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R Software Module

rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 30 Nov 2014 20:25:28 +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/2014/Nov/30/t1417379151ori9kbw9o85d4l8.htm/, Retrieved Sun, 24 May 2026 00:23:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=261615, Retrieved Sun, 24 May 2026 00:23:37 +0000

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341

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-       [Classical Decomposition] [] [2014-11-30 20:25:28] [12dd06d3a2c25ae97856df636a27679f] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261615&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261615&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261615&T=0

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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	'Herman Ole Andreas Wold' @ wold.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	123.2	NA	NA	0.567083	NA
2	136.9	NA	NA	0.442917	NA
3	146.8	NA	NA	0.02125	NA
4	149.6	NA	NA	-0.97875	NA
5	146.5	NA	NA	-0.282917	NA
6	157	NA	NA	-2.03042	NA
7	147.9	130.241	128.237	2.00375	17.6588
8	133.6	128.996	125.246	3.75042	4.60375
9	128.7	124.601	121.129	3.47208	4.09875
10	100.8	114.537	116.554	-2.01708	-13.7371
11	91.8	109.404	112.312	-2.90875	-17.6037
12	89.3	106.019	108.058	-2.03958	-16.7188
13	96.7	103.725	103.158	0.567083	-7.02542
14	91.6	98.9887	98.5458	0.442917	-7.38875
15	93.3	94.4796	94.4583	0.02125	-1.17958
16	93.3	90.9171	91.8958	-0.97875	2.38292
17	101	91.1046	91.3875	-0.282917	9.89542
18	100.4	89.6529	91.6833	-2.03042	10.7471
19	86.9	93.7496	91.7458	2.00375	-6.84958
20	83.9	95.1254	91.375	3.75042	-11.2254
21	80.3	94.4012	90.9292	3.47208	-14.1013
22	87.7	88.2412	90.2583	-2.01708	-0.54125
23	92.7	86.2829	89.1917	-2.90875	6.41708
24	95.5	85.7062	87.7458	-2.03958	9.79375
25	92	87.7004	87.1333	0.567083	4.29958
26	87.4	88.4762	88.0333	0.442917	-1.07625
27	86.8	90.1838	90.1625	0.02125	-3.38375
28	83.7	91.9296	92.9083	-0.97875	-8.22958
29	85	95.2004	95.4833	-0.282917	-10.2004
30	81.7	96.2196	98.25	-2.03042	-14.5196
31	90.9	103.795	101.792	2.00375	-12.8954
32	101.5	110.125	106.375	3.75042	-8.62542
33	113.8	114.826	111.354	3.47208	-1.02625
34	120.1	114.679	116.696	-2.01708	5.42125
35	122.1	119.5	122.408	-2.90875	2.60042
36	132.5	125.794	127.833	-2.03958	6.70625
37	140	132.971	132.404	0.567083	7.02875
38	149.4	136.639	136.196	0.442917	12.7613
39	144.3	139.4	139.379	0.02125	4.89958
40	154.4	140.267	141.246	-0.97875	14.1329
41	151.4	141.913	142.196	-0.282917	9.48708
