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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Thu, 02 Apr 2015 19:33:41 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Apr/02/t1427999671e255qhlwo3d88g6.htm/, Retrieved Thu, 09 May 2024 19:07:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=278623, Retrieved Thu, 09 May 2024 19:07:40 +0000

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

-       [Classical Decomposition] [opgave 9 oefening...] [2015-04-02 18:33:41] [24a1c9f91cd9dd71c8b0e9e460386711] [Current]

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Dataseries X:

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12,8
12,1
11,4
11,4
10,6
10,4
10,9
11,6
13,3
15,2
17,4
19,1
19,9
19,4
18,2
15,8
13,5
12,1
10,3
8,8
8,2
6,8
5,9
4,9
3,9
3,6
2,8
4
4,2
4,2
4,8
4
3,8
4
3,7
4
4,6
4,6
4,6
4,5
4,1
4,1
4,4
4,2
4,4
3,2
2,8
1,7
-0,2
-2,9
-5,2
-5,3
-4,8
-2,2
-0,8
-1,1
-1,5
-2
-2,8
-3,4
-4,1
-5,5
-8,6
-7,6
-8,6
-8,7
-4,6
-4,3
-1,5
1,2
1,8
0

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	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

\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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
R Framework error message & Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=278623&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=278623&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=278623&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	'Sir Ronald Aylmer Fisher' @ fisher.wessa.net
R Framework error message	Warning: there are blank lines in the 'Data' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	12.8	NA	NA	0.871181	NA
2	12.1	NA	NA	0.152847	NA
3	11.4	NA	NA	-1.07132	NA
4	11.4	NA	NA	-0.911319	NA
5	10.6	NA	NA	-1.26465	NA
6	10.4	NA	NA	-0.755486	NA
7	10.9	13.432	13.3125	0.119514	-2.53201
8	11.6	13.8995	13.9125	-0.0129861	-2.29951
9	13.3	14.9403	14.5	0.440347	-1.64035
10	15.2	15.532	14.9667	0.565347	-0.332014
11	17.4	16.1145	15.2708	0.843681	1.28549
12	19.1	16.4853	15.4625	1.02285	2.61465
13	19.9	16.3795	15.5083	0.871181	3.52049
14	19.4	15.5195	15.3667	0.152847	3.88049
15	18.2	13.9662	15.0375	-1.07132	4.23382
16	15.8	13.5637	14.475	-0.911319	2.23632
17	13.5	12.3812	13.6458	-1.26465	1.11882
18	12.1	11.8195	12.575	-0.755486	0.280486
19	10.3	11.4362	11.3167	0.119514	-1.13618
20	8.8	9.97868	9.99167	-0.0129861	-1.17868
21	8.2	9.13201	8.69167	0.440347	-0.932014
22	6.8	8.12368	7.55833	0.565347	-1.32368
23	5.9	7.52285	6.67917	0.843681	-1.62285
24	4.9	6.98535	5.9625	1.02285	-2.08535
25	3.9	6.27535	5.40417	0.871181	-2.37535
26	3.6	5.12785	4.975	0.152847	-1.52785
27	2.8	3.52035	4.59167	-1.07132	-0.720347
28	4	3.38035	4.29167	-0.911319	0.619653
29	4.2	2.81868	4.08333	-1.26465	1.38132
