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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 15 Dec 2010 20:07:32 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/15/t12924435508yvpcth4o3wvgqk.htm/, Retrieved Fri, 03 May 2024 08:28:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110707, Retrieved Fri, 03 May 2024 08:28:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W92
Estimated Impact105

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [opdracht 9 deel 2] [2010-12-15 20:07:32] [8926b0113c2f0aa20b2cf07d29df363f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110,04
111,73
110,99
115,83
125,33
123,03
123,46
130,34
131,21
132,97
133,91
133,14
135,31
133,09
135,39
131,85
130,25
127,65
118,3
119,73
122,51
123,28
133,52
153,2
163,63
168,45
166,26
162,31
161,56
156,59
157,97
158,68
163,55
162,89
164,95
159,82
159,05
166,76
164,55
163,22
160,68
155,24
157,6
156,56
154,82
151,11
149,65
148,99
148,53
146,7
145,11
142,7
143,59
140,96
140,77
139,81
140,58
139,59
138,05
136,06
135,98
134,75
132,22
135,37
138,84
138,83
136,55
135,63
139,14
136,09
135,97
134,51
134,54
134,08
132,86
134,48
129,08
133,13
134,78
134,13
132,43
127,84
128,12
128,94

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110707&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110707&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110707&T=0

As an alternative you can also use a QR Code:  
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'RServer@AstonUniversity' @ vre.aston.ac.uk






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1110.04NANA2.57600115740739NA
2111.73NANA3.60273726851851NA
3110.99NANA2.32794560185187NA
4115.83NANA1.27843171296296NA
5125.33NANA0.365931712962961NA
6123.03NANA-1.49802662037037NA
7123.46121.376556712963124.55125-3.174693287037042.08344328703706
8130.34124.010792824074126.494166666667-2.483373842592596.32920717592594
9131.21127.453709490741128.400833333333-0.9471238425925723.75629050925926
10132.97127.876487268518130.085-2.208512731481495.0935127314815
11133.91130.28009837963130.9575-0.6774016203703663.62990162037039
12133.14132.193084490741131.3550.8380844907407310.94691550925927
13135.31133.908501157407131.33252.576001157407391.40149884259259
14133.09134.278153935185130.6754166666673.60273726851851-1.18815393518517
15135.39132.198778935185129.8708333333332.327945601851873.19122106481481
16131.85130.383015046296129.1045833333331.278431712962961.46698495370373
17130.25129.050515046296128.6845833333330.3659317129629611.19948495370372
18127.65128.006140046296129.504166666667-1.49802662037037-0.356140046296275
19118.3128.345306712963131.52-3.17469328703704-10.045306712963
20119.73131.689959490741134.173333333333-2.48337384259259-11.9599594907407
21122.51135.985792824074136.932916666667-0.947123842592572-13.4757928240741
22123.28137.279820601852139.488333333333-2.20851273148149-13.9998206018519
23133.52141.384681712963142.062083333333-0.677401620370366-7.86468171296295
24153.2145.410584490741144.57250.8380844907407317.78941550925927
25163.63150.007251157407147.431252.5760011574073913.6227488425926
26168.45154.309820601852150.7070833333333.6027372685185114.1401793981481
27166.26156.367945601852154.042.327945601851879.89205439814813
28162.31158.67884837963157.4004166666671.278431712962963.63115162037036
29161.56160.72634837963160.3604166666670.3659317129629610.833651620370404
30156.59160.447806712963161.945833333333-1.49802662037037-3.85780671296294
31157.97158.856140046296162.030833333333-3.17469328703704-0.886140046296305
32158.68159.286209490741161.769583333333-2.48337384259259-0.606209490740724
33163.55160.680792824074161.627916666667-0.9471238425925722.86920717592594
34162.89159.386070601852161.594583333333-2.208512731481493.50392939814813
35164.95160.918431712963161.595833333333-0.6774016203703664.03156828703703
36159.82162.341001157407161.5029166666670.838084490740731-2.5210011574074
