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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Thu, 16 Dec 2010 16:07:33 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/16/t1292515686ykluk2wqqnv2wq5.htm/, Retrieved Fri, 03 May 2024 11:25:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=111051, Retrieved Fri, 03 May 2024 11:25:40 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

KDGP2W92

Estimated Impact

118

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Classical Decomposition] [Classical Decompo...] [2010-12-16 16:07:33] [ac6548ae9fe194312a6a7ae1d0184c66] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111051&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111051&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111051&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	95.05	NA	NA	0.98140733479096	NA
2	96.84	NA	NA	1.00247656470811	NA
3	96.92	NA	NA	1.00208045626739	NA
4	97.44	NA	NA	1.00383742651187	NA
5	97.78	NA	NA	1.00447763133924	NA
6	97.69	NA	NA	1.00487913979828	NA
7	96.67	96.9027028458144	97.6054166666667	0.992800462875415	0.997598592825789
8	98.29	98.3910699561508	97.7758333333333	1.00629231786468	0.998972773075892
9	98.2	98.2328456813316	97.9820833333333	1.00255926736264	0.999665634431092
10	98.71	98.3270751126927	98.195	1.00134502889855	1.00389439924729
11	98.54	98.3626227943673	98.38625	0.999759852564431	1.00180329886082
12	98.2	98.3994890586527	98.5883333333333	0.998084517018437	0.997972661641223
13	96.92	96.9712230718033	98.8083333333333	0.98140733479096	0.999471770385268
14	99.06	99.2806842844362	99.0354166666667	1.00247656470811	0.997777167975556
15	99.65	99.4798671618514	99.2733333333333	1.00208045626739	1.00171022381717
16	99.82	99.868439438911	99.4866666666667	1.00383742651187	0.99951496749941
17	99.99	100.121726436085	99.6754166666666	1.00447763133924	0.998684337148645
18	100.33	100.372771578393	99.8854166666666	1.00487913979828	0.999573872697544
19	99.31	99.3900816721768	100.110833333333	0.992800462875415	0.99919426897705
20	101.1	100.971371174542	100.34	1.00629231786468	1.00127391382291
21	101.1	100.804827935145	100.5475	1.00255926736264	1.00292815404680
22	100.93	100.879670482193	100.744166666667	1.00134502889855	1.00049890644533
23	100.85	100.941170080773	100.965416666667	0.999759852564431	0.999096799841927
24	100.93	100.989934248864	101.18375	0.998084517018437	0.999406532449892
25	99.6	99.5028450758412	101.387916666667	0.98140733479096	1.00097640347957
26	101.88	101.838670318716	101.587083333333	1.00247656470811	1.00040583484794
27	101.81	101.975882565004	101.764166666667	1.00208045626739	0.998373315721016
28	102.38	102.306509588552	101.915416666667	1.00383742651187	1.00071833563420
29	102.74	102.530798621914	102.07375	1.00447763133924	1.00204037597383
30	102.82	102.745542447241	102.246666666667	1.00487913979828	1.00072467915381
31	101.72	101.667317733897	102.404583333333	0.992800462875415	1.00051818290555
32	103.47	103.195696485489	102.550416666667	1.00629231786468	1.00265809063607
33	102.98	102.966596355396	102.70375	1.00255926736264	1.00013017468848
34	102.68	102.995846309932	102.8575	1.00134502889855	0.99693340730478
35	102.9	102.964434082400	102.989166666667	0.999759852564431	0.999374210299173
36	103.03	102.903345441698	103.100833333333	0.998084517018437	1.00123081089111
