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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationFri, 14 Dec 2012 16:47:32 -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/Dec/14/t1355521671bk3m7usz881ztek.htm/, Retrieved Thu, 28 Mar 2024 17:54:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199758, Retrieved Thu, 28 Mar 2024 17:54:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Blocked Bootstrap Plot - Central Tendency] [] [2012-12-04 22:05:27] [873b10c79bed0b14ae85834791a7b7d7]
- RMPD  [Bootstrap Plot - Central Tendency] [] [2012-12-14 20:18:15] [873b10c79bed0b14ae85834791a7b7d7]
- R PD    [Bootstrap Plot - Central Tendency] [] [2012-12-14 20:25:30] [873b10c79bed0b14ae85834791a7b7d7]
- RMPD      [Variability] [] [2012-12-14 21:36:17] [873b10c79bed0b14ae85834791a7b7d7]
- RMPD          [Classical Decomposition] [] [2012-12-14 21:47:32] [e3cb5e3bac8dbaf27c4382afdd169712] [Current]
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Dataseries X:
65
65.3
62.9
63.5
62.1
59.3
61.6
61.5
60.1
59.5
62.7
65.5
63.8
63.8
62.7
62.3
62.4
64.8
66.4
65.1
67.4
68.8
68.6
71.5
75
84.3
84
79.1
78.8
82.7
85.3
84.5
80.8
70.1
68.2
68.1
72.3
73.1
71.5
74.1
80.3
80.6
81.4
87.4
89.3
93.2
92.8
96.8
100.3
95.6
89
87.4
86.7
92.8
98.6
100.8
105.5
107.8
113.7
120.3
126.5
134.8
134.5
133.1
128.8
127.1
129.1
128.4
126.5
117.1
114.2
109.1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199758&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199758&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199758&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
165NANA1.02410131716589NA
265.3NANA1.0415858558711NA
362.9NANA1.00587881468006NA
463.5NANA0.981615631454441NA
562.1NANA0.97830125723152NA
659.3NANA0.996780283464658NA
761.662.80063351959862.36666666666671.006958314050210.980881824715616
861.562.695107112193862.25416666666661.007082906560910.980937792959583
960.162.219236790513662.18333333333331.000577380710480.965939203053087
1059.560.601700494715462.1250.9754800884461230.981820633980206
1162.760.737441471107662.08750.9782555501688361.03231216991295
1265.562.540001318036162.32916666666671.003382600195781.04732968691368
1363.864.270891829802362.75833333333331.024101317165890.992673326658523
1463.865.732747387598463.10833333333331.04158585587110.970596887177074
1562.763.936172158101563.56251.005878814680060.980665527566388
1662.363.072894386078964.25416666666670.9816156314544410.987746013662416
1762.463.479522828610264.88750.978301257231520.982994156532574
1864.865.172817533864265.38333333333330.9967802834646580.994279554759612
1966.466.559944558718866.11.006958314050210.997596984796499
2065.167.898368796091867.42083333333331.007082906560910.9587859201081
2167.469.202433093388969.16251.000577380710480.973954194775833
2268.869.015216257563270.750.9754800884461230.996881611487531
2368.670.56483368551272.13333333333330.9782555501688360.972155625077092
2471.573.81133252690273.56251.003382600195780.96868593957358
257576.905741830336475.09583333333331.024101317165890.975219771827431
2684.379.880955263180876.69166666666671.04158585587111.05532037921004
278478.517223809234678.05833333333331.005878814680061.06982896140198
2879.177.224519739547178.67083333333330.9816156314544411.0242860721799
