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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Mon, 11 Jan 2016 23:40:18 +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/Jan/11/t14525556306zhyuek5i4r0g32.htm/, Retrieved Tue, 07 May 2024 11:57:40 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=289714, Retrieved Tue, 07 May 2024 11:57:40 +0000

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-       [Classical Decomposition] [] [2016-01-11 23:40:18] [d1a83db1c928d515dd26931964d56abe] [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=289714&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=289714&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=289714&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	'Herman Ole Andreas Wold' @ wold.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	90.65	NA	NA	-0.0192622	NA
2	90.93	NA	NA	0.203967	NA
3	91.42	NA	NA	0.119071	NA
4	91.52	NA	NA	0.53178	NA
5	91.76	NA	NA	0.453655	NA
6	91.47	NA	NA	0.3698	NA
7	91.37	91.4926	91.5675	-0.0748872	-0.122613
8	91.35	91.3603	91.7304	-0.370095	-0.0103212
9	91.74	91.4803	91.8721	-0.391762	0.259679
10	91.78	91.7236	92.0062	-0.2827	0.0564497
11	91.88	91.9733	92.1642	-0.190825	-0.093342
12	91.99	92	92.3487	-0.348741	-0.0100087
13	92.55	92.5432	92.5625	-0.0192622	0.00676215
14	92.94	92.9736	92.7696	0.203967	-0.0335503
15	92.81	93.0699	92.9508	0.119071	-0.259905
16	93.35	93.6643	93.1325	0.53178	-0.31428
17	93.72	93.8066	93.3529	0.453655	-0.0865712
18	93.94	93.9648	93.595	0.3698	-0.0248003
19	94.03	93.743	93.8179	-0.0748872	0.28697
20	93.66	93.6745	94.0446	-0.370095	-0.0144878
21	93.78	93.9066	94.2983	-0.391762	-0.126571
22	94.1	94.2561	94.5388	-0.2827	-0.15605
23	94.85	94.5642	94.755	-0.190825	0.285825
24	94.83	94.6067	94.9554	-0.348741	0.223325
25	95.06	95.1187	95.1379	-0.0192622	-0.0586545
26	95.87	95.5519	95.3479	0.203967	0.318116
27	95.97	95.7116	95.5925	0.119071	0.258429
28	95.96	96.3955	95.8638	0.53178	-0.43553
29	96.3	96.5899	96.1362	0.453655	-0.289905
30	96.17	96.7561	96.3862	0.3698	-0.58605
31	96.18	96.5843	96.6592	-0.0748872	-0.40428
32	96.55	96.5778	96.9479	-0.370095	-0.0278212
33	96.76	96.8491	97.2408	-0.391762	-0.0890712
34	97.63	97.2902	97.5729	-0.2827	0.339783
35	97.86	97.7508	97.9417	-0.190825	0.109158
36	97.82	97.9854	98.3342	-0.348741	-0.165425
37	98.62	98.6932	98.7125	-0.0192622	-0.0732378
38	99.24	99.2361	99.0321	0.203967	0.00394965
39	99.63	99.4295	99.3104	0.119071	0.200512
40	100.27	100.073	99.5408	0.53178	0.197387
41	100.84	100.18	99.7267	0.453655	0.659679
42	101.05	100.278	99.9083	0.3698	0.771866
43	100.38	100.005	100.08	-0.0748872	0.375304
