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rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Fri, 11 Dec 2009 02:02:56 -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/11/t1260522271farxw4hbf39wgaz.htm/, Retrieved Mon, 29 Apr 2024 07:01:59 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=65897, Retrieved Mon, 29 Apr 2024 07:01:59 +0000

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Estimated Impact

160

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] [WS9 Berekening2 TVD] [2009-12-02 16:39:05] [42ad1186d39724f834063794eac7cea3]
-   P       [Classical Decomposition] [TG 8] [2009-12-02 18:03:32] [a21bac9c8d3d56fdec8be4e719e2c7ed]
F    D          [Classical Decomposition] [WS 9 Classical De...] [2009-12-11 09:02:56] [762da55b2e2304daaed24a7cc507d14d] [Current]

Feedback Forum

2009-12-14 18:59:26 [f1e24346ff4ab8a20729561498ad5c34] [reply] 
We zien hier 4 grafieken onder elkaar. De oorspronkelijke reeks, de uitgezuiverde trend, uitgezuiverde saisonaliteit en wat overblijft. Als je de laatste 3 reeksen optelt krijg je opnieuw de oorspronkelijke reeks. De random component zit inderdaad niet vol autocorrelatie, maar de lags liggen anderzijds ook niet allemaal binnen het betrouwbaarheidsinterval. 

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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	6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65897&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]6 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=65897&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65897&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	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	108.8	NA	NA	0.931589383338221	NA
2	128.4	NA	NA	1.07567516197286	NA
3	121.1	NA	NA	1.07522779088427	NA
4	119.5	NA	NA	1.02612324059806	NA
5	128.7	NA	NA	1.02207725546480	NA
6	108.7	NA	NA	0.958011024227935	NA
7	105.5	109.374607482350	114.954166666667	0.951462749493057	0.964574890172977
8	119.8	121.948216255011	114.8625	1.06168868216355	0.982384192889554
9	111.3	111.635304139402	114.654166666667	0.973669840224461	0.996996432786322
10	110.6	111.183928997099	114.504166666667	0.971003346286662	0.994748080928907
11	120.1	119.711506887963	113.995833333333	1.05013931989879	1.00324524452274
12	97.5	102.592191349491	113.570833333333	0.903332205447332	0.950364727738934
13	107.7	106.158491853821	113.954166666667	0.931589383338221	1.01452081806420
14	127.3	123.370977118604	114.691666666667	1.07567516197286	1.03184722187633
15	117.2	123.704957341235	115.05	1.07522779088427	0.947415548406103
16	119.8	118.312009640956	115.3	1.02612324059806	1.01257683276245
17	116.2	118.356546182824	115.8	1.02207725546480	0.981779240334603
18	111	111.253021901070	116.129166666667	0.958011024227935	0.997725707610038
19	112.4	110.857303600310	116.5125	0.951462749493057	1.01391605559208
20	130.6	123.956577345437	116.754166666667	1.06168868216355	1.05359475710635
21	109.1	114.211472258329	117.3	0.973669840224461	0.955245544451367
22	118.8	114.643128418245	118.066666666667	0.971003346286662	1.03625923017897
23	123.9	124.393378022512	118.454166666667	1.05013931989879	0.996033727595834
24	101.6	107.304574354575	118.7875	0.903332205447332	0.946837547337685