37	101.29	101.293095622389	103.212083333333	0.98140733479096	0.999969438959586
38	103.69	103.572954775661	103.317083333333	1.00247656470811	1.00113007516868
39	103.68	103.643511457642	103.428333333333	1.00208045626739	1.00035205814474
40	104.2	103.982081559638	103.584583333333	1.00383742651187	1.00209573069796
41	104.08	104.266870794745	103.802083333333	1.00447763133924	0.99820776442871
42	104.16	104.566048488843	104.058333333333	1.00487913979828	0.996116822862576
43	103.05	103.589213963280	104.340416666667	0.992800462875415	0.994794690077758
44	104.66	105.303878891415	104.645416666667	1.00629231786468	0.993885515916472
45	104.46	105.259950679488	104.99125	1.00255926736264	0.992400236991147
46	104.95	105.499626109274	105.357916666667	1.00134502889855	0.994790255382473
47	105.85	105.730852907767	105.75625	0.999759852564431	1.00112689048614
48	106.23	106.023607163027	106.227083333333	0.998084517018437	1.00194666869479
49	104.86	104.745604842239	106.73	0.98140733479096	1.00109212370231
50	107.44	107.483031076592	107.2175	1.00247656470811	0.999599647719639
51	108.23	107.913626801912	107.689583333333	1.00208045626739	1.00293172611712
52	108.45	108.552469709427	108.1375	1.00383742651187	0.99905603520858
53	109.39	108.975359691648	108.489583333333	1.00447763133924	1.00380489965369
54	110.15	109.282281251629	108.751666666667	1.00487913979828	1.00794015954309
55	109.13	108.178847769781	108.963333333333	0.992800462875415	1.00879240489086
56	110.28	109.828840014080	109.142083333333	1.00629231786468	1.00410784622565
57	110.17	109.537536886902	109.257916666667	1.00255926736264	1.00577393951949
58	109.99	109.466621332094	109.319583333333	1.00134502889855	1.00478117129713
59	109.26	109.314991979211	109.34125	0.999759852564431	0.999496940188945
60	109.11	109.072755362519	109.282083333333	0.998084517018437	1.00034146599999
61	107.06	107.125517629107	109.155	0.98140733479096	0.999388403150275
62	109.53	109.312968505753	109.042916666667	1.00247656470811	1.00198541396519
63	108.92	109.189609249559	108.962916666667	1.00208045626739	0.99753081587697
64	109.24	109.293218077074	108.875416666667	1.00383742651187	0.999513070636855
65	109.12	109.321067409767	108.83375	1.00447763133924	0.998160762472125
66	109	109.378163469552	108.847083333333	1.00487913979828	0.996542605419985
67	107.23	NA	NA	0.992800462875415	NA
68	109.49	NA	NA	1.00629231786468	NA
69	109.04	NA	NA	1.00255926736264	NA
70	109.02	NA	NA	1.00134502889855	NA
71	109.23	NA	NA	0.999759852564431	NA
72	109.46	NA	NA	0.998084517018437	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 95.05 & NA & NA & 0.98140733479096 & NA \tabularnewline
2 & 96.84 & NA & NA & 1.00247656470811 & NA \tabularnewline
3 & 96.92 & NA & NA & 1.00208045626739 & NA \tabularnewline
4 & 97.44 & NA & NA & 1.00383742651187 & NA \tabularnewline
5 & 97.78 & NA & NA & 1.00447763133924 & NA \tabularnewline
6 & 97.69 & NA & NA & 1.00487913979828 & NA \tabularnewline
7 & 96.67 & 96.9027028458144 & 97.6054166666667 & 0.992800462875415 & 0.997598592825789 \tabularnewline
8 & 98.29 & 98.3910699561508 & 97.7758333333333 & 1.00629231786468 & 0.998972773075892 \tabularnewline
9 & 98.2 & 98.2328456813316 & 97.9820833333333 & 1.00255926736264 & 0.999665634431092 \tabularnewline
10 & 98.71 & 98.3270751126927 & 98.195 & 1.00134502889855 & 1.00389439924729 \tabularnewline
