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*Unverified author*

R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Thu, 02 Apr 2015 16:15:29 +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/t1427987755umn9qc5bd67ghg5.htm/, Retrieved Thu, 09 May 2024 03:29:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=278571, Retrieved Thu, 09 May 2024 03:29:46 +0000

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Estimated Impact

106

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

-       [Classical Decomposition] [] [2015-04-02 15:15:29] [8a8c4bd73c288c653fbb494901d06130] [Current]

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Dataseries X:

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2
2,2
1,9
2,3
2,2
2,3
2,1
2,4
2,3
1,9
1,6
1,8
1,8
2
2,3
2,2
2,2
2
2
1,9
1,5
1,6
1,5
2
1,5
1,5
1,9
1,1
1,5
2,1
2,3
2,6
2,9
3,2
3,2
3,1
3
3,3
2,7
3,6
3,1
2,7
2,6
2,2
2,7
2,1
1,8
1,7
1,7
1,2
1,2
1,2
1,5
1,3
1,1
1,2
1,3
1,6
1,9
1,6
2,1
2,2
2,3
2,1
1,7
1,7
2,2
2
1,5
1,5
1,7
2,2
2,6
2,6
2,3
2,3
2,7
2,7
2,5
2,5
2,7
2,6
2,6
2,4
1,4
1,8
2,1
1,7
1,6
1,7
1,8
2
1,9
2
2,1
2,3

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	'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=278571&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=278571&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=278571&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	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	2	NA	NA	-0.0540675	NA
2	2.2	NA	NA	0.0215278	NA
3	1.9	NA	NA	0.0548611	NA
4	2.3	NA	NA	-0.0290675	NA
5	2.2	NA	NA	-0.0183532	NA
6	2.3	NA	NA	-0.0385913	NA
7	2.1	2.09177	2.075	0.0167659	0.00823413
8	2.4	2.08105	2.05833	0.0227183	0.318948
9	2.3	2.10486	2.06667	0.0381944	0.195139
10	1.9	2.0626	2.07917	-0.0165675	-0.162599
11	1.6	2.037	2.075	-0.037996	-0.437004
12	1.8	2.10308	2.0625	0.0405754	-0.303075
13	1.8	1.99177	2.04583	-0.0540675	-0.191766
14	2	2.04236	2.02083	0.0215278	-0.0423611
15	2.3	2.02153	1.96667	0.0548611	0.278472
16	2.2	1.89177	1.92083	-0.0290675	0.308234
17	2.2	1.88581	1.90417	-0.0183532	0.314187
18	2	1.86974	1.90833	-0.0385913	0.130258
19	2	1.92093	1.90417	0.0167659	0.0790675
20	1.9	1.89355	1.87083	0.0227183	0.00644841
21	1.5	1.87153	1.83333	0.0381944	-0.371528
22	1.6	1.75427	1.77083	-0.0165675	-0.154266
23	1.5	1.65784	1.69583	-0.037996	-0.157837
24	2	1.71141	1.67083	0.0405754	0.288591
25	1.5	1.63343	1.6875	-0.0540675	-0.133433
26	1.5	1.75069	1.72917	0.0215278	-0.250694
27	1.9	1.87153	1.81667	0.0548611	0.0284722
28	1.1	1.9126	1.94167	-0.0290675	-0.812599
29	1.5	2.06081	2.07917	-0.0183532	-0.560813
30	2.1	2.15724	2.19583	-0.0385913	-0.0572421
31	2.3	2.32093	2.30417	0.0167659	-0.0209325
32	2.6	2.46438	2.44167	0.0227183	0.135615
33	2.9	2.58819	2.55	0.0381944	0.311806
34	3.2	2.67093	2.6875	-0.0165675	0.529067
35	3.2	2.82034	2.85833	-0.037996	0.379663
36	3.1	2.99058	2.95	0.0405754	0.109425
37	3	2.93343	2.9875	-0.0540675	0.0665675
38	3.3	3.00486	2.98333	0.0215278	0.295139
39	2.7	3.01319	2.95833	0.0548611	-0.313194
40	3.6	2.8751	2.90417	-0.0290675	0.724901
41	3.1	2.78165	2.8	-0.0183532	0.318353