30	4.2	3.19868	3.95417	-0.755486	1.00132
31	4.8	4.06535	3.94583	0.119514	0.734653
32	4	4.00368	4.01667	-0.0129861	-0.00368056
33	3.8	4.57368	4.13333	0.440347	-0.773681
34	4	4.79451	4.22917	0.565347	-0.794514
35	3.7	5.08951	4.24583	0.843681	-1.38951
36	4	5.26035	4.2375	1.02285	-1.26035
37	4.6	5.08785	4.21667	0.871181	-0.487847
38	4.6	4.36118	4.20833	0.152847	0.238819
39	4.6	3.17035	4.24167	-1.07132	1.42965
40	4.5	3.32201	4.23333	-0.911319	1.17799
41	4.1	2.89785	4.1625	-1.26465	1.20215
42	4.1	3.27368	4.02917	-0.755486	0.826319
43	4.4	3.85285	3.73333	0.119514	0.547153
44	4.2	3.20785	3.22083	-0.0129861	0.992153
45	4.4	2.94035	2.5	0.440347	1.45965
46	3.2	2.24868	1.68333	0.565347	0.951319
47	2.8	1.74785	0.904167	0.843681	1.05215
48	1.7	1.29368	0.270833	1.02285	0.406319
49	-0.2	0.662847	-0.208333	0.871181	-0.862847
50	-2.9	-0.492986	-0.645833	0.152847	-2.40701
51	-5.2	-2.18382	-1.1125	-1.07132	-3.01618
52	-5.3	-2.48632	-1.575	-0.911319	-2.81368
53	-4.8	-3.28965	-2.025	-1.26465	-1.51035
54	-2.2	-3.22632	-2.47083	-0.755486	1.02632
55	-0.8	-2.72632	-2.84583	0.119514	1.92632
56	-1.1	-3.12965	-3.11667	-0.0129861	2.02965
57	-1.5	-2.92632	-3.36667	0.440347	1.42632
58	-2	-3.03882	-3.60417	0.565347	1.03882
59	-2.8	-3.01465	-3.85833	0.843681	0.214653
60	-3.4	-3.26465	-4.2875	1.02285	-0.135347
61	-4.1	-3.84549	-4.71667	0.871181	-0.254514
62	-5.5	-4.85549	-5.00833	0.152847	-0.644514
63	-8.6	-6.21299	-5.14167	-1.07132	-2.38701
64	-7.6	-5.91965	-5.00833	-0.911319	-1.68035
65	-8.6	-5.94799	-4.68333	-1.26465	-2.65201
66	-8.7	-5.10549	-4.35	-0.755486	-3.59451
67	-4.6	NA	NA	0.119514	NA
68	-4.3	NA	NA	-0.0129861	NA
69	-1.5	NA	NA	0.440347	NA
70	1.2	NA	NA	0.565347	NA
71	1.8	NA	NA	0.843681	NA
72	0	NA	NA	1.02285	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 12.8 & NA & NA & 0.871181 & NA \tabularnewline
2 & 12.1 & NA & NA & 0.152847 & NA \tabularnewline
3 & 11.4 & NA & NA & -1.07132 & NA \tabularnewline
4 & 11.4 & NA & NA & -0.911319 & NA \tabularnewline
5 & 10.6 & NA & NA & -1.26465 & NA \tabularnewline
6 & 10.4 & NA & NA & -0.755486 & NA \tabularnewline
7 & 10.9 & 13.432 & 13.3125 & 0.119514 & -2.53201 \tabularnewline
8 & 11.6 & 13.8995 & 13.9125 & -0.0129861 & -2.29951 \tabularnewline
9 & 13.3 & 14.9403 & 14.5 & 0.440347 & -1.64035 \tabularnewline
10 & 15.2 & 15.532 & 14.9667 & 0.565347 & -0.332014 \tabularnewline
11 & 17.4 & 16.1145 & 15.2708 & 0.843681 & 1.28549 \tabularnewline
12 & 19.1 & 16.4853 & 15.4625 & 1.02285 & 2.61465 \tabularnewline
13 & 19.9 & 16.3795 & 15.5083 & 0.871181 & 3.52049 \tabularnewline
14 & 19.4 & 15.5195 & 15.3667 & 0.152847 & 3.88049 \tabularnewline
15 & 18.2 & 13.9662 & 15.0375 & -1.07132 & 4.23382 \tabularnewline