42	145.5	140.345	142.375	-2.03042	5.15542
43	136.8	143.758	141.754	2.00375	-6.95792
44	146.6	144.33	140.579	3.75042	2.27042
45	145.1	142.789	139.317	3.47208	2.31125
46	133.6	135.725	137.742	-2.01708	-2.12458
47	131.4	132.662	135.571	-2.90875	-1.26208
48	127.5	131.735	133.775	-2.03958	-4.23542
49	130.1	134.446	133.879	0.567083	-4.34625
50	131.1	135.818	135.375	0.442917	-4.71792
51	132.3	136.7	136.679	0.02125	-4.40042
52	128.6	137.305	138.283	-0.97875	-8.70458
53	125.1	140.025	140.308	-0.282917	-14.9254
54	128.7	140.353	142.383	-2.03042	-11.6529
55	156.1	146.233	144.229	2.00375	9.86708
56	163.2	149.4	145.65	3.75042	13.7996
57	159.8	150.26	146.787	3.47208	9.54042
58	157.4	145.595	147.612	-2.01708	11.8046
59	156.2	145.529	148.437	-2.90875	10.6713
60	152.5	147.223	149.262	-2.03958	5.27708
61	149.4	148.534	147.967	0.567083	0.86625
62	145.9	144.655	144.212	0.442917	1.24458
63	144.8	139.913	139.892	0.02125	4.88708
64	135.9	134.659	135.638	-0.97875	1.24125
65	137.6	131.034	131.317	-0.282917	6.56625
66	136	124.907	126.938	-2.03042	11.0929
67	117.7	NA	NA	2.00375	NA
68	111.5	NA	NA	3.75042	NA
69	107.8	NA	NA	3.47208	NA
70	107.3	NA	NA	-2.01708	NA
71	102.6	NA	NA	-2.90875	NA
72	101	NA	NA	-2.03958	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 123.2 & NA & NA & 0.567083 & NA \tabularnewline
2 & 136.9 & NA & NA & 0.442917 & NA \tabularnewline
3 & 146.8 & NA & NA & 0.02125 & NA \tabularnewline
4 & 149.6 & NA & NA & -0.97875 & NA \tabularnewline
5 & 146.5 & NA & NA & -0.282917 & NA \tabularnewline
6 & 157 & NA & NA & -2.03042 & NA \tabularnewline
7 & 147.9 & 130.241 & 128.237 & 2.00375 & 17.6588 \tabularnewline
8 & 133.6 & 128.996 & 125.246 & 3.75042 & 4.60375 \tabularnewline
9 & 128.7 & 124.601 & 121.129 & 3.47208 & 4.09875 \tabularnewline
10 & 100.8 & 114.537 & 116.554 & -2.01708 & -13.7371 \tabularnewline
11 & 91.8 & 109.404 & 112.312 & -2.90875 & -17.6037 \tabularnewline
12 & 89.3 & 106.019 & 108.058 & -2.03958 & -16.7188 \tabularnewline
13 & 96.7 & 103.725 & 103.158 & 0.567083 & -7.02542 \tabularnewline
14 & 91.6 & 98.9887 & 98.5458 & 0.442917 & -7.38875 \tabularnewline
15 & 93.3 & 94.4796 & 94.4583 & 0.02125 & -1.17958 \tabularnewline
16 & 93.3 & 90.9171 & 91.8958 & -0.97875 & 2.38292 \tabularnewline
17 & 101 & 91.1046 & 91.3875 & -0.282917 & 9.89542 \tabularnewline
18 & 100.4 & 89.6529 & 91.6833 & -2.03042 & 10.7471 \tabularnewline
19 & 86.9 & 93.7496 & 91.7458 & 2.00375 & -6.84958 \tabularnewline
20 & 83.9 & 95.1254 & 91.375 & 3.75042 & -11.2254 \tabularnewline
21 & 80.3 & 94.4012 & 90.9292 & 3.47208 & -14.1013 \tabularnewline