37159.05164.007251157407161.431252.57600115740739-4.9572511574074
38166.76164.930237268519161.32753.602737268518511.82976273148148
39164.55163.203362268519160.8754166666672.327945601851871.34663773148151
40163.22161.299265046296160.0208333333331.278431712962961.92073495370371
41160.68159.258431712963158.89250.3659317129629611.42156828703705
42155.24156.30572337963157.80375-1.49802662037037-1.06572337962965
43157.6153.73947337963156.914166666667-3.174693287037043.86052662037034
44156.56153.156626157407155.64-2.483373842592593.40337384259257
45154.82153.047042824074153.994166666667-0.9471238425925721.77295717592591
46151.11150.120653935185152.329166666667-2.208512731481490.989346064814782
47149.65150.084681712963150.762083333333-0.677401620370366-0.434681712962941
48148.99150.293084490741149.4550.838084490740731-1.30308449074073
49148.53150.734751157407148.158752.57600115740739-2.2047511574074
50146.7150.362320601852146.7595833333333.60273726851851-3.66232060185186
51145.11147.796278935185145.4683333333332.32794560185187-2.68627893518516
52142.7145.673431712963144.3951.27843171296296-2.97343171296296
53143.59143.79759837963143.4316666666670.365931712962961-0.207598379629644
54140.96140.911556712963142.409583333333-1.498026620370370.0484432870370597
55140.77138.17322337963141.347916666667-3.174693287037042.59677662037038
56139.81137.843709490741140.327083333333-2.483373842592591.96629050925924
57140.58138.344959490741139.292083333333-0.9471238425925722.23504050925925
58139.59136.241070601852138.449583333333-2.208512731481493.34892939814816
59138.05137.26884837963137.94625-0.6774016203703660.781151620370395
60136.06138.497667824074137.6595833333330.838084490740731-2.43766782407405
61135.98139.971001157407137.3952.57600115740739-3.99100115740742
62134.75140.647737268519137.0453.60273726851851-5.89773726851851
63132.22139.138778935185136.8108333333332.32794560185187-6.9187789351852
64135.37137.883431712963136.6051.27843171296296-2.51343171296298
65138.84136.738431712963136.37250.3659317129629612.10156828703703
66138.83134.72322337963136.22125-1.498026620370374.1067766203704
67136.55132.92197337963136.096666666667-3.174693287037043.62802662037041
68135.63133.525376157407136.00875-2.483373842592592.10462384259259
69139.14135.060376157407136.0075-0.9471238425925724.07962384259258
70136.09133.788570601852135.997083333333-2.208512731481492.30142939814817
71135.97134.875931712963135.553333333333-0.6774016203703661.09406828703706
72134.51135.747251157407134.9091666666670.838084490740731-1.2372511574074
73134.54137.173917824074134.5979166666672.57600115740739-2.63391782407408
74134.08138.064403935185134.4616666666673.60273726851851-3.98440393518518
75132.86136.447528935185134.1195833333332.32794560185187-3.58752893518516
76134.48134.774681712963133.496251.27843171296296-0.294681712962927
77129.08133.19134837963132.8254166666670.365931712962961-4.11134837962962
78133.13130.76822337963132.26625-1.498026620370372.36177662037036
79134.78NANA-3.17469328703704NA
80134.13NANA-2.48337384259259NA
81132.43NANA-0.947123842592572NA
82127.84NANA-2.20851273148149NA
83128.12NANA-0.677401620370366NA
84128.94NANA0.838084490740731NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 110.04 & NA & NA & 2.57600115740739 & NA \tabularnewline
2 & 111.73 & NA & NA & 3.60273726851851 & NA \tabularnewline
3 & 110.99 & NA & NA & 2.32794560185187 & NA \tabularnewline
4 & 115.83 & NA & NA & 1.27843171296296 & NA \tabularnewline
5 & 125.33 & NA & NA & 0.365931712962961 & NA \tabularnewline
6 & 123.03 & NA & NA & -1.49802662037037 & NA \tabularnewline
7 & 123.46 & 121.376556712963 & 124.55125 & -3.17469328703704 & 2.08344328703706 \tabularnewline
8 & 130.34 & 124.010792824074 & 126.494166666667 & -2.48337384259259 & 6.32920717592594 \tabularnewline
9 & 131.21 & 127.453709490741 & 128.400833333333 & -0.947123842592572 & 3.75629050925926 \tabularnewline
