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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Tue, 01 Dec 2009 13:46:35 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/01/t125970043304x389kn1wzfetr.htm/, Retrieved Thu, 18 Apr 2024 23:49:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=62253, Retrieved Thu, 18 Apr 2024 23:49:45 +0000

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Estimated Impact

152

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

-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Classical Decomposition] [] [2009-11-27 14:58:37] [b98453cac15ba1066b407e146608df68]
-   PD      [Classical Decomposition] [BBWS9-populartheory1] [2009-12-01 20:46:35] [b32ceebc68d054278e6bda97f3d57f91] [Current]
-   PD        [Classical Decomposition] [workshop 9] [2009-12-04 12:04:40] [28d531aeb5ea2ff1b676cbab66947a19] 
-   PD        [Classical Decomposition] [shw-ws9] [2009-12-04 13:37:30] [2663058f2a5dda519058ac6b2228468f] 
-   PD          [Classical Decomposition] [ws 9 theorie 1] [2009-12-04 19:29:39] [134dc66689e3d457a82860db6471d419] 

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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	1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62253&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]1 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=62253&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62253&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	1 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	96.8	NA	NA	0.943155173191398	NA
2	114.1	NA	NA	1.07907892169394	NA
3	110.3	NA	NA	1.08648569742897	NA
4	103.9	NA	NA	1.02639952170399	NA
5	101.6	NA	NA	0.944324804192714	NA
6	94.6	NA	NA	0.962640888639396	NA
7	95.9	98.9115766938867	101.420833333333	0.97525896251316	0.969552839065472
8	104.7	107.007716613078	101.3375	1.05595378426622	0.978434110304188
9	102.8	101.428675792967	101.116666666667	1.00308563500544	1.01352008390440
10	98.1	101.321313490236	101.1375	1.00181746128029	0.96820695094378
11	113.9	110.161814579927	101.370833333333	1.08672101192743	1.03393358610085
12	80.9	84.8300208677867	101.583333333333	0.835078138157047	0.953671815383473
13	95.7	96.1703891597495	101.966666666667	0.943155173191398	0.995108794257158
14	113.2	110.731482014493	102.616666666667	1.07907892169394	1.02229282892812
15	105.9	111.885391716487	102.979166666667	1.08648569742897	0.946504260970425
16	108.8	106.112603885497	103.383333333333	1.02639952170399	1.02532588981986
17	102.3	98.1192818423071	103.904166666667	0.944324804192714	1.04260852789783
18	99	100.230971525874	104.120833333333	0.962640888639396	0.987718651160069
19	100.7	101.922688740646	104.508333333333	0.97525896251316	0.988003762893683
20	115.5	110.637557746493	104.775	1.05595378426622	1.04394929129444
21	100.7	105.608199272156	105.283333333333	1.00308563500544	0.953524448802428
22	109.9	106.088294910161	105.895833333333	1.00181746128029	1.03592955370870
23	114.6	115.251291319120	106.054166666667	1.08672101192743	0.994348945580863
24	85.4	88.7966420240326	106.333333333333	0.835078138157047	0.961748080258335
25	100.5	100.744691749728	106.816666666667	0.943155173191398	0.99757116980083
26	114.8	115.618810297332	107.145833333333	1.07907892169394	0.992918018311844
