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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Tue, 05 Jan 2016 21:32:35 +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/05/t1452029729evg4xg0rp86ytnm.htm/, Retrieved Sat, 04 May 2024 06:32:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=287336, Retrieved Sat, 04 May 2024 06:32:51 +0000

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Estimated Impact

172

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-       [Classical Decomposition] [] [2016-01-05 21:32:35] [f684f3a3d8618606ffff76ddc8e0eec7] [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	2 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 & 2 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=287336&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=287336&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=287336&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	'Herman Ole Andreas Wold' @ wold.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	94.9	NA	NA	0.0203993	NA
2	95.8	NA	NA	1.98811	NA
3	98.8	NA	NA	3.25373	NA
4	100.6	NA	NA	2.34436	NA
5	100.6	NA	NA	-0.0931424	NA
6	100.1	NA	NA	-1.06502	NA
7	99.4	100.136	100.592	-0.455642	-0.736024
8	99.9	100.75	101.892	-1.1421	-0.849566
9	101	103.149	103.425	-0.276476	-2.14852
10	100.4	103.265	105.146	-1.88064	-2.86519
11	101.6	104.9	106.771	-1.87127	-3.29957
12	106.8	107.428	108.25	-0.822309	-0.627691
13	109.3	109.837	109.817	0.0203993	-0.537066
14	112.6	113.313	111.325	1.98811	-0.713108
15	118.8	116.02	112.767	3.25373	2.7796
16	121.9	116.632	114.288	2.34436	5.26814
17	118.3	115.736	115.829	-0.0931424	2.56398
18	117.9	116.114	117.179	-1.06502	1.78585
19	119.2	117.919	118.375	-0.455642	1.28064
20	116.3	118.512	119.654	-1.1421	-2.21207
21	119.2	120.557	120.833	-0.276476	-1.35686
22	118.7	119.828	121.708	-1.88064	-1.12769
23	120.3	120.533	122.404	-1.87127	-0.232899
24	120.5	122.09	122.913	-0.822309	-1.59019
25	124.3	123.341	123.321	0.0203993	0.958767
26	128.3	126.063	124.075	1.98811	2.23689
27	131.4	128.266	125.012	3.25373	3.13377
28	130.3	128.194	125.85	2.34436	2.10564
29	126.6	126.453	126.546	-0.0931424	0.147309
30	121.8	126.006	127.071	-1.06502	-4.20582
31	125.1	126.919	127.375	-0.455642	-1.81936
32	128.5	126.258	127.4	-1.1421	2.2421
33	129.5	126.844	127.121	-0.276476	2.65564
34	128.5	124.678	126.558	-1.88064	3.82231
35	127.2	124.141	126.012	-1.87127	3.05877
36	126.2	124.957	125.779	-0.822309	1.24314
37	125.9	125.716	125.696	0.0203993	0.183767
38	127.3	127.326	125.338	1.98811	-0.0256076
39	125.7	127.983	124.729	3.25373	-2.2829
40	122.5	126.353	124.008	2.34436	-3.85269
41	121.3	123.161	123.254	-0.0931424	-1.86102
42	121.5	121.568	122.633	-1.06502	-0.068316
43	123.4	121.603	122.058	-0.455642	1.79731
44	121.6	120.258	121.4	-1.1421	1.3421
45	121.8	120.428	120.704	-0.276476	1.37231
46	118.9	118.207	120.088	-1.88064	0.693142
47	118.7	117.704	119.575	-1.87127	0.996267
48	119.8	118.303	119.125	-0.822309	1.49731
49	118.5	118.583	118.562	0.0203993	-0.0828993
50	118.9	119.876	117.887	1.98811	-0.975608
51	117.4	120.508	117.254	3.25373	-3.1079
52	116	118.999	116.654	2.34436	-2.99852
53	115.5	115.828	115.921	-0.0931424	-0.327691
54	116.5	113.489	114.554	-1.06502	3.01085
55	114.9	NA	NA	-0.455642	NA
56	113.9	NA	NA	-1.1421	NA
57	114.3	NA	NA	-0.276476	NA
58	112	NA	NA	-1.88064	NA
59	108	NA	NA	-1.87127	NA