16 & 15.8 & 13.5637 & 14.475 & -0.911319 & 2.23632 \tabularnewline
17 & 13.5 & 12.3812 & 13.6458 & -1.26465 & 1.11882 \tabularnewline
18 & 12.1 & 11.8195 & 12.575 & -0.755486 & 0.280486 \tabularnewline
19 & 10.3 & 11.4362 & 11.3167 & 0.119514 & -1.13618 \tabularnewline
20 & 8.8 & 9.97868 & 9.99167 & -0.0129861 & -1.17868 \tabularnewline
21 & 8.2 & 9.13201 & 8.69167 & 0.440347 & -0.932014 \tabularnewline
22 & 6.8 & 8.12368 & 7.55833 & 0.565347 & -1.32368 \tabularnewline
23 & 5.9 & 7.52285 & 6.67917 & 0.843681 & -1.62285 \tabularnewline
24 & 4.9 & 6.98535 & 5.9625 & 1.02285 & -2.08535 \tabularnewline
25 & 3.9 & 6.27535 & 5.40417 & 0.871181 & -2.37535 \tabularnewline
26 & 3.6 & 5.12785 & 4.975 & 0.152847 & -1.52785 \tabularnewline
27 & 2.8 & 3.52035 & 4.59167 & -1.07132 & -0.720347 \tabularnewline
28 & 4 & 3.38035 & 4.29167 & -0.911319 & 0.619653 \tabularnewline
29 & 4.2 & 2.81868 & 4.08333 & -1.26465 & 1.38132 \tabularnewline
30 & 4.2 & 3.19868 & 3.95417 & -0.755486 & 1.00132 \tabularnewline
31 & 4.8 & 4.06535 & 3.94583 & 0.119514 & 0.734653 \tabularnewline
32 & 4 & 4.00368 & 4.01667 & -0.0129861 & -0.00368056 \tabularnewline
33 & 3.8 & 4.57368 & 4.13333 & 0.440347 & -0.773681 \tabularnewline
34 & 4 & 4.79451 & 4.22917 & 0.565347 & -0.794514 \tabularnewline
35 & 3.7 & 5.08951 & 4.24583 & 0.843681 & -1.38951 \tabularnewline
36 & 4 & 5.26035 & 4.2375 & 1.02285 & -1.26035 \tabularnewline
37 & 4.6 & 5.08785 & 4.21667 & 0.871181 & -0.487847 \tabularnewline
38 & 4.6 & 4.36118 & 4.20833 & 0.152847 & 0.238819 \tabularnewline
39 & 4.6 & 3.17035 & 4.24167 & -1.07132 & 1.42965 \tabularnewline
40 & 4.5 & 3.32201 & 4.23333 & -0.911319 & 1.17799 \tabularnewline
41 & 4.1 & 2.89785 & 4.1625 & -1.26465 & 1.20215 \tabularnewline
42 & 4.1 & 3.27368 & 4.02917 & -0.755486 & 0.826319 \tabularnewline
43 & 4.4 & 3.85285 & 3.73333 & 0.119514 & 0.547153 \tabularnewline
44 & 4.2 & 3.20785 & 3.22083 & -0.0129861 & 0.992153 \tabularnewline
45 & 4.4 & 2.94035 & 2.5 & 0.440347 & 1.45965 \tabularnewline
46 & 3.2 & 2.24868 & 1.68333 & 0.565347 & 0.951319 \tabularnewline
47 & 2.8 & 1.74785 & 0.904167 & 0.843681 & 1.05215 \tabularnewline
48 & 1.7 & 1.29368 & 0.270833 & 1.02285 & 0.406319 \tabularnewline
49 & -0.2 & 0.662847 & -0.208333 & 0.871181 & -0.862847 \tabularnewline
50 & -2.9 & -0.492986 & -0.645833 & 0.152847 & -2.40701 \tabularnewline
51 & -5.2 & -2.18382 & -1.1125 & -1.07132 & -3.01618 \tabularnewline
52 & -5.3 & -2.48632 & -1.575 & -0.911319 & -2.81368 \tabularnewline
53 & -4.8 & -3.28965 & -2.025 & -1.26465 & -1.51035 \tabularnewline
54 & -2.2 & -3.22632 & -2.47083 & -0.755486 & 1.02632 \tabularnewline
55 & -0.8 & -2.72632 & -2.84583 & 0.119514 & 1.92632 \tabularnewline
56 & -1.1 & -3.12965 & -3.11667 & -0.0129861 & 2.02965 \tabularnewline