27	116.5	116.806266521092	107.508333333333	1.08648569742897	0.997377995802674
28	112.9	110.551781816867	107.708333333333	1.02639952170399	1.02124088951386
29	102	101.790344518606	107.791666666667	0.944324804192714	1.00205967945570
30	106	104.15373314708	108.195833333333	0.962640888639396	1.01772636272492
31	105.3	105.957822698044	108.645833333333	0.97525896251316	0.993791655195493
32	118.8	114.786576157173	108.704166666667	1.05595378426622	1.03496422645564
33	106.1	109.190050893822	108.854166666667	1.00308563500544	0.971700252280067
34	109.3	109.314981983368	109.116666666667	1.00181746128029	0.999862946660226
35	117.2	118.506926350686	109.05	1.08672101192743	0.988971730252976
36	92.5	91.1592172565686	109.1625	0.835078138157047	1.01470814234459
37	104.2	103.436613806545	109.670833333333	0.943155173191398	1.00738023186725
38	112.5	118.276042142003	109.608333333333	1.07907892169394	0.95116473262549
39	122.4	119.187481007957	109.7	1.08648569742897	1.02695349347830
40	113.3	113.066460645042	110.158333333333	1.02639952170399	1.00206550513411
41	100	103.966226254934	110.095833333333	0.944324804192714	0.961850820234562
42	110.7	106.070992916953	110.1875	0.962640888639396	1.04364065005662
43	112.8	107.424774720825	110.15	0.97525896251316	1.05003711009071
44	109.8	116.30890952949	110.145833333333	1.05595378426622	0.944037739191084
45	117.3	110.38539460887	110.045833333333	1.00308563500544	1.06264058225847
46	109.1	109.385944053542	109.1875	1.00181746128029	0.997385915932653
47	115.9	117.768861663419	108.370833333333	1.08672101192743	0.984131105310673
48	96	89.6143227009781	107.3125	0.835078138157047	1.07125732925896
49	99.8	99.667922927001	105.675	0.943155173191398	1.00132517132012
50	116.8	112.849174398318	104.579166666667	1.07907892169394	1.0350097873799
51	115.7	112.270188734326	103.333333333333	1.08648569742897	1.03054961699396
52	99.4	104.329234716537	101.645833333333	1.02639952170399	0.952753082777522
53	94.3	94.7866022208437	100.375	0.944324804192714	0.994866339657266
54	91	95.5581522122707	99.2666666666667	0.962640888639396	0.95229970330375
55	93.2	NA	NA	0.97525896251316	NA
56	103.1	NA	NA	1.05595378426622	NA
57	94.1	NA	NA	1.00308563500544	NA
58	91.8	NA	NA	1.00181746128029	NA
59	102.7	NA	NA	1.08672101192743	NA
60	82.6	NA	NA	0.835078138157047	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 96.8 & NA & NA & 0.943155173191398 & NA \tabularnewline
2 & 114.1 & NA & NA & 1.07907892169394 & NA \tabularnewline
3 & 110.3 & NA & NA & 1.08648569742897 & NA \tabularnewline
4 & 103.9 & NA & NA & 1.02639952170399 & NA \tabularnewline
5 & 101.6 & NA & NA & 0.944324804192714 & NA \tabularnewline
6 & 94.6 & NA & NA & 0.962640888639396 & NA \tabularnewline
7 & 95.9 & 98.9115766938867 & 101.420833333333 & 0.97525896251316 & 0.969552839065472 \tabularnewline
8 & 104.7 & 107.007716613078 & 101.3375 & 1.05595378426622 & 0.978434110304188 \tabularnewline
9 & 102.8 & 101.428675792967 & 101.116666666667 & 1.00308563500544 & 1.01352008390440 \tabularnewline
10 & 98.1 & 101.321313490236 & 101.1375 & 1.00181746128029 & 0.96820695094378 \tabularnewline
11 & 113.9 & 110.161814579927 & 101.370833333333 & 1.08672101192743 & 1.03393358610085 \tabularnewline