44	100.02	99.832	100.202	-0.370095	0.188012
45	99.97	99.8787	100.27	-0.391762	0.0913455
46	99.95	100.055	100.337	-0.2827	-0.1048
47	100	100.166	100.357	-0.190825	-0.166259
48	100.04	99.9525	100.301	-0.348741	0.0874913
49	100.51	100.249	100.269	-0.0192622	0.260512
50	100.29	100.443	100.239	0.203967	-0.153134
51	100.22	100.284	100.165	0.119071	-0.0636545
52	101.29	100.602	100.07	0.53178	0.687804
53	100.29	100.438	99.9842	0.453655	-0.147821
54	100.26	100.286	99.9158	0.3698	-0.0256337
55	100.39	NA	NA	-0.0748872	NA
56	99.3	NA	NA	-0.370095	NA
57	98.9	NA	NA	-0.391762	NA
58	98.76	NA	NA	-0.2827	NA
59	99.12	NA	NA	-0.190825	NA
60	99.28	NA	NA	-0.348741	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 90.65 & NA & NA & -0.0192622 & NA \tabularnewline
2 & 90.93 & NA & NA & 0.203967 & NA \tabularnewline
3 & 91.42 & NA & NA & 0.119071 & NA \tabularnewline
4 & 91.52 & NA & NA & 0.53178 & NA \tabularnewline
5 & 91.76 & NA & NA & 0.453655 & NA \tabularnewline
6 & 91.47 & NA & NA & 0.3698 & NA \tabularnewline
7 & 91.37 & 91.4926 & 91.5675 & -0.0748872 & -0.122613 \tabularnewline
8 & 91.35 & 91.3603 & 91.7304 & -0.370095 & -0.0103212 \tabularnewline
9 & 91.74 & 91.4803 & 91.8721 & -0.391762 & 0.259679 \tabularnewline
10 & 91.78 & 91.7236 & 92.0062 & -0.2827 & 0.0564497 \tabularnewline
11 & 91.88 & 91.9733 & 92.1642 & -0.190825 & -0.093342 \tabularnewline
12 & 91.99 & 92 & 92.3487 & -0.348741 & -0.0100087 \tabularnewline
13 & 92.55 & 92.5432 & 92.5625 & -0.0192622 & 0.00676215 \tabularnewline
14 & 92.94 & 92.9736 & 92.7696 & 0.203967 & -0.0335503 \tabularnewline
15 & 92.81 & 93.0699 & 92.9508 & 0.119071 & -0.259905 \tabularnewline
16 & 93.35 & 93.6643 & 93.1325 & 0.53178 & -0.31428 \tabularnewline
17 & 93.72 & 93.8066 & 93.3529 & 0.453655 & -0.0865712 \tabularnewline
18 & 93.94 & 93.9648 & 93.595 & 0.3698 & -0.0248003 \tabularnewline
19 & 94.03 & 93.743 & 93.8179 & -0.0748872 & 0.28697 \tabularnewline
20 & 93.66 & 93.6745 & 94.0446 & -0.370095 & -0.0144878 \tabularnewline
21 & 93.78 & 93.9066 & 94.2983 & -0.391762 & -0.126571 \tabularnewline
22 & 94.1 & 94.2561 & 94.5388 & -0.2827 & -0.15605 \tabularnewline
23 & 94.85 & 94.5642 & 94.755 & -0.190825 & 0.285825 \tabularnewline
24 & 94.83 & 94.6067 & 94.9554 & -0.348741 & 0.223325 \tabularnewline
25 & 95.06 & 95.1187 & 95.1379 & -0.0192622 & -0.0586545 \tabularnewline
26 & 95.87 & 95.5519 & 95.3479 & 0.203967 & 0.318116 \tabularnewline
27 & 95.97 & 95.7116 & 95.5925 & 0.119071 & 0.258429 \tabularnewline
28 & 95.96 & 96.3955 & 95.8638 & 0.53178 & -0.43553 \tabularnewline
29 & 96.3 & 96.5899 & 96.1362 & 0.453655 & -0.289905 \tabularnewline
