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R Software Module

rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Wed, 04 Dec 2013 04:05:51 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/04/t1386148030y54s7lk4fx4znj0.htm/, Retrieved Fri, 19 Apr 2024 17:01:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=230446, Retrieved Fri, 19 Apr 2024 17:01:05 +0000

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-       [Classical Decomposition] [] [2013-12-04 09:05:51] [ba1aac5cc07b687ee7a9bc35c791a1eb] [Current]

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Dataseries X:

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2,7
3
-0,3
1,1
1,7
1,6
3
3,3
6,7
5,6
6
4,8
5,9
4,3
3,7
5,6
1,7
3,2
3,6
1,7
0,5
2,1
1,5
2,7
1,4
1,2
2,3
1,6
4,7
3,5
4,4
3,9
3,5
3
1,6
2,2
4,1
4,3
3,5
1,8
0,6
-0,4
-2,5
-1,6
-1,9
-1,6
-0,7
-1,1
0,3
1,3
3,3
2,4
2
3,9
4,2
4,9
5,8
4,8
4,4
5,3
2,1
2
-0,9
0,1
-0,5
-0,1
0,7
-0,4
-1,5
-0,3
1
0,4
0,3
1,8
3
2,2
3,4
3,4
3,1
4,5
4,6
5,7
4,3
4,5

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230446&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]7 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=230446&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230446&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	7 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	2.7	NA	NA	0.0501157	NA
2	3	NA	NA	0.174421	NA
3	-0.3	NA	NA	0.180671	NA
4	1.1	NA	NA	-0.00543981	NA
5	1.7	NA	NA	-0.294329	NA
6	1.6	NA	NA	-0.0137731	NA
7	3	3.41053	3.4	0.0105324	-0.410532
8	3.3	3.35637	3.5875	-0.231134	-0.0563657
9	6.7	3.77928	3.80833	-0.0290509	2.92072
10	5.6	4.18623	4.1625	0.0237269	1.41377
11	6	4.38762	4.35	0.0376157	1.61238
12	4.8	4.51331	4.41667	0.0966435	0.28669
13	5.9	4.55845	4.50833	0.0501157	1.34155
14	4.3	4.64109	4.46667	0.174421	-0.341088
15	3.7	4.32234	4.14167	0.180671	-0.622338
16	5.6	3.73206	3.7375	-0.00543981	1.86794
17	1.7	3.10984	3.40417	-0.294329	-1.40984
18	3.2	3.11539	3.12917	-0.0137731	0.0846065
19	3.6	2.8647	2.85417	0.0105324	0.735301
20	1.7	2.30637	2.5375	-0.231134	-0.606366
21	0.5	2.32095	2.35	-0.0290509	-1.82095
22	2.1	2.14873	2.125	0.0237269	-0.0487269
23	1.5	2.12095	2.08333	0.0376157	-0.620949
24	2.7	2.31748	2.22083	0.0966435	0.382523
25	1.4	2.31678	2.26667	0.0501157	-0.916782
26	1.2	2.56609	2.39167	0.174421	-1.36609
27	2.3	2.789	2.60833	0.180671	-0.489005
28	1.6	2.76539	2.77083	-0.00543981	-1.16539
29	4.7	2.51817	2.8125	-0.294329	2.18183
30	3.5	2.78206	2.79583	-0.0137731	0.71794
31	4.4	2.89803	2.8875	0.0105324	1.50197
32	3.9	2.89803	3.12917	-0.231134	1.00197
33	3.5	3.27928	3.30833	-0.0290509	0.220718
34	3	3.39039	3.36667	0.0237269	-0.390394
35	1.6	3.24178	3.20417	0.0376157	-1.64178