57 & -1.5 & -2.92632 & -3.36667 & 0.440347 & 1.42632 \tabularnewline
58 & -2 & -3.03882 & -3.60417 & 0.565347 & 1.03882 \tabularnewline
59 & -2.8 & -3.01465 & -3.85833 & 0.843681 & 0.214653 \tabularnewline
60 & -3.4 & -3.26465 & -4.2875 & 1.02285 & -0.135347 \tabularnewline
61 & -4.1 & -3.84549 & -4.71667 & 0.871181 & -0.254514 \tabularnewline
62 & -5.5 & -4.85549 & -5.00833 & 0.152847 & -0.644514 \tabularnewline
63 & -8.6 & -6.21299 & -5.14167 & -1.07132 & -2.38701 \tabularnewline
64 & -7.6 & -5.91965 & -5.00833 & -0.911319 & -1.68035 \tabularnewline
65 & -8.6 & -5.94799 & -4.68333 & -1.26465 & -2.65201 \tabularnewline
66 & -8.7 & -5.10549 & -4.35 & -0.755486 & -3.59451 \tabularnewline
67 & -4.6 & NA & NA & 0.119514 & NA \tabularnewline
68 & -4.3 & NA & NA & -0.0129861 & NA \tabularnewline
69 & -1.5 & NA & NA & 0.440347 & NA \tabularnewline
70 & 1.2 & NA & NA & 0.565347 & NA \tabularnewline
71 & 1.8 & NA & NA & 0.843681 & NA \tabularnewline
72 & 0 & NA & NA & 1.02285 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=278623&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]12.8[/C][C]NA[/C][C]NA[/C][C]0.871181[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]12.1[/C][C]NA[/C][C]NA[/C][C]0.152847[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]11.4[/C][C]NA[/C][C]NA[/C][C]-1.07132[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]11.4[/C][C]NA[/C][C]NA[/C][C]-0.911319[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]10.6[/C][C]NA[/C][C]NA[/C][C]-1.26465[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]10.4[/C][C]NA[/C][C]NA[/C][C]-0.755486[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]10.9[/C][C]13.432[/C][C]13.3125[/C][C]0.119514[/C][C]-2.53201[/C][/ROW]
[ROW][C]8[/C][C]11.6[/C][C]13.8995[/C][C]13.9125[/C][C]-0.0129861[/C][C]-2.29951[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]14.9403[/C][C]14.5[/C][C]0.440347[/C][C]-1.64035[/C][/ROW]
[ROW][C]10[/C][C]15.2[/C][C]15.532[/C][C]14.9667[/C][C]0.565347[/C][C]-0.332014[/C][/ROW]
[ROW][C]11[/C][C]17.4[/C][C]16.1145[/C][C]15.2708[/C][C]0.843681[/C][C]1.28549[/C][/ROW]
[ROW][C]12[/C][C]19.1[/C][C]16.4853[/C][C]15.4625[/C][C]1.02285[/C][C]2.61465[/C][/ROW]
[ROW][C]13[/C][C]19.9[/C][C]16.3795[/C][C]15.5083[/C][C]0.871181[/C][C]3.52049[/C][/ROW]
[ROW][C]14[/C][C]19.4[/C][C]15.5195[/C][C]15.3667[/C][C]0.152847[/C][C]3.88049[/C][/ROW]
[ROW][C]15[/C][C]18.2[/C][C]13.9662[/C][C]15.0375[/C][C]-1.07132[/C][C]4.23382[/C][/ROW]
[ROW][C]16[/C][C]15.8[/C][C]13.5637[/C][C]14.475[/C][C]-0.911319[/C][C]2.23632[/C][/ROW]
[ROW][C]17[/C][C]13.5[/C][C]12.3812[/C][C]13.6458[/C][C]-1.26465[/C][C]1.11882[/C][/ROW]
[ROW][C]18[/C][C]12.1[/C][C]11.8195[/C][C]12.575[/C][C]-0.755486[/C][C]0.280486[/C][/ROW]
