049.69-0.180853587962963-0.0681464120370361
179.1629.670924189814819.72133333333333-0.0504091435185181-0.508924189814813
189.9159.809632523148159.75850.05113252314814820.105367476851853
1910.44410.32052835648159.775416666666660.5451116898148150.123471643518521
2010.20910.15555613425939.778208333333330.3773478009259260.053443865740741
219.98510.02610474537049.802291666666670.223813078703704-0.0411047453703688
229.84210.07986863425939.840166666666670.239701967592592-0.237868634259259
239.4299.335583912037049.88895833333333-0.5533744212962960.0934160879629626
2410.1329.804604745370379.92754166666666-0.1229369212962970.327395254629632
259.84910.07530613425939.9180.157306134259259-0.226306134259257
269.1729.133382523148159.91675-0.7833674768518520.0386174768518526
2710.31310.03640335648159.9398750.09652835648148150.276596643518518
289.8199.784604745370379.96545833333333-0.1808535879629630.0343952546296311
299.9559.931757523148159.98216666666667-0.05040914351851810.023242476851852
3010.04810.02667418981489.975541666666670.05113252314814820.0213258101851856
3110.08210.52648668981489.9813750.545111689814815-0.444486689814815
3210.54110.37380613425939.996458333333330.3773478009259260.167193865740742
3310.20810.229188078703710.0053750.223813078703704-0.0211880787037035
3410.23310.249201967592610.00950.239701967592592-0.0162019675925897
359.4399.4758339120370410.0292083333333-0.553374421296296-0.0368339120370358
369.9639.9382714120370410.0612083333333-0.1229369212962970.0247285879629633
3710.15810.243306134259310.0860.157306134259259-0.0853061342592571
389.2259.3218408564814810.1052083333333-0.783367476851852-0.0968408564814798
3910.47410.226153356481510.1296250.09652835648148150.247846643518519
409.7579.9921047453703710.1729583333333-0.180853587962963-0.23510474537037
4110.4910.161965856481510.212375-0.05040914351851810.328034143518519
4210.28110.270882523148110.219750.05113252314814820.0101174768518533
4310.44410.766403356481510.22129166666670.545111689814815-0.32240335648148
4410.6410.621097800925910.243750.3773478009259260.0189021990740752
4510.69510.476479745370410.25266666666670.2238130787037040.218520254629631
4610.78610.487035300925910.24733333333330.2397019675925920.298964699074071
479.8329.6937089120370410.2470833333333-0.5533744212962960.138291087962964
489.74710.119188078703710.242125-0.122936921296297-0.372188078703703
4910.41110.412014467592610.25470833333330.157306134259259-0.00101446759259183
509.5119.5135908564814810.2969583333333-0.783367476851852-0.00259085648148094
5110.40210.403153356481510.3066250.0965283564814815-0.00115335648148118
529.70110.114813078703710.2956666666667-0.180853587962963-0.413813078703702
5310.5410.249507523148110.2999166666667-0.05040914351851810.290492476851851
5410.11210.372840856481510.32170833333330.0511325231481482-0.260840856481479
5510.91510.908528356481510.36341666666670.5451116898148150.00647164351851792
5611.18310.777764467592610.40041666666670.3773478009259260.405235532407406
5710.38410.63077141203710.40695833333330.223813078703704-0.246771412037035
5810.83410.685701967592610.4460.2397019675925920.148298032407409
599.8869.9413339120370410.4947083333333-0.553374421296296-0.0553339120370371
6010.21610.395438078703710.518375-0.122936921296297-0.179438078703702
6110.94310.722431134259310.5651250.1573061342592590.220568865740741
629.8679.7842575231481510.567625-0.7833674768518520.0827424768518537
6310.20310.662278356481510.565750.0965283564814815-0.459278356481482
6410.83710.40502141203710.585875-0.1808535879629630.431978587962963
6510.57310.537965856481510.588375-0.05040914351851810.0350341435185193
6610.64710.646757523148110.5956250.05113252314814820.000242476851852302
6711.50211.136153356481510.59104166666670.5451116898148150.36584664351852
6810.65610.942139467592610.56479166666670.377347800925926-0.286139467592591
6910.86610.78739641203710.56358333333330.2238130787037040.0786035879629647
7010.83510.810451967592610.570750.2397019675925920.0245480324074094
719.9459.9994172453703710.5527916666667-0.553374421296296-0.054417245370372
7210.33110.424979745370410.5479166666667-0.122936921296297-0.0939797453703708
7310.71810.697514467592610.54020833333330.1573061342592590.0204855324074096
749.4629.7594241898148110.5427916666667-0.783367476851852-0.297424189814814
7510.57910.660403356481510.5638750.0965283564814815-0.0814033564814807
7610.63310.386313078703710.5671666666667-0.1808535879629630.246686921296297
7710.34610.517882523148110.5682916666667-0.0504091435185181-0.171882523148145
7810.75710.637215856481510.58608333333330.05113252314814820.119784143518519
7911.207NANA0.545111689814815NA
8011.013NANA0.377347800925926NA
8111.015NANA0.223813078703704NA
8210.765NANA0.239701967592592NA
8310.042NANA-0.553374421296296NA
8410.661NANA-0.122936921296297NA



Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')