36	2.2	2.96748	2.87083	0.0966435	-0.767477
37	4.1	2.47095	2.42083	0.0501157	1.62905
38	4.3	2.07859	1.90417	0.174421	2.22141
39	3.5	1.63067	1.45	0.180671	1.86933
40	1.8	1.02789	1.03333	-0.00543981	0.772106
41	0.6	0.451505	0.745833	-0.294329	0.148495
42	-0.4	0.498727	0.5125	-0.0137731	-0.898727
43	-2.5	0.227199	0.216667	0.0105324	-2.7272
44	-1.6	-0.297801	-0.0666667	-0.231134	-1.3022
45	-1.9	-0.229051	-0.2	-0.0290509	-1.67095
46	-1.6	-0.159606	-0.183333	0.0237269	-1.44039
47	-0.7	-0.0623843	-0.1	0.0376157	-0.637616
48	-1.1	0.234144	0.1375	0.0966435	-1.33414
49	0.3	0.645949	0.595833	0.0501157	-0.345949
50	1.3	1.32025	1.14583	0.174421	-0.0202546
51	3.3	1.91817	1.7375	0.180671	1.38183
52	2.4	2.31956	2.325	-0.00543981	0.0804398
53	2	2.50984	2.80417	-0.294329	-0.509838
54	3.9	3.26956	3.28333	-0.0137731	0.63044
55	4.2	3.63553	3.625	0.0105324	0.564468
56	4.9	3.49803	3.72917	-0.231134	1.40197
57	5.8	3.55428	3.58333	-0.0290509	2.24572
58	4.8	3.33623	3.3125	0.0237269	1.46377
59	4.4	3.15012	3.1125	0.0376157	1.24988
60	5.3	2.93831	2.84167	0.0966435	2.36169
61	2.1	2.57928	2.52917	0.0501157	-0.479282
62	2	2.33692	2.1625	0.174421	-0.336921
63	-0.9	1.81817	1.6375	0.180671	-2.71817
64	0.1	1.11539	1.12083	-0.00543981	-1.01539
65	-0.5	0.472338	0.766667	-0.294329	-0.972338
66	-0.1	0.40706	0.420833	-0.0137731	-0.50706
67	0.7	0.152199	0.141667	0.0105324	0.547801
68	-0.4	-0.172801	0.0583333	-0.231134	-0.227199
69	-1.5	0.183449	0.2125	-0.0290509	-1.68345
70	-0.3	0.486227	0.4625	0.0237269	-0.786227
71	1	0.750116	0.7125	0.0376157	0.249884
72	0.4	1.11748	1.02083	0.0966435	-0.717477
73	0.3	1.31678	1.26667	0.0501157	-1.01678
74	1.8	1.74525	1.57083	0.174421	0.0547454
75	3	2.20984	2.02917	0.180671	0.790162
76	2.2	2.52789	2.53333	-0.00543981	-0.327894
77	3.4	2.6265	2.92083	-0.294329	0.773495
78	3.4	3.21539	3.22917	-0.0137731	0.184606
79	3.1	NA	NA	0.0105324	NA
80	4.5	NA	NA	-0.231134	NA
81	4.6	NA	NA	-0.0290509	NA
82	5.7	NA	NA	0.0237269	NA
83	4.3	NA	NA	0.0376157	NA
84	4.5	NA	NA	0.0966435	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 2.7 & NA & NA & 0.0501157 & NA \tabularnewline
2 & 3 & NA & NA & 0.174421 & NA \tabularnewline
3 & -0.3 & NA & NA & 0.180671 & NA \tabularnewline
4 & 1.1 & NA & NA & -0.00543981 & NA \tabularnewline
5 & 1.7 & NA & NA & -0.294329 & NA \tabularnewline
6 & 1.6 & NA & NA & -0.0137731 & NA \tabularnewline
7 & 3 & 3.41053 & 3.4 & 0.0105324 & -0.410532 \tabularnewline
8 & 3.3 & 3.35637 & 3.5875 & -0.231134 & -0.0563657 \tabularnewline
9 & 6.7 & 3.77928 & 3.80833 & -0.0290509 & 2.92072 \tabularnewline
10 & 5.6 & 4.18623 & 4.1625 & 0.0237269 & 1.41377 \tabularnewline
11 & 6 & 4.38762 & 4.35 & 0.0376157 & 1.61238 \tabularnewline
