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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 28 Nov 2016 16:03:01 +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/2016/Nov/28/t1480349001ww79s1ejovwq35g.htm/, Retrieved Sat, 04 May 2024 09:52:08 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 09:52:08 +0200

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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	2 seconds
R Server	'George Udny Yule' @ yule.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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=0

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

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	100.55	NA	NA	-0.00773437	NA
2	100.41	NA	NA	-0.457526	NA
3	100.54	NA	NA	-0.362422	NA
4	100.66	NA	NA	-0.227943	NA
5	100.86	NA	NA	-0.0827344	NA
6	100.88	NA	NA	-0.0559635	NA
7	100.88	100.889	101.369	-0.480339	-0.00882812
8	101.37	101.383	101.518	-0.135339	-0.0129948
9	101.84	101.742	101.641	0.101224	0.0979427
10	102.25	102.223	101.762	0.460807	0.0266927
11	102.58	102.531	101.893	0.638099	0.0489844
12	102.59	102.637	102.027	0.60987	-0.0473698
13	102.59	102.155	102.162	-0.00773437	0.435234
14	101.95	101.835	102.292	-0.457526	0.115026
15	101.94	102.044	102.407	-0.362422	-0.104245
16	102.18	102.281	102.509	-0.227943	-0.101224
17	102.47	102.522	102.605	-0.0827344	-0.051849
18	102.5	102.639	102.695	-0.0559635	-0.13862
19	102.5	102.142	102.622	-0.480339	0.358255
20	102.87	102.251	102.387	-0.135339	0.618672
21	103.08	102.256	102.155	0.101224	0.824193
22	103.47	102.384	101.923	0.460807	1.08628
23	103.65	102.325	101.687	0.638099	1.32523
24	103.68	102.059	101.449	0.60987	1.62138
25	99.76	101.204	101.212	-0.00773437	-1.44393
26	99.13	100.515	100.973	-0.457526	-1.38539
27	99.19	100.378	100.74	-0.362422	-1.18758
28	99.37	100.293	100.521	-0.227943	-0.923307
29	99.61	100.229	100.311	-0.0827344	-0.618516
30	99.65	100.048	100.104	-0.0559635	-0.397786
31	99.66	99.593	100.073	-0.480339	0.0670052
32	99.98	100.095	100.23	-0.135339	-0.115078
33	100.38	100.507	100.405	0.101224	-0.126641
34	100.92	101.045	100.584	0.460807	-0.124557
35	101.16	101.389	100.75	0.638099	-0.228516
36	101.19	101.518	100.908	0.60987	-0.328203
37	101.52	101.026	101.034	-0.00773437	0.493984
38	101.14	100.674	101.132	-0.457526	0.465859
39	101.38	100.858	101.22	-0.362422	0.522422
40	101.46	101.057	101.285	-0.227943	0.402526
41	101.52	101.255	101.338	-0.0827344	0.264818
42	101.53	101.33	101.386	-0.0559635	0.199714
43	100.79	100.993	101.473	-0.480339	-0.202578
44	101.2	101.477	101.612	-0.135339	-0.276745
45	101.28	101.862	101.76	0.101224	-0.581641
46	101.59	102.365	101.904	0.460807	-0.774557
47	101.75	102.682	102.044	0.638099	-0.931849
48	101.76	102.792	102.182	0.60987	-1.03195
49	103.03	102.301	102.309	-0.00773437	0.728568
50	102.97	101.952	102.409	-0.457526	1.01836
51	103.11	102.127	102.489	-0.362422	0.983255
52	103.17	102.334	102.562	-0.227943	0.835859
53	103.17	102.551	102.633	-0.0827344	0.619401
54	103.2	102.649	102.705	-0.0559635	0.550547
55	102.17	NA	NA	-0.480339	NA
56	102.22	NA	NA	-0.135339	NA