11 & 98.54 & 98.3626227943673 & 98.38625 & 0.999759852564431 & 1.00180329886082 \tabularnewline
12 & 98.2 & 98.3994890586527 & 98.5883333333333 & 0.998084517018437 & 0.997972661641223 \tabularnewline
13 & 96.92 & 96.9712230718033 & 98.8083333333333 & 0.98140733479096 & 0.999471770385268 \tabularnewline
14 & 99.06 & 99.2806842844362 & 99.0354166666667 & 1.00247656470811 & 0.997777167975556 \tabularnewline
15 & 99.65 & 99.4798671618514 & 99.2733333333333 & 1.00208045626739 & 1.00171022381717 \tabularnewline
16 & 99.82 & 99.868439438911 & 99.4866666666667 & 1.00383742651187 & 0.99951496749941 \tabularnewline
17 & 99.99 & 100.121726436085 & 99.6754166666666 & 1.00447763133924 & 0.998684337148645 \tabularnewline
18 & 100.33 & 100.372771578393 & 99.8854166666666 & 1.00487913979828 & 0.999573872697544 \tabularnewline
19 & 99.31 & 99.3900816721768 & 100.110833333333 & 0.992800462875415 & 0.99919426897705 \tabularnewline
20 & 101.1 & 100.971371174542 & 100.34 & 1.00629231786468 & 1.00127391382291 \tabularnewline
21 & 101.1 & 100.804827935145 & 100.5475 & 1.00255926736264 & 1.00292815404680 \tabularnewline
22 & 100.93 & 100.879670482193 & 100.744166666667 & 1.00134502889855 & 1.00049890644533 \tabularnewline
23 & 100.85 & 100.941170080773 & 100.965416666667 & 0.999759852564431 & 0.999096799841927 \tabularnewline
24 & 100.93 & 100.989934248864 & 101.18375 & 0.998084517018437 & 0.999406532449892 \tabularnewline
25 & 99.6 & 99.5028450758412 & 101.387916666667 & 0.98140733479096 & 1.00097640347957 \tabularnewline
26 & 101.88 & 101.838670318716 & 101.587083333333 & 1.00247656470811 & 1.00040583484794 \tabularnewline
27 & 101.81 & 101.975882565004 & 101.764166666667 & 1.00208045626739 & 0.998373315721016 \tabularnewline
28 & 102.38 & 102.306509588552 & 101.915416666667 & 1.00383742651187 & 1.00071833563420 \tabularnewline
29 & 102.74 & 102.530798621914 & 102.07375 & 1.00447763133924 & 1.00204037597383 \tabularnewline
30 & 102.82 & 102.745542447241 & 102.246666666667 & 1.00487913979828 & 1.00072467915381 \tabularnewline
31 & 101.72 & 101.667317733897 & 102.404583333333 & 0.992800462875415 & 1.00051818290555 \tabularnewline
32 & 103.47 & 103.195696485489 & 102.550416666667 & 1.00629231786468 & 1.00265809063607 \tabularnewline
33 & 102.98 & 102.966596355396 & 102.70375 & 1.00255926736264 & 1.00013017468848 \tabularnewline
34 & 102.68 & 102.995846309932 & 102.8575 & 1.00134502889855 & 0.99693340730478 \tabularnewline
35 & 102.9 & 102.964434082400 & 102.989166666667 & 0.999759852564431 & 0.999374210299173 \tabularnewline
36 & 103.03 & 102.903345441698 & 103.100833333333 & 0.998084517018437 & 1.00123081089111 \tabularnewline
37 & 101.29 & 101.293095622389 & 103.212083333333 & 0.98140733479096 & 0.999969438959586 \tabularnewline
38 & 103.69 & 103.572954775661 & 103.317083333333 & 1.00247656470811 & 1.00113007516868 \tabularnewline
39 & 103.68 & 103.643511457642 & 103.428333333333 & 1.00208045626739 & 1.00035205814474 \tabularnewline
40 & 104.2 & 103.982081559638 & 103.584583333333 & 1.00383742651187 & 1.00209573069796 \tabularnewline
41 & 104.08 & 104.266870794745 & 103.802083333333 & 1.00447763133924 & 0.99820776442871 \tabularnewline
42 & 104.16 & 104.566048488843 & 104.058333333333 & 1.00487913979828 & 0.996116822862576 \tabularnewline
43 & 103.05 & 103.589213963280 & 104.340416666667 & 0.992800462875415 & 0.994794690077758 \tabularnewline