2978.877.000461454597578.70833333333330.978301257231521.02337049040237
3082.778.297091266148978.550.9967802834646581.05623336272972
3185.378.840640330489578.29583333333331.006958314050211.0819293151658
3284.578.267126554891877.71666666666671.007082906560911.07963590487427
3380.876.773468607431576.72916666666671.000577380710481.05244691252856
3470.174.1364867219053760.9754800884461230.945553304447152
3568.274.204759545098675.85416666666670.9782555501688360.919078512188304
3668.176.085666420679175.82916666666671.003382600195780.895043747444805
3772.377.400724133633375.57916666666671.024101317165890.93409978794479
3873.178.678791587862975.53751.04158585587110.92909408653496
3971.576.459363400868376.01251.005878814680060.935137265335746
4074.175.907518767345777.32916666666670.9816156314544410.976187882350816
4180.377.595594719413479.31666666666670.978301257231521.03485256206059
4280.681.274972362999681.53750.9967802834646580.99169520033812
4381.484.483802548812583.91.006958314050210.963498298421987
4487.486.613326143015386.00416666666661.007082906560911.00908259608557
4589.387.721452781372187.67083333333331.000577380710481.01799499630452
4693.286.773018367651188.95416666666670.9754800884461231.07406659066668
4792.887.822892016407289.7750.9782555501688361.05667210301686
4896.890.856294447727890.551.003382600195781.06541875374074
49100.393.986898382899191.7751.024101317165891.0671700175846
5095.696.919563888805593.051.04158585587110.986384958455659
518994.837607577418694.28333333333331.005878814680060.938446279629596
5287.493.809733845996195.56666666666670.9816156314544410.931673040917921
5386.794.940060759080597.04583333333330.978301257231520.913207757682077
5492.898.577416783473698.89583333333330.9967802834646580.941392085814505
5598.6101.669224441936100.9666666666671.006958314050210.96981166662003
56100.8104.426105052811103.6916666666671.007082906560910.965275875692409
57105.5107.282740574262107.2208333333331.000577380710480.983382783057933
58107.8108.298612319362111.0208333333330.9754800884461230.99539594913837
59113.7112.185531280404114.6791666666670.9782555501688361.01349967952472
60120.3118.261181715575117.86251.003382600195781.01723996204713
61126.5123.468215050812120.56251.024101317165891.0245551857046
62134.8128.09770050788122.9833333333331.04158585587111.05232177834221
63134.5125.743234158464125.0083333333331.005878814680061.06964005578623
64133.1123.949423796778126.2708333333330.9816156314544411.07382508060888
65128.8123.930388015041126.6791666666670.978301257231521.03929312304233
66127.1125.826897782689126.2333333333330.9967802834646581.01011788607798
67129.1NANA1.00695831405021NA
68128.4NANA1.00708290656091NA
69126.5NANA1.00057738071048NA
70117.1NANA0.975480088446123NA
71114.2NANA0.978255550168836NA
72109.1NANA1.00338260019578NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 65 & NA & NA & 1.02410131716589 & NA \tabularnewline
2 & 65.3 & NA & NA & 1.0415858558711 & NA \tabularnewline
3 & 62.9 & NA & NA & 1.00587881468006 & NA \tabularnewline
4 & 63.5 & NA & NA & 0.981615631454441 & NA \tabularnewline
5 & 62.1 & NA & NA & 0.97830125723152 & NA \tabularnewline
6 & 59.3 & NA & NA & 0.996780283464658 & NA \tabularnewline
7 & 61.6 & 62.800633519598 & 62.3666666666667 & 1.00695831405021 & 0.980881824715616 \tabularnewline