[ROW][C]19[/C][C]10.3[/C][C]11.4362[/C][C]11.3167[/C][C]0.119514[/C][C]-1.13618[/C][/ROW]
[ROW][C]20[/C][C]8.8[/C][C]9.97868[/C][C]9.99167[/C][C]-0.0129861[/C][C]-1.17868[/C][/ROW]
[ROW][C]21[/C][C]8.2[/C][C]9.13201[/C][C]8.69167[/C][C]0.440347[/C][C]-0.932014[/C][/ROW]
[ROW][C]22[/C][C]6.8[/C][C]8.12368[/C][C]7.55833[/C][C]0.565347[/C][C]-1.32368[/C][/ROW]
[ROW][C]23[/C][C]5.9[/C][C]7.52285[/C][C]6.67917[/C][C]0.843681[/C][C]-1.62285[/C][/ROW]
[ROW][C]24[/C][C]4.9[/C][C]6.98535[/C][C]5.9625[/C][C]1.02285[/C][C]-2.08535[/C][/ROW]
[ROW][C]25[/C][C]3.9[/C][C]6.27535[/C][C]5.40417[/C][C]0.871181[/C][C]-2.37535[/C][/ROW]
[ROW][C]26[/C][C]3.6[/C][C]5.12785[/C][C]4.975[/C][C]0.152847[/C][C]-1.52785[/C][/ROW]
[ROW][C]27[/C][C]2.8[/C][C]3.52035[/C][C]4.59167[/C][C]-1.07132[/C][C]-0.720347[/C][/ROW]
[ROW][C]28[/C][C]4[/C][C]3.38035[/C][C]4.29167[/C][C]-0.911319[/C][C]0.619653[/C][/ROW]
[ROW][C]29[/C][C]4.2[/C][C]2.81868[/C][C]4.08333[/C][C]-1.26465[/C][C]1.38132[/C][/ROW]
[ROW][C]30[/C][C]4.2[/C][C]3.19868[/C][C]3.95417[/C][C]-0.755486[/C][C]1.00132[/C][/ROW]
[ROW][C]31[/C][C]4.8[/C][C]4.06535[/C][C]3.94583[/C][C]0.119514[/C][C]0.734653[/C][/ROW]
[ROW][C]32[/C][C]4[/C][C]4.00368[/C][C]4.01667[/C][C]-0.0129861[/C][C]-0.00368056[/C][/ROW]
[ROW][C]33[/C][C]3.8[/C][C]4.57368[/C][C]4.13333[/C][C]0.440347[/C][C]-0.773681[/C][/ROW]
[ROW][C]34[/C][C]4[/C][C]4.79451[/C][C]4.22917[/C][C]0.565347[/C][C]-0.794514[/C][/ROW]
[ROW][C]35[/C][C]3.7[/C][C]5.08951[/C][C]4.24583[/C][C]0.843681[/C][C]-1.38951[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]5.26035[/C][C]4.2375[/C][C]1.02285[/C][C]-1.26035[/C][/ROW]
[ROW][C]37[/C][C]4.6[/C][C]5.08785[/C][C]4.21667[/C][C]0.871181[/C][C]-0.487847[/C][/ROW]
[ROW][C]38[/C][C]4.6[/C][C]4.36118[/C][C]4.20833[/C][C]0.152847[/C][C]0.238819[/C][/ROW]
[ROW][C]39[/C][C]4.6[/C][C]3.17035[/C][C]4.24167[/C][C]-1.07132[/C][C]1.42965[/C][/ROW]
[ROW][C]40[/C][C]4.5[/C][C]3.32201[/C][C]4.23333[/C][C]-0.911319[/C][C]1.17799[/C][/ROW]
[ROW][C]41[/C][C]4.1[/C][C]2.89785[/C][C]4.1625[/C][C]-1.26465[/C][C]1.20215[/C][/ROW]
[ROW][C]42[/C][C]4.1[/C][C]3.27368[/C][C]4.02917[/C][C]-0.755486[/C][C]0.826319[/C][/ROW]
[ROW][C]43[/C][C]4.4[/C][C]3.85285[/C][C]3.73333[/C][C]0.119514[/C][C]0.547153[/C][/ROW]
[ROW][C]44[/C][C]4.2[/C][C]3.20785[/C][C]3.22083[/C][C]-0.0129861[/C][C]0.992153[/C][/ROW]
[ROW][C]45[/C][C]4.4[/C][C]2.94035[/C][C]2.5[/C][C]0.440347[/C][C]1.45965[/C][/ROW]
[ROW][C]46[/C][C]3.2[/C][C]2.24868[/C][C]1.68333[/C][C]0.565347[/C][C]0.951319[/C][/ROW]
[ROW][C]47[/C][C]2.8[/C][C]1.74785[/C][C]0.904167[/C][C]0.843681[/C][C]1.05215[/C][/ROW]
[ROW][C]48[/C][C]1.7[/C][C]1.29368[/C][C]0.270833[/C][C]1.02285[/C][C]0.406319[/C][/ROW]