44 & 104.66 & 105.303878891415 & 104.645416666667 & 1.00629231786468 & 0.993885515916472 \tabularnewline
45 & 104.46 & 105.259950679488 & 104.99125 & 1.00255926736264 & 0.992400236991147 \tabularnewline
46 & 104.95 & 105.499626109274 & 105.357916666667 & 1.00134502889855 & 0.994790255382473 \tabularnewline
47 & 105.85 & 105.730852907767 & 105.75625 & 0.999759852564431 & 1.00112689048614 \tabularnewline
48 & 106.23 & 106.023607163027 & 106.227083333333 & 0.998084517018437 & 1.00194666869479 \tabularnewline
49 & 104.86 & 104.745604842239 & 106.73 & 0.98140733479096 & 1.00109212370231 \tabularnewline
50 & 107.44 & 107.483031076592 & 107.2175 & 1.00247656470811 & 0.999599647719639 \tabularnewline
51 & 108.23 & 107.913626801912 & 107.689583333333 & 1.00208045626739 & 1.00293172611712 \tabularnewline
52 & 108.45 & 108.552469709427 & 108.1375 & 1.00383742651187 & 0.99905603520858 \tabularnewline
53 & 109.39 & 108.975359691648 & 108.489583333333 & 1.00447763133924 & 1.00380489965369 \tabularnewline
54 & 110.15 & 109.282281251629 & 108.751666666667 & 1.00487913979828 & 1.00794015954309 \tabularnewline
55 & 109.13 & 108.178847769781 & 108.963333333333 & 0.992800462875415 & 1.00879240489086 \tabularnewline
56 & 110.28 & 109.828840014080 & 109.142083333333 & 1.00629231786468 & 1.00410784622565 \tabularnewline
57 & 110.17 & 109.537536886902 & 109.257916666667 & 1.00255926736264 & 1.00577393951949 \tabularnewline
58 & 109.99 & 109.466621332094 & 109.319583333333 & 1.00134502889855 & 1.00478117129713 \tabularnewline
59 & 109.26 & 109.314991979211 & 109.34125 & 0.999759852564431 & 0.999496940188945 \tabularnewline
60 & 109.11 & 109.072755362519 & 109.282083333333 & 0.998084517018437 & 1.00034146599999 \tabularnewline
61 & 107.06 & 107.125517629107 & 109.155 & 0.98140733479096 & 0.999388403150275 \tabularnewline
62 & 109.53 & 109.312968505753 & 109.042916666667 & 1.00247656470811 & 1.00198541396519 \tabularnewline
63 & 108.92 & 109.189609249559 & 108.962916666667 & 1.00208045626739 & 0.99753081587697 \tabularnewline
64 & 109.24 & 109.293218077074 & 108.875416666667 & 1.00383742651187 & 0.999513070636855 \tabularnewline
65 & 109.12 & 109.321067409767 & 108.83375 & 1.00447763133924 & 0.998160762472125 \tabularnewline
66 & 109 & 109.378163469552 & 108.847083333333 & 1.00487913979828 & 0.996542605419985 \tabularnewline
67 & 107.23 & NA & NA & 0.992800462875415 & NA \tabularnewline
68 & 109.49 & NA & NA & 1.00629231786468 & NA \tabularnewline
69 & 109.04 & NA & NA & 1.00255926736264 & NA \tabularnewline
70 & 109.02 & NA & NA & 1.00134502889855 & NA \tabularnewline
71 & 109.23 & NA & NA & 0.999759852564431 & NA \tabularnewline
72 & 109.46 & NA & NA & 0.998084517018437 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=111051&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]95.05[/C][C]NA[/C][C]NA[/C][C]0.98140733479096[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]96.84[/C][C]NA[/C][C]NA[/C][C]1.00247656470811[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]96.92[/C][C]NA[/C][C]NA[/C][C]1.00208045626739[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]97.44[/C][C]NA[/C][C]NA[/C][C]1.00383742651187[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]97.78[/C][C]NA[/C][C]NA[/C][C]1.00447763133924[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]97.69[/C][C]NA[/C][C]NA[/C][C]1.00487913979828[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]96.67[/C][C]96.9027028458144[/C][C]97.6054166666667[/C][C]0.992800462875415[/C][C]0.997598592825789[/C][/ROW]