60	97.7	NA	NA	-0.822309	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 94.9 & NA & NA & 0.0203993 & NA \tabularnewline
2 & 95.8 & NA & NA & 1.98811 & NA \tabularnewline
3 & 98.8 & NA & NA & 3.25373 & NA \tabularnewline
4 & 100.6 & NA & NA & 2.34436 & NA \tabularnewline
5 & 100.6 & NA & NA & -0.0931424 & NA \tabularnewline
6 & 100.1 & NA & NA & -1.06502 & NA \tabularnewline
7 & 99.4 & 100.136 & 100.592 & -0.455642 & -0.736024 \tabularnewline
8 & 99.9 & 100.75 & 101.892 & -1.1421 & -0.849566 \tabularnewline
9 & 101 & 103.149 & 103.425 & -0.276476 & -2.14852 \tabularnewline
10 & 100.4 & 103.265 & 105.146 & -1.88064 & -2.86519 \tabularnewline
11 & 101.6 & 104.9 & 106.771 & -1.87127 & -3.29957 \tabularnewline
12 & 106.8 & 107.428 & 108.25 & -0.822309 & -0.627691 \tabularnewline
13 & 109.3 & 109.837 & 109.817 & 0.0203993 & -0.537066 \tabularnewline
14 & 112.6 & 113.313 & 111.325 & 1.98811 & -0.713108 \tabularnewline
15 & 118.8 & 116.02 & 112.767 & 3.25373 & 2.7796 \tabularnewline
16 & 121.9 & 116.632 & 114.288 & 2.34436 & 5.26814 \tabularnewline
17 & 118.3 & 115.736 & 115.829 & -0.0931424 & 2.56398 \tabularnewline
18 & 117.9 & 116.114 & 117.179 & -1.06502 & 1.78585 \tabularnewline
19 & 119.2 & 117.919 & 118.375 & -0.455642 & 1.28064 \tabularnewline
20 & 116.3 & 118.512 & 119.654 & -1.1421 & -2.21207 \tabularnewline
21 & 119.2 & 120.557 & 120.833 & -0.276476 & -1.35686 \tabularnewline
22 & 118.7 & 119.828 & 121.708 & -1.88064 & -1.12769 \tabularnewline
23 & 120.3 & 120.533 & 122.404 & -1.87127 & -0.232899 \tabularnewline
24 & 120.5 & 122.09 & 122.913 & -0.822309 & -1.59019 \tabularnewline
25 & 124.3 & 123.341 & 123.321 & 0.0203993 & 0.958767 \tabularnewline
26 & 128.3 & 126.063 & 124.075 & 1.98811 & 2.23689 \tabularnewline
27 & 131.4 & 128.266 & 125.012 & 3.25373 & 3.13377 \tabularnewline
28 & 130.3 & 128.194 & 125.85 & 2.34436 & 2.10564 \tabularnewline
29 & 126.6 & 126.453 & 126.546 & -0.0931424 & 0.147309 \tabularnewline
30 & 121.8 & 126.006 & 127.071 & -1.06502 & -4.20582 \tabularnewline
31 & 125.1 & 126.919 & 127.375 & -0.455642 & -1.81936 \tabularnewline
32 & 128.5 & 126.258 & 127.4 & -1.1421 & 2.2421 \tabularnewline
33 & 129.5 & 126.844 & 127.121 & -0.276476 & 2.65564 \tabularnewline
34 & 128.5 & 124.678 & 126.558 & -1.88064 & 3.82231 \tabularnewline
35 & 127.2 & 124.141 & 126.012 & -1.87127 & 3.05877 \tabularnewline
36 & 126.2 & 124.957 & 125.779 & -0.822309 & 1.24314 \tabularnewline
37 & 125.9 & 125.716 & 125.696 & 0.0203993 & 0.183767 \tabularnewline
38 & 127.3 & 127.326 & 125.338 & 1.98811 & -0.0256076 \tabularnewline
39 & 125.7 & 127.983 & 124.729 & 3.25373 & -2.2829 \tabularnewline
40 & 122.5 & 126.353 & 124.008 & 2.34436 & -3.85269 \tabularnewline
41 & 121.3 & 123.161 & 123.254 & -0.0931424 & -1.86102 \tabularnewline
42 & 121.5 & 121.568 & 122.633 & -1.06502 & -0.068316 \tabularnewline
43 & 123.4 & 121.603 & 122.058 & -0.455642 & 1.79731 \tabularnewline
44 & 121.6 & 120.258 & 121.4 & -1.1421 & 1.3421 \tabularnewline
45 & 121.8 & 120.428 & 120.704 & -0.276476 & 1.37231 \tabularnewline
46 & 118.9 & 118.207 & 120.088 & -1.88064 & 0.693142 \tabularnewline
47 & 118.7 & 117.704 & 119.575 & -1.87127 & 0.996267 \tabularnewline
48 & 119.8 & 118.303 & 119.125 & -0.822309 & 1.49731 \tabularnewline
49 & 118.5 & 118.583 & 118.562 & 0.0203993 & -0.0828993 \tabularnewline
50 & 118.9 & 119.876 & 117.887 & 1.98811 & -0.975608 \tabularnewline