30 & 96.17 & 96.7561 & 96.3862 & 0.3698 & -0.58605 \tabularnewline
31 & 96.18 & 96.5843 & 96.6592 & -0.0748872 & -0.40428 \tabularnewline
32 & 96.55 & 96.5778 & 96.9479 & -0.370095 & -0.0278212 \tabularnewline
33 & 96.76 & 96.8491 & 97.2408 & -0.391762 & -0.0890712 \tabularnewline
34 & 97.63 & 97.2902 & 97.5729 & -0.2827 & 0.339783 \tabularnewline
35 & 97.86 & 97.7508 & 97.9417 & -0.190825 & 0.109158 \tabularnewline
36 & 97.82 & 97.9854 & 98.3342 & -0.348741 & -0.165425 \tabularnewline
37 & 98.62 & 98.6932 & 98.7125 & -0.0192622 & -0.0732378 \tabularnewline
38 & 99.24 & 99.2361 & 99.0321 & 0.203967 & 0.00394965 \tabularnewline
39 & 99.63 & 99.4295 & 99.3104 & 0.119071 & 0.200512 \tabularnewline
40 & 100.27 & 100.073 & 99.5408 & 0.53178 & 0.197387 \tabularnewline
41 & 100.84 & 100.18 & 99.7267 & 0.453655 & 0.659679 \tabularnewline
42 & 101.05 & 100.278 & 99.9083 & 0.3698 & 0.771866 \tabularnewline
43 & 100.38 & 100.005 & 100.08 & -0.0748872 & 0.375304 \tabularnewline
44 & 100.02 & 99.832 & 100.202 & -0.370095 & 0.188012 \tabularnewline
45 & 99.97 & 99.8787 & 100.27 & -0.391762 & 0.0913455 \tabularnewline
46 & 99.95 & 100.055 & 100.337 & -0.2827 & -0.1048 \tabularnewline
47 & 100 & 100.166 & 100.357 & -0.190825 & -0.166259 \tabularnewline
48 & 100.04 & 99.9525 & 100.301 & -0.348741 & 0.0874913 \tabularnewline
49 & 100.51 & 100.249 & 100.269 & -0.0192622 & 0.260512 \tabularnewline
50 & 100.29 & 100.443 & 100.239 & 0.203967 & -0.153134 \tabularnewline
51 & 100.22 & 100.284 & 100.165 & 0.119071 & -0.0636545 \tabularnewline
52 & 101.29 & 100.602 & 100.07 & 0.53178 & 0.687804 \tabularnewline
53 & 100.29 & 100.438 & 99.9842 & 0.453655 & -0.147821 \tabularnewline
54 & 100.26 & 100.286 & 99.9158 & 0.3698 & -0.0256337 \tabularnewline
55 & 100.39 & NA & NA & -0.0748872 & NA \tabularnewline
56 & 99.3 & NA & NA & -0.370095 & NA \tabularnewline
57 & 98.9 & NA & NA & -0.391762 & NA \tabularnewline
58 & 98.76 & NA & NA & -0.2827 & NA \tabularnewline
59 & 99.12 & NA & NA & -0.190825 & NA \tabularnewline
60 & 99.28 & NA & NA & -0.348741 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=289714&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]90.65[/C][C]NA[/C][C]NA[/C][C]-0.0192622[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]90.93[/C][C]NA[/C][C]NA[/C][C]0.203967[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]91.42[/C][C]NA[/C][C]NA[/C][C]0.119071[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]91.52[/C][C]NA[/C][C]NA[/C][C]0.53178[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]91.76[/C][C]NA[/C][C]NA[/C][C]0.453655[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]91.47[/C][C]NA[/C][C]NA[/C][C]0.3698[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]91.37[/C][C]91.4926[/C][C]91.5675[/C][C]-0.0748872[/C][C]-0.122613[/C][/ROW]
