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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Wed, 29 Dec 2010 19:20:10 +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/29/t1293650378k583hmh3n2bc2r4.htm/, Retrieved Fri, 03 May 2024 08:11:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=117053, Retrieved Fri, 03 May 2024 08:11:41 +0000

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-  M D  [Classical Decomposition] [Paper 'Classical ...] [2010-12-20 14:30:24] [40c8b935cbad1b0be3c22a481f9723f7]
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Dataseries X:

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9,3
14,2
17,3
23
16,3
18,4
14,2
9,1
5,9
7,2
6,8
8
14,3
14,6
17,5
17,2
17,2
14,1
10,4
6,8
4,1
6,5
6,1
6,3
9,3
16,4
16,1
18
17,6
14
10,5
6,9
2,8
0,7
3,6
6,7
12,5
14,4
16,5
18,7
19,4
15,8
11,3
9,7
2,9
0,1
2,5
6,7
10,3
11,2
17,4
20,5
17
14,2
10,6
6,1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117053&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117053&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117053&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	3 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	9.3	NA	NA	0.85914351851852	NA
2	14.2	NA	NA	3.99108796296296	NA
3	17.3	NA	NA	5.59108796296297	NA
4	23	NA	NA	6.99803240740741	NA
5	16.3	NA	NA	7.25636574074074	NA
6	18.4	NA	NA	3.90081018518519	NA
7	14.2	12.7577546296296	12.6833333333333	0.0744212962962968	1.44224537037037
8	9.1	10.1521990740741	12.9083333333333	-2.75613425925926	-1.05219907407407
9	5.9	5.69247685185185	12.9333333333333	-7.24085648148148	0.207523148148148
10	7.2	4.58136574074074	12.7	-8.11863425925926	2.61863425925926
11	6.8	5.96747685185185	12.4958333333333	-6.52835648148148	0.83252314814815
12	8	8.32719907407408	12.3541666666667	-4.02696759259259	-0.327199074074075
13	14.3	12.8758101851852	12.0166666666667	0.85914351851852	1.42418981481481
14	14.6	15.753587962963	11.7625	3.99108796296296	-1.15358796296296
15	17.5	17.1827546296296	11.5916666666667	5.59108796296297	0.317245370370371
16	17.2	18.4855324074074	11.4875	6.99803240740741	-1.28553240740741
17	17.2	18.6855324074074	11.4291666666667	7.25636574074074	-1.48553240740741
18	14.1	15.2299768518519	11.3291666666667	3.90081018518519	-1.12997685185185
19	10.4	11.1244212962963	11.05	0.0744212962962968	-0.724421296296297
20	6.8	8.1605324074074	10.9166666666667	-2.75613425925926	-1.36053240740741
21	4.1	3.69247685185185	10.9333333333333	-7.24085648148148	0.407523148148149
22	6.5	2.78969907407407	10.9083333333333	-8.11863425925926	3.71030092592593
23	6.1	4.42997685185185	10.9583333333333	-6.52835648148148	1.67002314814815
24	6.3	6.94386574074074	10.9708333333333	-4.02696759259259	-0.643865740740742
25	9.3	11.8299768518519	10.9708333333333	0.85914351851852	-2.52997685185185
26	16.4	14.9702546296296	10.9791666666667	3.99108796296296	1.42974537037037
27	16.1	16.5202546296296	10.9291666666667	5.59108796296297	-0.42025462962963
28	18	17.6313657407407	10.6333333333333	6.99803240740741	0.368634259259258
29	17.6	17.5438657407407	10.2875	7.25636574074074	0.0561342592592595
30	14	14.1008101851852	10.2	3.90081018518519	-0.100810185185185
31	10.5	10.4244212962963	10.35	0.0744212962962968	0.0755787037037035
32	6.9	7.64386574074074	10.4	-2.75613425925926	-0.743865740740741