25	112.8	110.890189596693	119.033333333333	0.931589383338221	1.01722253709055
26	128	128.054646053027	119.045833333333	1.07567516197286	0.999573259895585
27	129.6	128.090990704884	119.129166666667	1.07522779088427	1.01178076058911
28	125.8	122.279686171268	119.166666666667	1.02612324059806	1.02878903225022
29	119.5	121.844384817097	119.2125	1.02207725546480	0.980759188692885
30	115.7	114.749762139502	119.779166666667	0.958011024227935	1.00828095712602
31	113.6	114.437182195277	120.275	0.951462749493057	0.992684351543637
32	129.7	127.451302590892	120.045833333333	1.06168868216355	1.01764358122197
33	112	116.686216435566	119.841666666667	0.973669840224461	0.959839160282018
34	116.8	116.415209525218	119.891666666667	0.971003346286662	1.00330532819853
35	127	125.907328875366	119.895833333333	1.05013931989879	1.00867837586894
36	112.1	108.414920190437	120.016666666667	0.903332205447332	1.03399052273515
37	114.2	112.035268213713	120.2625	0.931589383338221	1.01932187801932
38	121.1	129.130321215000	120.045833333333	1.07567516197286	0.937812272598394
39	131.6	129.363343590764	120.3125	1.07522779088427	1.01728972324890
40	125	123.925758869728	120.770833333333	1.02612324059806	1.00866842487042
41	120.4	123.139016007353	120.479166666667	1.02207725546480	0.977756716789184
42	117.7	115.444320132067	120.504166666667	0.958011024227935	1.01953911517996
43	117.5	114.401502342171	120.2375	0.951462749493057	1.02708441405394
44	120.6	127.668064030167	120.25	1.06168868216355	0.944637180144776
45	127.5	117.384013154394	120.558333333333	0.973669840224461	1.08617857384293
46	112.3	116.500172318018	119.979166666667	0.971003346286662	0.963947072056229
47	124.5	125.719178913884	119.716666666667	1.05013931989879	0.99030236337513
48	115.2	107.959490203524	119.5125	0.903332205447332	1.06706691355087
49	104.7	110.536961955511	118.654166666667	0.931589383338221	0.947194478188574
50	130.9	127.207551862974	118.258333333333	1.07567516197286	1.02902695699233
51	129.2	126.446788207990	117.6	1.07522779088427	1.02177367911853
52	113.5	119.697276015763	116.65	1.02612324059806	0.948225421479541
53	125.6	118.718531877467	116.154166666667	1.02207725546480	1.05796456554597
54	107.6	110.618339597519	115.466666666667	0.958011024227935	0.972713931446621
55	107	NA	NA	0.951462749493057	NA
56	121.6	NA	NA	1.06168868216355	NA
57	110.7	NA	NA	0.973669840224461	NA
58	106.3	NA	NA	0.971003346286662	NA
59	118.6	NA	NA	1.05013931989879	NA
60	104.6	NA	NA	0.903332205447332	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 108.8 & NA & NA & 0.931589383338221 & NA \tabularnewline
2 & 128.4 & NA & NA & 1.07567516197286 & NA \tabularnewline
3 & 121.1 & NA & NA & 1.07522779088427 & NA \tabularnewline
4 & 119.5 & NA & NA & 1.02612324059806 & NA \tabularnewline
5 & 128.7 & NA & NA & 1.02207725546480 & NA \tabularnewline
6 & 108.7 & NA & NA & 0.958011024227935 & NA \tabularnewline
7 & 105.5 & 109.374607482350 & 114.954166666667 & 0.951462749493057 & 0.964574890172977 \tabularnewline
8 & 119.8 & 121.948216255011 & 114.8625 & 1.06168868216355 & 0.982384192889554 \tabularnewline
9 & 111.3 & 111.635304139402 & 114.654166666667 & 0.973669840224461 & 0.996996432786322 \tabularnewline
10 & 110.6 & 111.183928997099 & 114.504166666667 & 0.971003346286662 & 0.994748080928907 \tabularnewline