8 & 61.5 & 62.6951071121938 & 62.2541666666666 & 1.00708290656091 & 0.980937792959583 \tabularnewline
9 & 60.1 & 62.2192367905136 & 62.1833333333333 & 1.00057738071048 & 0.965939203053087 \tabularnewline
10 & 59.5 & 60.6017004947154 & 62.125 & 0.975480088446123 & 0.981820633980206 \tabularnewline
11 & 62.7 & 60.7374414711076 & 62.0875 & 0.978255550168836 & 1.03231216991295 \tabularnewline
12 & 65.5 & 62.5400013180361 & 62.3291666666667 & 1.00338260019578 & 1.04732968691368 \tabularnewline
13 & 63.8 & 64.2708918298023 & 62.7583333333333 & 1.02410131716589 & 0.992673326658523 \tabularnewline
14 & 63.8 & 65.7327473875984 & 63.1083333333333 & 1.0415858558711 & 0.970596887177074 \tabularnewline
15 & 62.7 & 63.9361721581015 & 63.5625 & 1.00587881468006 & 0.980665527566388 \tabularnewline
16 & 62.3 & 63.0728943860789 & 64.2541666666667 & 0.981615631454441 & 0.987746013662416 \tabularnewline
17 & 62.4 & 63.4795228286102 & 64.8875 & 0.97830125723152 & 0.982994156532574 \tabularnewline
18 & 64.8 & 65.1728175338642 & 65.3833333333333 & 0.996780283464658 & 0.994279554759612 \tabularnewline
19 & 66.4 & 66.5599445587188 & 66.1 & 1.00695831405021 & 0.997596984796499 \tabularnewline
20 & 65.1 & 67.8983687960918 & 67.4208333333333 & 1.00708290656091 & 0.9587859201081 \tabularnewline
21 & 67.4 & 69.2024330933889 & 69.1625 & 1.00057738071048 & 0.973954194775833 \tabularnewline
22 & 68.8 & 69.0152162575632 & 70.75 & 0.975480088446123 & 0.996881611487531 \tabularnewline
23 & 68.6 & 70.564833685512 & 72.1333333333333 & 0.978255550168836 & 0.972155625077092 \tabularnewline
24 & 71.5 & 73.811332526902 & 73.5625 & 1.00338260019578 & 0.96868593957358 \tabularnewline
25 & 75 & 76.9057418303364 & 75.0958333333333 & 1.02410131716589 & 0.975219771827431 \tabularnewline
26 & 84.3 & 79.8809552631808 & 76.6916666666667 & 1.0415858558711 & 1.05532037921004 \tabularnewline
27 & 84 & 78.5172238092346 & 78.0583333333333 & 1.00587881468006 & 1.06982896140198 \tabularnewline
28 & 79.1 & 77.2245197395471 & 78.6708333333333 & 0.981615631454441 & 1.0242860721799 \tabularnewline
29 & 78.8 & 77.0004614545975 & 78.7083333333333 & 0.97830125723152 & 1.02337049040237 \tabularnewline
30 & 82.7 & 78.2970912661489 & 78.55 & 0.996780283464658 & 1.05623336272972 \tabularnewline
31 & 85.3 & 78.8406403304895 & 78.2958333333333 & 1.00695831405021 & 1.0819293151658 \tabularnewline
32 & 84.5 & 78.2671265548918 & 77.7166666666667 & 1.00708290656091 & 1.07963590487427 \tabularnewline
33 & 80.8 & 76.7734686074315 & 76.7291666666667 & 1.00057738071048 & 1.05244691252856 \tabularnewline
34 & 70.1 & 74.1364867219053 & 76 & 0.975480088446123 & 0.945553304447152 \tabularnewline
35 & 68.2 & 74.2047595450986 & 75.8541666666667 & 0.978255550168836 & 0.919078512188304 \tabularnewline
36 & 68.1 & 76.0856664206791 & 75.8291666666667 & 1.00338260019578 & 0.895043747444805 \tabularnewline
37 & 72.3 & 77.4007241336333 & 75.5791666666667 & 1.02410131716589 & 0.93409978794479 \tabularnewline
38 & 73.1 & 78.6787915878629 & 75.5375 & 1.0415858558711 & 0.92909408653496 \tabularnewline
39 & 71.5 & 76.4593634008683 & 76.0125 & 1.00587881468006 & 0.935137265335746 \tabularnewline
40 & 74.1 & 75.9075187673457 & 77.3291666666667 & 0.981615631454441 & 0.976187882350816 \tabularnewline