42	2.7	2.64474	2.68333	-0.0385913	0.0552579
43	2.6	2.5876	2.57083	0.0167659	0.0124008
44	2.2	2.45188	2.42917	0.0227183	-0.251885
45	2.7	2.31736	2.27917	0.0381944	0.382639
46	2.1	2.1001	2.11667	-0.0165675	-9.92063e-05
47	1.8	1.912	1.95	-0.037996	-0.112004
48	1.7	1.86558	1.825	0.0405754	-0.165575
49	1.7	1.6501	1.70417	-0.0540675	0.0499008
50	1.2	1.62153	1.6	0.0215278	-0.421528
51	1.2	1.55486	1.5	0.0548611	-0.354861
52	1.2	1.39177	1.42083	-0.0290675	-0.191766
53	1.5	1.38581	1.40417	-0.0183532	0.114187
54	1.3	1.36558	1.40417	-0.0385913	-0.0655754
55	1.1	1.43343	1.41667	0.0167659	-0.333433
56	1.2	1.49772	1.475	0.0227183	-0.297718
57	1.3	1.60069	1.5625	0.0381944	-0.300694
58	1.6	1.62927	1.64583	-0.0165675	-0.0292659
59	1.9	1.65367	1.69167	-0.037996	0.246329
60	1.6	1.75724	1.71667	0.0405754	-0.157242
61	2.1	1.7251	1.77917	-0.0540675	0.374901
62	2.2	1.87986	1.85833	0.0215278	0.320139
63	2.3	1.95486	1.9	0.0548611	0.345139
64	2.1	1.8751	1.90417	-0.0290675	0.224901
65	1.7	1.87331	1.89167	-0.0183532	-0.173313
66	1.7	1.86974	1.90833	-0.0385913	-0.169742
67	2.2	1.97093	1.95417	0.0167659	0.229067
68	2	2.01438	1.99167	0.0227183	-0.0143849
69	1.5	2.04653	2.00833	0.0381944	-0.546528
70	1.5	2.0001	2.01667	-0.0165675	-0.500099
71	1.7	2.02867	2.06667	-0.037996	-0.328671
72	2.2	2.19058	2.15	0.0405754	0.0094246
73	2.6	2.1501	2.20417	-0.0540675	0.449901
74	2.6	2.25903	2.2375	0.0215278	0.340972
75	2.3	2.36319	2.30833	0.0548611	-0.0631944
76	2.3	2.3751	2.40417	-0.0290675	-0.0750992
77	2.7	2.46915	2.4875	-0.0183532	0.230853
78	2.7	2.49474	2.53333	-0.0385913	0.205258
79	2.5	2.50843	2.49167	0.0167659	-0.00843254
80	2.5	2.43105	2.40833	0.0227183	0.0689484
81	2.7	2.40486	2.36667	0.0381944	0.295139
82	2.6	2.31677	2.33333	-0.0165675	0.283234
83	2.6	2.2245	2.2625	-0.037996	0.375496
84	2.4	2.21558	2.175	0.0405754	0.184425
85	1.4	2.0501	2.10417	-0.0540675	-0.650099
86	1.8	2.07569	2.05417	0.0215278	-0.275694
87	2.1	2.05486	2	0.0548611	0.0451389
88	1.7	1.9126	1.94167	-0.0290675	-0.212599
89	1.6	1.87748	1.89583	-0.0183532	-0.27748
90	1.7	1.83224	1.87083	-0.0385913	-0.132242
91	1.8	NA	NA	0.0167659	NA
92	2	NA	NA	0.0227183	NA
93	1.9	NA	NA	0.0381944	NA
94	2	NA	NA	-0.0165675	NA
95	2.1	NA	NA	-0.037996	NA
96	2.3	NA	NA	0.0405754	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 2 & NA & NA & -0.0540675 & NA \tabularnewline
2 & 2.2 & NA & NA & 0.0215278 & NA \tabularnewline
3 & 1.9 & NA & NA & 0.0548611 & NA \tabularnewline
4 & 2.3 & NA & NA & -0.0290675 & NA \tabularnewline
5 & 2.2 & NA & NA & -0.0183532 & NA \tabularnewline
6 & 2.3 & NA & NA & -0.0385913 & NA \tabularnewline
7 & 2.1 & 2.09177 & 2.075 & 0.0167659 & 0.00823413 \tabularnewline
8 & 2.4 & 2.08105 & 2.05833 & 0.0227183 & 0.318948 \tabularnewline
9 & 2.3 & 2.10486 & 2.06667 & 0.0381944 & 0.195139 \tabularnewline
10 & 1.9 & 2.0626 & 2.07917 & -0.0165675 & -0.162599 \tabularnewline
11 & 1.6 & 2.037 & 2.075 & -0.037996 & -0.437004 \tabularnewline
12 & 1.8 & 2.10308 & 2.0625 & 0.0405754 & -0.303075 \tabularnewline
13 & 1.8 & 1.99177 & 2.04583 & -0.0540675 & -0.191766 \tabularnewline