[ROW][C]8[/C][C]98.29[/C][C]98.3910699561508[/C][C]97.7758333333333[/C][C]1.00629231786468[/C][C]0.998972773075892[/C][/ROW]
[ROW][C]9[/C][C]98.2[/C][C]98.2328456813316[/C][C]97.9820833333333[/C][C]1.00255926736264[/C][C]0.999665634431092[/C][/ROW]
[ROW][C]10[/C][C]98.71[/C][C]98.3270751126927[/C][C]98.195[/C][C]1.00134502889855[/C][C]1.00389439924729[/C][/ROW]
[ROW][C]11[/C][C]98.54[/C][C]98.3626227943673[/C][C]98.38625[/C][C]0.999759852564431[/C][C]1.00180329886082[/C][/ROW]
[ROW][C]12[/C][C]98.2[/C][C]98.3994890586527[/C][C]98.5883333333333[/C][C]0.998084517018437[/C][C]0.997972661641223[/C][/ROW]
[ROW][C]13[/C][C]96.92[/C][C]96.9712230718033[/C][C]98.8083333333333[/C][C]0.98140733479096[/C][C]0.999471770385268[/C][/ROW]
[ROW][C]14[/C][C]99.06[/C][C]99.2806842844362[/C][C]99.0354166666667[/C][C]1.00247656470811[/C][C]0.997777167975556[/C][/ROW]
[ROW][C]15[/C][C]99.65[/C][C]99.4798671618514[/C][C]99.2733333333333[/C][C]1.00208045626739[/C][C]1.00171022381717[/C][/ROW]
[ROW][C]16[/C][C]99.82[/C][C]99.868439438911[/C][C]99.4866666666667[/C][C]1.00383742651187[/C][C]0.99951496749941[/C][/ROW]
[ROW][C]17[/C][C]99.99[/C][C]100.121726436085[/C][C]99.6754166666666[/C][C]1.00447763133924[/C][C]0.998684337148645[/C][/ROW]
[ROW][C]18[/C][C]100.33[/C][C]100.372771578393[/C][C]99.8854166666666[/C][C]1.00487913979828[/C][C]0.999573872697544[/C][/ROW]
[ROW][C]19[/C][C]99.31[/C][C]99.3900816721768[/C][C]100.110833333333[/C][C]0.992800462875415[/C][C]0.99919426897705[/C][/ROW]
[ROW][C]20[/C][C]101.1[/C][C]100.971371174542[/C][C]100.34[/C][C]1.00629231786468[/C][C]1.00127391382291[/C][/ROW]
[ROW][C]21[/C][C]101.1[/C][C]100.804827935145[/C][C]100.5475[/C][C]1.00255926736264[/C][C]1.00292815404680[/C][/ROW]
[ROW][C]22[/C][C]100.93[/C][C]100.879670482193[/C][C]100.744166666667[/C][C]1.00134502889855[/C][C]1.00049890644533[/C][/ROW]
[ROW][C]23[/C][C]100.85[/C][C]100.941170080773[/C][C]100.965416666667[/C][C]0.999759852564431[/C][C]0.999096799841927[/C][/ROW]
[ROW][C]24[/C][C]100.93[/C][C]100.989934248864[/C][C]101.18375[/C][C]0.998084517018437[/C][C]0.999406532449892[/C][/ROW]
[ROW][C]25[/C][C]99.6[/C][C]99.5028450758412[/C][C]101.387916666667[/C][C]0.98140733479096[/C][C]1.00097640347957[/C][/ROW]
[ROW][C]26[/C][C]101.88[/C][C]101.838670318716[/C][C]101.587083333333[/C][C]1.00247656470811[/C][C]1.00040583484794[/C][/ROW]
[ROW][C]27[/C][C]101.81[/C][C]101.975882565004[/C][C]101.764166666667[/C][C]1.00208045626739[/C][C]0.998373315721016[/C][/ROW]
[ROW][C]28[/C][C]102.38[/C][C]102.306509588552[/C][C]101.915416666667[/C][C]1.00383742651187[/C][C]1.00071833563420[/C][/ROW]
[ROW][C]29[/C][C]102.74[/C][C]102.530798621914[/C][C]102.07375[/C][C]1.00447763133924[/C][C]1.00204037597383[/C][/ROW]
[ROW][C]30[/C][C]102.82[/C][C]102.745542447241[/C][C]102.246666666667[/C][C]1.00487913979828[/C][C]1.00072467915381[/C][/ROW]
[ROW][C]31[/C][C]101.72[/C][C]101.667317733897[/C][C]102.404583333333[/C][C]0.992800462875415[/C][C]1.00051818290555[/C][/ROW]
[ROW][C]32[/C][C]103.47[/C][C]103.195696485489[/C][C]102.550416666667[/C][C]1.00629231786468[/C][C]1.00265809063607[/C][/ROW]