41 & 80.3 & 77.5955947194134 & 79.3166666666667 & 0.97830125723152 & 1.03485256206059 \tabularnewline
42 & 80.6 & 81.2749723629996 & 81.5375 & 0.996780283464658 & 0.99169520033812 \tabularnewline
43 & 81.4 & 84.4838025488125 & 83.9 & 1.00695831405021 & 0.963498298421987 \tabularnewline
44 & 87.4 & 86.6133261430153 & 86.0041666666666 & 1.00708290656091 & 1.00908259608557 \tabularnewline
45 & 89.3 & 87.7214527813721 & 87.6708333333333 & 1.00057738071048 & 1.01799499630452 \tabularnewline
46 & 93.2 & 86.7730183676511 & 88.9541666666667 & 0.975480088446123 & 1.07406659066668 \tabularnewline
47 & 92.8 & 87.8228920164072 & 89.775 & 0.978255550168836 & 1.05667210301686 \tabularnewline
48 & 96.8 & 90.8562944477278 & 90.55 & 1.00338260019578 & 1.06541875374074 \tabularnewline
49 & 100.3 & 93.9868983828991 & 91.775 & 1.02410131716589 & 1.0671700175846 \tabularnewline
50 & 95.6 & 96.9195638888055 & 93.05 & 1.0415858558711 & 0.986384958455659 \tabularnewline
51 & 89 & 94.8376075774186 & 94.2833333333333 & 1.00587881468006 & 0.938446279629596 \tabularnewline
52 & 87.4 & 93.8097338459961 & 95.5666666666667 & 0.981615631454441 & 0.931673040917921 \tabularnewline
53 & 86.7 & 94.9400607590805 & 97.0458333333333 & 0.97830125723152 & 0.913207757682077 \tabularnewline
54 & 92.8 & 98.5774167834736 & 98.8958333333333 & 0.996780283464658 & 0.941392085814505 \tabularnewline
55 & 98.6 & 101.669224441936 & 100.966666666667 & 1.00695831405021 & 0.96981166662003 \tabularnewline
56 & 100.8 & 104.426105052811 & 103.691666666667 & 1.00708290656091 & 0.965275875692409 \tabularnewline
57 & 105.5 & 107.282740574262 & 107.220833333333 & 1.00057738071048 & 0.983382783057933 \tabularnewline
58 & 107.8 & 108.298612319362 & 111.020833333333 & 0.975480088446123 & 0.99539594913837 \tabularnewline
59 & 113.7 & 112.185531280404 & 114.679166666667 & 0.978255550168836 & 1.01349967952472 \tabularnewline
60 & 120.3 & 118.261181715575 & 117.8625 & 1.00338260019578 & 1.01723996204713 \tabularnewline
61 & 126.5 & 123.468215050812 & 120.5625 & 1.02410131716589 & 1.0245551857046 \tabularnewline
62 & 134.8 & 128.09770050788 & 122.983333333333 & 1.0415858558711 & 1.05232177834221 \tabularnewline
63 & 134.5 & 125.743234158464 & 125.008333333333 & 1.00587881468006 & 1.06964005578623 \tabularnewline
64 & 133.1 & 123.949423796778 & 126.270833333333 & 0.981615631454441 & 1.07382508060888 \tabularnewline
65 & 128.8 & 123.930388015041 & 126.679166666667 & 0.97830125723152 & 1.03929312304233 \tabularnewline
66 & 127.1 & 125.826897782689 & 126.233333333333 & 0.996780283464658 & 1.01011788607798 \tabularnewline
67 & 129.1 & NA & NA & 1.00695831405021 & NA \tabularnewline
68 & 128.4 & NA & NA & 1.00708290656091 & NA \tabularnewline
69 & 126.5 & NA & NA & 1.00057738071048 & NA \tabularnewline
70 & 117.1 & NA & NA & 0.975480088446123 & NA \tabularnewline
71 & 114.2 & NA & NA & 0.978255550168836 & NA \tabularnewline