14 & 2 & 2.04236 & 2.02083 & 0.0215278 & -0.0423611 \tabularnewline
15 & 2.3 & 2.02153 & 1.96667 & 0.0548611 & 0.278472 \tabularnewline
16 & 2.2 & 1.89177 & 1.92083 & -0.0290675 & 0.308234 \tabularnewline
17 & 2.2 & 1.88581 & 1.90417 & -0.0183532 & 0.314187 \tabularnewline
18 & 2 & 1.86974 & 1.90833 & -0.0385913 & 0.130258 \tabularnewline
19 & 2 & 1.92093 & 1.90417 & 0.0167659 & 0.0790675 \tabularnewline
20 & 1.9 & 1.89355 & 1.87083 & 0.0227183 & 0.00644841 \tabularnewline
21 & 1.5 & 1.87153 & 1.83333 & 0.0381944 & -0.371528 \tabularnewline
22 & 1.6 & 1.75427 & 1.77083 & -0.0165675 & -0.154266 \tabularnewline
23 & 1.5 & 1.65784 & 1.69583 & -0.037996 & -0.157837 \tabularnewline
24 & 2 & 1.71141 & 1.67083 & 0.0405754 & 0.288591 \tabularnewline
25 & 1.5 & 1.63343 & 1.6875 & -0.0540675 & -0.133433 \tabularnewline
26 & 1.5 & 1.75069 & 1.72917 & 0.0215278 & -0.250694 \tabularnewline
27 & 1.9 & 1.87153 & 1.81667 & 0.0548611 & 0.0284722 \tabularnewline
28 & 1.1 & 1.9126 & 1.94167 & -0.0290675 & -0.812599 \tabularnewline
29 & 1.5 & 2.06081 & 2.07917 & -0.0183532 & -0.560813 \tabularnewline
30 & 2.1 & 2.15724 & 2.19583 & -0.0385913 & -0.0572421 \tabularnewline
31 & 2.3 & 2.32093 & 2.30417 & 0.0167659 & -0.0209325 \tabularnewline
32 & 2.6 & 2.46438 & 2.44167 & 0.0227183 & 0.135615 \tabularnewline
33 & 2.9 & 2.58819 & 2.55 & 0.0381944 & 0.311806 \tabularnewline
34 & 3.2 & 2.67093 & 2.6875 & -0.0165675 & 0.529067 \tabularnewline
35 & 3.2 & 2.82034 & 2.85833 & -0.037996 & 0.379663 \tabularnewline
36 & 3.1 & 2.99058 & 2.95 & 0.0405754 & 0.109425 \tabularnewline
37 & 3 & 2.93343 & 2.9875 & -0.0540675 & 0.0665675 \tabularnewline
38 & 3.3 & 3.00486 & 2.98333 & 0.0215278 & 0.295139 \tabularnewline
39 & 2.7 & 3.01319 & 2.95833 & 0.0548611 & -0.313194 \tabularnewline
40 & 3.6 & 2.8751 & 2.90417 & -0.0290675 & 0.724901 \tabularnewline
41 & 3.1 & 2.78165 & 2.8 & -0.0183532 & 0.318353 \tabularnewline
42 & 2.7 & 2.64474 & 2.68333 & -0.0385913 & 0.0552579 \tabularnewline
43 & 2.6 & 2.5876 & 2.57083 & 0.0167659 & 0.0124008 \tabularnewline
44 & 2.2 & 2.45188 & 2.42917 & 0.0227183 & -0.251885 \tabularnewline
45 & 2.7 & 2.31736 & 2.27917 & 0.0381944 & 0.382639 \tabularnewline
46 & 2.1 & 2.1001 & 2.11667 & -0.0165675 & -9.92063e-05 \tabularnewline
47 & 1.8 & 1.912 & 1.95 & -0.037996 & -0.112004 \tabularnewline
48 & 1.7 & 1.86558 & 1.825 & 0.0405754 & -0.165575 \tabularnewline
49 & 1.7 & 1.6501 & 1.70417 & -0.0540675 & 0.0499008 \tabularnewline
50 & 1.2 & 1.62153 & 1.6 & 0.0215278 & -0.421528 \tabularnewline
51 & 1.2 & 1.55486 & 1.5 & 0.0548611 & -0.354861 \tabularnewline
52 & 1.2 & 1.39177 & 1.42083 & -0.0290675 & -0.191766 \tabularnewline
53 & 1.5 & 1.38581 & 1.40417 & -0.0183532 & 0.114187 \tabularnewline
54 & 1.3 & 1.36558 & 1.40417 & -0.0385913 & -0.0655754 \tabularnewline
55 & 1.1 & 1.43343 & 1.41667 & 0.0167659 & -0.333433 \tabularnewline
56 & 1.2 & 1.49772 & 1.475 & 0.0227183 & -0.297718 \tabularnewline
57 & 1.3 & 1.60069 & 1.5625 & 0.0381944 & -0.300694 \tabularnewline
58 & 1.6 & 1.62927 & 1.64583 & -0.0165675 & -0.0292659 \tabularnewline