[ROW][C]33[/C][C]102.98[/C][C]102.966596355396[/C][C]102.70375[/C][C]1.00255926736264[/C][C]1.00013017468848[/C][/ROW]
[ROW][C]34[/C][C]102.68[/C][C]102.995846309932[/C][C]102.8575[/C][C]1.00134502889855[/C][C]0.99693340730478[/C][/ROW]
[ROW][C]35[/C][C]102.9[/C][C]102.964434082400[/C][C]102.989166666667[/C][C]0.999759852564431[/C][C]0.999374210299173[/C][/ROW]
[ROW][C]36[/C][C]103.03[/C][C]102.903345441698[/C][C]103.100833333333[/C][C]0.998084517018437[/C][C]1.00123081089111[/C][/ROW]
[ROW][C]37[/C][C]101.29[/C][C]101.293095622389[/C][C]103.212083333333[/C][C]0.98140733479096[/C][C]0.999969438959586[/C][/ROW]
[ROW][C]38[/C][C]103.69[/C][C]103.572954775661[/C][C]103.317083333333[/C][C]1.00247656470811[/C][C]1.00113007516868[/C][/ROW]
[ROW][C]39[/C][C]103.68[/C][C]103.643511457642[/C][C]103.428333333333[/C][C]1.00208045626739[/C][C]1.00035205814474[/C][/ROW]
[ROW][C]40[/C][C]104.2[/C][C]103.982081559638[/C][C]103.584583333333[/C][C]1.00383742651187[/C][C]1.00209573069796[/C][/ROW]
[ROW][C]41[/C][C]104.08[/C][C]104.266870794745[/C][C]103.802083333333[/C][C]1.00447763133924[/C][C]0.99820776442871[/C][/ROW]
[ROW][C]42[/C][C]104.16[/C][C]104.566048488843[/C][C]104.058333333333[/C][C]1.00487913979828[/C][C]0.996116822862576[/C][/ROW]
[ROW][C]43[/C][C]103.05[/C][C]103.589213963280[/C][C]104.340416666667[/C][C]0.992800462875415[/C][C]0.994794690077758[/C][/ROW]
[ROW][C]44[/C][C]104.66[/C][C]105.303878891415[/C][C]104.645416666667[/C][C]1.00629231786468[/C][C]0.993885515916472[/C][/ROW]
[ROW][C]45[/C][C]104.46[/C][C]105.259950679488[/C][C]104.99125[/C][C]1.00255926736264[/C][C]0.992400236991147[/C][/ROW]
[ROW][C]46[/C][C]104.95[/C][C]105.499626109274[/C][C]105.357916666667[/C][C]1.00134502889855[/C][C]0.994790255382473[/C][/ROW]
[ROW][C]47[/C][C]105.85[/C][C]105.730852907767[/C][C]105.75625[/C][C]0.999759852564431[/C][C]1.00112689048614[/C][/ROW]
[ROW][C]48[/C][C]106.23[/C][C]106.023607163027[/C][C]106.227083333333[/C][C]0.998084517018437[/C][C]1.00194666869479[/C][/ROW]
[ROW][C]49[/C][C]104.86[/C][C]104.745604842239[/C][C]106.73[/C][C]0.98140733479096[/C][C]1.00109212370231[/C][/ROW]
[ROW][C]50[/C][C]107.44[/C][C]107.483031076592[/C][C]107.2175[/C][C]1.00247656470811[/C][C]0.999599647719639[/C][/ROW]
[ROW][C]51[/C][C]108.23[/C][C]107.913626801912[/C][C]107.689583333333[/C][C]1.00208045626739[/C][C]1.00293172611712[/C][/ROW]
[ROW][C]52[/C][C]108.45[/C][C]108.552469709427[/C][C]108.1375[/C][C]1.00383742651187[/C][C]0.99905603520858[/C][/ROW]
[ROW][C]53[/C][C]109.39[/C][C]108.975359691648[/C][C]108.489583333333[/C][C]1.00447763133924[/C][C]1.00380489965369[/C][/ROW]
[ROW][C]54[/C][C]110.15[/C][C]109.282281251629[/C][C]108.751666666667[/C][C]1.00487913979828[/C][C]1.00794015954309[/C][/ROW]
[ROW][C]55[/C][C]109.13[/C][C]108.178847769781[/C][C]108.963333333333[/C][C]0.992800462875415[/C][C]1.00879240489086[/C][/ROW]
[ROW][C]56[/C][C]110.28[/C][C]109.828840014080[/C][C]109.142083333333[/C][C]1.00629231786468[/C][C]1.00410784622565[/C][/ROW]
[ROW][C]57[/C][C]110.17[/C][C]109.537536886902[/C][C]109.257916666667[/C][C]1.00255926736264[/C][C]1.00577393951949[/C][/ROW]
[ROW][C]58[/C][C]109.99[/C][C]109.466621332094[/C][C]109.319583333333[/C][C]1.00134502889855[/C][C]1.00478117129713[/C][/ROW]
[ROW][C]59[/C][C]109.26[/C][C]109.314991979211[/C][C]109.34125[/C][C]0.999759852564431[/C][C]0.999496940188945[/C][/ROW]