72 & 109.1 & NA & NA & 1.00338260019578 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199758&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]65[/C][C]NA[/C][C]NA[/C][C]1.02410131716589[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]65.3[/C][C]NA[/C][C]NA[/C][C]1.0415858558711[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]62.9[/C][C]NA[/C][C]NA[/C][C]1.00587881468006[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]63.5[/C][C]NA[/C][C]NA[/C][C]0.981615631454441[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]62.1[/C][C]NA[/C][C]NA[/C][C]0.97830125723152[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]59.3[/C][C]NA[/C][C]NA[/C][C]0.996780283464658[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]61.6[/C][C]62.800633519598[/C][C]62.3666666666667[/C][C]1.00695831405021[/C][C]0.980881824715616[/C][/ROW]
[ROW][C]8[/C][C]61.5[/C][C]62.6951071121938[/C][C]62.2541666666666[/C][C]1.00708290656091[/C][C]0.980937792959583[/C][/ROW]
[ROW][C]9[/C][C]60.1[/C][C]62.2192367905136[/C][C]62.1833333333333[/C][C]1.00057738071048[/C][C]0.965939203053087[/C][/ROW]
[ROW][C]10[/C][C]59.5[/C][C]60.6017004947154[/C][C]62.125[/C][C]0.975480088446123[/C][C]0.981820633980206[/C][/ROW]
[ROW][C]11[/C][C]62.7[/C][C]60.7374414711076[/C][C]62.0875[/C][C]0.978255550168836[/C][C]1.03231216991295[/C][/ROW]
[ROW][C]12[/C][C]65.5[/C][C]62.5400013180361[/C][C]62.3291666666667[/C][C]1.00338260019578[/C][C]1.04732968691368[/C][/ROW]
[ROW][C]13[/C][C]63.8[/C][C]64.2708918298023[/C][C]62.7583333333333[/C][C]1.02410131716589[/C][C]0.992673326658523[/C][/ROW]
[ROW][C]14[/C][C]63.8[/C][C]65.7327473875984[/C][C]63.1083333333333[/C][C]1.0415858558711[/C][C]0.970596887177074[/C][/ROW]
[ROW][C]15[/C][C]62.7[/C][C]63.9361721581015[/C][C]63.5625[/C][C]1.00587881468006[/C][C]0.980665527566388[/C][/ROW]
[ROW][C]16[/C][C]62.3[/C][C]63.0728943860789[/C][C]64.2541666666667[/C][C]0.981615631454441[/C][C]0.987746013662416[/C][/ROW]
[ROW][C]17[/C][C]62.4[/C][C]63.4795228286102[/C][C]64.8875[/C][C]0.97830125723152[/C][C]0.982994156532574[/C][/ROW]
[ROW][C]18[/C][C]64.8[/C][C]65.1728175338642[/C][C]65.3833333333333[/C][C]0.996780283464658[/C][C]0.994279554759612[/C][/ROW]
[ROW][C]19[/C][C]66.4[/C][C]66.5599445587188[/C][C]66.1[/C][C]1.00695831405021[/C][C]0.997596984796499[/C][/ROW]
[ROW][C]20[/C][C]65.1[/C][C]67.8983687960918[/C][C]67.4208333333333[/C][C]1.00708290656091[/C][C]0.9587859201081[/C][/ROW]
[ROW][C]21[/C][C]67.4[/C][C]69.2024330933889[/C][C]69.1625[/C][C]1.00057738071048[/C][C]0.973954194775833[/C][/ROW]
[ROW][C]22[/C][C]68.8[/C][C]69.0152162575632[/C][C]70.75[/C][C]0.975480088446123[/C][C]0.996881611487531[/C][/ROW]
[ROW][C]23[/C][C]68.6[/C][C]70.564833685512[/C][C]72.1333333333333[/C][C]0.978255550168836[/C][C]0.972155625077092[/C][/ROW]
[ROW][C]24[/C][C]71.5[/C][C]73.811332526902[/C][C]73.5625[/C][C]1.00338260019578[/C][C]0.96868593957358[/C][/ROW]
[ROW][C]25[/C][C]75[/C][C]76.9057418303364[/C][C]75.0958333333333[/C][C]1.02410131716589[/C][C]0.975219771827431[/C][/ROW]
[ROW][C]26[/C][C]84.3[/C][C]79.8809552631808[/C][C]76.6916666666667[/C][C]1.0415858558711[/C][C]1.05532037921004[/C][/ROW]
[ROW][C]27[/C][C]84[/C][C]78.5172238092346[/C][C]78.0583333333333[/C][C]1.00587881468006[/C][C]1.06982896140198[/C][/ROW]
[ROW][C]28[/C][C]79.1[/C][C]77.2245197395471[/C][C]78.6708333333333[/C][C]0.981615631454441[/C][C]1.0242860721799[/C][/ROW]
[ROW][C]29[/C][C]78.8[/C][C]77.0004614545975[/C][C]78.7083333333333[/C][C]0.97830125723152[/C][C]1.02337049040237[/C][/ROW]
[ROW][C]30[/C][C]82.7[/C][C]78.2970912661489[/C][C]78.55[/C][C]0.996780283464658[/C][C]1.05623336272972[/C][/ROW]