33	2.8	3.09247685185185	10.3333333333333	-7.24085648148148	-0.292476851851852
34	0.7	2.26053240740741	10.3791666666667	-8.11863425925926	-1.56053240740741
35	3.6	3.95497685185185	10.4833333333333	-6.52835648148148	-0.354976851851848
36	6.7	6.60636574074074	10.6333333333333	-4.02696759259259	0.093634259259261
37	12.5	11.6008101851852	10.7416666666667	0.85914351851852	0.899189814814813
38	14.4	14.8827546296296	10.8916666666667	3.99108796296296	-0.482754629629627
39	16.5	16.603587962963	11.0125	5.59108796296297	-0.103587962962965
40	18.7	17.9896990740741	10.9916666666667	6.99803240740741	0.710300925925926
41	19.4	18.1771990740741	10.9208333333333	7.25636574074074	1.22280092592593
42	15.8	14.7758101851852	10.875	3.90081018518519	1.02418981481482
43	11.3	10.8577546296296	10.7833333333333	0.0744212962962968	0.442245370370372
44	9.7	7.80219907407407	10.5583333333333	-2.75613425925926	1.89780092592593
45	2.9	3.22164351851852	10.4625	-7.24085648148148	-0.321643518518519
46	0.1	2.45636574074074	10.575	-8.11863425925926	-2.35636574074074
47	2.5	4.02164351851852	10.55	-6.52835648148148	-1.52164351851852
48	6.7	6.35636574074074	10.3833333333333	-4.02696759259259	0.343634259259261
49	10.3	NA	10.2875	NA	NA
50	11.2	NA	10.1083333333333	NA	NA
51	17.4	NA	NA	NA	NA
52	20.5	NA	NA	NA	NA
53	17	NA	NA	NA	NA
54	14.2	NA	NA	NA	NA
55	10.6	NA	NA	NA	NA
56	6.1	NA	NA	NA	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 9.3 & NA & NA & 0.85914351851852 & NA \tabularnewline
2 & 14.2 & NA & NA & 3.99108796296296 & NA \tabularnewline
3 & 17.3 & NA & NA & 5.59108796296297 & NA \tabularnewline
4 & 23 & NA & NA & 6.99803240740741 & NA \tabularnewline
5 & 16.3 & NA & NA & 7.25636574074074 & NA \tabularnewline
6 & 18.4 & NA & NA & 3.90081018518519 & NA \tabularnewline
7 & 14.2 & 12.7577546296296 & 12.6833333333333 & 0.0744212962962968 & 1.44224537037037 \tabularnewline
8 & 9.1 & 10.1521990740741 & 12.9083333333333 & -2.75613425925926 & -1.05219907407407 \tabularnewline
9 & 5.9 & 5.69247685185185 & 12.9333333333333 & -7.24085648148148 & 0.207523148148148 \tabularnewline
10 & 7.2 & 4.58136574074074 & 12.7 & -8.11863425925926 & 2.61863425925926 \tabularnewline
11 & 6.8 & 5.96747685185185 & 12.4958333333333 & -6.52835648148148 & 0.83252314814815 \tabularnewline
12 & 8 & 8.32719907407408 & 12.3541666666667 & -4.02696759259259 & -0.327199074074075 \tabularnewline
13 & 14.3 & 12.8758101851852 & 12.0166666666667 & 0.85914351851852 & 1.42418981481481 \tabularnewline
14 & 14.6 & 15.753587962963 & 11.7625 & 3.99108796296296 & -1.15358796296296 \tabularnewline
15 & 17.5 & 17.1827546296296 & 11.5916666666667 & 5.59108796296297 & 0.317245370370371 \tabularnewline
16 & 17.2 & 18.4855324074074 & 11.4875 & 6.99803240740741 & -1.28553240740741 \tabularnewline
17 & 17.2 & 18.6855324074074 & 11.4291666666667 & 7.25636574074074 & -1.48553240740741 \tabularnewline
18 & 14.1 & 15.2299768518519 & 11.3291666666667 & 3.90081018518519 & -1.12997685185185 \tabularnewline
19 & 10.4 & 11.1244212962963 & 11.05 & 0.0744212962962968 & -0.724421296296297 \tabularnewline
20 & 6.8 & 8.1605324074074 & 10.9166666666667 & -2.75613425925926 & -1.36053240740741 \tabularnewline
21 & 4.1 & 3.69247685185185 & 10.9333333333333 & -7.24085648148148 & 0.407523148148149 \tabularnewline
22 & 6.5 & 2.78969907407407 & 10.9083333333333 & -8.11863425925926 & 3.71030092592593 \tabularnewline