22 & 87.7 & 88.2412 & 90.2583 & -2.01708 & -0.54125 \tabularnewline
23 & 92.7 & 86.2829 & 89.1917 & -2.90875 & 6.41708 \tabularnewline
24 & 95.5 & 85.7062 & 87.7458 & -2.03958 & 9.79375 \tabularnewline
25 & 92 & 87.7004 & 87.1333 & 0.567083 & 4.29958 \tabularnewline
26 & 87.4 & 88.4762 & 88.0333 & 0.442917 & -1.07625 \tabularnewline
27 & 86.8 & 90.1838 & 90.1625 & 0.02125 & -3.38375 \tabularnewline
28 & 83.7 & 91.9296 & 92.9083 & -0.97875 & -8.22958 \tabularnewline
29 & 85 & 95.2004 & 95.4833 & -0.282917 & -10.2004 \tabularnewline
30 & 81.7 & 96.2196 & 98.25 & -2.03042 & -14.5196 \tabularnewline
31 & 90.9 & 103.795 & 101.792 & 2.00375 & -12.8954 \tabularnewline
32 & 101.5 & 110.125 & 106.375 & 3.75042 & -8.62542 \tabularnewline
33 & 113.8 & 114.826 & 111.354 & 3.47208 & -1.02625 \tabularnewline
34 & 120.1 & 114.679 & 116.696 & -2.01708 & 5.42125 \tabularnewline
35 & 122.1 & 119.5 & 122.408 & -2.90875 & 2.60042 \tabularnewline
36 & 132.5 & 125.794 & 127.833 & -2.03958 & 6.70625 \tabularnewline
37 & 140 & 132.971 & 132.404 & 0.567083 & 7.02875 \tabularnewline
38 & 149.4 & 136.639 & 136.196 & 0.442917 & 12.7613 \tabularnewline
39 & 144.3 & 139.4 & 139.379 & 0.02125 & 4.89958 \tabularnewline
40 & 154.4 & 140.267 & 141.246 & -0.97875 & 14.1329 \tabularnewline
41 & 151.4 & 141.913 & 142.196 & -0.282917 & 9.48708 \tabularnewline
42 & 145.5 & 140.345 & 142.375 & -2.03042 & 5.15542 \tabularnewline
43 & 136.8 & 143.758 & 141.754 & 2.00375 & -6.95792 \tabularnewline
44 & 146.6 & 144.33 & 140.579 & 3.75042 & 2.27042 \tabularnewline
45 & 145.1 & 142.789 & 139.317 & 3.47208 & 2.31125 \tabularnewline
46 & 133.6 & 135.725 & 137.742 & -2.01708 & -2.12458 \tabularnewline
47 & 131.4 & 132.662 & 135.571 & -2.90875 & -1.26208 \tabularnewline
48 & 127.5 & 131.735 & 133.775 & -2.03958 & -4.23542 \tabularnewline
49 & 130.1 & 134.446 & 133.879 & 0.567083 & -4.34625 \tabularnewline
50 & 131.1 & 135.818 & 135.375 & 0.442917 & -4.71792 \tabularnewline
51 & 132.3 & 136.7 & 136.679 & 0.02125 & -4.40042 \tabularnewline
52 & 128.6 & 137.305 & 138.283 & -0.97875 & -8.70458 \tabularnewline
53 & 125.1 & 140.025 & 140.308 & -0.282917 & -14.9254 \tabularnewline
54 & 128.7 & 140.353 & 142.383 & -2.03042 & -11.6529 \tabularnewline
55 & 156.1 & 146.233 & 144.229 & 2.00375 & 9.86708 \tabularnewline
56 & 163.2 & 149.4 & 145.65 & 3.75042 & 13.7996 \tabularnewline
57 & 159.8 & 150.26 & 146.787 & 3.47208 & 9.54042 \tabularnewline
58 & 157.4 & 145.595 & 147.612 & -2.01708 & 11.8046 \tabularnewline
59 & 156.2 & 145.529 & 148.437 & -2.90875 & 10.6713 \tabularnewline