[ROW][C]49[/C][C]-0.2[/C][C]0.662847[/C][C]-0.208333[/C][C]0.871181[/C][C]-0.862847[/C][/ROW]
[ROW][C]50[/C][C]-2.9[/C][C]-0.492986[/C][C]-0.645833[/C][C]0.152847[/C][C]-2.40701[/C][/ROW]
[ROW][C]51[/C][C]-5.2[/C][C]-2.18382[/C][C]-1.1125[/C][C]-1.07132[/C][C]-3.01618[/C][/ROW]
[ROW][C]52[/C][C]-5.3[/C][C]-2.48632[/C][C]-1.575[/C][C]-0.911319[/C][C]-2.81368[/C][/ROW]
[ROW][C]53[/C][C]-4.8[/C][C]-3.28965[/C][C]-2.025[/C][C]-1.26465[/C][C]-1.51035[/C][/ROW]
[ROW][C]54[/C][C]-2.2[/C][C]-3.22632[/C][C]-2.47083[/C][C]-0.755486[/C][C]1.02632[/C][/ROW]
[ROW][C]55[/C][C]-0.8[/C][C]-2.72632[/C][C]-2.84583[/C][C]0.119514[/C][C]1.92632[/C][/ROW]
[ROW][C]56[/C][C]-1.1[/C][C]-3.12965[/C][C]-3.11667[/C][C]-0.0129861[/C][C]2.02965[/C][/ROW]
[ROW][C]57[/C][C]-1.5[/C][C]-2.92632[/C][C]-3.36667[/C][C]0.440347[/C][C]1.42632[/C][/ROW]
[ROW][C]58[/C][C]-2[/C][C]-3.03882[/C][C]-3.60417[/C][C]0.565347[/C][C]1.03882[/C][/ROW]
[ROW][C]59[/C][C]-2.8[/C][C]-3.01465[/C][C]-3.85833[/C][C]0.843681[/C][C]0.214653[/C][/ROW]
[ROW][C]60[/C][C]-3.4[/C][C]-3.26465[/C][C]-4.2875[/C][C]1.02285[/C][C]-0.135347[/C][/ROW]
[ROW][C]61[/C][C]-4.1[/C][C]-3.84549[/C][C]-4.71667[/C][C]0.871181[/C][C]-0.254514[/C][/ROW]
[ROW][C]62[/C][C]-5.5[/C][C]-4.85549[/C][C]-5.00833[/C][C]0.152847[/C][C]-0.644514[/C][/ROW]
[ROW][C]63[/C][C]-8.6[/C][C]-6.21299[/C][C]-5.14167[/C][C]-1.07132[/C][C]-2.38701[/C][/ROW]
[ROW][C]64[/C][C]-7.6[/C][C]-5.91965[/C][C]-5.00833[/C][C]-0.911319[/C][C]-1.68035[/C][/ROW]
[ROW][C]65[/C][C]-8.6[/C][C]-5.94799[/C][C]-4.68333[/C][C]-1.26465[/C][C]-2.65201[/C][/ROW]
[ROW][C]66[/C][C]-8.7[/C][C]-5.10549[/C][C]-4.35[/C][C]-0.755486[/C][C]-3.59451[/C][/ROW]
[ROW][C]67[/C][C]-4.6[/C][C]NA[/C][C]NA[/C][C]0.119514[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]-4.3[/C][C]NA[/C][C]NA[/C][C]-0.0129861[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]-1.5[/C][C]NA[/C][C]NA[/C][C]0.440347[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]1.2[/C][C]NA[/C][C]NA[/C][C]0.565347[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]1.8[/C][C]NA[/C][C]NA[/C][C]0.843681[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]NA[/C][C]NA[/C][C]1.02285[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=278623&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=278623&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	12.8	NA	NA	0.871181	NA
2	12.1	NA	NA	0.152847	NA
3	11.4	NA	NA	-1.07132	NA
4	11.4	NA	NA	-0.911319	NA
5	10.6	NA	NA	-1.26465	NA
6	10.4	NA	NA	-0.755486	NA
7	10.9	13.432	13.3125	0.119514	-2.53201
8	11.6	13.8995	13.9125	-0.0129861	-2.29951
9	13.3	14.9403	14.5	0.440347	-1.64035
10	15.2	15.532	14.9667	0.565347	-0.332014
11	17.4	16.1145	15.2708	0.843681	1.28549
12	19.1	16.4853	15.4625	1.02285	2.61465