12 & 80.9 & 84.8300208677867 & 101.583333333333 & 0.835078138157047 & 0.953671815383473 \tabularnewline
13 & 95.7 & 96.1703891597495 & 101.966666666667 & 0.943155173191398 & 0.995108794257158 \tabularnewline
14 & 113.2 & 110.731482014493 & 102.616666666667 & 1.07907892169394 & 1.02229282892812 \tabularnewline
15 & 105.9 & 111.885391716487 & 102.979166666667 & 1.08648569742897 & 0.946504260970425 \tabularnewline
16 & 108.8 & 106.112603885497 & 103.383333333333 & 1.02639952170399 & 1.02532588981986 \tabularnewline
17 & 102.3 & 98.1192818423071 & 103.904166666667 & 0.944324804192714 & 1.04260852789783 \tabularnewline
18 & 99 & 100.230971525874 & 104.120833333333 & 0.962640888639396 & 0.987718651160069 \tabularnewline
19 & 100.7 & 101.922688740646 & 104.508333333333 & 0.97525896251316 & 0.988003762893683 \tabularnewline
20 & 115.5 & 110.637557746493 & 104.775 & 1.05595378426622 & 1.04394929129444 \tabularnewline
21 & 100.7 & 105.608199272156 & 105.283333333333 & 1.00308563500544 & 0.953524448802428 \tabularnewline
22 & 109.9 & 106.088294910161 & 105.895833333333 & 1.00181746128029 & 1.03592955370870 \tabularnewline
23 & 114.6 & 115.251291319120 & 106.054166666667 & 1.08672101192743 & 0.994348945580863 \tabularnewline
24 & 85.4 & 88.7966420240326 & 106.333333333333 & 0.835078138157047 & 0.961748080258335 \tabularnewline
25 & 100.5 & 100.744691749728 & 106.816666666667 & 0.943155173191398 & 0.99757116980083 \tabularnewline
26 & 114.8 & 115.618810297332 & 107.145833333333 & 1.07907892169394 & 0.992918018311844 \tabularnewline
27 & 116.5 & 116.806266521092 & 107.508333333333 & 1.08648569742897 & 0.997377995802674 \tabularnewline
28 & 112.9 & 110.551781816867 & 107.708333333333 & 1.02639952170399 & 1.02124088951386 \tabularnewline
29 & 102 & 101.790344518606 & 107.791666666667 & 0.944324804192714 & 1.00205967945570 \tabularnewline
30 & 106 & 104.15373314708 & 108.195833333333 & 0.962640888639396 & 1.01772636272492 \tabularnewline
31 & 105.3 & 105.957822698044 & 108.645833333333 & 0.97525896251316 & 0.993791655195493 \tabularnewline
32 & 118.8 & 114.786576157173 & 108.704166666667 & 1.05595378426622 & 1.03496422645564 \tabularnewline
33 & 106.1 & 109.190050893822 & 108.854166666667 & 1.00308563500544 & 0.971700252280067 \tabularnewline
34 & 109.3 & 109.314981983368 & 109.116666666667 & 1.00181746128029 & 0.999862946660226 \tabularnewline
35 & 117.2 & 118.506926350686 & 109.05 & 1.08672101192743 & 0.988971730252976 \tabularnewline
36 & 92.5 & 91.1592172565686 & 109.1625 & 0.835078138157047 & 1.01470814234459 \tabularnewline
37 & 104.2 & 103.436613806545 & 109.670833333333 & 0.943155173191398 & 1.00738023186725 \tabularnewline
38 & 112.5 & 118.276042142003 & 109.608333333333 & 1.07907892169394 & 0.95116473262549 \tabularnewline
39 & 122.4 & 119.187481007957 & 109.7 & 1.08648569742897 & 1.02695349347830 \tabularnewline
40 & 113.3 & 113.066460645042 & 110.158333333333 & 1.02639952170399 & 1.00206550513411 \tabularnewline
41 & 100 & 103.966226254934 & 110.095833333333 & 0.944324804192714 & 0.961850820234562 \tabularnewline
42 & 110.7 & 106.070992916953 & 110.1875 & 0.962640888639396 & 1.04364065005662 \tabularnewline
43 & 112.8 & 107.424774720825 & 110.15 & 0.97525896251316 & 1.05003711009071 \tabularnewline