[ROW][C]60[/C][C]109.11[/C][C]109.072755362519[/C][C]109.282083333333[/C][C]0.998084517018437[/C][C]1.00034146599999[/C][/ROW]
[ROW][C]61[/C][C]107.06[/C][C]107.125517629107[/C][C]109.155[/C][C]0.98140733479096[/C][C]0.999388403150275[/C][/ROW]
[ROW][C]62[/C][C]109.53[/C][C]109.312968505753[/C][C]109.042916666667[/C][C]1.00247656470811[/C][C]1.00198541396519[/C][/ROW]
[ROW][C]63[/C][C]108.92[/C][C]109.189609249559[/C][C]108.962916666667[/C][C]1.00208045626739[/C][C]0.99753081587697[/C][/ROW]
[ROW][C]64[/C][C]109.24[/C][C]109.293218077074[/C][C]108.875416666667[/C][C]1.00383742651187[/C][C]0.999513070636855[/C][/ROW]
[ROW][C]65[/C][C]109.12[/C][C]109.321067409767[/C][C]108.83375[/C][C]1.00447763133924[/C][C]0.998160762472125[/C][/ROW]
[ROW][C]66[/C][C]109[/C][C]109.378163469552[/C][C]108.847083333333[/C][C]1.00487913979828[/C][C]0.996542605419985[/C][/ROW]
[ROW][C]67[/C][C]107.23[/C][C]NA[/C][C]NA[/C][C]0.992800462875415[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]109.49[/C][C]NA[/C][C]NA[/C][C]1.00629231786468[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]109.04[/C][C]NA[/C][C]NA[/C][C]1.00255926736264[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]109.02[/C][C]NA[/C][C]NA[/C][C]1.00134502889855[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]109.23[/C][C]NA[/C][C]NA[/C][C]0.999759852564431[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]109.46[/C][C]NA[/C][C]NA[/C][C]0.998084517018437[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=111051&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=111051&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
t	Observations	Fit	Trend	Seasonal	Random
1	95.05	NA	NA	0.98140733479096	NA
2	96.84	NA	NA	1.00247656470811	NA
3	96.92	NA	NA	1.00208045626739	NA
4	97.44	NA	NA	1.00383742651187	NA
5	97.78	NA	NA	1.00447763133924	NA
6	97.69	NA	NA	1.00487913979828	NA
7	96.67	96.9027028458144	97.6054166666667	0.992800462875415	0.997598592825789
8	98.29	98.3910699561508	97.7758333333333	1.00629231786468	0.998972773075892
9	98.2	98.2328456813316	97.9820833333333	1.00255926736264	0.999665634431092
10	98.71	98.3270751126927	98.195	1.00134502889855	1.00389439924729
11	98.54	98.3626227943673	98.38625	0.999759852564431	1.00180329886082
12	98.2	98.3994890586527	98.5883333333333	0.998084517018437	0.997972661641223
13	96.92	96.9712230718033	98.8083333333333	0.98140733479096	0.999471770385268
14	99.06	99.2806842844362	99.0354166666667	1.00247656470811	0.997777167975556
15	99.65	99.4798671618514	99.2733333333333	1.00208045626739	1.00171022381717
16	99.82	99.868439438911	99.4866666666667	1.00383742651187	0.99951496749941
17	99.99	100.121726436085	99.6754166666666	1.00447763133924	0.998684337148645
18	100.33	100.372771578393	99.8854166666666	1.00487913979828	0.999573872697544
19	99.31	99.3900816721768	100.110833333333	0.992800462875415	0.99919426897705
20	101.1	100.971371174542	100.34	1.00629231786468	1.00127391382291
21	101.1	100.804827935145	100.5475	1.00255926736264	1.00292815404680
22	100.93	100.879670482193	100.744166666667	1.00134502889855	1.00049890644533
23	100.85	100.941170080773	100.965416666667	0.999759852564431	0.999096799841927
24	100.93	100.989934248864	101.18375	0.998084517018437	0.999406532449892
25	99.6	99.5028450758412	101.387916666667	0.98140733479096	1.00097640347957
26	101.88	101.838670318716	101.587083333333	1.00247656470811	1.00040583484794