10 & 132.97 & 127.876487268518 & 130.085 & -2.20851273148149 & 5.0935127314815 \tabularnewline
11 & 133.91 & 130.28009837963 & 130.9575 & -0.677401620370366 & 3.62990162037039 \tabularnewline
12 & 133.14 & 132.193084490741 & 131.355 & 0.838084490740731 & 0.94691550925927 \tabularnewline
13 & 135.31 & 133.908501157407 & 131.3325 & 2.57600115740739 & 1.40149884259259 \tabularnewline
14 & 133.09 & 134.278153935185 & 130.675416666667 & 3.60273726851851 & -1.18815393518517 \tabularnewline
15 & 135.39 & 132.198778935185 & 129.870833333333 & 2.32794560185187 & 3.19122106481481 \tabularnewline
16 & 131.85 & 130.383015046296 & 129.104583333333 & 1.27843171296296 & 1.46698495370373 \tabularnewline
17 & 130.25 & 129.050515046296 & 128.684583333333 & 0.365931712962961 & 1.19948495370372 \tabularnewline
18 & 127.65 & 128.006140046296 & 129.504166666667 & -1.49802662037037 & -0.356140046296275 \tabularnewline
19 & 118.3 & 128.345306712963 & 131.52 & -3.17469328703704 & -10.045306712963 \tabularnewline
20 & 119.73 & 131.689959490741 & 134.173333333333 & -2.48337384259259 & -11.9599594907407 \tabularnewline
21 & 122.51 & 135.985792824074 & 136.932916666667 & -0.947123842592572 & -13.4757928240741 \tabularnewline
22 & 123.28 & 137.279820601852 & 139.488333333333 & -2.20851273148149 & -13.9998206018519 \tabularnewline
23 & 133.52 & 141.384681712963 & 142.062083333333 & -0.677401620370366 & -7.86468171296295 \tabularnewline
24 & 153.2 & 145.410584490741 & 144.5725 & 0.838084490740731 & 7.78941550925927 \tabularnewline
25 & 163.63 & 150.007251157407 & 147.43125 & 2.57600115740739 & 13.6227488425926 \tabularnewline
26 & 168.45 & 154.309820601852 & 150.707083333333 & 3.60273726851851 & 14.1401793981481 \tabularnewline
27 & 166.26 & 156.367945601852 & 154.04 & 2.32794560185187 & 9.89205439814813 \tabularnewline
28 & 162.31 & 158.67884837963 & 157.400416666667 & 1.27843171296296 & 3.63115162037036 \tabularnewline
29 & 161.56 & 160.72634837963 & 160.360416666667 & 0.365931712962961 & 0.833651620370404 \tabularnewline
30 & 156.59 & 160.447806712963 & 161.945833333333 & -1.49802662037037 & -3.85780671296294 \tabularnewline
31 & 157.97 & 158.856140046296 & 162.030833333333 & -3.17469328703704 & -0.886140046296305 \tabularnewline
32 & 158.68 & 159.286209490741 & 161.769583333333 & -2.48337384259259 & -0.606209490740724 \tabularnewline
33 & 163.55 & 160.680792824074 & 161.627916666667 & -0.947123842592572 & 2.86920717592594 \tabularnewline
34 & 162.89 & 159.386070601852 & 161.594583333333 & -2.20851273148149 & 3.50392939814813 \tabularnewline
35 & 164.95 & 160.918431712963 & 161.595833333333 & -0.677401620370366 & 4.03156828703703 \tabularnewline
36 & 159.82 & 162.341001157407 & 161.502916666667 & 0.838084490740731 & -2.5210011574074 \tabularnewline
37 & 159.05 & 164.007251157407 & 161.43125 & 2.57600115740739 & -4.9572511574074 \tabularnewline
38 & 166.76 & 164.930237268519 & 161.3275 & 3.60273726851851 & 1.82976273148148 \tabularnewline
39 & 164.55 & 163.203362268519 & 160.875416666667 & 2.32794560185187 & 1.34663773148151 \tabularnewline
40 & 163.22 & 161.299265046296 & 160.020833333333 & 1.27843171296296 & 1.92073495370371 \tabularnewline
41 & 160.68 & 159.258431712963 & 158.8925 & 0.365931712962961 & 1.42156828703705 \tabularnewline
42 & 155.24 & 156.30572337963 & 157.80375 & -1.49802662037037 & -1.06572337962965 \tabularnewline
43 & 157.6 & 153.73947337963 & 156.914166666667 & -3.17469328703704 & 3.86052662037034 \tabularnewline
44 & 156.56 & 153.156626157407 & 155.64 & -2.48337384259259 & 3.40337384259257 \tabularnewline
45 & 154.82 & 153.047042824074 & 153.994166666667 & -0.947123842592572 & 1.77295717592591 \tabularnewline
46 & 151.11 & 150.120653935185 & 152.329166666667 & -2.20851273148149 & 0.989346064814782 \tabularnewline
47 & 149.65 & 150.084681712963 & 150.762083333333 & -0.677401620370366 & -0.434681712962941 \tabularnewline