44 & 109.8 & 116.30890952949 & 110.145833333333 & 1.05595378426622 & 0.944037739191084 \tabularnewline
45 & 117.3 & 110.38539460887 & 110.045833333333 & 1.00308563500544 & 1.06264058225847 \tabularnewline
46 & 109.1 & 109.385944053542 & 109.1875 & 1.00181746128029 & 0.997385915932653 \tabularnewline
47 & 115.9 & 117.768861663419 & 108.370833333333 & 1.08672101192743 & 0.984131105310673 \tabularnewline
48 & 96 & 89.6143227009781 & 107.3125 & 0.835078138157047 & 1.07125732925896 \tabularnewline
49 & 99.8 & 99.667922927001 & 105.675 & 0.943155173191398 & 1.00132517132012 \tabularnewline
50 & 116.8 & 112.849174398318 & 104.579166666667 & 1.07907892169394 & 1.0350097873799 \tabularnewline
51 & 115.7 & 112.270188734326 & 103.333333333333 & 1.08648569742897 & 1.03054961699396 \tabularnewline
52 & 99.4 & 104.329234716537 & 101.645833333333 & 1.02639952170399 & 0.952753082777522 \tabularnewline
53 & 94.3 & 94.7866022208437 & 100.375 & 0.944324804192714 & 0.994866339657266 \tabularnewline
54 & 91 & 95.5581522122707 & 99.2666666666667 & 0.962640888639396 & 0.95229970330375 \tabularnewline
55 & 93.2 & NA & NA & 0.97525896251316 & NA \tabularnewline
56 & 103.1 & NA & NA & 1.05595378426622 & NA \tabularnewline
57 & 94.1 & NA & NA & 1.00308563500544 & NA \tabularnewline
58 & 91.8 & NA & NA & 1.00181746128029 & NA \tabularnewline
59 & 102.7 & NA & NA & 1.08672101192743 & NA \tabularnewline
60 & 82.6 & NA & NA & 0.835078138157047 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=62253&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]96.8[/C][C]NA[/C][C]NA[/C][C]0.943155173191398[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]114.1[/C][C]NA[/C][C]NA[/C][C]1.07907892169394[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]110.3[/C][C]NA[/C][C]NA[/C][C]1.08648569742897[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]103.9[/C][C]NA[/C][C]NA[/C][C]1.02639952170399[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]101.6[/C][C]NA[/C][C]NA[/C][C]0.944324804192714[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]94.6[/C][C]NA[/C][C]NA[/C][C]0.962640888639396[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]95.9[/C][C]98.9115766938867[/C][C]101.420833333333[/C][C]0.97525896251316[/C][C]0.969552839065472[/C][/ROW]
[ROW][C]8[/C][C]104.7[/C][C]107.007716613078[/C][C]101.3375[/C][C]1.05595378426622[/C][C]0.978434110304188[/C][/ROW]
[ROW][C]9[/C][C]102.8[/C][C]101.428675792967[/C][C]101.116666666667[/C][C]1.00308563500544[/C][C]1.01352008390440[/C][/ROW]
[ROW][C]10[/C][C]98.1[/C][C]101.321313490236[/C][C]101.1375[/C][C]1.00181746128029[/C][C]0.96820695094378[/C][/ROW]
[ROW][C]11[/C][C]113.9[/C][C]110.161814579927[/C][C]101.370833333333[/C][C]1.08672101192743[/C][C]1.03393358610085[/C][/ROW]
[ROW][C]12[/C][C]80.9[/C][C]84.8300208677867[/C][C]101.583333333333[/C][C]0.835078138157047[/C][C]0.953671815383473[/C][/ROW]
[ROW][C]13[/C][C]95.7[/C][C]96.1703891597495[/C][C]101.966666666667[/C][C]0.943155173191398[/C][C]0.995108794257158[/C][/ROW]
[ROW][C]14[/C][C]113.2[/C][C]110.731482014493[/C][C]102.616666666667[/C][C]1.07907892169394[/C][C]1.02229282892812[/C][/ROW]
[ROW][C]15[/C][C]105.9[/C][C]111.885391716487[/C][C]102.979166666667[/C][C]1.08648569742897[/C][C]0.946504260970425[/C][/ROW]