59 & 1.9 & 1.65367 & 1.69167 & -0.037996 & 0.246329 \tabularnewline
60 & 1.6 & 1.75724 & 1.71667 & 0.0405754 & -0.157242 \tabularnewline
61 & 2.1 & 1.7251 & 1.77917 & -0.0540675 & 0.374901 \tabularnewline
62 & 2.2 & 1.87986 & 1.85833 & 0.0215278 & 0.320139 \tabularnewline
63 & 2.3 & 1.95486 & 1.9 & 0.0548611 & 0.345139 \tabularnewline
64 & 2.1 & 1.8751 & 1.90417 & -0.0290675 & 0.224901 \tabularnewline
65 & 1.7 & 1.87331 & 1.89167 & -0.0183532 & -0.173313 \tabularnewline
66 & 1.7 & 1.86974 & 1.90833 & -0.0385913 & -0.169742 \tabularnewline
67 & 2.2 & 1.97093 & 1.95417 & 0.0167659 & 0.229067 \tabularnewline
68 & 2 & 2.01438 & 1.99167 & 0.0227183 & -0.0143849 \tabularnewline
69 & 1.5 & 2.04653 & 2.00833 & 0.0381944 & -0.546528 \tabularnewline
70 & 1.5 & 2.0001 & 2.01667 & -0.0165675 & -0.500099 \tabularnewline
71 & 1.7 & 2.02867 & 2.06667 & -0.037996 & -0.328671 \tabularnewline
72 & 2.2 & 2.19058 & 2.15 & 0.0405754 & 0.0094246 \tabularnewline
73 & 2.6 & 2.1501 & 2.20417 & -0.0540675 & 0.449901 \tabularnewline
74 & 2.6 & 2.25903 & 2.2375 & 0.0215278 & 0.340972 \tabularnewline
75 & 2.3 & 2.36319 & 2.30833 & 0.0548611 & -0.0631944 \tabularnewline
76 & 2.3 & 2.3751 & 2.40417 & -0.0290675 & -0.0750992 \tabularnewline
77 & 2.7 & 2.46915 & 2.4875 & -0.0183532 & 0.230853 \tabularnewline
78 & 2.7 & 2.49474 & 2.53333 & -0.0385913 & 0.205258 \tabularnewline
79 & 2.5 & 2.50843 & 2.49167 & 0.0167659 & -0.00843254 \tabularnewline
80 & 2.5 & 2.43105 & 2.40833 & 0.0227183 & 0.0689484 \tabularnewline
81 & 2.7 & 2.40486 & 2.36667 & 0.0381944 & 0.295139 \tabularnewline
82 & 2.6 & 2.31677 & 2.33333 & -0.0165675 & 0.283234 \tabularnewline
83 & 2.6 & 2.2245 & 2.2625 & -0.037996 & 0.375496 \tabularnewline
84 & 2.4 & 2.21558 & 2.175 & 0.0405754 & 0.184425 \tabularnewline
85 & 1.4 & 2.0501 & 2.10417 & -0.0540675 & -0.650099 \tabularnewline
86 & 1.8 & 2.07569 & 2.05417 & 0.0215278 & -0.275694 \tabularnewline
87 & 2.1 & 2.05486 & 2 & 0.0548611 & 0.0451389 \tabularnewline
88 & 1.7 & 1.9126 & 1.94167 & -0.0290675 & -0.212599 \tabularnewline
89 & 1.6 & 1.87748 & 1.89583 & -0.0183532 & -0.27748 \tabularnewline
90 & 1.7 & 1.83224 & 1.87083 & -0.0385913 & -0.132242 \tabularnewline
91 & 1.8 & NA & NA & 0.0167659 & NA \tabularnewline
92 & 2 & NA & NA & 0.0227183 & NA \tabularnewline
93 & 1.9 & NA & NA & 0.0381944 & NA \tabularnewline
94 & 2 & NA & NA & -0.0165675 & NA \tabularnewline
95 & 2.1 & NA & NA & -0.037996 & NA \tabularnewline
96 & 2.3 & NA & NA & 0.0405754 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=278571&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]2[/C][C]NA[/C][C]NA[/C][C]-0.0540675[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]2.2[/C][C]NA[/C][C]NA[/C][C]0.0215278[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]1.9[/C][C]NA[/C][C]NA[/C][C]0.0548611[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]2.3[/C][C]NA[/C][C]NA[/C][C]-0.0290675[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]2.2[/C][C]NA[/C][C]NA[/C][C]-0.0183532[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]2.3[/C][C]NA[/C][C]NA[/C][C]-0.0385913[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.09177[/C][C]2.075[/C][C]0.0167659[/C][C]0.00823413[/C][/ROW]