48 & 148.99 & 150.293084490741 & 149.455 & 0.838084490740731 & -1.30308449074073 \tabularnewline
49 & 148.53 & 150.734751157407 & 148.15875 & 2.57600115740739 & -2.2047511574074 \tabularnewline
50 & 146.7 & 150.362320601852 & 146.759583333333 & 3.60273726851851 & -3.66232060185186 \tabularnewline
51 & 145.11 & 147.796278935185 & 145.468333333333 & 2.32794560185187 & -2.68627893518516 \tabularnewline
52 & 142.7 & 145.673431712963 & 144.395 & 1.27843171296296 & -2.97343171296296 \tabularnewline
53 & 143.59 & 143.79759837963 & 143.431666666667 & 0.365931712962961 & -0.207598379629644 \tabularnewline
54 & 140.96 & 140.911556712963 & 142.409583333333 & -1.49802662037037 & 0.0484432870370597 \tabularnewline
55 & 140.77 & 138.17322337963 & 141.347916666667 & -3.17469328703704 & 2.59677662037038 \tabularnewline
56 & 139.81 & 137.843709490741 & 140.327083333333 & -2.48337384259259 & 1.96629050925924 \tabularnewline
57 & 140.58 & 138.344959490741 & 139.292083333333 & -0.947123842592572 & 2.23504050925925 \tabularnewline
58 & 139.59 & 136.241070601852 & 138.449583333333 & -2.20851273148149 & 3.34892939814816 \tabularnewline
59 & 138.05 & 137.26884837963 & 137.94625 & -0.677401620370366 & 0.781151620370395 \tabularnewline
60 & 136.06 & 138.497667824074 & 137.659583333333 & 0.838084490740731 & -2.43766782407405 \tabularnewline
61 & 135.98 & 139.971001157407 & 137.395 & 2.57600115740739 & -3.99100115740742 \tabularnewline
62 & 134.75 & 140.647737268519 & 137.045 & 3.60273726851851 & -5.89773726851851 \tabularnewline
63 & 132.22 & 139.138778935185 & 136.810833333333 & 2.32794560185187 & -6.9187789351852 \tabularnewline
64 & 135.37 & 137.883431712963 & 136.605 & 1.27843171296296 & -2.51343171296298 \tabularnewline
65 & 138.84 & 136.738431712963 & 136.3725 & 0.365931712962961 & 2.10156828703703 \tabularnewline
66 & 138.83 & 134.72322337963 & 136.22125 & -1.49802662037037 & 4.1067766203704 \tabularnewline
67 & 136.55 & 132.92197337963 & 136.096666666667 & -3.17469328703704 & 3.62802662037041 \tabularnewline
68 & 135.63 & 133.525376157407 & 136.00875 & -2.48337384259259 & 2.10462384259259 \tabularnewline
69 & 139.14 & 135.060376157407 & 136.0075 & -0.947123842592572 & 4.07962384259258 \tabularnewline
70 & 136.09 & 133.788570601852 & 135.997083333333 & -2.20851273148149 & 2.30142939814817 \tabularnewline
71 & 135.97 & 134.875931712963 & 135.553333333333 & -0.677401620370366 & 1.09406828703706 \tabularnewline
72 & 134.51 & 135.747251157407 & 134.909166666667 & 0.838084490740731 & -1.2372511574074 \tabularnewline
73 & 134.54 & 137.173917824074 & 134.597916666667 & 2.57600115740739 & -2.63391782407408 \tabularnewline
74 & 134.08 & 138.064403935185 & 134.461666666667 & 3.60273726851851 & -3.98440393518518 \tabularnewline
75 & 132.86 & 136.447528935185 & 134.119583333333 & 2.32794560185187 & -3.58752893518516 \tabularnewline
76 & 134.48 & 134.774681712963 & 133.49625 & 1.27843171296296 & -0.294681712962927 \tabularnewline
77 & 129.08 & 133.19134837963 & 132.825416666667 & 0.365931712962961 & -4.11134837962962 \tabularnewline
78 & 133.13 & 130.76822337963 & 132.26625 & -1.49802662037037 & 2.36177662037036 \tabularnewline
79 & 134.78 & NA & NA & -3.17469328703704 & NA \tabularnewline
80 & 134.13 & NA & NA & -2.48337384259259 & NA \tabularnewline
81 & 132.43 & NA & NA & -0.947123842592572 & NA \tabularnewline
82 & 127.84 & NA & NA & -2.20851273148149 & NA \tabularnewline
83 & 128.12 & NA & NA & -0.677401620370366 & NA \tabularnewline
84 & 128.94 & NA & NA & 0.838084490740731 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110707&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]110.04[/C][C]NA[/C][C]NA[/C][C]2.57600115740739[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]111.73[/C][C]NA[/C][C]NA[/C][C]3.60273726851851[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]110.99[/C][C]NA[/C][C]NA[/C][C]2.32794560185187[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]115.83[/C][C]NA[/C][C]NA[/C][C]1.27843171296296[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]125.33[/C][C]NA[/C][C]NA[/C][C]0.365931712962961[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]123.03[/C][C]NA[/C][C]NA[/C][C]-1.49802662037037[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]123.46[/C][C]121.376556712963[/C][C]124.55125[/C][C]-3.17469328703704[/C][C]2.08344328703706[/C][/ROW]