60 & 152.5 & 147.223 & 149.262 & -2.03958 & 5.27708 \tabularnewline
61 & 149.4 & 148.534 & 147.967 & 0.567083 & 0.86625 \tabularnewline
62 & 145.9 & 144.655 & 144.212 & 0.442917 & 1.24458 \tabularnewline
63 & 144.8 & 139.913 & 139.892 & 0.02125 & 4.88708 \tabularnewline
64 & 135.9 & 134.659 & 135.638 & -0.97875 & 1.24125 \tabularnewline
65 & 137.6 & 131.034 & 131.317 & -0.282917 & 6.56625 \tabularnewline
66 & 136 & 124.907 & 126.938 & -2.03042 & 11.0929 \tabularnewline
67 & 117.7 & NA & NA & 2.00375 & NA \tabularnewline
68 & 111.5 & NA & NA & 3.75042 & NA \tabularnewline
69 & 107.8 & NA & NA & 3.47208 & NA \tabularnewline
70 & 107.3 & NA & NA & -2.01708 & NA \tabularnewline
71 & 102.6 & NA & NA & -2.90875 & NA \tabularnewline
72 & 101 & NA & NA & -2.03958 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261615&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]123.2[/C][C]NA[/C][C]NA[/C][C]0.567083[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]136.9[/C][C]NA[/C][C]NA[/C][C]0.442917[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]146.8[/C][C]NA[/C][C]NA[/C][C]0.02125[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]149.6[/C][C]NA[/C][C]NA[/C][C]-0.97875[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]146.5[/C][C]NA[/C][C]NA[/C][C]-0.282917[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]157[/C][C]NA[/C][C]NA[/C][C]-2.03042[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]147.9[/C][C]130.241[/C][C]128.237[/C][C]2.00375[/C][C]17.6588[/C][/ROW]
[ROW][C]8[/C][C]133.6[/C][C]128.996[/C][C]125.246[/C][C]3.75042[/C][C]4.60375[/C][/ROW]
[ROW][C]9[/C][C]128.7[/C][C]124.601[/C][C]121.129[/C][C]3.47208[/C][C]4.09875[/C][/ROW]
[ROW][C]10[/C][C]100.8[/C][C]114.537[/C][C]116.554[/C][C]-2.01708[/C][C]-13.7371[/C][/ROW]
[ROW][C]11[/C][C]91.8[/C][C]109.404[/C][C]112.312[/C][C]-2.90875[/C][C]-17.6037[/C][/ROW]
[ROW][C]12[/C][C]89.3[/C][C]106.019[/C][C]108.058[/C][C]-2.03958[/C][C]-16.7188[/C][/ROW]
[ROW][C]13[/C][C]96.7[/C][C]103.725[/C][C]103.158[/C][C]0.567083[/C][C]-7.02542[/C][/ROW]
[ROW][C]14[/C][C]91.6[/C][C]98.9887[/C][C]98.5458[/C][C]0.442917[/C][C]-7.38875[/C][/ROW]
[ROW][C]15[/C][C]93.3[/C][C]94.4796[/C][C]94.4583[/C][C]0.02125[/C][C]-1.17958[/C][/ROW]
[ROW][C]16[/C][C]93.3[/C][C]90.9171[/C][C]91.8958[/C][C]-0.97875[/C][C]2.38292[/C][/ROW]
[ROW][C]17[/C][C]101[/C][C]91.1046[/C][C]91.3875[/C][C]-0.282917[/C][C]9.89542[/C][/ROW]
[ROW][C]18[/C][C]100.4[/C][C]89.6529[/C][C]91.6833[/C][C]-2.03042[/C][C]10.7471[/C][/ROW]
[ROW][C]19[/C][C]86.9[/C][C]93.7496[/C][C]91.7458[/C][C]2.00375[/C][C]-6.84958[/C][/ROW]