12 & 4.8 & 4.51331 & 4.41667 & 0.0966435 & 0.28669 \tabularnewline
13 & 5.9 & 4.55845 & 4.50833 & 0.0501157 & 1.34155 \tabularnewline
14 & 4.3 & 4.64109 & 4.46667 & 0.174421 & -0.341088 \tabularnewline
15 & 3.7 & 4.32234 & 4.14167 & 0.180671 & -0.622338 \tabularnewline
16 & 5.6 & 3.73206 & 3.7375 & -0.00543981 & 1.86794 \tabularnewline
17 & 1.7 & 3.10984 & 3.40417 & -0.294329 & -1.40984 \tabularnewline
18 & 3.2 & 3.11539 & 3.12917 & -0.0137731 & 0.0846065 \tabularnewline
19 & 3.6 & 2.8647 & 2.85417 & 0.0105324 & 0.735301 \tabularnewline
20 & 1.7 & 2.30637 & 2.5375 & -0.231134 & -0.606366 \tabularnewline
21 & 0.5 & 2.32095 & 2.35 & -0.0290509 & -1.82095 \tabularnewline
22 & 2.1 & 2.14873 & 2.125 & 0.0237269 & -0.0487269 \tabularnewline
23 & 1.5 & 2.12095 & 2.08333 & 0.0376157 & -0.620949 \tabularnewline
24 & 2.7 & 2.31748 & 2.22083 & 0.0966435 & 0.382523 \tabularnewline
25 & 1.4 & 2.31678 & 2.26667 & 0.0501157 & -0.916782 \tabularnewline
26 & 1.2 & 2.56609 & 2.39167 & 0.174421 & -1.36609 \tabularnewline
27 & 2.3 & 2.789 & 2.60833 & 0.180671 & -0.489005 \tabularnewline
28 & 1.6 & 2.76539 & 2.77083 & -0.00543981 & -1.16539 \tabularnewline
29 & 4.7 & 2.51817 & 2.8125 & -0.294329 & 2.18183 \tabularnewline
30 & 3.5 & 2.78206 & 2.79583 & -0.0137731 & 0.71794 \tabularnewline
31 & 4.4 & 2.89803 & 2.8875 & 0.0105324 & 1.50197 \tabularnewline
32 & 3.9 & 2.89803 & 3.12917 & -0.231134 & 1.00197 \tabularnewline
33 & 3.5 & 3.27928 & 3.30833 & -0.0290509 & 0.220718 \tabularnewline
34 & 3 & 3.39039 & 3.36667 & 0.0237269 & -0.390394 \tabularnewline
35 & 1.6 & 3.24178 & 3.20417 & 0.0376157 & -1.64178 \tabularnewline
36 & 2.2 & 2.96748 & 2.87083 & 0.0966435 & -0.767477 \tabularnewline
37 & 4.1 & 2.47095 & 2.42083 & 0.0501157 & 1.62905 \tabularnewline
38 & 4.3 & 2.07859 & 1.90417 & 0.174421 & 2.22141 \tabularnewline
39 & 3.5 & 1.63067 & 1.45 & 0.180671 & 1.86933 \tabularnewline
40 & 1.8 & 1.02789 & 1.03333 & -0.00543981 & 0.772106 \tabularnewline
41 & 0.6 & 0.451505 & 0.745833 & -0.294329 & 0.148495 \tabularnewline
42 & -0.4 & 0.498727 & 0.5125 & -0.0137731 & -0.898727 \tabularnewline
43 & -2.5 & 0.227199 & 0.216667 & 0.0105324 & -2.7272 \tabularnewline
44 & -1.6 & -0.297801 & -0.0666667 & -0.231134 & -1.3022 \tabularnewline
45 & -1.9 & -0.229051 & -0.2 & -0.0290509 & -1.67095 \tabularnewline
46 & -1.6 & -0.159606 & -0.183333 & 0.0237269 & -1.44039 \tabularnewline
47 & -0.7 & -0.0623843 & -0.1 & 0.0376157 & -0.637616 \tabularnewline
48 & -1.1 & 0.234144 & 0.1375 & 0.0966435 & -1.33414 \tabularnewline
49 & 0.3 & 0.645949 & 0.595833 & 0.0501157 & -0.345949 \tabularnewline
50 & 1.3 & 1.32025 & 1.14583 & 0.174421 & -0.0202546 \tabularnewline
51 & 3.3 & 1.91817 & 1.7375 & 0.180671 & 1.38183 \tabularnewline