11 & 120.1 & 119.711506887963 & 113.995833333333 & 1.05013931989879 & 1.00324524452274 \tabularnewline
12 & 97.5 & 102.592191349491 & 113.570833333333 & 0.903332205447332 & 0.950364727738934 \tabularnewline
13 & 107.7 & 106.158491853821 & 113.954166666667 & 0.931589383338221 & 1.01452081806420 \tabularnewline
14 & 127.3 & 123.370977118604 & 114.691666666667 & 1.07567516197286 & 1.03184722187633 \tabularnewline
15 & 117.2 & 123.704957341235 & 115.05 & 1.07522779088427 & 0.947415548406103 \tabularnewline
16 & 119.8 & 118.312009640956 & 115.3 & 1.02612324059806 & 1.01257683276245 \tabularnewline
17 & 116.2 & 118.356546182824 & 115.8 & 1.02207725546480 & 0.981779240334603 \tabularnewline
18 & 111 & 111.253021901070 & 116.129166666667 & 0.958011024227935 & 0.997725707610038 \tabularnewline
19 & 112.4 & 110.857303600310 & 116.5125 & 0.951462749493057 & 1.01391605559208 \tabularnewline
20 & 130.6 & 123.956577345437 & 116.754166666667 & 1.06168868216355 & 1.05359475710635 \tabularnewline
21 & 109.1 & 114.211472258329 & 117.3 & 0.973669840224461 & 0.955245544451367 \tabularnewline
22 & 118.8 & 114.643128418245 & 118.066666666667 & 0.971003346286662 & 1.03625923017897 \tabularnewline
23 & 123.9 & 124.393378022512 & 118.454166666667 & 1.05013931989879 & 0.996033727595834 \tabularnewline
24 & 101.6 & 107.304574354575 & 118.7875 & 0.903332205447332 & 0.946837547337685 \tabularnewline
25 & 112.8 & 110.890189596693 & 119.033333333333 & 0.931589383338221 & 1.01722253709055 \tabularnewline
26 & 128 & 128.054646053027 & 119.045833333333 & 1.07567516197286 & 0.999573259895585 \tabularnewline
27 & 129.6 & 128.090990704884 & 119.129166666667 & 1.07522779088427 & 1.01178076058911 \tabularnewline
28 & 125.8 & 122.279686171268 & 119.166666666667 & 1.02612324059806 & 1.02878903225022 \tabularnewline
29 & 119.5 & 121.844384817097 & 119.2125 & 1.02207725546480 & 0.980759188692885 \tabularnewline
30 & 115.7 & 114.749762139502 & 119.779166666667 & 0.958011024227935 & 1.00828095712602 \tabularnewline
31 & 113.6 & 114.437182195277 & 120.275 & 0.951462749493057 & 0.992684351543637 \tabularnewline
32 & 129.7 & 127.451302590892 & 120.045833333333 & 1.06168868216355 & 1.01764358122197 \tabularnewline
33 & 112 & 116.686216435566 & 119.841666666667 & 0.973669840224461 & 0.959839160282018 \tabularnewline
34 & 116.8 & 116.415209525218 & 119.891666666667 & 0.971003346286662 & 1.00330532819853 \tabularnewline
35 & 127 & 125.907328875366 & 119.895833333333 & 1.05013931989879 & 1.00867837586894 \tabularnewline
36 & 112.1 & 108.414920190437 & 120.016666666667 & 0.903332205447332 & 1.03399052273515 \tabularnewline
37 & 114.2 & 112.035268213713 & 120.2625 & 0.931589383338221 & 1.01932187801932 \tabularnewline
38 & 121.1 & 129.130321215000 & 120.045833333333 & 1.07567516197286 & 0.937812272598394 \tabularnewline
39 & 131.6 & 129.363343590764 & 120.3125 & 1.07522779088427 & 1.01728972324890 \tabularnewline
40 & 125 & 123.925758869728 & 120.770833333333 & 1.02612324059806 & 1.00866842487042 \tabularnewline
41 & 120.4 & 123.139016007353 & 120.479166666667 & 1.02207725546480 & 0.977756716789184 \tabularnewline
42 & 117.7 & 115.444320132067 & 120.504166666667 & 0.958011024227935 & 1.01953911517996 \tabularnewline