[ROW][C]8[/C][C]2.4[/C][C]2.08105[/C][C]2.05833[/C][C]0.0227183[/C][C]0.318948[/C][/ROW]
[ROW][C]9[/C][C]2.3[/C][C]2.10486[/C][C]2.06667[/C][C]0.0381944[/C][C]0.195139[/C][/ROW]
[ROW][C]10[/C][C]1.9[/C][C]2.0626[/C][C]2.07917[/C][C]-0.0165675[/C][C]-0.162599[/C][/ROW]
[ROW][C]11[/C][C]1.6[/C][C]2.037[/C][C]2.075[/C][C]-0.037996[/C][C]-0.437004[/C][/ROW]
[ROW][C]12[/C][C]1.8[/C][C]2.10308[/C][C]2.0625[/C][C]0.0405754[/C][C]-0.303075[/C][/ROW]
[ROW][C]13[/C][C]1.8[/C][C]1.99177[/C][C]2.04583[/C][C]-0.0540675[/C][C]-0.191766[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]2.04236[/C][C]2.02083[/C][C]0.0215278[/C][C]-0.0423611[/C][/ROW]
[ROW][C]15[/C][C]2.3[/C][C]2.02153[/C][C]1.96667[/C][C]0.0548611[/C][C]0.278472[/C][/ROW]
[ROW][C]16[/C][C]2.2[/C][C]1.89177[/C][C]1.92083[/C][C]-0.0290675[/C][C]0.308234[/C][/ROW]
[ROW][C]17[/C][C]2.2[/C][C]1.88581[/C][C]1.90417[/C][C]-0.0183532[/C][C]0.314187[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.86974[/C][C]1.90833[/C][C]-0.0385913[/C][C]0.130258[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]1.92093[/C][C]1.90417[/C][C]0.0167659[/C][C]0.0790675[/C][/ROW]
[ROW][C]20[/C][C]1.9[/C][C]1.89355[/C][C]1.87083[/C][C]0.0227183[/C][C]0.00644841[/C][/ROW]
[ROW][C]21[/C][C]1.5[/C][C]1.87153[/C][C]1.83333[/C][C]0.0381944[/C][C]-0.371528[/C][/ROW]
[ROW][C]22[/C][C]1.6[/C][C]1.75427[/C][C]1.77083[/C][C]-0.0165675[/C][C]-0.154266[/C][/ROW]
[ROW][C]23[/C][C]1.5[/C][C]1.65784[/C][C]1.69583[/C][C]-0.037996[/C][C]-0.157837[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.71141[/C][C]1.67083[/C][C]0.0405754[/C][C]0.288591[/C][/ROW]
[ROW][C]25[/C][C]1.5[/C][C]1.63343[/C][C]1.6875[/C][C]-0.0540675[/C][C]-0.133433[/C][/ROW]
[ROW][C]26[/C][C]1.5[/C][C]1.75069[/C][C]1.72917[/C][C]0.0215278[/C][C]-0.250694[/C][/ROW]
[ROW][C]27[/C][C]1.9[/C][C]1.87153[/C][C]1.81667[/C][C]0.0548611[/C][C]0.0284722[/C][/ROW]
[ROW][C]28[/C][C]1.1[/C][C]1.9126[/C][C]1.94167[/C][C]-0.0290675[/C][C]-0.812599[/C][/ROW]
[ROW][C]29[/C][C]1.5[/C][C]2.06081[/C][C]2.07917[/C][C]-0.0183532[/C][C]-0.560813[/C][/ROW]
[ROW][C]30[/C][C]2.1[/C][C]2.15724[/C][C]2.19583[/C][C]-0.0385913[/C][C]-0.0572421[/C][/ROW]
[ROW][C]31[/C][C]2.3[/C][C]2.32093[/C][C]2.30417[/C][C]0.0167659[/C][C]-0.0209325[/C][/ROW]
[ROW][C]32[/C][C]2.6[/C][C]2.46438[/C][C]2.44167[/C][C]0.0227183[/C][C]0.135615[/C][/ROW]
[ROW][C]33[/C][C]2.9[/C][C]2.58819[/C][C]2.55[/C][C]0.0381944[/C][C]0.311806[/C][/ROW]
[ROW][C]34[/C][C]3.2[/C][C]2.67093[/C][C]2.6875[/C][C]-0.0165675[/C][C]0.529067[/C][/ROW]
[ROW][C]35[/C][C]3.2[/C][C]2.82034[/C][C]2.85833[/C][C]-0.037996[/C][C]0.379663[/C][/ROW]
[ROW][C]36[/C][C]3.1[/C][C]2.99058[/C][C]2.95[/C][C]0.0405754[/C][C]0.109425[/C][/ROW]
[ROW][C]37[/C][C]3[/C][C]2.93343[/C][C]2.9875[/C][C]-0.0540675[/C][C]0.0665675[/C][/ROW]
[ROW][C]38[/C][C]3.3[/C][C]3.00486[/C][C]2.98333[/C][C]0.0215278[/C][C]0.295139[/C][/ROW]