52 & 2.4 & 2.31956 & 2.325 & -0.00543981 & 0.0804398 \tabularnewline
53 & 2 & 2.50984 & 2.80417 & -0.294329 & -0.509838 \tabularnewline
54 & 3.9 & 3.26956 & 3.28333 & -0.0137731 & 0.63044 \tabularnewline
55 & 4.2 & 3.63553 & 3.625 & 0.0105324 & 0.564468 \tabularnewline
56 & 4.9 & 3.49803 & 3.72917 & -0.231134 & 1.40197 \tabularnewline
57 & 5.8 & 3.55428 & 3.58333 & -0.0290509 & 2.24572 \tabularnewline
58 & 4.8 & 3.33623 & 3.3125 & 0.0237269 & 1.46377 \tabularnewline
59 & 4.4 & 3.15012 & 3.1125 & 0.0376157 & 1.24988 \tabularnewline
60 & 5.3 & 2.93831 & 2.84167 & 0.0966435 & 2.36169 \tabularnewline
61 & 2.1 & 2.57928 & 2.52917 & 0.0501157 & -0.479282 \tabularnewline
62 & 2 & 2.33692 & 2.1625 & 0.174421 & -0.336921 \tabularnewline
63 & -0.9 & 1.81817 & 1.6375 & 0.180671 & -2.71817 \tabularnewline
64 & 0.1 & 1.11539 & 1.12083 & -0.00543981 & -1.01539 \tabularnewline
65 & -0.5 & 0.472338 & 0.766667 & -0.294329 & -0.972338 \tabularnewline
66 & -0.1 & 0.40706 & 0.420833 & -0.0137731 & -0.50706 \tabularnewline
67 & 0.7 & 0.152199 & 0.141667 & 0.0105324 & 0.547801 \tabularnewline
68 & -0.4 & -0.172801 & 0.0583333 & -0.231134 & -0.227199 \tabularnewline
69 & -1.5 & 0.183449 & 0.2125 & -0.0290509 & -1.68345 \tabularnewline
70 & -0.3 & 0.486227 & 0.4625 & 0.0237269 & -0.786227 \tabularnewline
71 & 1 & 0.750116 & 0.7125 & 0.0376157 & 0.249884 \tabularnewline
72 & 0.4 & 1.11748 & 1.02083 & 0.0966435 & -0.717477 \tabularnewline
73 & 0.3 & 1.31678 & 1.26667 & 0.0501157 & -1.01678 \tabularnewline
74 & 1.8 & 1.74525 & 1.57083 & 0.174421 & 0.0547454 \tabularnewline
75 & 3 & 2.20984 & 2.02917 & 0.180671 & 0.790162 \tabularnewline
76 & 2.2 & 2.52789 & 2.53333 & -0.00543981 & -0.327894 \tabularnewline
77 & 3.4 & 2.6265 & 2.92083 & -0.294329 & 0.773495 \tabularnewline
78 & 3.4 & 3.21539 & 3.22917 & -0.0137731 & 0.184606 \tabularnewline
79 & 3.1 & NA & NA & 0.0105324 & NA \tabularnewline
80 & 4.5 & NA & NA & -0.231134 & NA \tabularnewline
81 & 4.6 & NA & NA & -0.0290509 & NA \tabularnewline
82 & 5.7 & NA & NA & 0.0237269 & NA \tabularnewline
83 & 4.3 & NA & NA & 0.0376157 & NA \tabularnewline
84 & 4.5 & NA & NA & 0.0966435 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230446&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]2.7[/C][C]NA[/C][C]NA[/C][C]0.0501157[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]3[/C][C]NA[/C][C]NA[/C][C]0.174421[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]-0.3[/C][C]NA[/C][C]NA[/C][C]0.180671[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]1.1[/C][C]NA[/C][C]NA[/C][C]-0.00543981[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]1.7[/C][C]NA[/C][C]NA[/C][C]-0.294329[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]1.6[/C][C]NA[/C][C]NA[/C][C]-0.0137731[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]3[/C][C]3.41053[/C][C]3.4[/C][C]0.0105324[/C][C]-0.410532[/C][/ROW]