[ROW][C]8[/C][C]91.35[/C][C]91.3603[/C][C]91.7304[/C][C]-0.370095[/C][C]-0.0103212[/C][/ROW]
[ROW][C]9[/C][C]91.74[/C][C]91.4803[/C][C]91.8721[/C][C]-0.391762[/C][C]0.259679[/C][/ROW]
[ROW][C]10[/C][C]91.78[/C][C]91.7236[/C][C]92.0062[/C][C]-0.2827[/C][C]0.0564497[/C][/ROW]
[ROW][C]11[/C][C]91.88[/C][C]91.9733[/C][C]92.1642[/C][C]-0.190825[/C][C]-0.093342[/C][/ROW]
[ROW][C]12[/C][C]91.99[/C][C]92[/C][C]92.3487[/C][C]-0.348741[/C][C]-0.0100087[/C][/ROW]
[ROW][C]13[/C][C]92.55[/C][C]92.5432[/C][C]92.5625[/C][C]-0.0192622[/C][C]0.00676215[/C][/ROW]
[ROW][C]14[/C][C]92.94[/C][C]92.9736[/C][C]92.7696[/C][C]0.203967[/C][C]-0.0335503[/C][/ROW]
[ROW][C]15[/C][C]92.81[/C][C]93.0699[/C][C]92.9508[/C][C]0.119071[/C][C]-0.259905[/C][/ROW]
[ROW][C]16[/C][C]93.35[/C][C]93.6643[/C][C]93.1325[/C][C]0.53178[/C][C]-0.31428[/C][/ROW]
[ROW][C]17[/C][C]93.72[/C][C]93.8066[/C][C]93.3529[/C][C]0.453655[/C][C]-0.0865712[/C][/ROW]
[ROW][C]18[/C][C]93.94[/C][C]93.9648[/C][C]93.595[/C][C]0.3698[/C][C]-0.0248003[/C][/ROW]
[ROW][C]19[/C][C]94.03[/C][C]93.743[/C][C]93.8179[/C][C]-0.0748872[/C][C]0.28697[/C][/ROW]
[ROW][C]20[/C][C]93.66[/C][C]93.6745[/C][C]94.0446[/C][C]-0.370095[/C][C]-0.0144878[/C][/ROW]
[ROW][C]21[/C][C]93.78[/C][C]93.9066[/C][C]94.2983[/C][C]-0.391762[/C][C]-0.126571[/C][/ROW]
[ROW][C]22[/C][C]94.1[/C][C]94.2561[/C][C]94.5388[/C][C]-0.2827[/C][C]-0.15605[/C][/ROW]
[ROW][C]23[/C][C]94.85[/C][C]94.5642[/C][C]94.755[/C][C]-0.190825[/C][C]0.285825[/C][/ROW]
[ROW][C]24[/C][C]94.83[/C][C]94.6067[/C][C]94.9554[/C][C]-0.348741[/C][C]0.223325[/C][/ROW]
[ROW][C]25[/C][C]95.06[/C][C]95.1187[/C][C]95.1379[/C][C]-0.0192622[/C][C]-0.0586545[/C][/ROW]
[ROW][C]26[/C][C]95.87[/C][C]95.5519[/C][C]95.3479[/C][C]0.203967[/C][C]0.318116[/C][/ROW]
[ROW][C]27[/C][C]95.97[/C][C]95.7116[/C][C]95.5925[/C][C]0.119071[/C][C]0.258429[/C][/ROW]
[ROW][C]28[/C][C]95.96[/C][C]96.3955[/C][C]95.8638[/C][C]0.53178[/C][C]-0.43553[/C][/ROW]
[ROW][C]29[/C][C]96.3[/C][C]96.5899[/C][C]96.1362[/C][C]0.453655[/C][C]-0.289905[/C][/ROW]
[ROW][C]30[/C][C]96.17[/C][C]96.7561[/C][C]96.3862[/C][C]0.3698[/C][C]-0.58605[/C][/ROW]
[ROW][C]31[/C][C]96.18[/C][C]96.5843[/C][C]96.6592[/C][C]-0.0748872[/C][C]-0.40428[/C][/ROW]
[ROW][C]32[/C][C]96.55[/C][C]96.5778[/C][C]96.9479[/C][C]-0.370095[/C][C]-0.0278212[/C][/ROW]
[ROW][C]33[/C][C]96.76[/C][C]96.8491[/C][C]97.2408[/C][C]-0.391762[/C][C]-0.0890712[/C][/ROW]