43 & 117.5 & 114.401502342171 & 120.2375 & 0.951462749493057 & 1.02708441405394 \tabularnewline
44 & 120.6 & 127.668064030167 & 120.25 & 1.06168868216355 & 0.944637180144776 \tabularnewline
45 & 127.5 & 117.384013154394 & 120.558333333333 & 0.973669840224461 & 1.08617857384293 \tabularnewline
46 & 112.3 & 116.500172318018 & 119.979166666667 & 0.971003346286662 & 0.963947072056229 \tabularnewline
47 & 124.5 & 125.719178913884 & 119.716666666667 & 1.05013931989879 & 0.99030236337513 \tabularnewline
48 & 115.2 & 107.959490203524 & 119.5125 & 0.903332205447332 & 1.06706691355087 \tabularnewline
49 & 104.7 & 110.536961955511 & 118.654166666667 & 0.931589383338221 & 0.947194478188574 \tabularnewline
50 & 130.9 & 127.207551862974 & 118.258333333333 & 1.07567516197286 & 1.02902695699233 \tabularnewline
51 & 129.2 & 126.446788207990 & 117.6 & 1.07522779088427 & 1.02177367911853 \tabularnewline
52 & 113.5 & 119.697276015763 & 116.65 & 1.02612324059806 & 0.948225421479541 \tabularnewline
53 & 125.6 & 118.718531877467 & 116.154166666667 & 1.02207725546480 & 1.05796456554597 \tabularnewline
54 & 107.6 & 110.618339597519 & 115.466666666667 & 0.958011024227935 & 0.972713931446621 \tabularnewline
55 & 107 & NA & NA & 0.951462749493057 & NA \tabularnewline
56 & 121.6 & NA & NA & 1.06168868216355 & NA \tabularnewline
57 & 110.7 & NA & NA & 0.973669840224461 & NA \tabularnewline
58 & 106.3 & NA & NA & 0.971003346286662 & NA \tabularnewline
59 & 118.6 & NA & NA & 1.05013931989879 & NA \tabularnewline
60 & 104.6 & NA & NA & 0.903332205447332 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=65897&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]108.8[/C][C]NA[/C][C]NA[/C][C]0.931589383338221[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]128.4[/C][C]NA[/C][C]NA[/C][C]1.07567516197286[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]121.1[/C][C]NA[/C][C]NA[/C][C]1.07522779088427[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]119.5[/C][C]NA[/C][C]NA[/C][C]1.02612324059806[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]128.7[/C][C]NA[/C][C]NA[/C][C]1.02207725546480[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]108.7[/C][C]NA[/C][C]NA[/C][C]0.958011024227935[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]105.5[/C][C]109.374607482350[/C][C]114.954166666667[/C][C]0.951462749493057[/C][C]0.964574890172977[/C][/ROW]
[ROW][C]8[/C][C]119.8[/C][C]121.948216255011[/C][C]114.8625[/C][C]1.06168868216355[/C][C]0.982384192889554[/C][/ROW]
[ROW][C]9[/C][C]111.3[/C][C]111.635304139402[/C][C]114.654166666667[/C][C]0.973669840224461[/C][C]0.996996432786322[/C][/ROW]
[ROW][C]10[/C][C]110.6[/C][C]111.183928997099[/C][C]114.504166666667[/C][C]0.971003346286662[/C][C]0.994748080928907[/C][/ROW]
[ROW][C]11[/C][C]120.1[/C][C]119.711506887963[/C][C]113.995833333333[/C][C]1.05013931989879[/C][C]1.00324524452274[/C][/ROW]
[ROW][C]12[/C][C]97.5[/C][C]102.592191349491[/C][C]113.570833333333[/C][C]0.903332205447332[/C][C]0.950364727738934[/C][/ROW]
[ROW][C]13[/C][C]107.7[/C][C]106.158491853821[/C][C]113.954166666667[/C][C]0.931589383338221[/C][C]1.01452081806420[/C][/ROW]
[ROW][C]14[/C][C]127.3[/C][C]123.370977118604[/C][C]114.691666666667[/C][C]1.07567516197286[/C][C]1.03184722187633[/C][/ROW]