[ROW][C]8[/C][C]3.3[/C][C]3.35637[/C][C]3.5875[/C][C]-0.231134[/C][C]-0.0563657[/C][/ROW]
[ROW][C]9[/C][C]6.7[/C][C]3.77928[/C][C]3.80833[/C][C]-0.0290509[/C][C]2.92072[/C][/ROW]
[ROW][C]10[/C][C]5.6[/C][C]4.18623[/C][C]4.1625[/C][C]0.0237269[/C][C]1.41377[/C][/ROW]
[ROW][C]11[/C][C]6[/C][C]4.38762[/C][C]4.35[/C][C]0.0376157[/C][C]1.61238[/C][/ROW]
[ROW][C]12[/C][C]4.8[/C][C]4.51331[/C][C]4.41667[/C][C]0.0966435[/C][C]0.28669[/C][/ROW]
[ROW][C]13[/C][C]5.9[/C][C]4.55845[/C][C]4.50833[/C][C]0.0501157[/C][C]1.34155[/C][/ROW]
[ROW][C]14[/C][C]4.3[/C][C]4.64109[/C][C]4.46667[/C][C]0.174421[/C][C]-0.341088[/C][/ROW]
[ROW][C]15[/C][C]3.7[/C][C]4.32234[/C][C]4.14167[/C][C]0.180671[/C][C]-0.622338[/C][/ROW]
[ROW][C]16[/C][C]5.6[/C][C]3.73206[/C][C]3.7375[/C][C]-0.00543981[/C][C]1.86794[/C][/ROW]
[ROW][C]17[/C][C]1.7[/C][C]3.10984[/C][C]3.40417[/C][C]-0.294329[/C][C]-1.40984[/C][/ROW]
[ROW][C]18[/C][C]3.2[/C][C]3.11539[/C][C]3.12917[/C][C]-0.0137731[/C][C]0.0846065[/C][/ROW]
[ROW][C]19[/C][C]3.6[/C][C]2.8647[/C][C]2.85417[/C][C]0.0105324[/C][C]0.735301[/C][/ROW]
[ROW][C]20[/C][C]1.7[/C][C]2.30637[/C][C]2.5375[/C][C]-0.231134[/C][C]-0.606366[/C][/ROW]
[ROW][C]21[/C][C]0.5[/C][C]2.32095[/C][C]2.35[/C][C]-0.0290509[/C][C]-1.82095[/C][/ROW]
[ROW][C]22[/C][C]2.1[/C][C]2.14873[/C][C]2.125[/C][C]0.0237269[/C][C]-0.0487269[/C][/ROW]
[ROW][C]23[/C][C]1.5[/C][C]2.12095[/C][C]2.08333[/C][C]0.0376157[/C][C]-0.620949[/C][/ROW]
[ROW][C]24[/C][C]2.7[/C][C]2.31748[/C][C]2.22083[/C][C]0.0966435[/C][C]0.382523[/C][/ROW]
[ROW][C]25[/C][C]1.4[/C][C]2.31678[/C][C]2.26667[/C][C]0.0501157[/C][C]-0.916782[/C][/ROW]
[ROW][C]26[/C][C]1.2[/C][C]2.56609[/C][C]2.39167[/C][C]0.174421[/C][C]-1.36609[/C][/ROW]
[ROW][C]27[/C][C]2.3[/C][C]2.789[/C][C]2.60833[/C][C]0.180671[/C][C]-0.489005[/C][/ROW]
[ROW][C]28[/C][C]1.6[/C][C]2.76539[/C][C]2.77083[/C][C]-0.00543981[/C][C]-1.16539[/C][/ROW]
[ROW][C]29[/C][C]4.7[/C][C]2.51817[/C][C]2.8125[/C][C]-0.294329[/C][C]2.18183[/C][/ROW]
[ROW][C]30[/C][C]3.5[/C][C]2.78206[/C][C]2.79583[/C][C]-0.0137731[/C][C]0.71794[/C][/ROW]
[ROW][C]31[/C][C]4.4[/C][C]2.89803[/C][C]2.8875[/C][C]0.0105324[/C][C]1.50197[/C][/ROW]
[ROW][C]32[/C][C]3.9[/C][C]2.89803[/C][C]3.12917[/C][C]-0.231134[/C][C]1.00197[/C][/ROW]
[ROW][C]33[/C][C]3.5[/C][C]3.27928[/C][C]3.30833[/C][C]-0.0290509[/C][C]0.220718[/C][/ROW]
[ROW][C]34[/C][C]3[/C][C]3.39039[/C][C]3.36667[/C][C]0.0237269[/C][C]-0.390394[/C][/ROW]
[ROW][C]35[/C][C]1.6[/C][C]3.24178[/C][C]3.20417[/C][C]0.0376157[/C][C]-1.64178[/C][/ROW]