[ROW][C]34[/C][C]97.63[/C][C]97.2902[/C][C]97.5729[/C][C]-0.2827[/C][C]0.339783[/C][/ROW]
[ROW][C]35[/C][C]97.86[/C][C]97.7508[/C][C]97.9417[/C][C]-0.190825[/C][C]0.109158[/C][/ROW]
[ROW][C]36[/C][C]97.82[/C][C]97.9854[/C][C]98.3342[/C][C]-0.348741[/C][C]-0.165425[/C][/ROW]
[ROW][C]37[/C][C]98.62[/C][C]98.6932[/C][C]98.7125[/C][C]-0.0192622[/C][C]-0.0732378[/C][/ROW]
[ROW][C]38[/C][C]99.24[/C][C]99.2361[/C][C]99.0321[/C][C]0.203967[/C][C]0.00394965[/C][/ROW]
[ROW][C]39[/C][C]99.63[/C][C]99.4295[/C][C]99.3104[/C][C]0.119071[/C][C]0.200512[/C][/ROW]
[ROW][C]40[/C][C]100.27[/C][C]100.073[/C][C]99.5408[/C][C]0.53178[/C][C]0.197387[/C][/ROW]
[ROW][C]41[/C][C]100.84[/C][C]100.18[/C][C]99.7267[/C][C]0.453655[/C][C]0.659679[/C][/ROW]
[ROW][C]42[/C][C]101.05[/C][C]100.278[/C][C]99.9083[/C][C]0.3698[/C][C]0.771866[/C][/ROW]
[ROW][C]43[/C][C]100.38[/C][C]100.005[/C][C]100.08[/C][C]-0.0748872[/C][C]0.375304[/C][/ROW]
[ROW][C]44[/C][C]100.02[/C][C]99.832[/C][C]100.202[/C][C]-0.370095[/C][C]0.188012[/C][/ROW]
[ROW][C]45[/C][C]99.97[/C][C]99.8787[/C][C]100.27[/C][C]-0.391762[/C][C]0.0913455[/C][/ROW]
[ROW][C]46[/C][C]99.95[/C][C]100.055[/C][C]100.337[/C][C]-0.2827[/C][C]-0.1048[/C][/ROW]
[ROW][C]47[/C][C]100[/C][C]100.166[/C][C]100.357[/C][C]-0.190825[/C][C]-0.166259[/C][/ROW]
[ROW][C]48[/C][C]100.04[/C][C]99.9525[/C][C]100.301[/C][C]-0.348741[/C][C]0.0874913[/C][/ROW]
[ROW][C]49[/C][C]100.51[/C][C]100.249[/C][C]100.269[/C][C]-0.0192622[/C][C]0.260512[/C][/ROW]
[ROW][C]50[/C][C]100.29[/C][C]100.443[/C][C]100.239[/C][C]0.203967[/C][C]-0.153134[/C][/ROW]
[ROW][C]51[/C][C]100.22[/C][C]100.284[/C][C]100.165[/C][C]0.119071[/C][C]-0.0636545[/C][/ROW]
[ROW][C]52[/C][C]101.29[/C][C]100.602[/C][C]100.07[/C][C]0.53178[/C][C]0.687804[/C][/ROW]
[ROW][C]53[/C][C]100.29[/C][C]100.438[/C][C]99.9842[/C][C]0.453655[/C][C]-0.147821[/C][/ROW]
[ROW][C]54[/C][C]100.26[/C][C]100.286[/C][C]99.9158[/C][C]0.3698[/C][C]-0.0256337[/C][/ROW]
[ROW][C]55[/C][C]100.39[/C][C]NA[/C][C]NA[/C][C]-0.0748872[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]99.3[/C][C]NA[/C][C]NA[/C][C]-0.370095[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]98.9[/C][C]NA[/C][C]NA[/C][C]-0.391762[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]98.76[/C][C]NA[/C][C]NA[/C][C]-0.2827[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]99.12[/C][C]NA[/C][C]NA[/C][C]-0.190825[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]99.28[/C][C]NA[/C][C]NA[/C][C]-0.348741[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=289714&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=289714&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	90.65	NA	NA	-0.0192622	NA