23 & 6.1 & 4.42997685185185 & 10.9583333333333 & -6.52835648148148 & 1.67002314814815 \tabularnewline
24 & 6.3 & 6.94386574074074 & 10.9708333333333 & -4.02696759259259 & -0.643865740740742 \tabularnewline
25 & 9.3 & 11.8299768518519 & 10.9708333333333 & 0.85914351851852 & -2.52997685185185 \tabularnewline
26 & 16.4 & 14.9702546296296 & 10.9791666666667 & 3.99108796296296 & 1.42974537037037 \tabularnewline
27 & 16.1 & 16.5202546296296 & 10.9291666666667 & 5.59108796296297 & -0.42025462962963 \tabularnewline
28 & 18 & 17.6313657407407 & 10.6333333333333 & 6.99803240740741 & 0.368634259259258 \tabularnewline
29 & 17.6 & 17.5438657407407 & 10.2875 & 7.25636574074074 & 0.0561342592592595 \tabularnewline
30 & 14 & 14.1008101851852 & 10.2 & 3.90081018518519 & -0.100810185185185 \tabularnewline
31 & 10.5 & 10.4244212962963 & 10.35 & 0.0744212962962968 & 0.0755787037037035 \tabularnewline
32 & 6.9 & 7.64386574074074 & 10.4 & -2.75613425925926 & -0.743865740740741 \tabularnewline
33 & 2.8 & 3.09247685185185 & 10.3333333333333 & -7.24085648148148 & -0.292476851851852 \tabularnewline
34 & 0.7 & 2.26053240740741 & 10.3791666666667 & -8.11863425925926 & -1.56053240740741 \tabularnewline
35 & 3.6 & 3.95497685185185 & 10.4833333333333 & -6.52835648148148 & -0.354976851851848 \tabularnewline
36 & 6.7 & 6.60636574074074 & 10.6333333333333 & -4.02696759259259 & 0.093634259259261 \tabularnewline
37 & 12.5 & 11.6008101851852 & 10.7416666666667 & 0.85914351851852 & 0.899189814814813 \tabularnewline
38 & 14.4 & 14.8827546296296 & 10.8916666666667 & 3.99108796296296 & -0.482754629629627 \tabularnewline
39 & 16.5 & 16.603587962963 & 11.0125 & 5.59108796296297 & -0.103587962962965 \tabularnewline
40 & 18.7 & 17.9896990740741 & 10.9916666666667 & 6.99803240740741 & 0.710300925925926 \tabularnewline
41 & 19.4 & 18.1771990740741 & 10.9208333333333 & 7.25636574074074 & 1.22280092592593 \tabularnewline
42 & 15.8 & 14.7758101851852 & 10.875 & 3.90081018518519 & 1.02418981481482 \tabularnewline
43 & 11.3 & 10.8577546296296 & 10.7833333333333 & 0.0744212962962968 & 0.442245370370372 \tabularnewline
44 & 9.7 & 7.80219907407407 & 10.5583333333333 & -2.75613425925926 & 1.89780092592593 \tabularnewline
45 & 2.9 & 3.22164351851852 & 10.4625 & -7.24085648148148 & -0.321643518518519 \tabularnewline
46 & 0.1 & 2.45636574074074 & 10.575 & -8.11863425925926 & -2.35636574074074 \tabularnewline
47 & 2.5 & 4.02164351851852 & 10.55 & -6.52835648148148 & -1.52164351851852 \tabularnewline
48 & 6.7 & 6.35636574074074 & 10.3833333333333 & -4.02696759259259 & 0.343634259259261 \tabularnewline
49 & 10.3 & NA & 10.2875 & NA & NA \tabularnewline
50 & 11.2 & NA & 10.1083333333333 & NA & NA \tabularnewline
51 & 17.4 & NA & NA & NA & NA \tabularnewline
52 & 20.5 & NA & NA & NA & NA \tabularnewline
53 & 17 & NA & NA & NA & NA \tabularnewline
54 & 14.2 & NA & NA & NA & NA \tabularnewline
55 & 10.6 & NA & NA & NA & NA \tabularnewline