[ROW][C]36[/C][C]2.2[/C][C]2.96748[/C][C]2.87083[/C][C]0.0966435[/C][C]-0.767477[/C][/ROW]
[ROW][C]37[/C][C]4.1[/C][C]2.47095[/C][C]2.42083[/C][C]0.0501157[/C][C]1.62905[/C][/ROW]
[ROW][C]38[/C][C]4.3[/C][C]2.07859[/C][C]1.90417[/C][C]0.174421[/C][C]2.22141[/C][/ROW]
[ROW][C]39[/C][C]3.5[/C][C]1.63067[/C][C]1.45[/C][C]0.180671[/C][C]1.86933[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]1.02789[/C][C]1.03333[/C][C]-0.00543981[/C][C]0.772106[/C][/ROW]
[ROW][C]41[/C][C]0.6[/C][C]0.451505[/C][C]0.745833[/C][C]-0.294329[/C][C]0.148495[/C][/ROW]
[ROW][C]42[/C][C]-0.4[/C][C]0.498727[/C][C]0.5125[/C][C]-0.0137731[/C][C]-0.898727[/C][/ROW]
[ROW][C]43[/C][C]-2.5[/C][C]0.227199[/C][C]0.216667[/C][C]0.0105324[/C][C]-2.7272[/C][/ROW]
[ROW][C]44[/C][C]-1.6[/C][C]-0.297801[/C][C]-0.0666667[/C][C]-0.231134[/C][C]-1.3022[/C][/ROW]
[ROW][C]45[/C][C]-1.9[/C][C]-0.229051[/C][C]-0.2[/C][C]-0.0290509[/C][C]-1.67095[/C][/ROW]
[ROW][C]46[/C][C]-1.6[/C][C]-0.159606[/C][C]-0.183333[/C][C]0.0237269[/C][C]-1.44039[/C][/ROW]
[ROW][C]47[/C][C]-0.7[/C][C]-0.0623843[/C][C]-0.1[/C][C]0.0376157[/C][C]-0.637616[/C][/ROW]
[ROW][C]48[/C][C]-1.1[/C][C]0.234144[/C][C]0.1375[/C][C]0.0966435[/C][C]-1.33414[/C][/ROW]
[ROW][C]49[/C][C]0.3[/C][C]0.645949[/C][C]0.595833[/C][C]0.0501157[/C][C]-0.345949[/C][/ROW]
[ROW][C]50[/C][C]1.3[/C][C]1.32025[/C][C]1.14583[/C][C]0.174421[/C][C]-0.0202546[/C][/ROW]
[ROW][C]51[/C][C]3.3[/C][C]1.91817[/C][C]1.7375[/C][C]0.180671[/C][C]1.38183[/C][/ROW]
[ROW][C]52[/C][C]2.4[/C][C]2.31956[/C][C]2.325[/C][C]-0.00543981[/C][C]0.0804398[/C][/ROW]
[ROW][C]53[/C][C]2[/C][C]2.50984[/C][C]2.80417[/C][C]-0.294329[/C][C]-0.509838[/C][/ROW]
[ROW][C]54[/C][C]3.9[/C][C]3.26956[/C][C]3.28333[/C][C]-0.0137731[/C][C]0.63044[/C][/ROW]
[ROW][C]55[/C][C]4.2[/C][C]3.63553[/C][C]3.625[/C][C]0.0105324[/C][C]0.564468[/C][/ROW]
[ROW][C]56[/C][C]4.9[/C][C]3.49803[/C][C]3.72917[/C][C]-0.231134[/C][C]1.40197[/C][/ROW]
[ROW][C]57[/C][C]5.8[/C][C]3.55428[/C][C]3.58333[/C][C]-0.0290509[/C][C]2.24572[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]3.33623[/C][C]3.3125[/C][C]0.0237269[/C][C]1.46377[/C][/ROW]
[ROW][C]59[/C][C]4.4[/C][C]3.15012[/C][C]3.1125[/C][C]0.0376157[/C][C]1.24988[/C][/ROW]
[ROW][C]60[/C][C]5.3[/C][C]2.93831[/C][C]2.84167[/C][C]0.0966435[/C][C]2.36169[/C][/ROW]
[ROW][C]61[/C][C]2.1[/C][C]2.57928[/C][C]2.52917[/C][C]0.0501157[/C][C]-0.479282[/C][/ROW]
[ROW][C]62[/C][C]2[/C][C]2.33692[/C][C]2.1625[/C][C]0.174421[/C][C]-0.336921[/C][/ROW]
[ROW][C]63[/C][C]-0.9[/C][C]1.81817[/C][C]1.6375[/C][C]0.180671[/C][C]-2.71817[/C][/ROW]
[ROW][C]64[/C][C]0.1[/C][C]1.11539[/C][C]1.12083[/C][C]-0.00543981[/C][C]-1.01539[/C][/ROW]