2	90.93	NA	NA	0.203967	NA
3	91.42	NA	NA	0.119071	NA
4	91.52	NA	NA	0.53178	NA
5	91.76	NA	NA	0.453655	NA
6	91.47	NA	NA	0.3698	NA
7	91.37	91.4926	91.5675	-0.0748872	-0.122613
8	91.35	91.3603	91.7304	-0.370095	-0.0103212
9	91.74	91.4803	91.8721	-0.391762	0.259679
10	91.78	91.7236	92.0062	-0.2827	0.0564497
11	91.88	91.9733	92.1642	-0.190825	-0.093342
12	91.99	92	92.3487	-0.348741	-0.0100087
13	92.55	92.5432	92.5625	-0.0192622	0.00676215
14	92.94	92.9736	92.7696	0.203967	-0.0335503
15	92.81	93.0699	92.9508	0.119071	-0.259905
16	93.35	93.6643	93.1325	0.53178	-0.31428
17	93.72	93.8066	93.3529	0.453655	-0.0865712
18	93.94	93.9648	93.595	0.3698	-0.0248003
19	94.03	93.743	93.8179	-0.0748872	0.28697
20	93.66	93.6745	94.0446	-0.370095	-0.0144878
21	93.78	93.9066	94.2983	-0.391762	-0.126571
22	94.1	94.2561	94.5388	-0.2827	-0.15605
23	94.85	94.5642	94.755	-0.190825	0.285825
24	94.83	94.6067	94.9554	-0.348741	0.223325
25	95.06	95.1187	95.1379	-0.0192622	-0.0586545
26	95.87	95.5519	95.3479	0.203967	0.318116
27	95.97	95.7116	95.5925	0.119071	0.258429
28	95.96	96.3955	95.8638	0.53178	-0.43553
29	96.3	96.5899	96.1362	0.453655	-0.289905
30	96.17	96.7561	96.3862	0.3698	-0.58605
31	96.18	96.5843	96.6592	-0.0748872	-0.40428
32	96.55	96.5778	96.9479	-0.370095	-0.0278212
33	96.76	96.8491	97.2408	-0.391762	-0.0890712
34	97.63	97.2902	97.5729	-0.2827	0.339783
35	97.86	97.7508	97.9417	-0.190825	0.109158
36	97.82	97.9854	98.3342	-0.348741	-0.165425
37	98.62	98.6932	98.7125	-0.0192622	-0.0732378
38	99.24	99.2361	99.0321	0.203967	0.00394965
39	99.63	99.4295	99.3104	0.119071	0.200512
40	100.27	100.073	99.5408	0.53178	0.197387
41	100.84	100.18	99.7267	0.453655	0.659679
42	101.05	100.278	99.9083	0.3698	0.771866
43	100.38	100.005	100.08	-0.0748872	0.375304
44	100.02	99.832	100.202	-0.370095	0.188012
45	99.97	99.8787	100.27	-0.391762	0.0913455
46	99.95	100.055	100.337	-0.2827	-0.1048
47	100	100.166	100.357	-0.190825	-0.166259
48	100.04	99.9525	100.301	-0.348741	0.0874913
49	100.51	100.249	100.269	-0.0192622	0.260512
50	100.29	100.443	100.239	0.203967	-0.153134
51	100.22	100.284	100.165	0.119071	-0.0636545
52	101.29	100.602	100.07	0.53178	0.687804
53	100.29	100.438	99.9842	0.453655	-0.147821
54	100.26	100.286	99.9158	0.3698	-0.0256337
55	100.39	NA	NA	-0.0748872	NA
56	99.3	NA	NA	-0.370095	NA
57	98.9	NA	NA	-0.391762	NA
58	98.76	NA	NA	-0.2827	NA
59	99.12	NA	NA	-0.190825	NA
60	99.28	NA	NA	-0.348741	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 = multiplicative ; 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