[ROW][C]16[/C][C]108.8[/C][C]106.112603885497[/C][C]103.383333333333[/C][C]1.02639952170399[/C][C]1.02532588981986[/C][/ROW]
[ROW][C]17[/C][C]102.3[/C][C]98.1192818423071[/C][C]103.904166666667[/C][C]0.944324804192714[/C][C]1.04260852789783[/C][/ROW]
[ROW][C]18[/C][C]99[/C][C]100.230971525874[/C][C]104.120833333333[/C][C]0.962640888639396[/C][C]0.987718651160069[/C][/ROW]
[ROW][C]19[/C][C]100.7[/C][C]101.922688740646[/C][C]104.508333333333[/C][C]0.97525896251316[/C][C]0.988003762893683[/C][/ROW]
[ROW][C]20[/C][C]115.5[/C][C]110.637557746493[/C][C]104.775[/C][C]1.05595378426622[/C][C]1.04394929129444[/C][/ROW]
[ROW][C]21[/C][C]100.7[/C][C]105.608199272156[/C][C]105.283333333333[/C][C]1.00308563500544[/C][C]0.953524448802428[/C][/ROW]
[ROW][C]22[/C][C]109.9[/C][C]106.088294910161[/C][C]105.895833333333[/C][C]1.00181746128029[/C][C]1.03592955370870[/C][/ROW]
[ROW][C]23[/C][C]114.6[/C][C]115.251291319120[/C][C]106.054166666667[/C][C]1.08672101192743[/C][C]0.994348945580863[/C][/ROW]
[ROW][C]24[/C][C]85.4[/C][C]88.7966420240326[/C][C]106.333333333333[/C][C]0.835078138157047[/C][C]0.961748080258335[/C][/ROW]
[ROW][C]25[/C][C]100.5[/C][C]100.744691749728[/C][C]106.816666666667[/C][C]0.943155173191398[/C][C]0.99757116980083[/C][/ROW]
[ROW][C]26[/C][C]114.8[/C][C]115.618810297332[/C][C]107.145833333333[/C][C]1.07907892169394[/C][C]0.992918018311844[/C][/ROW]
[ROW][C]27[/C][C]116.5[/C][C]116.806266521092[/C][C]107.508333333333[/C][C]1.08648569742897[/C][C]0.997377995802674[/C][/ROW]
[ROW][C]28[/C][C]112.9[/C][C]110.551781816867[/C][C]107.708333333333[/C][C]1.02639952170399[/C][C]1.02124088951386[/C][/ROW]
[ROW][C]29[/C][C]102[/C][C]101.790344518606[/C][C]107.791666666667[/C][C]0.944324804192714[/C][C]1.00205967945570[/C][/ROW]
[ROW][C]30[/C][C]106[/C][C]104.15373314708[/C][C]108.195833333333[/C][C]0.962640888639396[/C][C]1.01772636272492[/C][/ROW]
[ROW][C]31[/C][C]105.3[/C][C]105.957822698044[/C][C]108.645833333333[/C][C]0.97525896251316[/C][C]0.993791655195493[/C][/ROW]
[ROW][C]32[/C][C]118.8[/C][C]114.786576157173[/C][C]108.704166666667[/C][C]1.05595378426622[/C][C]1.03496422645564[/C][/ROW]
[ROW][C]33[/C][C]106.1[/C][C]109.190050893822[/C][C]108.854166666667[/C][C]1.00308563500544[/C][C]0.971700252280067[/C][/ROW]
[ROW][C]34[/C][C]109.3[/C][C]109.314981983368[/C][C]109.116666666667[/C][C]1.00181746128029[/C][C]0.999862946660226[/C][/ROW]
[ROW][C]35[/C][C]117.2[/C][C]118.506926350686[/C][C]109.05[/C][C]1.08672101192743[/C][C]0.988971730252976[/C][/ROW]
[ROW][C]36[/C][C]92.5[/C][C]91.1592172565686[/C][C]109.1625[/C][C]0.835078138157047[/C][C]1.01470814234459[/C][/ROW]
[ROW][C]37[/C][C]104.2[/C][C]103.436613806545[/C][C]109.670833333333[/C][C]0.943155173191398[/C][C]1.00738023186725[/C][/ROW]
[ROW][C]38[/C][C]112.5[/C][C]118.276042142003[/C][C]109.608333333333[/C][C]1.07907892169394[/C][C]0.95116473262549[/C][/ROW]
[ROW][C]39[/C][C]122.4[/C][C]119.187481007957[/C][C]109.7[/C][C]1.08648569742897[/C][C]1.02695349347830[/C][/ROW]
[ROW][C]40[/C][C]113.3[/C][C]113.066460645042[/C][C]110.158333333333[/C][C]1.02639952170399[/C][C]1.00206550513411[/C][/ROW]
[ROW][C]41[/C][C]100[/C][C]103.966226254934[/C][C]110.095833333333[/C][C]0.944324804192714[/C][C]0.961850820234562[/C][/ROW]