56 & 6.1 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=117053&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]9.3[/C][C]NA[/C][C]NA[/C][C]0.85914351851852[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]14.2[/C][C]NA[/C][C]NA[/C][C]3.99108796296296[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]17.3[/C][C]NA[/C][C]NA[/C][C]5.59108796296297[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]23[/C][C]NA[/C][C]NA[/C][C]6.99803240740741[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]16.3[/C][C]NA[/C][C]NA[/C][C]7.25636574074074[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]18.4[/C][C]NA[/C][C]NA[/C][C]3.90081018518519[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]14.2[/C][C]12.7577546296296[/C][C]12.6833333333333[/C][C]0.0744212962962968[/C][C]1.44224537037037[/C][/ROW]
[ROW][C]8[/C][C]9.1[/C][C]10.1521990740741[/C][C]12.9083333333333[/C][C]-2.75613425925926[/C][C]-1.05219907407407[/C][/ROW]
[ROW][C]9[/C][C]5.9[/C][C]5.69247685185185[/C][C]12.9333333333333[/C][C]-7.24085648148148[/C][C]0.207523148148148[/C][/ROW]
[ROW][C]10[/C][C]7.2[/C][C]4.58136574074074[/C][C]12.7[/C][C]-8.11863425925926[/C][C]2.61863425925926[/C][/ROW]
[ROW][C]11[/C][C]6.8[/C][C]5.96747685185185[/C][C]12.4958333333333[/C][C]-6.52835648148148[/C][C]0.83252314814815[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]8.32719907407408[/C][C]12.3541666666667[/C][C]-4.02696759259259[/C][C]-0.327199074074075[/C][/ROW]
[ROW][C]13[/C][C]14.3[/C][C]12.8758101851852[/C][C]12.0166666666667[/C][C]0.85914351851852[/C][C]1.42418981481481[/C][/ROW]
[ROW][C]14[/C][C]14.6[/C][C]15.753587962963[/C][C]11.7625[/C][C]3.99108796296296[/C][C]-1.15358796296296[/C][/ROW]
[ROW][C]15[/C][C]17.5[/C][C]17.1827546296296[/C][C]11.5916666666667[/C][C]5.59108796296297[/C][C]0.317245370370371[/C][/ROW]
[ROW][C]16[/C][C]17.2[/C][C]18.4855324074074[/C][C]11.4875[/C][C]6.99803240740741[/C][C]-1.28553240740741[/C][/ROW]
[ROW][C]17[/C][C]17.2[/C][C]18.6855324074074[/C][C]11.4291666666667[/C][C]7.25636574074074[/C][C]-1.48553240740741[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]15.2299768518519[/C][C]11.3291666666667[/C][C]3.90081018518519[/C][C]-1.12997685185185[/C][/ROW]
[ROW][C]19[/C][C]10.4[/C][C]11.1244212962963[/C][C]11.05[/C][C]0.0744212962962968[/C][C]-0.724421296296297[/C][/ROW]
[ROW][C]20[/C][C]6.8[/C][C]8.1605324074074[/C][C]10.9166666666667[/C][C]-2.75613425925926[/C][C]-1.36053240740741[/C][/ROW]
[ROW][C]21[/C][C]4.1[/C][C]3.69247685185185[/C][C]10.9333333333333[/C][C]-7.24085648148148[/C][C]0.407523148148149[/C][/ROW]
[ROW][C]22[/C][C]6.5[/C][C]2.78969907407407[/C][C]10.9083333333333[/C][C]-8.11863425925926[/C][C]3.71030092592593[/C][/ROW]
[ROW][C]23[/C][C]6.1[/C][C]4.42997685185185[/C][C]10.9583333333333[/C][C]-6.52835648148148[/C][C]1.67002314814815[/C][/ROW]
[ROW][C]24[/C][C]6.3[/C][C]6.94386574074074[/C][C]10.9708333333333[/C][C]-4.02696759259259[/C][C]-0.643865740740742[/C][/ROW]
[ROW][C]25[/C][C]9.3[/C][C]11.8299768518519[/C][C]10.9708333333333[/C][C]0.85914351851852[/C][C]-2.52997685185185[/C][/ROW]
[ROW][C]26[/C][C]16.4[/C][C]14.9702546296296[/C][C]10.9791666666667[/C][C]3.99108796296296[/C][C]1.42974537037037[/C][/ROW]
[ROW][C]27[/C][C]16.1[/C][C]16.5202546296296[/C][C]10.9291666666667[/C][C]5.59108796296297[/C][C]-0.42025462962963[/C][/ROW]
[ROW][C]28[/C][C]18[/C][C]17.6313657407407[/C][C]10.6333333333333[/C][C]6.99803240740741[/C][C]0.368634259259258[/C][/ROW]
[ROW][C]29[/C][C]17.6[/C][C]17.5438657407407[/C][C]10.2875[/C][C]7.25636574074074[/C][C]0.0561342592592595[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1008101851852[/C][C]10.2[/C][C]3.90081018518519[/C][C]-0.100810185185185[/C][/ROW]