[ROW][C]65[/C][C]-0.5[/C][C]0.472338[/C][C]0.766667[/C][C]-0.294329[/C][C]-0.972338[/C][/ROW]
[ROW][C]66[/C][C]-0.1[/C][C]0.40706[/C][C]0.420833[/C][C]-0.0137731[/C][C]-0.50706[/C][/ROW]
[ROW][C]67[/C][C]0.7[/C][C]0.152199[/C][C]0.141667[/C][C]0.0105324[/C][C]0.547801[/C][/ROW]
[ROW][C]68[/C][C]-0.4[/C][C]-0.172801[/C][C]0.0583333[/C][C]-0.231134[/C][C]-0.227199[/C][/ROW]
[ROW][C]69[/C][C]-1.5[/C][C]0.183449[/C][C]0.2125[/C][C]-0.0290509[/C][C]-1.68345[/C][/ROW]
[ROW][C]70[/C][C]-0.3[/C][C]0.486227[/C][C]0.4625[/C][C]0.0237269[/C][C]-0.786227[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]0.750116[/C][C]0.7125[/C][C]0.0376157[/C][C]0.249884[/C][/ROW]
[ROW][C]72[/C][C]0.4[/C][C]1.11748[/C][C]1.02083[/C][C]0.0966435[/C][C]-0.717477[/C][/ROW]
[ROW][C]73[/C][C]0.3[/C][C]1.31678[/C][C]1.26667[/C][C]0.0501157[/C][C]-1.01678[/C][/ROW]
[ROW][C]74[/C][C]1.8[/C][C]1.74525[/C][C]1.57083[/C][C]0.174421[/C][C]0.0547454[/C][/ROW]
[ROW][C]75[/C][C]3[/C][C]2.20984[/C][C]2.02917[/C][C]0.180671[/C][C]0.790162[/C][/ROW]
[ROW][C]76[/C][C]2.2[/C][C]2.52789[/C][C]2.53333[/C][C]-0.00543981[/C][C]-0.327894[/C][/ROW]
[ROW][C]77[/C][C]3.4[/C][C]2.6265[/C][C]2.92083[/C][C]-0.294329[/C][C]0.773495[/C][/ROW]
[ROW][C]78[/C][C]3.4[/C][C]3.21539[/C][C]3.22917[/C][C]-0.0137731[/C][C]0.184606[/C][/ROW]
[ROW][C]79[/C][C]3.1[/C][C]NA[/C][C]NA[/C][C]0.0105324[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]4.5[/C][C]NA[/C][C]NA[/C][C]-0.231134[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]4.6[/C][C]NA[/C][C]NA[/C][C]-0.0290509[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]5.7[/C][C]NA[/C][C]NA[/C][C]0.0237269[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]4.3[/C][C]NA[/C][C]NA[/C][C]0.0376157[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]4.5[/C][C]NA[/C][C]NA[/C][C]0.0966435[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230446&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230446&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	2.7	NA	NA	0.0501157	NA
2	3	NA	NA	0.174421	NA
3	-0.3	NA	NA	0.180671	NA
4	1.1	NA	NA	-0.00543981	NA
5	1.7	NA	NA	-0.294329	NA
6	1.6	NA	NA	-0.0137731	NA
7	3	3.41053	3.4	0.0105324	-0.410532
8	3.3	3.35637	3.5875	-0.231134	-0.0563657
9	6.7	3.77928	3.80833	-0.0290509	2.92072
10	5.6	4.18623	4.1625	0.0237269	1.41377
11	6	4.38762	4.35	0.0376157	1.61238
12	4.8	4.51331	4.41667	0.0966435	0.28669
13	5.9	4.55845	4.50833	0.0501157	1.34155
14	4.3	4.64109	4.46667	0.174421	-0.341088
15	3.7	4.32234	4.14167	0.180671	-0.622338
16	5.6	3.73206	3.7375	-0.00543981	1.86794
17	1.7	3.10984	3.40417	-0.294329	-1.40984
18	3.2	3.11539	3.12917	-0.0137731	0.0846065
19	3.6	2.8647	2.85417	0.0105324	0.735301
20	1.7	2.30637	2.5375	-0.231134	-0.606366
21	0.5	2.32095	2.35	-0.0290509	-1.82095