13	19.9	16.3795	15.5083	0.871181	3.52049
14	19.4	15.5195	15.3667	0.152847	3.88049
15	18.2	13.9662	15.0375	-1.07132	4.23382
16	15.8	13.5637	14.475	-0.911319	2.23632
17	13.5	12.3812	13.6458	-1.26465	1.11882
18	12.1	11.8195	12.575	-0.755486	0.280486
19	10.3	11.4362	11.3167	0.119514	-1.13618
20	8.8	9.97868	9.99167	-0.0129861	-1.17868
21	8.2	9.13201	8.69167	0.440347	-0.932014
22	6.8	8.12368	7.55833	0.565347	-1.32368
23	5.9	7.52285	6.67917	0.843681	-1.62285
24	4.9	6.98535	5.9625	1.02285	-2.08535
25	3.9	6.27535	5.40417	0.871181	-2.37535
26	3.6	5.12785	4.975	0.152847	-1.52785
27	2.8	3.52035	4.59167	-1.07132	-0.720347
28	4	3.38035	4.29167	-0.911319	0.619653
29	4.2	2.81868	4.08333	-1.26465	1.38132
30	4.2	3.19868	3.95417	-0.755486	1.00132
31	4.8	4.06535	3.94583	0.119514	0.734653
32	4	4.00368	4.01667	-0.0129861	-0.00368056
33	3.8	4.57368	4.13333	0.440347	-0.773681
34	4	4.79451	4.22917	0.565347	-0.794514
35	3.7	5.08951	4.24583	0.843681	-1.38951
36	4	5.26035	4.2375	1.02285	-1.26035
37	4.6	5.08785	4.21667	0.871181	-0.487847
38	4.6	4.36118	4.20833	0.152847	0.238819
39	4.6	3.17035	4.24167	-1.07132	1.42965
40	4.5	3.32201	4.23333	-0.911319	1.17799
41	4.1	2.89785	4.1625	-1.26465	1.20215
42	4.1	3.27368	4.02917	-0.755486	0.826319
43	4.4	3.85285	3.73333	0.119514	0.547153
44	4.2	3.20785	3.22083	-0.0129861	0.992153
45	4.4	2.94035	2.5	0.440347	1.45965
46	3.2	2.24868	1.68333	0.565347	0.951319
47	2.8	1.74785	0.904167	0.843681	1.05215
48	1.7	1.29368	0.270833	1.02285	0.406319
49	-0.2	0.662847	-0.208333	0.871181	-0.862847
50	-2.9	-0.492986	-0.645833	0.152847	-2.40701
51	-5.2	-2.18382	-1.1125	-1.07132	-3.01618
52	-5.3	-2.48632	-1.575	-0.911319	-2.81368
53	-4.8	-3.28965	-2.025	-1.26465	-1.51035
54	-2.2	-3.22632	-2.47083	-0.755486	1.02632
55	-0.8	-2.72632	-2.84583	0.119514	1.92632
56	-1.1	-3.12965	-3.11667	-0.0129861	2.02965
57	-1.5	-2.92632	-3.36667	0.440347	1.42632
58	-2	-3.03882	-3.60417	0.565347	1.03882
59	-2.8	-3.01465	-3.85833	0.843681	0.214653
60	-3.4	-3.26465	-4.2875	1.02285	-0.135347
61	-4.1	-3.84549	-4.71667	0.871181	-0.254514
62	-5.5	-4.85549	-5.00833	0.152847	-0.644514
63	-8.6	-6.21299	-5.14167	-1.07132	-2.38701
64	-7.6	-5.91965	-5.00833	-0.911319	-1.68035
65	-8.6	-5.94799	-4.68333	-1.26465	-2.65201
66	-8.7	-5.10549	-4.35	-0.755486	-3.59451
67	-4.6	NA	NA	0.119514	NA
68	-4.3	NA	NA	-0.0129861	NA
69	-1.5	NA	NA	0.440347	NA
70	1.2	NA	NA	0.565347	NA
71	1.8	NA	NA	0.843681	NA
72	0	NA	NA	1.02285	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 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- '12'
par1 <- 'additive'
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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
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