[ROW][C]31[/C][C]10.5[/C][C]10.4244212962963[/C][C]10.35[/C][C]0.0744212962962968[/C][C]0.0755787037037035[/C][/ROW]
[ROW][C]32[/C][C]6.9[/C][C]7.64386574074074[/C][C]10.4[/C][C]-2.75613425925926[/C][C]-0.743865740740741[/C][/ROW]
[ROW][C]33[/C][C]2.8[/C][C]3.09247685185185[/C][C]10.3333333333333[/C][C]-7.24085648148148[/C][C]-0.292476851851852[/C][/ROW]
[ROW][C]34[/C][C]0.7[/C][C]2.26053240740741[/C][C]10.3791666666667[/C][C]-8.11863425925926[/C][C]-1.56053240740741[/C][/ROW]
[ROW][C]35[/C][C]3.6[/C][C]3.95497685185185[/C][C]10.4833333333333[/C][C]-6.52835648148148[/C][C]-0.354976851851848[/C][/ROW]
[ROW][C]36[/C][C]6.7[/C][C]6.60636574074074[/C][C]10.6333333333333[/C][C]-4.02696759259259[/C][C]0.093634259259261[/C][/ROW]
[ROW][C]37[/C][C]12.5[/C][C]11.6008101851852[/C][C]10.7416666666667[/C][C]0.85914351851852[/C][C]0.899189814814813[/C][/ROW]
[ROW][C]38[/C][C]14.4[/C][C]14.8827546296296[/C][C]10.8916666666667[/C][C]3.99108796296296[/C][C]-0.482754629629627[/C][/ROW]
[ROW][C]39[/C][C]16.5[/C][C]16.603587962963[/C][C]11.0125[/C][C]5.59108796296297[/C][C]-0.103587962962965[/C][/ROW]
[ROW][C]40[/C][C]18.7[/C][C]17.9896990740741[/C][C]10.9916666666667[/C][C]6.99803240740741[/C][C]0.710300925925926[/C][/ROW]
[ROW][C]41[/C][C]19.4[/C][C]18.1771990740741[/C][C]10.9208333333333[/C][C]7.25636574074074[/C][C]1.22280092592593[/C][/ROW]
[ROW][C]42[/C][C]15.8[/C][C]14.7758101851852[/C][C]10.875[/C][C]3.90081018518519[/C][C]1.02418981481482[/C][/ROW]
[ROW][C]43[/C][C]11.3[/C][C]10.8577546296296[/C][C]10.7833333333333[/C][C]0.0744212962962968[/C][C]0.442245370370372[/C][/ROW]
[ROW][C]44[/C][C]9.7[/C][C]7.80219907407407[/C][C]10.5583333333333[/C][C]-2.75613425925926[/C][C]1.89780092592593[/C][/ROW]
[ROW][C]45[/C][C]2.9[/C][C]3.22164351851852[/C][C]10.4625[/C][C]-7.24085648148148[/C][C]-0.321643518518519[/C][/ROW]
[ROW][C]46[/C][C]0.1[/C][C]2.45636574074074[/C][C]10.575[/C][C]-8.11863425925926[/C][C]-2.35636574074074[/C][/ROW]
[ROW][C]47[/C][C]2.5[/C][C]4.02164351851852[/C][C]10.55[/C][C]-6.52835648148148[/C][C]-1.52164351851852[/C][/ROW]
[ROW][C]48[/C][C]6.7[/C][C]6.35636574074074[/C][C]10.3833333333333[/C][C]-4.02696759259259[/C][C]0.343634259259261[/C][/ROW]
[ROW][C]49[/C][C]10.3[/C][C]NA[/C][C]10.2875[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]50[/C][C]11.2[/C][C]NA[/C][C]10.1083333333333[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]51[/C][C]17.4[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]52[/C][C]20.5[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]53[/C][C]17[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]54[/C][C]14.2[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]55[/C][C]10.6[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]6.1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=117053&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=117053&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	9.3	NA	NA	0.85914351851852	NA
2	14.2	NA	NA	3.99108796296296	NA
3	17.3	NA	NA	5.59108796296297	NA
4	23	NA	NA	6.99803240740741	NA
5	16.3	NA	NA	7.25636574074074	NA
6	18.4	NA	NA	3.90081018518519	NA
7	14.2	12.7577546296296	12.6833333333333	0.0744212962962968	1.44224537037037
8	9.1	10.1521990740741	12.9083333333333	-2.75613425925926	-1.05219907407407
9	5.9	5.69247685185185	12.9333333333333	-7.24085648148148	0.207523148148148