27	101.81	101.975882565004	101.764166666667	1.00208045626739	0.998373315721016
28	102.38	102.306509588552	101.915416666667	1.00383742651187	1.00071833563420
29	102.74	102.530798621914	102.07375	1.00447763133924	1.00204037597383
30	102.82	102.745542447241	102.246666666667	1.00487913979828	1.00072467915381
31	101.72	101.667317733897	102.404583333333	0.992800462875415	1.00051818290555
32	103.47	103.195696485489	102.550416666667	1.00629231786468	1.00265809063607
33	102.98	102.966596355396	102.70375	1.00255926736264	1.00013017468848
34	102.68	102.995846309932	102.8575	1.00134502889855	0.99693340730478
35	102.9	102.964434082400	102.989166666667	0.999759852564431	0.999374210299173
36	103.03	102.903345441698	103.100833333333	0.998084517018437	1.00123081089111
37	101.29	101.293095622389	103.212083333333	0.98140733479096	0.999969438959586
38	103.69	103.572954775661	103.317083333333	1.00247656470811	1.00113007516868
39	103.68	103.643511457642	103.428333333333	1.00208045626739	1.00035205814474
40	104.2	103.982081559638	103.584583333333	1.00383742651187	1.00209573069796
41	104.08	104.266870794745	103.802083333333	1.00447763133924	0.99820776442871
42	104.16	104.566048488843	104.058333333333	1.00487913979828	0.996116822862576
43	103.05	103.589213963280	104.340416666667	0.992800462875415	0.994794690077758
44	104.66	105.303878891415	104.645416666667	1.00629231786468	0.993885515916472
45	104.46	105.259950679488	104.99125	1.00255926736264	0.992400236991147
46	104.95	105.499626109274	105.357916666667	1.00134502889855	0.994790255382473
47	105.85	105.730852907767	105.75625	0.999759852564431	1.00112689048614
48	106.23	106.023607163027	106.227083333333	0.998084517018437	1.00194666869479
49	104.86	104.745604842239	106.73	0.98140733479096	1.00109212370231
50	107.44	107.483031076592	107.2175	1.00247656470811	0.999599647719639
51	108.23	107.913626801912	107.689583333333	1.00208045626739	1.00293172611712
52	108.45	108.552469709427	108.1375	1.00383742651187	0.99905603520858
53	109.39	108.975359691648	108.489583333333	1.00447763133924	1.00380489965369
54	110.15	109.282281251629	108.751666666667	1.00487913979828	1.00794015954309
55	109.13	108.178847769781	108.963333333333	0.992800462875415	1.00879240489086
56	110.28	109.828840014080	109.142083333333	1.00629231786468	1.00410784622565
57	110.17	109.537536886902	109.257916666667	1.00255926736264	1.00577393951949
58	109.99	109.466621332094	109.319583333333	1.00134502889855	1.00478117129713
59	109.26	109.314991979211	109.34125	0.999759852564431	0.999496940188945
60	109.11	109.072755362519	109.282083333333	0.998084517018437	1.00034146599999
61	107.06	107.125517629107	109.155	0.98140733479096	0.999388403150275
62	109.53	109.312968505753	109.042916666667	1.00247656470811	1.00198541396519
63	108.92	109.189609249559	108.962916666667	1.00208045626739	0.99753081587697
64	109.24	109.293218077074	108.875416666667	1.00383742651187	0.999513070636855
65	109.12	109.321067409767	108.83375	1.00447763133924	0.998160762472125
66	109	109.378163469552	108.847083333333	1.00487913979828	0.996542605419985
67	107.23	NA	NA	0.992800462875415	NA
68	109.49	NA	NA	1.00629231786468	NA
69	109.04	NA	NA	1.00255926736264	NA
70	109.02	NA	NA	1.00134502889855	NA
71	109.23	NA	NA	0.999759852564431	NA
72	109.46	NA	NA	0.998084517018437	NA

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code