[ROW][C]20[/C][C]83.9[/C][C]95.1254[/C][C]91.375[/C][C]3.75042[/C][C]-11.2254[/C][/ROW]
[ROW][C]21[/C][C]80.3[/C][C]94.4012[/C][C]90.9292[/C][C]3.47208[/C][C]-14.1013[/C][/ROW]
[ROW][C]22[/C][C]87.7[/C][C]88.2412[/C][C]90.2583[/C][C]-2.01708[/C][C]-0.54125[/C][/ROW]
[ROW][C]23[/C][C]92.7[/C][C]86.2829[/C][C]89.1917[/C][C]-2.90875[/C][C]6.41708[/C][/ROW]
[ROW][C]24[/C][C]95.5[/C][C]85.7062[/C][C]87.7458[/C][C]-2.03958[/C][C]9.79375[/C][/ROW]
[ROW][C]25[/C][C]92[/C][C]87.7004[/C][C]87.1333[/C][C]0.567083[/C][C]4.29958[/C][/ROW]
[ROW][C]26[/C][C]87.4[/C][C]88.4762[/C][C]88.0333[/C][C]0.442917[/C][C]-1.07625[/C][/ROW]
[ROW][C]27[/C][C]86.8[/C][C]90.1838[/C][C]90.1625[/C][C]0.02125[/C][C]-3.38375[/C][/ROW]
[ROW][C]28[/C][C]83.7[/C][C]91.9296[/C][C]92.9083[/C][C]-0.97875[/C][C]-8.22958[/C][/ROW]
[ROW][C]29[/C][C]85[/C][C]95.2004[/C][C]95.4833[/C][C]-0.282917[/C][C]-10.2004[/C][/ROW]
[ROW][C]30[/C][C]81.7[/C][C]96.2196[/C][C]98.25[/C][C]-2.03042[/C][C]-14.5196[/C][/ROW]
[ROW][C]31[/C][C]90.9[/C][C]103.795[/C][C]101.792[/C][C]2.00375[/C][C]-12.8954[/C][/ROW]
[ROW][C]32[/C][C]101.5[/C][C]110.125[/C][C]106.375[/C][C]3.75042[/C][C]-8.62542[/C][/ROW]
[ROW][C]33[/C][C]113.8[/C][C]114.826[/C][C]111.354[/C][C]3.47208[/C][C]-1.02625[/C][/ROW]
[ROW][C]34[/C][C]120.1[/C][C]114.679[/C][C]116.696[/C][C]-2.01708[/C][C]5.42125[/C][/ROW]
[ROW][C]35[/C][C]122.1[/C][C]119.5[/C][C]122.408[/C][C]-2.90875[/C][C]2.60042[/C][/ROW]
[ROW][C]36[/C][C]132.5[/C][C]125.794[/C][C]127.833[/C][C]-2.03958[/C][C]6.70625[/C][/ROW]
[ROW][C]37[/C][C]140[/C][C]132.971[/C][C]132.404[/C][C]0.567083[/C][C]7.02875[/C][/ROW]
[ROW][C]38[/C][C]149.4[/C][C]136.639[/C][C]136.196[/C][C]0.442917[/C][C]12.7613[/C][/ROW]
[ROW][C]39[/C][C]144.3[/C][C]139.4[/C][C]139.379[/C][C]0.02125[/C][C]4.89958[/C][/ROW]
[ROW][C]40[/C][C]154.4[/C][C]140.267[/C][C]141.246[/C][C]-0.97875[/C][C]14.1329[/C][/ROW]
[ROW][C]41[/C][C]151.4[/C][C]141.913[/C][C]142.196[/C][C]-0.282917[/C][C]9.48708[/C][/ROW]
[ROW][C]42[/C][C]145.5[/C][C]140.345[/C][C]142.375[/C][C]-2.03042[/C][C]5.15542[/C][/ROW]
[ROW][C]43[/C][C]136.8[/C][C]143.758[/C][C]141.754[/C][C]2.00375[/C][C]-6.95792[/C][/ROW]
[ROW][C]44[/C][C]146.6[/C][C]144.33[/C][C]140.579[/C][C]3.75042[/C][C]2.27042[/C][/ROW]
[ROW][C]45[/C][C]145.1[/C][C]142.789[/C][C]139.317[/C][C]3.47208[/C][C]2.31125[/C][/ROW]
[ROW][C]46[/C][C]133.6[/C][C]135.725[/C][C]137.742[/C][C]-2.01708[/C][C]-2.12458[/C][/ROW]
[ROW][C]47[/C][C]131.4[/C][C]132.662[/C][C]135.571[/C][C]-2.90875[/C][C]-1.26208[/C][/ROW]
[ROW][C]48[/C][C]127.5[/C][C]131.735[/C][C]133.775[/C][C]-2.03958[/C][C]-4.23542[/C][/ROW]