57	102.18	NA	NA	0.101224	NA
58	102.44	NA	NA	0.460807	NA
59	102.61	NA	NA	0.638099	NA
60	102.63	NA	NA	0.60987	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 100.55 & NA & NA & -0.00773437 & NA \tabularnewline
2 & 100.41 & NA & NA & -0.457526 & NA \tabularnewline
3 & 100.54 & NA & NA & -0.362422 & NA \tabularnewline
4 & 100.66 & NA & NA & -0.227943 & NA \tabularnewline
5 & 100.86 & NA & NA & -0.0827344 & NA \tabularnewline
6 & 100.88 & NA & NA & -0.0559635 & NA \tabularnewline
7 & 100.88 & 100.889 & 101.369 & -0.480339 & -0.00882812 \tabularnewline
8 & 101.37 & 101.383 & 101.518 & -0.135339 & -0.0129948 \tabularnewline
9 & 101.84 & 101.742 & 101.641 & 0.101224 & 0.0979427 \tabularnewline
10 & 102.25 & 102.223 & 101.762 & 0.460807 & 0.0266927 \tabularnewline
11 & 102.58 & 102.531 & 101.893 & 0.638099 & 0.0489844 \tabularnewline
12 & 102.59 & 102.637 & 102.027 & 0.60987 & -0.0473698 \tabularnewline
13 & 102.59 & 102.155 & 102.162 & -0.00773437 & 0.435234 \tabularnewline
14 & 101.95 & 101.835 & 102.292 & -0.457526 & 0.115026 \tabularnewline
15 & 101.94 & 102.044 & 102.407 & -0.362422 & -0.104245 \tabularnewline
16 & 102.18 & 102.281 & 102.509 & -0.227943 & -0.101224 \tabularnewline
17 & 102.47 & 102.522 & 102.605 & -0.0827344 & -0.051849 \tabularnewline
18 & 102.5 & 102.639 & 102.695 & -0.0559635 & -0.13862 \tabularnewline
19 & 102.5 & 102.142 & 102.622 & -0.480339 & 0.358255 \tabularnewline
20 & 102.87 & 102.251 & 102.387 & -0.135339 & 0.618672 \tabularnewline
21 & 103.08 & 102.256 & 102.155 & 0.101224 & 0.824193 \tabularnewline
22 & 103.47 & 102.384 & 101.923 & 0.460807 & 1.08628 \tabularnewline
23 & 103.65 & 102.325 & 101.687 & 0.638099 & 1.32523 \tabularnewline
24 & 103.68 & 102.059 & 101.449 & 0.60987 & 1.62138 \tabularnewline
25 & 99.76 & 101.204 & 101.212 & -0.00773437 & -1.44393 \tabularnewline
26 & 99.13 & 100.515 & 100.973 & -0.457526 & -1.38539 \tabularnewline
27 & 99.19 & 100.378 & 100.74 & -0.362422 & -1.18758 \tabularnewline
28 & 99.37 & 100.293 & 100.521 & -0.227943 & -0.923307 \tabularnewline
29 & 99.61 & 100.229 & 100.311 & -0.0827344 & -0.618516 \tabularnewline
30 & 99.65 & 100.048 & 100.104 & -0.0559635 & -0.397786 \tabularnewline
31 & 99.66 & 99.593 & 100.073 & -0.480339 & 0.0670052 \tabularnewline
32 & 99.98 & 100.095 & 100.23 & -0.135339 & -0.115078 \tabularnewline
33 & 100.38 & 100.507 & 100.405 & 0.101224 & -0.126641 \tabularnewline
34 & 100.92 & 101.045 & 100.584 & 0.460807 & -0.124557 \tabularnewline
35 & 101.16 & 101.389 & 100.75 & 0.638099 & -0.228516 \tabularnewline
36 & 101.19 & 101.518 & 100.908 & 0.60987 & -0.328203 \tabularnewline
37 & 101.52 & 101.026 & 101.034 & -0.00773437 & 0.493984 \tabularnewline
38 & 101.14 & 100.674 & 101.132 & -0.457526 & 0.465859 \tabularnewline
39 & 101.38 & 100.858 & 101.22 & -0.362422 & 0.522422 \tabularnewline
40 & 101.46 & 101.057 & 101.285 & -0.227943 & 0.402526 \tabularnewline
41 & 101.52 & 101.255 & 101.338 & -0.0827344 & 0.264818 \tabularnewline
42 & 101.53 & 101.33 & 101.386 & -0.0559635 & 0.199714 \tabularnewline
43 & 100.79 & 100.993 & 101.473 & -0.480339 & -0.202578 \tabularnewline