[ROW][C]31[/C][C]85.3[/C][C]78.8406403304895[/C][C]78.2958333333333[/C][C]1.00695831405021[/C][C]1.0819293151658[/C][/ROW]
[ROW][C]32[/C][C]84.5[/C][C]78.2671265548918[/C][C]77.7166666666667[/C][C]1.00708290656091[/C][C]1.07963590487427[/C][/ROW]
[ROW][C]33[/C][C]80.8[/C][C]76.7734686074315[/C][C]76.7291666666667[/C][C]1.00057738071048[/C][C]1.05244691252856[/C][/ROW]
[ROW][C]34[/C][C]70.1[/C][C]74.1364867219053[/C][C]76[/C][C]0.975480088446123[/C][C]0.945553304447152[/C][/ROW]
[ROW][C]35[/C][C]68.2[/C][C]74.2047595450986[/C][C]75.8541666666667[/C][C]0.978255550168836[/C][C]0.919078512188304[/C][/ROW]
[ROW][C]36[/C][C]68.1[/C][C]76.0856664206791[/C][C]75.8291666666667[/C][C]1.00338260019578[/C][C]0.895043747444805[/C][/ROW]
[ROW][C]37[/C][C]72.3[/C][C]77.4007241336333[/C][C]75.5791666666667[/C][C]1.02410131716589[/C][C]0.93409978794479[/C][/ROW]
[ROW][C]38[/C][C]73.1[/C][C]78.6787915878629[/C][C]75.5375[/C][C]1.0415858558711[/C][C]0.92909408653496[/C][/ROW]
[ROW][C]39[/C][C]71.5[/C][C]76.4593634008683[/C][C]76.0125[/C][C]1.00587881468006[/C][C]0.935137265335746[/C][/ROW]
[ROW][C]40[/C][C]74.1[/C][C]75.9075187673457[/C][C]77.3291666666667[/C][C]0.981615631454441[/C][C]0.976187882350816[/C][/ROW]
[ROW][C]41[/C][C]80.3[/C][C]77.5955947194134[/C][C]79.3166666666667[/C][C]0.97830125723152[/C][C]1.03485256206059[/C][/ROW]
[ROW][C]42[/C][C]80.6[/C][C]81.2749723629996[/C][C]81.5375[/C][C]0.996780283464658[/C][C]0.99169520033812[/C][/ROW]
[ROW][C]43[/C][C]81.4[/C][C]84.4838025488125[/C][C]83.9[/C][C]1.00695831405021[/C][C]0.963498298421987[/C][/ROW]
[ROW][C]44[/C][C]87.4[/C][C]86.6133261430153[/C][C]86.0041666666666[/C][C]1.00708290656091[/C][C]1.00908259608557[/C][/ROW]
[ROW][C]45[/C][C]89.3[/C][C]87.7214527813721[/C][C]87.6708333333333[/C][C]1.00057738071048[/C][C]1.01799499630452[/C][/ROW]
[ROW][C]46[/C][C]93.2[/C][C]86.7730183676511[/C][C]88.9541666666667[/C][C]0.975480088446123[/C][C]1.07406659066668[/C][/ROW]
[ROW][C]47[/C][C]92.8[/C][C]87.8228920164072[/C][C]89.775[/C][C]0.978255550168836[/C][C]1.05667210301686[/C][/ROW]
[ROW][C]48[/C][C]96.8[/C][C]90.8562944477278[/C][C]90.55[/C][C]1.00338260019578[/C][C]1.06541875374074[/C][/ROW]
[ROW][C]49[/C][C]100.3[/C][C]93.9868983828991[/C][C]91.775[/C][C]1.02410131716589[/C][C]1.0671700175846[/C][/ROW]
[ROW][C]50[/C][C]95.6[/C][C]96.9195638888055[/C][C]93.05[/C][C]1.0415858558711[/C][C]0.986384958455659[/C][/ROW]
[ROW][C]51[/C][C]89[/C][C]94.8376075774186[/C][C]94.2833333333333[/C][C]1.00587881468006[/C][C]0.938446279629596[/C][/ROW]
[ROW][C]52[/C][C]87.4[/C][C]93.8097338459961[/C][C]95.5666666666667[/C][C]0.981615631454441[/C][C]0.931673040917921[/C][/ROW]
[ROW][C]53[/C][C]86.7[/C][C]94.9400607590805[/C][C]97.0458333333333[/C][C]0.97830125723152[/C][C]0.913207757682077[/C][/ROW]
[ROW][C]54[/C][C]92.8[/C][C]98.5774167834736[/C][C]98.8958333333333[/C][C]0.996780283464658[/C][C]0.941392085814505[/C][/ROW]
[ROW][C]55[/C][C]98.6[/C][C]101.669224441936[/C][C]100.966666666667[/C][C]1.00695831405021[/C][C]0.96981166662003[/C][/ROW]
[ROW][C]56[/C][C]100.8[/C][C]104.426105052811[/C][C]103.691666666667[/C][C]1.00708290656091[/C][C]0.965275875692409[/C][/ROW]
[ROW][C]57[/C][C]105.5[/C][C]107.282740574262[/C][C]107.220833333333[/C][C]1.00057738071048[/C][C]0.983382783057933[/C][/ROW]