[ROW][C]49[/C][C]130.1[/C][C]134.446[/C][C]133.879[/C][C]0.567083[/C][C]-4.34625[/C][/ROW]
[ROW][C]50[/C][C]131.1[/C][C]135.818[/C][C]135.375[/C][C]0.442917[/C][C]-4.71792[/C][/ROW]
[ROW][C]51[/C][C]132.3[/C][C]136.7[/C][C]136.679[/C][C]0.02125[/C][C]-4.40042[/C][/ROW]
[ROW][C]52[/C][C]128.6[/C][C]137.305[/C][C]138.283[/C][C]-0.97875[/C][C]-8.70458[/C][/ROW]
[ROW][C]53[/C][C]125.1[/C][C]140.025[/C][C]140.308[/C][C]-0.282917[/C][C]-14.9254[/C][/ROW]
[ROW][C]54[/C][C]128.7[/C][C]140.353[/C][C]142.383[/C][C]-2.03042[/C][C]-11.6529[/C][/ROW]
[ROW][C]55[/C][C]156.1[/C][C]146.233[/C][C]144.229[/C][C]2.00375[/C][C]9.86708[/C][/ROW]
[ROW][C]56[/C][C]163.2[/C][C]149.4[/C][C]145.65[/C][C]3.75042[/C][C]13.7996[/C][/ROW]
[ROW][C]57[/C][C]159.8[/C][C]150.26[/C][C]146.787[/C][C]3.47208[/C][C]9.54042[/C][/ROW]
[ROW][C]58[/C][C]157.4[/C][C]145.595[/C][C]147.612[/C][C]-2.01708[/C][C]11.8046[/C][/ROW]
[ROW][C]59[/C][C]156.2[/C][C]145.529[/C][C]148.437[/C][C]-2.90875[/C][C]10.6713[/C][/ROW]
[ROW][C]60[/C][C]152.5[/C][C]147.223[/C][C]149.262[/C][C]-2.03958[/C][C]5.27708[/C][/ROW]
[ROW][C]61[/C][C]149.4[/C][C]148.534[/C][C]147.967[/C][C]0.567083[/C][C]0.86625[/C][/ROW]
[ROW][C]62[/C][C]145.9[/C][C]144.655[/C][C]144.212[/C][C]0.442917[/C][C]1.24458[/C][/ROW]
[ROW][C]63[/C][C]144.8[/C][C]139.913[/C][C]139.892[/C][C]0.02125[/C][C]4.88708[/C][/ROW]
[ROW][C]64[/C][C]135.9[/C][C]134.659[/C][C]135.638[/C][C]-0.97875[/C][C]1.24125[/C][/ROW]
[ROW][C]65[/C][C]137.6[/C][C]131.034[/C][C]131.317[/C][C]-0.282917[/C][C]6.56625[/C][/ROW]
[ROW][C]66[/C][C]136[/C][C]124.907[/C][C]126.938[/C][C]-2.03042[/C][C]11.0929[/C][/ROW]
[ROW][C]67[/C][C]117.7[/C][C]NA[/C][C]NA[/C][C]2.00375[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]111.5[/C][C]NA[/C][C]NA[/C][C]3.75042[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]107.8[/C][C]NA[/C][C]NA[/C][C]3.47208[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]107.3[/C][C]NA[/C][C]NA[/C][C]-2.01708[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]102.6[/C][C]NA[/C][C]NA[/C][C]-2.90875[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]101[/C][C]NA[/C][C]NA[/C][C]-2.03958[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261615&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261615&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	123.2	NA	NA	0.567083	NA
2	136.9	NA	NA	0.442917	NA
3	146.8	NA	NA	0.02125	NA
4	149.6	NA	NA	-0.97875	NA
5	146.5	NA	NA	-0.282917	NA
6	157	NA	NA	-2.03042	NA
7	147.9	130.241	128.237	2.00375	17.6588
8	133.6	128.996	125.246	3.75042	4.60375
9	128.7	124.601	121.129	3.47208	4.09875