44 & 101.2 & 101.477 & 101.612 & -0.135339 & -0.276745 \tabularnewline
45 & 101.28 & 101.862 & 101.76 & 0.101224 & -0.581641 \tabularnewline
46 & 101.59 & 102.365 & 101.904 & 0.460807 & -0.774557 \tabularnewline
47 & 101.75 & 102.682 & 102.044 & 0.638099 & -0.931849 \tabularnewline
48 & 101.76 & 102.792 & 102.182 & 0.60987 & -1.03195 \tabularnewline
49 & 103.03 & 102.301 & 102.309 & -0.00773437 & 0.728568 \tabularnewline
50 & 102.97 & 101.952 & 102.409 & -0.457526 & 1.01836 \tabularnewline
51 & 103.11 & 102.127 & 102.489 & -0.362422 & 0.983255 \tabularnewline
52 & 103.17 & 102.334 & 102.562 & -0.227943 & 0.835859 \tabularnewline
53 & 103.17 & 102.551 & 102.633 & -0.0827344 & 0.619401 \tabularnewline
54 & 103.2 & 102.649 & 102.705 & -0.0559635 & 0.550547 \tabularnewline
55 & 102.17 & NA & NA & -0.480339 & NA \tabularnewline
56 & 102.22 & NA & NA & -0.135339 & NA \tabularnewline
57 & 102.18 & NA & NA & 0.101224 & NA \tabularnewline
58 & 102.44 & NA & NA & 0.460807 & NA \tabularnewline
59 & 102.61 & NA & NA & 0.638099 & NA \tabularnewline
60 & 102.63 & NA & NA & 0.60987 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]100.55[/C][C]NA[/C][C]NA[/C][C]-0.00773437[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]100.41[/C][C]NA[/C][C]NA[/C][C]-0.457526[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]100.54[/C][C]NA[/C][C]NA[/C][C]-0.362422[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]100.66[/C][C]NA[/C][C]NA[/C][C]-0.227943[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]100.86[/C][C]NA[/C][C]NA[/C][C]-0.0827344[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]100.88[/C][C]NA[/C][C]NA[/C][C]-0.0559635[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]100.88[/C][C]100.889[/C][C]101.369[/C][C]-0.480339[/C][C]-0.00882812[/C][/ROW]
[ROW][C]8[/C][C]101.37[/C][C]101.383[/C][C]101.518[/C][C]-0.135339[/C][C]-0.0129948[/C][/ROW]
[ROW][C]9[/C][C]101.84[/C][C]101.742[/C][C]101.641[/C][C]0.101224[/C][C]0.0979427[/C][/ROW]
[ROW][C]10[/C][C]102.25[/C][C]102.223[/C][C]101.762[/C][C]0.460807[/C][C]0.0266927[/C][/ROW]
[ROW][C]11[/C][C]102.58[/C][C]102.531[/C][C]101.893[/C][C]0.638099[/C][C]0.0489844[/C][/ROW]
[ROW][C]12[/C][C]102.59[/C][C]102.637[/C][C]102.027[/C][C]0.60987[/C][C]-0.0473698[/C][/ROW]
[ROW][C]13[/C][C]102.59[/C][C]102.155[/C][C]102.162[/C][C]-0.00773437[/C][C]0.435234[/C][/ROW]
[ROW][C]14[/C][C]101.95[/C][C]101.835[/C][C]102.292[/C][C]-0.457526[/C][C]0.115026[/C][/ROW]
[ROW][C]15[/C][C]101.94[/C][C]102.044[/C][C]102.407[/C][C]-0.362422[/C][C]-0.104245[/C][/ROW]
[ROW][C]16[/C][C]102.18[/C][C]102.281[/C][C]102.509[/C][C]-0.227943[/C][C]-0.101224[/C][/ROW]
[ROW][C]17[/C][C]102.47[/C][C]102.522[/C][C]102.605[/C][C]-0.0827344[/C][C]-0.051849[/C][/ROW]
[ROW][C]18[/C][C]102.5[/C][C]102.639[/C][C]102.695[/C][C]-0.0559635[/C][C]-0.13862[/C][/ROW]
[ROW][C]19[/C][C]102.5[/C][C]102.142[/C][C]102.622[/C][C]-0.480339[/C][C]0.358255[/C][/ROW]
[ROW][C]20[/C][C]102.87[/C][C]102.251[/C][C]102.387[/C][C]-0.135339[/C][C]0.618672[/C][/ROW]
[ROW][C]21[/C][C]103.08[/C][C]102.256[/C][C]102.155[/C][C]0.101224[/C][C]0.824193[/C][/ROW]