[ROW][C]39[/C][C]2.7[/C][C]3.01319[/C][C]2.95833[/C][C]0.0548611[/C][C]-0.313194[/C][/ROW]
[ROW][C]40[/C][C]3.6[/C][C]2.8751[/C][C]2.90417[/C][C]-0.0290675[/C][C]0.724901[/C][/ROW]
[ROW][C]41[/C][C]3.1[/C][C]2.78165[/C][C]2.8[/C][C]-0.0183532[/C][C]0.318353[/C][/ROW]
[ROW][C]42[/C][C]2.7[/C][C]2.64474[/C][C]2.68333[/C][C]-0.0385913[/C][C]0.0552579[/C][/ROW]
[ROW][C]43[/C][C]2.6[/C][C]2.5876[/C][C]2.57083[/C][C]0.0167659[/C][C]0.0124008[/C][/ROW]
[ROW][C]44[/C][C]2.2[/C][C]2.45188[/C][C]2.42917[/C][C]0.0227183[/C][C]-0.251885[/C][/ROW]
[ROW][C]45[/C][C]2.7[/C][C]2.31736[/C][C]2.27917[/C][C]0.0381944[/C][C]0.382639[/C][/ROW]
[ROW][C]46[/C][C]2.1[/C][C]2.1001[/C][C]2.11667[/C][C]-0.0165675[/C][C]-9.92063e-05[/C][/ROW]
[ROW][C]47[/C][C]1.8[/C][C]1.912[/C][C]1.95[/C][C]-0.037996[/C][C]-0.112004[/C][/ROW]
[ROW][C]48[/C][C]1.7[/C][C]1.86558[/C][C]1.825[/C][C]0.0405754[/C][C]-0.165575[/C][/ROW]
[ROW][C]49[/C][C]1.7[/C][C]1.6501[/C][C]1.70417[/C][C]-0.0540675[/C][C]0.0499008[/C][/ROW]
[ROW][C]50[/C][C]1.2[/C][C]1.62153[/C][C]1.6[/C][C]0.0215278[/C][C]-0.421528[/C][/ROW]
[ROW][C]51[/C][C]1.2[/C][C]1.55486[/C][C]1.5[/C][C]0.0548611[/C][C]-0.354861[/C][/ROW]
[ROW][C]52[/C][C]1.2[/C][C]1.39177[/C][C]1.42083[/C][C]-0.0290675[/C][C]-0.191766[/C][/ROW]
[ROW][C]53[/C][C]1.5[/C][C]1.38581[/C][C]1.40417[/C][C]-0.0183532[/C][C]0.114187[/C][/ROW]
[ROW][C]54[/C][C]1.3[/C][C]1.36558[/C][C]1.40417[/C][C]-0.0385913[/C][C]-0.0655754[/C][/ROW]
[ROW][C]55[/C][C]1.1[/C][C]1.43343[/C][C]1.41667[/C][C]0.0167659[/C][C]-0.333433[/C][/ROW]
[ROW][C]56[/C][C]1.2[/C][C]1.49772[/C][C]1.475[/C][C]0.0227183[/C][C]-0.297718[/C][/ROW]
[ROW][C]57[/C][C]1.3[/C][C]1.60069[/C][C]1.5625[/C][C]0.0381944[/C][C]-0.300694[/C][/ROW]
[ROW][C]58[/C][C]1.6[/C][C]1.62927[/C][C]1.64583[/C][C]-0.0165675[/C][C]-0.0292659[/C][/ROW]
[ROW][C]59[/C][C]1.9[/C][C]1.65367[/C][C]1.69167[/C][C]-0.037996[/C][C]0.246329[/C][/ROW]
[ROW][C]60[/C][C]1.6[/C][C]1.75724[/C][C]1.71667[/C][C]0.0405754[/C][C]-0.157242[/C][/ROW]
[ROW][C]61[/C][C]2.1[/C][C]1.7251[/C][C]1.77917[/C][C]-0.0540675[/C][C]0.374901[/C][/ROW]
[ROW][C]62[/C][C]2.2[/C][C]1.87986[/C][C]1.85833[/C][C]0.0215278[/C][C]0.320139[/C][/ROW]
[ROW][C]63[/C][C]2.3[/C][C]1.95486[/C][C]1.9[/C][C]0.0548611[/C][C]0.345139[/C][/ROW]
[ROW][C]64[/C][C]2.1[/C][C]1.8751[/C][C]1.90417[/C][C]-0.0290675[/C][C]0.224901[/C][/ROW]
[ROW][C]65[/C][C]1.7[/C][C]1.87331[/C][C]1.89167[/C][C]-0.0183532[/C][C]-0.173313[/C][/ROW]
[ROW][C]66[/C][C]1.7[/C][C]1.86974[/C][C]1.90833[/C][C]-0.0385913[/C][C]-0.169742[/C][/ROW]
[ROW][C]67[/C][C]2.2[/C][C]1.97093[/C][C]1.95417[/C][C]0.0167659[/C][C]0.229067[/C][/ROW]
[ROW][C]68[/C][C]2[/C][C]2.01438[/C][C]1.99167[/C][C]0.0227183[/C][C]-0.0143849[/C][/ROW]
[ROW][C]69[/C][C]1.5[/C][C]2.04653[/C][C]2.00833[/C][C]0.0381944[/C][C]-0.546528[/C][/ROW]
[ROW][C]70[/C][C]1.5[/C][C]2.0001[/C][C]2.01667[/C][C]-0.0165675[/C][C]-0.500099[/C][/ROW]
[ROW][C]71[/C][C]1.7[/C][C]2.02867[/C][C]2.06667[/C][C]-0.037996[/C][C]-0.328671[/C][/ROW]
[ROW][C]72[/C][C]2.2[/C][C]2.19058[/C][C]2.15[/C][C]0.0405754[/C][C]0.0094246[/C][/ROW]