[ROW][C]8[/C][C]130.34[/C][C]124.010792824074[/C][C]126.494166666667[/C][C]-2.48337384259259[/C][C]6.32920717592594[/C][/ROW]
[ROW][C]9[/C][C]131.21[/C][C]127.453709490741[/C][C]128.400833333333[/C][C]-0.947123842592572[/C][C]3.75629050925926[/C][/ROW]
[ROW][C]10[/C][C]132.97[/C][C]127.876487268518[/C][C]130.085[/C][C]-2.20851273148149[/C][C]5.0935127314815[/C][/ROW]
[ROW][C]11[/C][C]133.91[/C][C]130.28009837963[/C][C]130.9575[/C][C]-0.677401620370366[/C][C]3.62990162037039[/C][/ROW]
[ROW][C]12[/C][C]133.14[/C][C]132.193084490741[/C][C]131.355[/C][C]0.838084490740731[/C][C]0.94691550925927[/C][/ROW]
[ROW][C]13[/C][C]135.31[/C][C]133.908501157407[/C][C]131.3325[/C][C]2.57600115740739[/C][C]1.40149884259259[/C][/ROW]
[ROW][C]14[/C][C]133.09[/C][C]134.278153935185[/C][C]130.675416666667[/C][C]3.60273726851851[/C][C]-1.18815393518517[/C][/ROW]
[ROW][C]15[/C][C]135.39[/C][C]132.198778935185[/C][C]129.870833333333[/C][C]2.32794560185187[/C][C]3.19122106481481[/C][/ROW]
[ROW][C]16[/C][C]131.85[/C][C]130.383015046296[/C][C]129.104583333333[/C][C]1.27843171296296[/C][C]1.46698495370373[/C][/ROW]
[ROW][C]17[/C][C]130.25[/C][C]129.050515046296[/C][C]128.684583333333[/C][C]0.365931712962961[/C][C]1.19948495370372[/C][/ROW]
[ROW][C]18[/C][C]127.65[/C][C]128.006140046296[/C][C]129.504166666667[/C][C]-1.49802662037037[/C][C]-0.356140046296275[/C][/ROW]
[ROW][C]19[/C][C]118.3[/C][C]128.345306712963[/C][C]131.52[/C][C]-3.17469328703704[/C][C]-10.045306712963[/C][/ROW]
[ROW][C]20[/C][C]119.73[/C][C]131.689959490741[/C][C]134.173333333333[/C][C]-2.48337384259259[/C][C]-11.9599594907407[/C][/ROW]
[ROW][C]21[/C][C]122.51[/C][C]135.985792824074[/C][C]136.932916666667[/C][C]-0.947123842592572[/C][C]-13.4757928240741[/C][/ROW]
[ROW][C]22[/C][C]123.28[/C][C]137.279820601852[/C][C]139.488333333333[/C][C]-2.20851273148149[/C][C]-13.9998206018519[/C][/ROW]
[ROW][C]23[/C][C]133.52[/C][C]141.384681712963[/C][C]142.062083333333[/C][C]-0.677401620370366[/C][C]-7.86468171296295[/C][/ROW]
[ROW][C]24[/C][C]153.2[/C][C]145.410584490741[/C][C]144.5725[/C][C]0.838084490740731[/C][C]7.78941550925927[/C][/ROW]
[ROW][C]25[/C][C]163.63[/C][C]150.007251157407[/C][C]147.43125[/C][C]2.57600115740739[/C][C]13.6227488425926[/C][/ROW]
[ROW][C]26[/C][C]168.45[/C][C]154.309820601852[/C][C]150.707083333333[/C][C]3.60273726851851[/C][C]14.1401793981481[/C][/ROW]
[ROW][C]27[/C][C]166.26[/C][C]156.367945601852[/C][C]154.04[/C][C]2.32794560185187[/C][C]9.89205439814813[/C][/ROW]
[ROW][C]28[/C][C]162.31[/C][C]158.67884837963[/C][C]157.400416666667[/C][C]1.27843171296296[/C][C]3.63115162037036[/C][/ROW]
[ROW][C]29[/C][C]161.56[/C][C]160.72634837963[/C][C]160.360416666667[/C][C]0.365931712962961[/C][C]0.833651620370404[/C][/ROW]
[ROW][C]30[/C][C]156.59[/C][C]160.447806712963[/C][C]161.945833333333[/C][C]-1.49802662037037[/C][C]-3.85780671296294[/C][/ROW]
[ROW][C]31[/C][C]157.97[/C][C]158.856140046296[/C][C]162.030833333333[/C][C]-3.17469328703704[/C][C]-0.886140046296305[/C][/ROW]
[ROW][C]32[/C][C]158.68[/C][C]159.286209490741[/C][C]161.769583333333[/C][C]-2.48337384259259[/C][C]-0.606209490740724[/C][/ROW]
[ROW][C]33[/C][C]163.55[/C][C]160.680792824074[/C][C]161.627916666667[/C][C]-0.947123842592572[/C][C]2.86920717592594[/C][/ROW]
[ROW][C]34[/C][C]162.89[/C][C]159.386070601852[/C][C]161.594583333333[/C][C]-2.20851273148149[/C][C]3.50392939814813[/C][/ROW]
[ROW][C]35[/C][C]164.95[/C][C]160.918431712963[/C][C]161.595833333333[/C][C]-0.677401620370366[/C][C]4.03156828703703[/C][/ROW]