[ROW][C]15[/C][C]117.2[/C][C]123.704957341235[/C][C]115.05[/C][C]1.07522779088427[/C][C]0.947415548406103[/C][/ROW]
[ROW][C]16[/C][C]119.8[/C][C]118.312009640956[/C][C]115.3[/C][C]1.02612324059806[/C][C]1.01257683276245[/C][/ROW]
[ROW][C]17[/C][C]116.2[/C][C]118.356546182824[/C][C]115.8[/C][C]1.02207725546480[/C][C]0.981779240334603[/C][/ROW]
[ROW][C]18[/C][C]111[/C][C]111.253021901070[/C][C]116.129166666667[/C][C]0.958011024227935[/C][C]0.997725707610038[/C][/ROW]
[ROW][C]19[/C][C]112.4[/C][C]110.857303600310[/C][C]116.5125[/C][C]0.951462749493057[/C][C]1.01391605559208[/C][/ROW]
[ROW][C]20[/C][C]130.6[/C][C]123.956577345437[/C][C]116.754166666667[/C][C]1.06168868216355[/C][C]1.05359475710635[/C][/ROW]
[ROW][C]21[/C][C]109.1[/C][C]114.211472258329[/C][C]117.3[/C][C]0.973669840224461[/C][C]0.955245544451367[/C][/ROW]
[ROW][C]22[/C][C]118.8[/C][C]114.643128418245[/C][C]118.066666666667[/C][C]0.971003346286662[/C][C]1.03625923017897[/C][/ROW]
[ROW][C]23[/C][C]123.9[/C][C]124.393378022512[/C][C]118.454166666667[/C][C]1.05013931989879[/C][C]0.996033727595834[/C][/ROW]
[ROW][C]24[/C][C]101.6[/C][C]107.304574354575[/C][C]118.7875[/C][C]0.903332205447332[/C][C]0.946837547337685[/C][/ROW]
[ROW][C]25[/C][C]112.8[/C][C]110.890189596693[/C][C]119.033333333333[/C][C]0.931589383338221[/C][C]1.01722253709055[/C][/ROW]
[ROW][C]26[/C][C]128[/C][C]128.054646053027[/C][C]119.045833333333[/C][C]1.07567516197286[/C][C]0.999573259895585[/C][/ROW]
[ROW][C]27[/C][C]129.6[/C][C]128.090990704884[/C][C]119.129166666667[/C][C]1.07522779088427[/C][C]1.01178076058911[/C][/ROW]
[ROW][C]28[/C][C]125.8[/C][C]122.279686171268[/C][C]119.166666666667[/C][C]1.02612324059806[/C][C]1.02878903225022[/C][/ROW]
[ROW][C]29[/C][C]119.5[/C][C]121.844384817097[/C][C]119.2125[/C][C]1.02207725546480[/C][C]0.980759188692885[/C][/ROW]
[ROW][C]30[/C][C]115.7[/C][C]114.749762139502[/C][C]119.779166666667[/C][C]0.958011024227935[/C][C]1.00828095712602[/C][/ROW]
[ROW][C]31[/C][C]113.6[/C][C]114.437182195277[/C][C]120.275[/C][C]0.951462749493057[/C][C]0.992684351543637[/C][/ROW]
[ROW][C]32[/C][C]129.7[/C][C]127.451302590892[/C][C]120.045833333333[/C][C]1.06168868216355[/C][C]1.01764358122197[/C][/ROW]
[ROW][C]33[/C][C]112[/C][C]116.686216435566[/C][C]119.841666666667[/C][C]0.973669840224461[/C][C]0.959839160282018[/C][/ROW]
[ROW][C]34[/C][C]116.8[/C][C]116.415209525218[/C][C]119.891666666667[/C][C]0.971003346286662[/C][C]1.00330532819853[/C][/ROW]
[ROW][C]35[/C][C]127[/C][C]125.907328875366[/C][C]119.895833333333[/C][C]1.05013931989879[/C][C]1.00867837586894[/C][/ROW]
[ROW][C]36[/C][C]112.1[/C][C]108.414920190437[/C][C]120.016666666667[/C][C]0.903332205447332[/C][C]1.03399052273515[/C][/ROW]
[ROW][C]37[/C][C]114.2[/C][C]112.035268213713[/C][C]120.2625[/C][C]0.931589383338221[/C][C]1.01932187801932[/C][/ROW]
[ROW][C]38[/C][C]121.1[/C][C]129.130321215000[/C][C]120.045833333333[/C][C]1.07567516197286[/C][C]0.937812272598394[/C][/ROW]
[ROW][C]39[/C][C]131.6[/C][C]129.363343590764[/C][C]120.3125[/C][C]1.07522779088427[/C][C]1.01728972324890[/C][/ROW]
[ROW][C]40[/C][C]125[/C][C]123.925758869728[/C][C]120.770833333333[/C][C]1.02612324059806[/C][C]1.00866842487042[/C][/ROW]