[ROW][C]73[/C][C]2.6[/C][C]2.1501[/C][C]2.20417[/C][C]-0.0540675[/C][C]0.449901[/C][/ROW]
[ROW][C]74[/C][C]2.6[/C][C]2.25903[/C][C]2.2375[/C][C]0.0215278[/C][C]0.340972[/C][/ROW]
[ROW][C]75[/C][C]2.3[/C][C]2.36319[/C][C]2.30833[/C][C]0.0548611[/C][C]-0.0631944[/C][/ROW]
[ROW][C]76[/C][C]2.3[/C][C]2.3751[/C][C]2.40417[/C][C]-0.0290675[/C][C]-0.0750992[/C][/ROW]
[ROW][C]77[/C][C]2.7[/C][C]2.46915[/C][C]2.4875[/C][C]-0.0183532[/C][C]0.230853[/C][/ROW]
[ROW][C]78[/C][C]2.7[/C][C]2.49474[/C][C]2.53333[/C][C]-0.0385913[/C][C]0.205258[/C][/ROW]
[ROW][C]79[/C][C]2.5[/C][C]2.50843[/C][C]2.49167[/C][C]0.0167659[/C][C]-0.00843254[/C][/ROW]
[ROW][C]80[/C][C]2.5[/C][C]2.43105[/C][C]2.40833[/C][C]0.0227183[/C][C]0.0689484[/C][/ROW]
[ROW][C]81[/C][C]2.7[/C][C]2.40486[/C][C]2.36667[/C][C]0.0381944[/C][C]0.295139[/C][/ROW]
[ROW][C]82[/C][C]2.6[/C][C]2.31677[/C][C]2.33333[/C][C]-0.0165675[/C][C]0.283234[/C][/ROW]
[ROW][C]83[/C][C]2.6[/C][C]2.2245[/C][C]2.2625[/C][C]-0.037996[/C][C]0.375496[/C][/ROW]
[ROW][C]84[/C][C]2.4[/C][C]2.21558[/C][C]2.175[/C][C]0.0405754[/C][C]0.184425[/C][/ROW]
[ROW][C]85[/C][C]1.4[/C][C]2.0501[/C][C]2.10417[/C][C]-0.0540675[/C][C]-0.650099[/C][/ROW]
[ROW][C]86[/C][C]1.8[/C][C]2.07569[/C][C]2.05417[/C][C]0.0215278[/C][C]-0.275694[/C][/ROW]
[ROW][C]87[/C][C]2.1[/C][C]2.05486[/C][C]2[/C][C]0.0548611[/C][C]0.0451389[/C][/ROW]
[ROW][C]88[/C][C]1.7[/C][C]1.9126[/C][C]1.94167[/C][C]-0.0290675[/C][C]-0.212599[/C][/ROW]
[ROW][C]89[/C][C]1.6[/C][C]1.87748[/C][C]1.89583[/C][C]-0.0183532[/C][C]-0.27748[/C][/ROW]
[ROW][C]90[/C][C]1.7[/C][C]1.83224[/C][C]1.87083[/C][C]-0.0385913[/C][C]-0.132242[/C][/ROW]
[ROW][C]91[/C][C]1.8[/C][C]NA[/C][C]NA[/C][C]0.0167659[/C][C]NA[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]NA[/C][C]NA[/C][C]0.0227183[/C][C]NA[/C][/ROW]
[ROW][C]93[/C][C]1.9[/C][C]NA[/C][C]NA[/C][C]0.0381944[/C][C]NA[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]NA[/C][C]NA[/C][C]-0.0165675[/C][C]NA[/C][/ROW]
[ROW][C]95[/C][C]2.1[/C][C]NA[/C][C]NA[/C][C]-0.037996[/C][C]NA[/C][/ROW]
[ROW][C]96[/C][C]2.3[/C][C]NA[/C][C]NA[/C][C]0.0405754[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=278571&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=278571&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	2	NA	NA	-0.0540675	NA
2	2.2	NA	NA	0.0215278	NA
3	1.9	NA	NA	0.0548611	NA
4	2.3	NA	NA	-0.0290675	NA
5	2.2	NA	NA	-0.0183532	NA
6	2.3	NA	NA	-0.0385913	NA
7	2.1	2.09177	2.075	0.0167659	0.00823413
8	2.4	2.08105	2.05833	0.0227183	0.318948
9	2.3	2.10486	2.06667	0.0381944	0.195139
10	1.9	2.0626	2.07917	-0.0165675	-0.162599
11	1.6	2.037	2.075	-0.037996	-0.437004
12	1.8	2.10308	2.0625	0.0405754	-0.303075
13	1.8	1.99177	2.04583	-0.0540675	-0.191766
14	2	2.04236	2.02083	0.0215278	-0.0423611
15	2.3	2.02153	1.96667	0.0548611	0.278472
16	2.2	1.89177	1.92083	-0.0290675	0.308234
17	2.2	1.88581	1.90417	-0.0183532	0.314187
18	2	1.86974	1.90833	-0.0385913	0.130258
19	2	1.92093	1.90417	0.0167659	0.0790675
20	1.9	1.89355	1.87083	0.0227183	0.00644841
21	1.5	1.87153	1.83333	0.0381944	-0.371528
22	1.6	1.75427	1.77083	-0.0165675	-0.154266