[ROW][C]36[/C][C]159.82[/C][C]162.341001157407[/C][C]161.502916666667[/C][C]0.838084490740731[/C][C]-2.5210011574074[/C][/ROW]
[ROW][C]37[/C][C]159.05[/C][C]164.007251157407[/C][C]161.43125[/C][C]2.57600115740739[/C][C]-4.9572511574074[/C][/ROW]
[ROW][C]38[/C][C]166.76[/C][C]164.930237268519[/C][C]161.3275[/C][C]3.60273726851851[/C][C]1.82976273148148[/C][/ROW]
[ROW][C]39[/C][C]164.55[/C][C]163.203362268519[/C][C]160.875416666667[/C][C]2.32794560185187[/C][C]1.34663773148151[/C][/ROW]
[ROW][C]40[/C][C]163.22[/C][C]161.299265046296[/C][C]160.020833333333[/C][C]1.27843171296296[/C][C]1.92073495370371[/C][/ROW]
[ROW][C]41[/C][C]160.68[/C][C]159.258431712963[/C][C]158.8925[/C][C]0.365931712962961[/C][C]1.42156828703705[/C][/ROW]
[ROW][C]42[/C][C]155.24[/C][C]156.30572337963[/C][C]157.80375[/C][C]-1.49802662037037[/C][C]-1.06572337962965[/C][/ROW]
[ROW][C]43[/C][C]157.6[/C][C]153.73947337963[/C][C]156.914166666667[/C][C]-3.17469328703704[/C][C]3.86052662037034[/C][/ROW]
[ROW][C]44[/C][C]156.56[/C][C]153.156626157407[/C][C]155.64[/C][C]-2.48337384259259[/C][C]3.40337384259257[/C][/ROW]
[ROW][C]45[/C][C]154.82[/C][C]153.047042824074[/C][C]153.994166666667[/C][C]-0.947123842592572[/C][C]1.77295717592591[/C][/ROW]
[ROW][C]46[/C][C]151.11[/C][C]150.120653935185[/C][C]152.329166666667[/C][C]-2.20851273148149[/C][C]0.989346064814782[/C][/ROW]
[ROW][C]47[/C][C]149.65[/C][C]150.084681712963[/C][C]150.762083333333[/C][C]-0.677401620370366[/C][C]-0.434681712962941[/C][/ROW]
[ROW][C]48[/C][C]148.99[/C][C]150.293084490741[/C][C]149.455[/C][C]0.838084490740731[/C][C]-1.30308449074073[/C][/ROW]
[ROW][C]49[/C][C]148.53[/C][C]150.734751157407[/C][C]148.15875[/C][C]2.57600115740739[/C][C]-2.2047511574074[/C][/ROW]
[ROW][C]50[/C][C]146.7[/C][C]150.362320601852[/C][C]146.759583333333[/C][C]3.60273726851851[/C][C]-3.66232060185186[/C][/ROW]
[ROW][C]51[/C][C]145.11[/C][C]147.796278935185[/C][C]145.468333333333[/C][C]2.32794560185187[/C][C]-2.68627893518516[/C][/ROW]
[ROW][C]52[/C][C]142.7[/C][C]145.673431712963[/C][C]144.395[/C][C]1.27843171296296[/C][C]-2.97343171296296[/C][/ROW]
[ROW][C]53[/C][C]143.59[/C][C]143.79759837963[/C][C]143.431666666667[/C][C]0.365931712962961[/C][C]-0.207598379629644[/C][/ROW]
[ROW][C]54[/C][C]140.96[/C][C]140.911556712963[/C][C]142.409583333333[/C][C]-1.49802662037037[/C][C]0.0484432870370597[/C][/ROW]
[ROW][C]55[/C][C]140.77[/C][C]138.17322337963[/C][C]141.347916666667[/C][C]-3.17469328703704[/C][C]2.59677662037038[/C][/ROW]
[ROW][C]56[/C][C]139.81[/C][C]137.843709490741[/C][C]140.327083333333[/C][C]-2.48337384259259[/C][C]1.96629050925924[/C][/ROW]
[ROW][C]57[/C][C]140.58[/C][C]138.344959490741[/C][C]139.292083333333[/C][C]-0.947123842592572[/C][C]2.23504050925925[/C][/ROW]
[ROW][C]58[/C][C]139.59[/C][C]136.241070601852[/C][C]138.449583333333[/C][C]-2.20851273148149[/C][C]3.34892939814816[/C][/ROW]
[ROW][C]59[/C][C]138.05[/C][C]137.26884837963[/C][C]137.94625[/C][C]-0.677401620370366[/C][C]0.781151620370395[/C][/ROW]
[ROW][C]60[/C][C]136.06[/C][C]138.497667824074[/C][C]137.659583333333[/C][C]0.838084490740731[/C][C]-2.43766782407405[/C][/ROW]
[ROW][C]61[/C][C]135.98[/C][C]139.971001157407[/C][C]137.395[/C][C]2.57600115740739[/C][C]-3.99100115740742[/C][/ROW]
[ROW][C]62[/C][C]134.75[/C][C]140.647737268519[/C][C]137.045[/C][C]3.60273726851851[/C][C]-5.89773726851851[/C][/ROW]
[ROW][C]63[/C][C]132.22[/C][C]139.138778935185[/C][C]136.810833333333[/C][C]2.32794560185187[/C][C]-6.9187789351852[/C][/ROW]
[ROW][C]64[/C][C]135.37[/C][C]137.883431712963[/C][C]136.605[/C][C]1.27843171296296[/C][C]-2.51343171296298[/C][/ROW]
[ROW][C]65[/C][C]138.84[/C][C]136.738431712963[/C][C]136.3725[/C][C]0.365931712962961[/C][C]2.10156828703703[/C][/ROW]
[ROW][C]66[/C][C]138.83[/C][C]134.72322337963[/C][C]136.22125[/C][C]-1.49802662037037[/C][C]4.1067766203704[/C][/ROW]