10	100.8	114.537	116.554	-2.01708	-13.7371
11	91.8	109.404	112.312	-2.90875	-17.6037
12	89.3	106.019	108.058	-2.03958	-16.7188
13	96.7	103.725	103.158	0.567083	-7.02542
14	91.6	98.9887	98.5458	0.442917	-7.38875
15	93.3	94.4796	94.4583	0.02125	-1.17958
16	93.3	90.9171	91.8958	-0.97875	2.38292
17	101	91.1046	91.3875	-0.282917	9.89542
18	100.4	89.6529	91.6833	-2.03042	10.7471
19	86.9	93.7496	91.7458	2.00375	-6.84958
20	83.9	95.1254	91.375	3.75042	-11.2254
21	80.3	94.4012	90.9292	3.47208	-14.1013
22	87.7	88.2412	90.2583	-2.01708	-0.54125
23	92.7	86.2829	89.1917	-2.90875	6.41708
24	95.5	85.7062	87.7458	-2.03958	9.79375
25	92	87.7004	87.1333	0.567083	4.29958
26	87.4	88.4762	88.0333	0.442917	-1.07625
27	86.8	90.1838	90.1625	0.02125	-3.38375
28	83.7	91.9296	92.9083	-0.97875	-8.22958
29	85	95.2004	95.4833	-0.282917	-10.2004
30	81.7	96.2196	98.25	-2.03042	-14.5196
31	90.9	103.795	101.792	2.00375	-12.8954
32	101.5	110.125	106.375	3.75042	-8.62542
33	113.8	114.826	111.354	3.47208	-1.02625
34	120.1	114.679	116.696	-2.01708	5.42125
35	122.1	119.5	122.408	-2.90875	2.60042
36	132.5	125.794	127.833	-2.03958	6.70625
37	140	132.971	132.404	0.567083	7.02875
38	149.4	136.639	136.196	0.442917	12.7613
39	144.3	139.4	139.379	0.02125	4.89958
40	154.4	140.267	141.246	-0.97875	14.1329
41	151.4	141.913	142.196	-0.282917	9.48708
42	145.5	140.345	142.375	-2.03042	5.15542
43	136.8	143.758	141.754	2.00375	-6.95792
44	146.6	144.33	140.579	3.75042	2.27042
45	145.1	142.789	139.317	3.47208	2.31125
46	133.6	135.725	137.742	-2.01708	-2.12458
47	131.4	132.662	135.571	-2.90875	-1.26208
48	127.5	131.735	133.775	-2.03958	-4.23542
49	130.1	134.446	133.879	0.567083	-4.34625
50	131.1	135.818	135.375	0.442917	-4.71792
51	132.3	136.7	136.679	0.02125	-4.40042
52	128.6	137.305	138.283	-0.97875	-8.70458
53	125.1	140.025	140.308	-0.282917	-14.9254
54	128.7	140.353	142.383	-2.03042	-11.6529
55	156.1	146.233	144.229	2.00375	9.86708
56	163.2	149.4	145.65	3.75042	13.7996
57	159.8	150.26	146.787	3.47208	9.54042
58	157.4	145.595	147.612	-2.01708	11.8046
59	156.2	145.529	148.437	-2.90875	10.6713
60	152.5	147.223	149.262	-2.03958	5.27708
61	149.4	148.534	147.967	0.567083	0.86625
62	145.9	144.655	144.212	0.442917	1.24458
63	144.8	139.913	139.892	0.02125	4.88708
64	135.9	134.659	135.638	-0.97875	1.24125
65	137.6	131.034	131.317	-0.282917	6.56625
66	136	124.907	126.938	-2.03042	11.0929
67	117.7	NA	NA	2.00375	NA
68	111.5	NA	NA	3.75042	NA
69	107.8	NA	NA	3.47208	NA
70	107.3	NA	NA	-2.01708	NA
71	102.6	NA	NA	-2.90875	NA
72	101	NA	NA	-2.03958	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