23	1.5	1.65784	1.69583	-0.037996	-0.157837
24	2	1.71141	1.67083	0.0405754	0.288591
25	1.5	1.63343	1.6875	-0.0540675	-0.133433
26	1.5	1.75069	1.72917	0.0215278	-0.250694
27	1.9	1.87153	1.81667	0.0548611	0.0284722
28	1.1	1.9126	1.94167	-0.0290675	-0.812599
29	1.5	2.06081	2.07917	-0.0183532	-0.560813
30	2.1	2.15724	2.19583	-0.0385913	-0.0572421
31	2.3	2.32093	2.30417	0.0167659	-0.0209325
32	2.6	2.46438	2.44167	0.0227183	0.135615
33	2.9	2.58819	2.55	0.0381944	0.311806
34	3.2	2.67093	2.6875	-0.0165675	0.529067
35	3.2	2.82034	2.85833	-0.037996	0.379663
36	3.1	2.99058	2.95	0.0405754	0.109425
37	3	2.93343	2.9875	-0.0540675	0.0665675
38	3.3	3.00486	2.98333	0.0215278	0.295139
39	2.7	3.01319	2.95833	0.0548611	-0.313194
40	3.6	2.8751	2.90417	-0.0290675	0.724901
41	3.1	2.78165	2.8	-0.0183532	0.318353
42	2.7	2.64474	2.68333	-0.0385913	0.0552579
43	2.6	2.5876	2.57083	0.0167659	0.0124008
44	2.2	2.45188	2.42917	0.0227183	-0.251885
45	2.7	2.31736	2.27917	0.0381944	0.382639
46	2.1	2.1001	2.11667	-0.0165675	-9.92063e-05
47	1.8	1.912	1.95	-0.037996	-0.112004
48	1.7	1.86558	1.825	0.0405754	-0.165575
49	1.7	1.6501	1.70417	-0.0540675	0.0499008
50	1.2	1.62153	1.6	0.0215278	-0.421528
51	1.2	1.55486	1.5	0.0548611	-0.354861
52	1.2	1.39177	1.42083	-0.0290675	-0.191766
53	1.5	1.38581	1.40417	-0.0183532	0.114187
54	1.3	1.36558	1.40417	-0.0385913	-0.0655754
55	1.1	1.43343	1.41667	0.0167659	-0.333433
56	1.2	1.49772	1.475	0.0227183	-0.297718
57	1.3	1.60069	1.5625	0.0381944	-0.300694
58	1.6	1.62927	1.64583	-0.0165675	-0.0292659
59	1.9	1.65367	1.69167	-0.037996	0.246329
60	1.6	1.75724	1.71667	0.0405754	-0.157242
61	2.1	1.7251	1.77917	-0.0540675	0.374901
62	2.2	1.87986	1.85833	0.0215278	0.320139
63	2.3	1.95486	1.9	0.0548611	0.345139
64	2.1	1.8751	1.90417	-0.0290675	0.224901
65	1.7	1.87331	1.89167	-0.0183532	-0.173313
66	1.7	1.86974	1.90833	-0.0385913	-0.169742
67	2.2	1.97093	1.95417	0.0167659	0.229067
68	2	2.01438	1.99167	0.0227183	-0.0143849
69	1.5	2.04653	2.00833	0.0381944	-0.546528
70	1.5	2.0001	2.01667	-0.0165675	-0.500099
71	1.7	2.02867	2.06667	-0.037996	-0.328671
72	2.2	2.19058	2.15	0.0405754	0.0094246
73	2.6	2.1501	2.20417	-0.0540675	0.449901
74	2.6	2.25903	2.2375	0.0215278	0.340972
75	2.3	2.36319	2.30833	0.0548611	-0.0631944
76	2.3	2.3751	2.40417	-0.0290675	-0.0750992
77	2.7	2.46915	2.4875	-0.0183532	0.230853
78	2.7	2.49474	2.53333	-0.0385913	0.205258
79	2.5	2.50843	2.49167	0.0167659	-0.00843254
80	2.5	2.43105	2.40833	0.0227183	0.0689484
81	2.7	2.40486	2.36667	0.0381944	0.295139
82	2.6	2.31677	2.33333	-0.0165675	0.283234
83	2.6	2.2245	2.2625	-0.037996	0.375496
84	2.4	2.21558	2.175	0.0405754	0.184425
85	1.4	2.0501	2.10417	-0.0540675	-0.650099
86	1.8	2.07569	2.05417	0.0215278	-0.275694
87	2.1	2.05486	2	0.0548611	0.0451389
88	1.7	1.9126	1.94167	-0.0290675	-0.212599
89	1.6	1.87748	1.89583	-0.0183532	-0.27748
90	1.7	1.83224	1.87083	-0.0385913	-0.132242
91	1.8	NA	NA	0.0167659	NA
92	2	NA	NA	0.0227183	NA
93	1.9	NA	NA	0.0381944	NA
94	2	NA	NA	-0.0165675	NA
95	2.1	NA	NA	-0.037996	NA
96	2.3	NA	NA	0.0405754	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