[ROW][C]42[/C][C]110.7[/C][C]106.070992916953[/C][C]110.1875[/C][C]0.962640888639396[/C][C]1.04364065005662[/C][/ROW]
[ROW][C]43[/C][C]112.8[/C][C]107.424774720825[/C][C]110.15[/C][C]0.97525896251316[/C][C]1.05003711009071[/C][/ROW]
[ROW][C]44[/C][C]109.8[/C][C]116.30890952949[/C][C]110.145833333333[/C][C]1.05595378426622[/C][C]0.944037739191084[/C][/ROW]
[ROW][C]45[/C][C]117.3[/C][C]110.38539460887[/C][C]110.045833333333[/C][C]1.00308563500544[/C][C]1.06264058225847[/C][/ROW]
[ROW][C]46[/C][C]109.1[/C][C]109.385944053542[/C][C]109.1875[/C][C]1.00181746128029[/C][C]0.997385915932653[/C][/ROW]
[ROW][C]47[/C][C]115.9[/C][C]117.768861663419[/C][C]108.370833333333[/C][C]1.08672101192743[/C][C]0.984131105310673[/C][/ROW]
[ROW][C]48[/C][C]96[/C][C]89.6143227009781[/C][C]107.3125[/C][C]0.835078138157047[/C][C]1.07125732925896[/C][/ROW]
[ROW][C]49[/C][C]99.8[/C][C]99.667922927001[/C][C]105.675[/C][C]0.943155173191398[/C][C]1.00132517132012[/C][/ROW]
[ROW][C]50[/C][C]116.8[/C][C]112.849174398318[/C][C]104.579166666667[/C][C]1.07907892169394[/C][C]1.0350097873799[/C][/ROW]
[ROW][C]51[/C][C]115.7[/C][C]112.270188734326[/C][C]103.333333333333[/C][C]1.08648569742897[/C][C]1.03054961699396[/C][/ROW]
[ROW][C]52[/C][C]99.4[/C][C]104.329234716537[/C][C]101.645833333333[/C][C]1.02639952170399[/C][C]0.952753082777522[/C][/ROW]
[ROW][C]53[/C][C]94.3[/C][C]94.7866022208437[/C][C]100.375[/C][C]0.944324804192714[/C][C]0.994866339657266[/C][/ROW]
[ROW][C]54[/C][C]91[/C][C]95.5581522122707[/C][C]99.2666666666667[/C][C]0.962640888639396[/C][C]0.95229970330375[/C][/ROW]
[ROW][C]55[/C][C]93.2[/C][C]NA[/C][C]NA[/C][C]0.97525896251316[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]103.1[/C][C]NA[/C][C]NA[/C][C]1.05595378426622[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]94.1[/C][C]NA[/C][C]NA[/C][C]1.00308563500544[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]91.8[/C][C]NA[/C][C]NA[/C][C]1.00181746128029[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]102.7[/C][C]NA[/C][C]NA[/C][C]1.08672101192743[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]82.6[/C][C]NA[/C][C]NA[/C][C]0.835078138157047[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=62253&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=62253&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	96.8	NA	NA	0.943155173191398	NA
2	114.1	NA	NA	1.07907892169394	NA
3	110.3	NA	NA	1.08648569742897	NA
4	103.9	NA	NA	1.02639952170399	NA
5	101.6	NA	NA	0.944324804192714	NA
6	94.6	NA	NA	0.962640888639396	NA
7	95.9	98.9115766938867	101.420833333333	0.97525896251316	0.969552839065472
8	104.7	107.007716613078	101.3375	1.05595378426622	0.978434110304188
9	102.8	101.428675792967	101.116666666667	1.00308563500544	1.01352008390440
10	98.1	101.321313490236	101.1375	1.00181746128029	0.96820695094378
11	113.9	110.161814579927	101.370833333333	1.08672101192743	1.03393358610085
12	80.9	84.8300208677867	101.583333333333	0.835078138157047	0.953671815383473
13	95.7	96.1703891597495	101.966666666667	0.943155173191398	0.995108794257158
14	113.2	110.731482014493	102.616666666667	1.07907892169394	1.02229282892812
15	105.9	111.885391716487	102.979166666667	1.08648569742897	0.946504260970425
16	108.8	106.112603885497	103.383333333333	1.02639952170399	1.02532588981986