[ROW][C]41[/C][C]120.4[/C][C]123.139016007353[/C][C]120.479166666667[/C][C]1.02207725546480[/C][C]0.977756716789184[/C][/ROW]
[ROW][C]42[/C][C]117.7[/C][C]115.444320132067[/C][C]120.504166666667[/C][C]0.958011024227935[/C][C]1.01953911517996[/C][/ROW]
[ROW][C]43[/C][C]117.5[/C][C]114.401502342171[/C][C]120.2375[/C][C]0.951462749493057[/C][C]1.02708441405394[/C][/ROW]
[ROW][C]44[/C][C]120.6[/C][C]127.668064030167[/C][C]120.25[/C][C]1.06168868216355[/C][C]0.944637180144776[/C][/ROW]
[ROW][C]45[/C][C]127.5[/C][C]117.384013154394[/C][C]120.558333333333[/C][C]0.973669840224461[/C][C]1.08617857384293[/C][/ROW]
[ROW][C]46[/C][C]112.3[/C][C]116.500172318018[/C][C]119.979166666667[/C][C]0.971003346286662[/C][C]0.963947072056229[/C][/ROW]
[ROW][C]47[/C][C]124.5[/C][C]125.719178913884[/C][C]119.716666666667[/C][C]1.05013931989879[/C][C]0.99030236337513[/C][/ROW]
[ROW][C]48[/C][C]115.2[/C][C]107.959490203524[/C][C]119.5125[/C][C]0.903332205447332[/C][C]1.06706691355087[/C][/ROW]
[ROW][C]49[/C][C]104.7[/C][C]110.536961955511[/C][C]118.654166666667[/C][C]0.931589383338221[/C][C]0.947194478188574[/C][/ROW]
[ROW][C]50[/C][C]130.9[/C][C]127.207551862974[/C][C]118.258333333333[/C][C]1.07567516197286[/C][C]1.02902695699233[/C][/ROW]
[ROW][C]51[/C][C]129.2[/C][C]126.446788207990[/C][C]117.6[/C][C]1.07522779088427[/C][C]1.02177367911853[/C][/ROW]
[ROW][C]52[/C][C]113.5[/C][C]119.697276015763[/C][C]116.65[/C][C]1.02612324059806[/C][C]0.948225421479541[/C][/ROW]
[ROW][C]53[/C][C]125.6[/C][C]118.718531877467[/C][C]116.154166666667[/C][C]1.02207725546480[/C][C]1.05796456554597[/C][/ROW]
[ROW][C]54[/C][C]107.6[/C][C]110.618339597519[/C][C]115.466666666667[/C][C]0.958011024227935[/C][C]0.972713931446621[/C][/ROW]
[ROW][C]55[/C][C]107[/C][C]NA[/C][C]NA[/C][C]0.951462749493057[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]121.6[/C][C]NA[/C][C]NA[/C][C]1.06168868216355[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]110.7[/C][C]NA[/C][C]NA[/C][C]0.973669840224461[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]106.3[/C][C]NA[/C][C]NA[/C][C]0.971003346286662[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]118.6[/C][C]NA[/C][C]NA[/C][C]1.05013931989879[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]104.6[/C][C]NA[/C][C]NA[/C][C]0.903332205447332[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=65897&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=65897&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	108.8	NA	NA	0.931589383338221	NA
2	128.4	NA	NA	1.07567516197286	NA
3	121.1	NA	NA	1.07522779088427	NA
4	119.5	NA	NA	1.02612324059806	NA
5	128.7	NA	NA	1.02207725546480	NA
6	108.7	NA	NA	0.958011024227935	NA
7	105.5	109.374607482350	114.954166666667	0.951462749493057	0.964574890172977
8	119.8	121.948216255011	114.8625	1.06168868216355	0.982384192889554
9	111.3	111.635304139402	114.654166666667	0.973669840224461	0.996996432786322
10	110.6	111.183928997099	114.504166666667	0.971003346286662	0.994748080928907
11	120.1	119.711506887963	113.995833333333	1.05013931989879	1.00324524452274
12	97.5	102.592191349491	113.570833333333	0.903332205447332	0.950364727738934
13	107.7	106.158491853821	113.954166666667	0.931589383338221	1.01452081806420
14	127.3	123.370977118604	114.691666666667	1.07567516197286	1.03184722187633