[ROW][C]22[/C][C]103.47[/C][C]102.384[/C][C]101.923[/C][C]0.460807[/C][C]1.08628[/C][/ROW]
[ROW][C]23[/C][C]103.65[/C][C]102.325[/C][C]101.687[/C][C]0.638099[/C][C]1.32523[/C][/ROW]
[ROW][C]24[/C][C]103.68[/C][C]102.059[/C][C]101.449[/C][C]0.60987[/C][C]1.62138[/C][/ROW]
[ROW][C]25[/C][C]99.76[/C][C]101.204[/C][C]101.212[/C][C]-0.00773437[/C][C]-1.44393[/C][/ROW]
[ROW][C]26[/C][C]99.13[/C][C]100.515[/C][C]100.973[/C][C]-0.457526[/C][C]-1.38539[/C][/ROW]
[ROW][C]27[/C][C]99.19[/C][C]100.378[/C][C]100.74[/C][C]-0.362422[/C][C]-1.18758[/C][/ROW]
[ROW][C]28[/C][C]99.37[/C][C]100.293[/C][C]100.521[/C][C]-0.227943[/C][C]-0.923307[/C][/ROW]
[ROW][C]29[/C][C]99.61[/C][C]100.229[/C][C]100.311[/C][C]-0.0827344[/C][C]-0.618516[/C][/ROW]
[ROW][C]30[/C][C]99.65[/C][C]100.048[/C][C]100.104[/C][C]-0.0559635[/C][C]-0.397786[/C][/ROW]
[ROW][C]31[/C][C]99.66[/C][C]99.593[/C][C]100.073[/C][C]-0.480339[/C][C]0.0670052[/C][/ROW]
[ROW][C]32[/C][C]99.98[/C][C]100.095[/C][C]100.23[/C][C]-0.135339[/C][C]-0.115078[/C][/ROW]
[ROW][C]33[/C][C]100.38[/C][C]100.507[/C][C]100.405[/C][C]0.101224[/C][C]-0.126641[/C][/ROW]
[ROW][C]34[/C][C]100.92[/C][C]101.045[/C][C]100.584[/C][C]0.460807[/C][C]-0.124557[/C][/ROW]
[ROW][C]35[/C][C]101.16[/C][C]101.389[/C][C]100.75[/C][C]0.638099[/C][C]-0.228516[/C][/ROW]
[ROW][C]36[/C][C]101.19[/C][C]101.518[/C][C]100.908[/C][C]0.60987[/C][C]-0.328203[/C][/ROW]
[ROW][C]37[/C][C]101.52[/C][C]101.026[/C][C]101.034[/C][C]-0.00773437[/C][C]0.493984[/C][/ROW]
[ROW][C]38[/C][C]101.14[/C][C]100.674[/C][C]101.132[/C][C]-0.457526[/C][C]0.465859[/C][/ROW]
[ROW][C]39[/C][C]101.38[/C][C]100.858[/C][C]101.22[/C][C]-0.362422[/C][C]0.522422[/C][/ROW]
[ROW][C]40[/C][C]101.46[/C][C]101.057[/C][C]101.285[/C][C]-0.227943[/C][C]0.402526[/C][/ROW]
[ROW][C]41[/C][C]101.52[/C][C]101.255[/C][C]101.338[/C][C]-0.0827344[/C][C]0.264818[/C][/ROW]
[ROW][C]42[/C][C]101.53[/C][C]101.33[/C][C]101.386[/C][C]-0.0559635[/C][C]0.199714[/C][/ROW]
[ROW][C]43[/C][C]100.79[/C][C]100.993[/C][C]101.473[/C][C]-0.480339[/C][C]-0.202578[/C][/ROW]
[ROW][C]44[/C][C]101.2[/C][C]101.477[/C][C]101.612[/C][C]-0.135339[/C][C]-0.276745[/C][/ROW]
[ROW][C]45[/C][C]101.28[/C][C]101.862[/C][C]101.76[/C][C]0.101224[/C][C]-0.581641[/C][/ROW]
[ROW][C]46[/C][C]101.59[/C][C]102.365[/C][C]101.904[/C][C]0.460807[/C][C]-0.774557[/C][/ROW]
[ROW][C]47[/C][C]101.75[/C][C]102.682[/C][C]102.044[/C][C]0.638099[/C][C]-0.931849[/C][/ROW]
[ROW][C]48[/C][C]101.76[/C][C]102.792[/C][C]102.182[/C][C]0.60987[/C][C]-1.03195[/C][/ROW]
[ROW][C]49[/C][C]103.03[/C][C]102.301[/C][C]102.309[/C][C]-0.00773437[/C][C]0.728568[/C][/ROW]
[ROW][C]50[/C][C]102.97[/C][C]101.952[/C][C]102.409[/C][C]-0.457526[/C][C]1.01836[/C][/ROW]
[ROW][C]51[/C][C]103.11[/C][C]102.127[/C][C]102.489[/C][C]-0.362422[/C][C]0.983255[/C][/ROW]
[ROW][C]52[/C][C]103.17[/C][C]102.334[/C][C]102.562[/C][C]-0.227943[/C][C]0.835859[/C][/ROW]
[ROW][C]53[/C][C]103.17[/C][C]102.551[/C][C]102.633[/C][C]-0.0827344[/C][C]0.619401[/C][/ROW]
[ROW][C]54[/C][C]103.2[/C][C]102.649[/C][C]102.705[/C][C]-0.0559635[/C][C]0.550547[/C][/ROW]