[ROW][C]58[/C][C]107.8[/C][C]108.298612319362[/C][C]111.020833333333[/C][C]0.975480088446123[/C][C]0.99539594913837[/C][/ROW]
[ROW][C]59[/C][C]113.7[/C][C]112.185531280404[/C][C]114.679166666667[/C][C]0.978255550168836[/C][C]1.01349967952472[/C][/ROW]
[ROW][C]60[/C][C]120.3[/C][C]118.261181715575[/C][C]117.8625[/C][C]1.00338260019578[/C][C]1.01723996204713[/C][/ROW]
[ROW][C]61[/C][C]126.5[/C][C]123.468215050812[/C][C]120.5625[/C][C]1.02410131716589[/C][C]1.0245551857046[/C][/ROW]
[ROW][C]62[/C][C]134.8[/C][C]128.09770050788[/C][C]122.983333333333[/C][C]1.0415858558711[/C][C]1.05232177834221[/C][/ROW]
[ROW][C]63[/C][C]134.5[/C][C]125.743234158464[/C][C]125.008333333333[/C][C]1.00587881468006[/C][C]1.06964005578623[/C][/ROW]
[ROW][C]64[/C][C]133.1[/C][C]123.949423796778[/C][C]126.270833333333[/C][C]0.981615631454441[/C][C]1.07382508060888[/C][/ROW]
[ROW][C]65[/C][C]128.8[/C][C]123.930388015041[/C][C]126.679166666667[/C][C]0.97830125723152[/C][C]1.03929312304233[/C][/ROW]
[ROW][C]66[/C][C]127.1[/C][C]125.826897782689[/C][C]126.233333333333[/C][C]0.996780283464658[/C][C]1.01011788607798[/C][/ROW]
[ROW][C]67[/C][C]129.1[/C][C]NA[/C][C]NA[/C][C]1.00695831405021[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]128.4[/C][C]NA[/C][C]NA[/C][C]1.00708290656091[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]126.5[/C][C]NA[/C][C]NA[/C][C]1.00057738071048[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]117.1[/C][C]NA[/C][C]NA[/C][C]0.975480088446123[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]114.2[/C][C]NA[/C][C]NA[/C][C]0.978255550168836[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]109.1[/C][C]NA[/C][C]NA[/C][C]1.00338260019578[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199758&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199758&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
165NANA1.02410131716589NA
265.3NANA1.0415858558711NA
362.9NANA1.00587881468006NA
463.5NANA0.981615631454441NA
562.1NANA0.97830125723152NA
659.3NANA0.996780283464658NA
761.662.80063351959862.36666666666671.006958314050210.980881824715616
861.562.695107112193862.25416666666661.007082906560910.980937792959583
960.162.219236790513662.18333333333331.000577380710480.965939203053087
1059.560.601700494715462.1250.9754800884461230.981820633980206
1162.760.737441471107662.08750.9782555501688361.03231216991295
1265.562.540001318036162.32916666666671.003382600195781.04732968691368
1363.864.270891829802362.75833333333331.024101317165890.992673326658523
1463.865.732747387598463.10833333333331.04158585587110.970596887177074
1562.763.936172158101563.56251.005878814680060.980665527566388
1662.363.072894386078964.25416666666670.9816156314544410.987746013662416
1762.463.479522828610264.88750.978301257231520.982994156532574
1864.865.172817533864265.38333333333330.9967802834646580.994279554759612
1966.466.559944558718866.11.006958314050210.997596984796499
2065.167.898368796091867.42083333333331.007082906560910.9587859201081
2167.469.202433093388969.16251.000577380710480.973954194775833
2268.869.015216257563270.750.9754800884461230.996881611487531
2368.670.56483368551272.13333333333330.9782555501688360.972155625077092