15	117.2	123.704957341235	115.05	1.07522779088427	0.947415548406103
16	119.8	118.312009640956	115.3	1.02612324059806	1.01257683276245
17	116.2	118.356546182824	115.8	1.02207725546480	0.981779240334603
18	111	111.253021901070	116.129166666667	0.958011024227935	0.997725707610038
19	112.4	110.857303600310	116.5125	0.951462749493057	1.01391605559208
20	130.6	123.956577345437	116.754166666667	1.06168868216355	1.05359475710635
21	109.1	114.211472258329	117.3	0.973669840224461	0.955245544451367
22	118.8	114.643128418245	118.066666666667	0.971003346286662	1.03625923017897
23	123.9	124.393378022512	118.454166666667	1.05013931989879	0.996033727595834
24	101.6	107.304574354575	118.7875	0.903332205447332	0.946837547337685
25	112.8	110.890189596693	119.033333333333	0.931589383338221	1.01722253709055
26	128	128.054646053027	119.045833333333	1.07567516197286	0.999573259895585
27	129.6	128.090990704884	119.129166666667	1.07522779088427	1.01178076058911
28	125.8	122.279686171268	119.166666666667	1.02612324059806	1.02878903225022
29	119.5	121.844384817097	119.2125	1.02207725546480	0.980759188692885
30	115.7	114.749762139502	119.779166666667	0.958011024227935	1.00828095712602
31	113.6	114.437182195277	120.275	0.951462749493057	0.992684351543637
32	129.7	127.451302590892	120.045833333333	1.06168868216355	1.01764358122197
33	112	116.686216435566	119.841666666667	0.973669840224461	0.959839160282018
34	116.8	116.415209525218	119.891666666667	0.971003346286662	1.00330532819853
35	127	125.907328875366	119.895833333333	1.05013931989879	1.00867837586894
36	112.1	108.414920190437	120.016666666667	0.903332205447332	1.03399052273515
37	114.2	112.035268213713	120.2625	0.931589383338221	1.01932187801932
38	121.1	129.130321215000	120.045833333333	1.07567516197286	0.937812272598394
39	131.6	129.363343590764	120.3125	1.07522779088427	1.01728972324890
40	125	123.925758869728	120.770833333333	1.02612324059806	1.00866842487042
41	120.4	123.139016007353	120.479166666667	1.02207725546480	0.977756716789184
42	117.7	115.444320132067	120.504166666667	0.958011024227935	1.01953911517996
43	117.5	114.401502342171	120.2375	0.951462749493057	1.02708441405394
44	120.6	127.668064030167	120.25	1.06168868216355	0.944637180144776
45	127.5	117.384013154394	120.558333333333	0.973669840224461	1.08617857384293
46	112.3	116.500172318018	119.979166666667	0.971003346286662	0.963947072056229
47	124.5	125.719178913884	119.716666666667	1.05013931989879	0.99030236337513
48	115.2	107.959490203524	119.5125	0.903332205447332	1.06706691355087
49	104.7	110.536961955511	118.654166666667	0.931589383338221	0.947194478188574
50	130.9	127.207551862974	118.258333333333	1.07567516197286	1.02902695699233
51	129.2	126.446788207990	117.6	1.07522779088427	1.02177367911853
52	113.5	119.697276015763	116.65	1.02612324059806	0.948225421479541
53	125.6	118.718531877467	116.154166666667	1.02207725546480	1.05796456554597
54	107.6	110.618339597519	115.466666666667	0.958011024227935	0.972713931446621
55	107	NA	NA	0.951462749493057	NA
56	121.6	NA	NA	1.06168868216355	NA
57	110.7	NA	NA	0.973669840224461	NA
58	106.3	NA	NA	0.971003346286662	NA
59	118.6	NA	NA	1.05013931989879	NA
60	104.6	NA	NA	0.903332205447332	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