[ROW][C]67[/C][C]136.55[/C][C]132.92197337963[/C][C]136.096666666667[/C][C]-3.17469328703704[/C][C]3.62802662037041[/C][/ROW]
[ROW][C]68[/C][C]135.63[/C][C]133.525376157407[/C][C]136.00875[/C][C]-2.48337384259259[/C][C]2.10462384259259[/C][/ROW]
[ROW][C]69[/C][C]139.14[/C][C]135.060376157407[/C][C]136.0075[/C][C]-0.947123842592572[/C][C]4.07962384259258[/C][/ROW]
[ROW][C]70[/C][C]136.09[/C][C]133.788570601852[/C][C]135.997083333333[/C][C]-2.20851273148149[/C][C]2.30142939814817[/C][/ROW]
[ROW][C]71[/C][C]135.97[/C][C]134.875931712963[/C][C]135.553333333333[/C][C]-0.677401620370366[/C][C]1.09406828703706[/C][/ROW]
[ROW][C]72[/C][C]134.51[/C][C]135.747251157407[/C][C]134.909166666667[/C][C]0.838084490740731[/C][C]-1.2372511574074[/C][/ROW]
[ROW][C]73[/C][C]134.54[/C][C]137.173917824074[/C][C]134.597916666667[/C][C]2.57600115740739[/C][C]-2.63391782407408[/C][/ROW]
[ROW][C]74[/C][C]134.08[/C][C]138.064403935185[/C][C]134.461666666667[/C][C]3.60273726851851[/C][C]-3.98440393518518[/C][/ROW]
[ROW][C]75[/C][C]132.86[/C][C]136.447528935185[/C][C]134.119583333333[/C][C]2.32794560185187[/C][C]-3.58752893518516[/C][/ROW]
[ROW][C]76[/C][C]134.48[/C][C]134.774681712963[/C][C]133.49625[/C][C]1.27843171296296[/C][C]-0.294681712962927[/C][/ROW]
[ROW][C]77[/C][C]129.08[/C][C]133.19134837963[/C][C]132.825416666667[/C][C]0.365931712962961[/C][C]-4.11134837962962[/C][/ROW]
[ROW][C]78[/C][C]133.13[/C][C]130.76822337963[/C][C]132.26625[/C][C]-1.49802662037037[/C][C]2.36177662037036[/C][/ROW]
[ROW][C]79[/C][C]134.78[/C][C]NA[/C][C]NA[/C][C]-3.17469328703704[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]134.13[/C][C]NA[/C][C]NA[/C][C]-2.48337384259259[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]132.43[/C][C]NA[/C][C]NA[/C][C]-0.947123842592572[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]127.84[/C][C]NA[/C][C]NA[/C][C]-2.20851273148149[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]128.12[/C][C]NA[/C][C]NA[/C][C]-0.677401620370366[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]128.94[/C][C]NA[/C][C]NA[/C][C]0.838084490740731[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110707&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110707&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1110.04NANA2.57600115740739NA
2111.73NANA3.60273726851851NA
3110.99NANA2.32794560185187NA
4115.83NANA1.27843171296296NA
5125.33NANA0.365931712962961NA
6123.03NANA-1.49802662037037NA
7123.46121.376556712963124.55125-3.174693287037042.08344328703706
8130.34124.010792824074126.494166666667-2.483373842592596.32920717592594
9131.21127.453709490741128.400833333333-0.9471238425925723.75629050925926
10132.97127.876487268518130.085-2.208512731481495.0935127314815
11133.91130.28009837963130.9575-0.6774016203703663.62990162037039
12133.14132.193084490741131.3550.8380844907407310.94691550925927
13135.31133.908501157407131.33252.576001157407391.40149884259259
14133.09134.278153935185130.6754166666673.60273726851851-1.18815393518517
15135.39132.198778935185129.8708333333332.327945601851873.19122106481481
16131.85130.383015046296129.1045833333331.278431712962961.46698495370373
17130.25129.050515046296128.6845833333330.3659317129629611.19948495370372
18127.65128.006140046296129.504166666667-1.49802662037037-0.356140046296275
19118.3128.345306712963131.52-3.17469328703704-10.045306712963
20119.73131.689959490741134.173333333333-2.48337384259259-11.9599594907407
21122.51135.985792824074136.932916666667-0.947123842592572-13.4757928240741
22123.28137.279820601852139.488333333333-2.20851273148149-13.9998206018519
23133.52141.384681712963142.062083333333-0.677401620370366-7.86468171296295
24153.2145.410584490741144.57250.8380844907407317.78941550925927
25163.63150.007251157407147.431252.5760011574073913.6227488425926
26168.45154.309820601852150.7070833333333.6027372685185114.1401793981481
27166.26156.367945601852154.042.327945601851879.89205439814813
28162.31158.67884837963157.4004166666671.278431712962963.63115162037036