2471.573.81133252690273.56251.003382600195780.96868593957358
257576.905741830336475.09583333333331.024101317165890.975219771827431
2684.379.880955263180876.69166666666671.04158585587111.05532037921004
278478.517223809234678.05833333333331.005878814680061.06982896140198
2879.177.224519739547178.67083333333330.9816156314544411.0242860721799
2978.877.000461454597578.70833333333330.978301257231521.02337049040237
3082.778.297091266148978.550.9967802834646581.05623336272972
3185.378.840640330489578.29583333333331.006958314050211.0819293151658
3284.578.267126554891877.71666666666671.007082906560911.07963590487427
3380.876.773468607431576.72916666666671.000577380710481.05244691252856
3470.174.1364867219053760.9754800884461230.945553304447152
3568.274.204759545098675.85416666666670.9782555501688360.919078512188304
3668.176.085666420679175.82916666666671.003382600195780.895043747444805
3772.377.400724133633375.57916666666671.024101317165890.93409978794479
3873.178.678791587862975.53751.04158585587110.92909408653496
3971.576.459363400868376.01251.005878814680060.935137265335746
4074.175.907518767345777.32916666666670.9816156314544410.976187882350816
4180.377.595594719413479.31666666666670.978301257231521.03485256206059
4280.681.274972362999681.53750.9967802834646580.99169520033812
4381.484.483802548812583.91.006958314050210.963498298421987
4487.486.613326143015386.00416666666661.007082906560911.00908259608557
4589.387.721452781372187.67083333333331.000577380710481.01799499630452
4693.286.773018367651188.95416666666670.9754800884461231.07406659066668
4792.887.822892016407289.7750.9782555501688361.05667210301686
4896.890.856294447727890.551.003382600195781.06541875374074
49100.393.986898382899191.7751.024101317165891.0671700175846
5095.696.919563888805593.051.04158585587110.986384958455659
518994.837607577418694.28333333333331.005878814680060.938446279629596
5287.493.809733845996195.56666666666670.9816156314544410.931673040917921
5386.794.940060759080597.04583333333330.978301257231520.913207757682077
5492.898.577416783473698.89583333333330.9967802834646580.941392085814505
5598.6101.669224441936100.9666666666671.006958314050210.96981166662003
56100.8104.426105052811103.6916666666671.007082906560910.965275875692409
57105.5107.282740574262107.2208333333331.000577380710480.983382783057933
58107.8108.298612319362111.0208333333330.9754800884461230.99539594913837
59113.7112.185531280404114.6791666666670.9782555501688361.01349967952472
60120.3118.261181715575117.86251.003382600195781.01723996204713
61126.5123.468215050812120.56251.024101317165891.0245551857046
62134.8128.09770050788122.9833333333331.04158585587111.05232177834221
63134.5125.743234158464125.0083333333331.005878814680061.06964005578623
64133.1123.949423796778126.2708333333330.9816156314544411.07382508060888
65128.8123.930388015041126.6791666666670.978301257231521.03929312304233
66127.1125.826897782689126.2333333333330.9967802834646581.01011788607798
67129.1NANA1.00695831405021NA
68128.4NANA1.00708290656091NA
69126.5NANA1.00057738071048NA
70117.1NANA0.975480088446123NA
71114.2NANA0.978255550168836NA
72109.1NANA1.00338260019578NA



Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
Parameters (R input):
par1 = multiplicative ; 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')