[ROW][C]55[/C][C]102.17[/C][C]NA[/C][C]NA[/C][C]-0.480339[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]102.22[/C][C]NA[/C][C]NA[/C][C]-0.135339[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]102.18[/C][C]NA[/C][C]NA[/C][C]0.101224[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]102.44[/C][C]NA[/C][C]NA[/C][C]0.460807[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]102.61[/C][C]NA[/C][C]NA[/C][C]0.638099[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]102.63[/C][C]NA[/C][C]NA[/C][C]0.60987[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	100.55	NA	NA	-0.00773437	NA
2	100.41	NA	NA	-0.457526	NA
3	100.54	NA	NA	-0.362422	NA
4	100.66	NA	NA	-0.227943	NA
5	100.86	NA	NA	-0.0827344	NA
6	100.88	NA	NA	-0.0559635	NA
7	100.88	100.889	101.369	-0.480339	-0.00882812
8	101.37	101.383	101.518	-0.135339	-0.0129948
9	101.84	101.742	101.641	0.101224	0.0979427
10	102.25	102.223	101.762	0.460807	0.0266927
11	102.58	102.531	101.893	0.638099	0.0489844
12	102.59	102.637	102.027	0.60987	-0.0473698
13	102.59	102.155	102.162	-0.00773437	0.435234
14	101.95	101.835	102.292	-0.457526	0.115026
15	101.94	102.044	102.407	-0.362422	-0.104245
16	102.18	102.281	102.509	-0.227943	-0.101224
17	102.47	102.522	102.605	-0.0827344	-0.051849
18	102.5	102.639	102.695	-0.0559635	-0.13862
19	102.5	102.142	102.622	-0.480339	0.358255
20	102.87	102.251	102.387	-0.135339	0.618672
21	103.08	102.256	102.155	0.101224	0.824193
22	103.47	102.384	101.923	0.460807	1.08628
23	103.65	102.325	101.687	0.638099	1.32523
24	103.68	102.059	101.449	0.60987	1.62138
25	99.76	101.204	101.212	-0.00773437	-1.44393
26	99.13	100.515	100.973	-0.457526	-1.38539
27	99.19	100.378	100.74	-0.362422	-1.18758
28	99.37	100.293	100.521	-0.227943	-0.923307
29	99.61	100.229	100.311	-0.0827344	-0.618516
30	99.65	100.048	100.104	-0.0559635	-0.397786
31	99.66	99.593	100.073	-0.480339	0.0670052
32	99.98	100.095	100.23	-0.135339	-0.115078
33	100.38	100.507	100.405	0.101224	-0.126641
34	100.92	101.045	100.584	0.460807	-0.124557
35	101.16	101.389	100.75	0.638099	-0.228516
36	101.19	101.518	100.908	0.60987	-0.328203
37	101.52	101.026	101.034	-0.00773437	0.493984
38	101.14	100.674	101.132	-0.457526	0.465859
39	101.38	100.858	101.22	-0.362422	0.522422
40	101.46	101.057	101.285	-0.227943	0.402526
41	101.52	101.255	101.338	-0.0827344	0.264818
42	101.53	101.33	101.386	-0.0559635	0.199714
43	100.79	100.993	101.473	-0.480339	-0.202578
44	101.2	101.477	101.612	-0.135339	-0.276745
45	101.28	101.862	101.76	0.101224	-0.581641
46	101.59	102.365	101.904	0.460807	-0.774557
47	101.75	102.682	102.044	0.638099	-0.931849
48	101.76	102.792	102.182	0.60987	-1.03195
49	103.03	102.301	102.309	-0.00773437	0.728568
50	102.97	101.952	102.409	-0.457526	1.01836
51	103.11	102.127	102.489	-0.362422	0.983255
52	103.17	102.334	102.562	-0.227943	0.835859
53	103.17	102.551	102.633	-0.0827344	0.619401
54	103.2	102.649	102.705	-0.0559635	0.550547
55	102.17	NA	NA	-0.480339	NA
56	102.22	NA	NA	-0.135339	NA
57	102.18	NA	NA	0.101224	NA
58	102.44	NA	NA	0.460807	NA
59	102.61	NA	NA	0.638099	NA
60	102.63	NA	NA	0.60987	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