22	2.1	2.14873	2.125	0.0237269	-0.0487269
23	1.5	2.12095	2.08333	0.0376157	-0.620949
24	2.7	2.31748	2.22083	0.0966435	0.382523
25	1.4	2.31678	2.26667	0.0501157	-0.916782
26	1.2	2.56609	2.39167	0.174421	-1.36609
27	2.3	2.789	2.60833	0.180671	-0.489005
28	1.6	2.76539	2.77083	-0.00543981	-1.16539
29	4.7	2.51817	2.8125	-0.294329	2.18183
30	3.5	2.78206	2.79583	-0.0137731	0.71794
31	4.4	2.89803	2.8875	0.0105324	1.50197
32	3.9	2.89803	3.12917	-0.231134	1.00197
33	3.5	3.27928	3.30833	-0.0290509	0.220718
34	3	3.39039	3.36667	0.0237269	-0.390394
35	1.6	3.24178	3.20417	0.0376157	-1.64178
36	2.2	2.96748	2.87083	0.0966435	-0.767477
37	4.1	2.47095	2.42083	0.0501157	1.62905
38	4.3	2.07859	1.90417	0.174421	2.22141
39	3.5	1.63067	1.45	0.180671	1.86933
40	1.8	1.02789	1.03333	-0.00543981	0.772106
41	0.6	0.451505	0.745833	-0.294329	0.148495
42	-0.4	0.498727	0.5125	-0.0137731	-0.898727
43	-2.5	0.227199	0.216667	0.0105324	-2.7272
44	-1.6	-0.297801	-0.0666667	-0.231134	-1.3022
45	-1.9	-0.229051	-0.2	-0.0290509	-1.67095
46	-1.6	-0.159606	-0.183333	0.0237269	-1.44039
47	-0.7	-0.0623843	-0.1	0.0376157	-0.637616
48	-1.1	0.234144	0.1375	0.0966435	-1.33414
49	0.3	0.645949	0.595833	0.0501157	-0.345949
50	1.3	1.32025	1.14583	0.174421	-0.0202546
51	3.3	1.91817	1.7375	0.180671	1.38183
52	2.4	2.31956	2.325	-0.00543981	0.0804398
53	2	2.50984	2.80417	-0.294329	-0.509838
54	3.9	3.26956	3.28333	-0.0137731	0.63044
55	4.2	3.63553	3.625	0.0105324	0.564468
56	4.9	3.49803	3.72917	-0.231134	1.40197
57	5.8	3.55428	3.58333	-0.0290509	2.24572
58	4.8	3.33623	3.3125	0.0237269	1.46377
59	4.4	3.15012	3.1125	0.0376157	1.24988
60	5.3	2.93831	2.84167	0.0966435	2.36169
61	2.1	2.57928	2.52917	0.0501157	-0.479282
62	2	2.33692	2.1625	0.174421	-0.336921
63	-0.9	1.81817	1.6375	0.180671	-2.71817
64	0.1	1.11539	1.12083	-0.00543981	-1.01539
65	-0.5	0.472338	0.766667	-0.294329	-0.972338
66	-0.1	0.40706	0.420833	-0.0137731	-0.50706
67	0.7	0.152199	0.141667	0.0105324	0.547801
68	-0.4	-0.172801	0.0583333	-0.231134	-0.227199
69	-1.5	0.183449	0.2125	-0.0290509	-1.68345
70	-0.3	0.486227	0.4625	0.0237269	-0.786227
71	1	0.750116	0.7125	0.0376157	0.249884
72	0.4	1.11748	1.02083	0.0966435	-0.717477
73	0.3	1.31678	1.26667	0.0501157	-1.01678
74	1.8	1.74525	1.57083	0.174421	0.0547454
75	3	2.20984	2.02917	0.180671	0.790162
76	2.2	2.52789	2.53333	-0.00543981	-0.327894
77	3.4	2.6265	2.92083	-0.294329	0.773495
78	3.4	3.21539	3.22917	-0.0137731	0.184606
79	3.1	NA	NA	0.0105324	NA
80	4.5	NA	NA	-0.231134	NA
81	4.6	NA	NA	-0.0290509	NA
82	5.7	NA	NA	0.0237269	NA
83	4.3	NA	NA	0.0376157	NA
84	4.5	NA	NA	0.0966435	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 <- 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