10	7.2	4.58136574074074	12.7	-8.11863425925926	2.61863425925926
11	6.8	5.96747685185185	12.4958333333333	-6.52835648148148	0.83252314814815
12	8	8.32719907407408	12.3541666666667	-4.02696759259259	-0.327199074074075
13	14.3	12.8758101851852	12.0166666666667	0.85914351851852	1.42418981481481
14	14.6	15.753587962963	11.7625	3.99108796296296	-1.15358796296296
15	17.5	17.1827546296296	11.5916666666667	5.59108796296297	0.317245370370371
16	17.2	18.4855324074074	11.4875	6.99803240740741	-1.28553240740741
17	17.2	18.6855324074074	11.4291666666667	7.25636574074074	-1.48553240740741
18	14.1	15.2299768518519	11.3291666666667	3.90081018518519	-1.12997685185185
19	10.4	11.1244212962963	11.05	0.0744212962962968	-0.724421296296297
20	6.8	8.1605324074074	10.9166666666667	-2.75613425925926	-1.36053240740741
21	4.1	3.69247685185185	10.9333333333333	-7.24085648148148	0.407523148148149
22	6.5	2.78969907407407	10.9083333333333	-8.11863425925926	3.71030092592593
23	6.1	4.42997685185185	10.9583333333333	-6.52835648148148	1.67002314814815
24	6.3	6.94386574074074	10.9708333333333	-4.02696759259259	-0.643865740740742
25	9.3	11.8299768518519	10.9708333333333	0.85914351851852	-2.52997685185185
26	16.4	14.9702546296296	10.9791666666667	3.99108796296296	1.42974537037037
27	16.1	16.5202546296296	10.9291666666667	5.59108796296297	-0.42025462962963
28	18	17.6313657407407	10.6333333333333	6.99803240740741	0.368634259259258
29	17.6	17.5438657407407	10.2875	7.25636574074074	0.0561342592592595
30	14	14.1008101851852	10.2	3.90081018518519	-0.100810185185185
31	10.5	10.4244212962963	10.35	0.0744212962962968	0.0755787037037035
32	6.9	7.64386574074074	10.4	-2.75613425925926	-0.743865740740741
33	2.8	3.09247685185185	10.3333333333333	-7.24085648148148	-0.292476851851852
34	0.7	2.26053240740741	10.3791666666667	-8.11863425925926	-1.56053240740741
35	3.6	3.95497685185185	10.4833333333333	-6.52835648148148	-0.354976851851848
36	6.7	6.60636574074074	10.6333333333333	-4.02696759259259	0.093634259259261
37	12.5	11.6008101851852	10.7416666666667	0.85914351851852	0.899189814814813
38	14.4	14.8827546296296	10.8916666666667	3.99108796296296	-0.482754629629627
39	16.5	16.603587962963	11.0125	5.59108796296297	-0.103587962962965
40	18.7	17.9896990740741	10.9916666666667	6.99803240740741	0.710300925925926
41	19.4	18.1771990740741	10.9208333333333	7.25636574074074	1.22280092592593
42	15.8	14.7758101851852	10.875	3.90081018518519	1.02418981481482
43	11.3	10.8577546296296	10.7833333333333	0.0744212962962968	0.442245370370372
44	9.7	7.80219907407407	10.5583333333333	-2.75613425925926	1.89780092592593
45	2.9	3.22164351851852	10.4625	-7.24085648148148	-0.321643518518519
46	0.1	2.45636574074074	10.575	-8.11863425925926	-2.35636574074074
47	2.5	4.02164351851852	10.55	-6.52835648148148	-1.52164351851852
48	6.7	6.35636574074074	10.3833333333333	-4.02696759259259	0.343634259259261
49	10.3	NA	10.2875	NA	NA
50	11.2	NA	10.1083333333333	NA	NA
51	17.4	NA	NA	NA	NA
52	20.5	NA	NA	NA	NA
53	17	NA	NA	NA	NA
54	14.2	NA	NA	NA	NA
55	10.6	NA	NA	NA	NA
56	6.1	NA	NA	NA	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 ; par3 = ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;

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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$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