29161.56160.72634837963160.3604166666670.3659317129629610.833651620370404
30156.59160.447806712963161.945833333333-1.49802662037037-3.85780671296294
31157.97158.856140046296162.030833333333-3.17469328703704-0.886140046296305
32158.68159.286209490741161.769583333333-2.48337384259259-0.606209490740724
33163.55160.680792824074161.627916666667-0.9471238425925722.86920717592594
34162.89159.386070601852161.594583333333-2.208512731481493.50392939814813
35164.95160.918431712963161.595833333333-0.6774016203703664.03156828703703
36159.82162.341001157407161.5029166666670.838084490740731-2.5210011574074
37159.05164.007251157407161.431252.57600115740739-4.9572511574074
38166.76164.930237268519161.32753.602737268518511.82976273148148
39164.55163.203362268519160.8754166666672.327945601851871.34663773148151
40163.22161.299265046296160.0208333333331.278431712962961.92073495370371
41160.68159.258431712963158.89250.3659317129629611.42156828703705
42155.24156.30572337963157.80375-1.49802662037037-1.06572337962965
43157.6153.73947337963156.914166666667-3.174693287037043.86052662037034
44156.56153.156626157407155.64-2.483373842592593.40337384259257
45154.82153.047042824074153.994166666667-0.9471238425925721.77295717592591
46151.11150.120653935185152.329166666667-2.208512731481490.989346064814782
47149.65150.084681712963150.762083333333-0.677401620370366-0.434681712962941
48148.99150.293084490741149.4550.838084490740731-1.30308449074073
49148.53150.734751157407148.158752.57600115740739-2.2047511574074
50146.7150.362320601852146.7595833333333.60273726851851-3.66232060185186
51145.11147.796278935185145.4683333333332.32794560185187-2.68627893518516
52142.7145.673431712963144.3951.27843171296296-2.97343171296296
53143.59143.79759837963143.4316666666670.365931712962961-0.207598379629644
54140.96140.911556712963142.409583333333-1.498026620370370.0484432870370597
55140.77138.17322337963141.347916666667-3.174693287037042.59677662037038
56139.81137.843709490741140.327083333333-2.483373842592591.96629050925924
57140.58138.344959490741139.292083333333-0.9471238425925722.23504050925925
58139.59136.241070601852138.449583333333-2.208512731481493.34892939814816
59138.05137.26884837963137.94625-0.6774016203703660.781151620370395
60136.06138.497667824074137.6595833333330.838084490740731-2.43766782407405
61135.98139.971001157407137.3952.57600115740739-3.99100115740742
62134.75140.647737268519137.0453.60273726851851-5.89773726851851
63132.22139.138778935185136.8108333333332.32794560185187-6.9187789351852
64135.37137.883431712963136.6051.27843171296296-2.51343171296298
65138.84136.738431712963136.37250.3659317129629612.10156828703703
66138.83134.72322337963136.22125-1.498026620370374.1067766203704
67136.55132.92197337963136.096666666667-3.174693287037043.62802662037041
68135.63133.525376157407136.00875-2.483373842592592.10462384259259
69139.14135.060376157407136.0075-0.9471238425925724.07962384259258
70136.09133.788570601852135.997083333333-2.208512731481492.30142939814817
71135.97134.875931712963135.553333333333-0.6774016203703661.09406828703706
72134.51135.747251157407134.9091666666670.838084490740731-1.2372511574074
73134.54137.173917824074134.5979166666672.57600115740739-2.63391782407408
74134.08138.064403935185134.4616666666673.60273726851851-3.98440393518518
75132.86136.447528935185134.1195833333332.32794560185187-3.58752893518516
76134.48134.774681712963133.496251.27843171296296-0.294681712962927
77129.08133.19134837963132.8254166666670.365931712962961-4.11134837962962
78133.13130.76822337963132.26625-1.498026620370372.36177662037036
79134.78NANA-3.17469328703704NA
80134.13NANA-2.48337384259259NA
81132.43NANA-0.947123842592572NA
82127.84NANA-2.20851273148149NA
83128.12NANA-0.677401620370366NA
84128.94NANA0.838084490740731NA


Figures (Output of Computation)

Input Parameters & R Code

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')