17	102.3	98.1192818423071	103.904166666667	0.944324804192714	1.04260852789783
18	99	100.230971525874	104.120833333333	0.962640888639396	0.987718651160069
19	100.7	101.922688740646	104.508333333333	0.97525896251316	0.988003762893683
20	115.5	110.637557746493	104.775	1.05595378426622	1.04394929129444
21	100.7	105.608199272156	105.283333333333	1.00308563500544	0.953524448802428
22	109.9	106.088294910161	105.895833333333	1.00181746128029	1.03592955370870
23	114.6	115.251291319120	106.054166666667	1.08672101192743	0.994348945580863
24	85.4	88.7966420240326	106.333333333333	0.835078138157047	0.961748080258335
25	100.5	100.744691749728	106.816666666667	0.943155173191398	0.99757116980083
26	114.8	115.618810297332	107.145833333333	1.07907892169394	0.992918018311844
27	116.5	116.806266521092	107.508333333333	1.08648569742897	0.997377995802674
28	112.9	110.551781816867	107.708333333333	1.02639952170399	1.02124088951386
29	102	101.790344518606	107.791666666667	0.944324804192714	1.00205967945570
30	106	104.15373314708	108.195833333333	0.962640888639396	1.01772636272492
31	105.3	105.957822698044	108.645833333333	0.97525896251316	0.993791655195493
32	118.8	114.786576157173	108.704166666667	1.05595378426622	1.03496422645564
33	106.1	109.190050893822	108.854166666667	1.00308563500544	0.971700252280067
34	109.3	109.314981983368	109.116666666667	1.00181746128029	0.999862946660226
35	117.2	118.506926350686	109.05	1.08672101192743	0.988971730252976
36	92.5	91.1592172565686	109.1625	0.835078138157047	1.01470814234459
37	104.2	103.436613806545	109.670833333333	0.943155173191398	1.00738023186725
38	112.5	118.276042142003	109.608333333333	1.07907892169394	0.95116473262549
39	122.4	119.187481007957	109.7	1.08648569742897	1.02695349347830
40	113.3	113.066460645042	110.158333333333	1.02639952170399	1.00206550513411
41	100	103.966226254934	110.095833333333	0.944324804192714	0.961850820234562
42	110.7	106.070992916953	110.1875	0.962640888639396	1.04364065005662
43	112.8	107.424774720825	110.15	0.97525896251316	1.05003711009071
44	109.8	116.30890952949	110.145833333333	1.05595378426622	0.944037739191084
45	117.3	110.38539460887	110.045833333333	1.00308563500544	1.06264058225847
46	109.1	109.385944053542	109.1875	1.00181746128029	0.997385915932653
47	115.9	117.768861663419	108.370833333333	1.08672101192743	0.984131105310673
48	96	89.6143227009781	107.3125	0.835078138157047	1.07125732925896
49	99.8	99.667922927001	105.675	0.943155173191398	1.00132517132012
50	116.8	112.849174398318	104.579166666667	1.07907892169394	1.0350097873799
51	115.7	112.270188734326	103.333333333333	1.08648569742897	1.03054961699396
52	99.4	104.329234716537	101.645833333333	1.02639952170399	0.952753082777522
53	94.3	94.7866022208437	100.375	0.944324804192714	0.994866339657266
54	91	95.5581522122707	99.2666666666667	0.962640888639396	0.95229970330375
55	93.2	NA	NA	0.97525896251316	NA
56	103.1	NA	NA	1.05595378426622	NA
57	94.1	NA	NA	1.00308563500544	NA
58	91.8	NA	NA	1.00181746128029	NA
59	102.7	NA	NA	1.08672101192743	NA
60	82.6	NA	NA	0.835078138157047	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 = FALSE ; par2 = 0.5 ; par3 = 1 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 1 ; par8 = 2 ; par9 = 1 ;

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