51 & 117.4 & 120.508 & 117.254 & 3.25373 & -3.1079 \tabularnewline
52 & 116 & 118.999 & 116.654 & 2.34436 & -2.99852 \tabularnewline
53 & 115.5 & 115.828 & 115.921 & -0.0931424 & -0.327691 \tabularnewline
54 & 116.5 & 113.489 & 114.554 & -1.06502 & 3.01085 \tabularnewline
55 & 114.9 & NA & NA & -0.455642 & NA \tabularnewline
56 & 113.9 & NA & NA & -1.1421 & NA \tabularnewline
57 & 114.3 & NA & NA & -0.276476 & NA \tabularnewline
58 & 112 & NA & NA & -1.88064 & NA \tabularnewline
59 & 108 & NA & NA & -1.87127 & NA \tabularnewline
60 & 97.7 & NA & NA & -0.822309 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=287336&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]94.9[/C][C]NA[/C][C]NA[/C][C]0.0203993[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]95.8[/C][C]NA[/C][C]NA[/C][C]1.98811[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]98.8[/C][C]NA[/C][C]NA[/C][C]3.25373[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]100.6[/C][C]NA[/C][C]NA[/C][C]2.34436[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]100.6[/C][C]NA[/C][C]NA[/C][C]-0.0931424[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]100.1[/C][C]NA[/C][C]NA[/C][C]-1.06502[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]99.4[/C][C]100.136[/C][C]100.592[/C][C]-0.455642[/C][C]-0.736024[/C][/ROW]
[ROW][C]8[/C][C]99.9[/C][C]100.75[/C][C]101.892[/C][C]-1.1421[/C][C]-0.849566[/C][/ROW]
[ROW][C]9[/C][C]101[/C][C]103.149[/C][C]103.425[/C][C]-0.276476[/C][C]-2.14852[/C][/ROW]
[ROW][C]10[/C][C]100.4[/C][C]103.265[/C][C]105.146[/C][C]-1.88064[/C][C]-2.86519[/C][/ROW]
[ROW][C]11[/C][C]101.6[/C][C]104.9[/C][C]106.771[/C][C]-1.87127[/C][C]-3.29957[/C][/ROW]
[ROW][C]12[/C][C]106.8[/C][C]107.428[/C][C]108.25[/C][C]-0.822309[/C][C]-0.627691[/C][/ROW]
[ROW][C]13[/C][C]109.3[/C][C]109.837[/C][C]109.817[/C][C]0.0203993[/C][C]-0.537066[/C][/ROW]
[ROW][C]14[/C][C]112.6[/C][C]113.313[/C][C]111.325[/C][C]1.98811[/C][C]-0.713108[/C][/ROW]
[ROW][C]15[/C][C]118.8[/C][C]116.02[/C][C]112.767[/C][C]3.25373[/C][C]2.7796[/C][/ROW]
[ROW][C]16[/C][C]121.9[/C][C]116.632[/C][C]114.288[/C][C]2.34436[/C][C]5.26814[/C][/ROW]
[ROW][C]17[/C][C]118.3[/C][C]115.736[/C][C]115.829[/C][C]-0.0931424[/C][C]2.56398[/C][/ROW]
[ROW][C]18[/C][C]117.9[/C][C]116.114[/C][C]117.179[/C][C]-1.06502[/C][C]1.78585[/C][/ROW]
[ROW][C]19[/C][C]119.2[/C][C]117.919[/C][C]118.375[/C][C]-0.455642[/C][C]1.28064[/C][/ROW]
[ROW][C]20[/C][C]116.3[/C][C]118.512[/C][C]119.654[/C][C]-1.1421[/C][C]-2.21207[/C][/ROW]
[ROW][C]21[/C][C]119.2[/C][C]120.557[/C][C]120.833[/C][C]-0.276476[/C][C]-1.35686[/C][/ROW]
[ROW][C]22[/C][C]118.7[/C][C]119.828[/C][C]121.708[/C][C]-1.88064[/C][C]-1.12769[/C][/ROW]
[ROW][C]23[/C][C]120.3[/C][C]120.533[/C][C]122.404[/C][C]-1.87127[/C][C]-0.232899[/C][/ROW]
[ROW][C]24[/C][C]120.5[/C][C]122.09[/C][C]122.913[/C][C]-0.822309[/C][C]-1.59019[/C][/ROW]
[ROW][C]25[/C][C]124.3[/C][C]123.341[/C][C]123.321[/C][C]0.0203993[/C][C]0.958767[/C][/ROW]
[ROW][C]26[/C][C]128.3[/C][C]126.063[/C][C]124.075[/C][C]1.98811[/C][C]2.23689[/C][/ROW]
[ROW][C]27[/C][C]131.4[/C][C]128.266[/C][C]125.012[/C][C]3.25373[/C][C]3.13377[/C][/ROW]
[ROW][C]28[/C][C]130.3[/C][C]128.194[/C][C]125.85[/C][C]2.34436[/C][C]2.10564[/C][/ROW]
[ROW][C]29[/C][C]126.6[/C][C]126.453[/C][C]126.546[/C][C]-0.0931424[/C][C]0.147309[/C][/ROW]
[ROW][C]30[/C][C]121.8[/C][C]126.006[/C][C]127.071[/C][C]-1.06502[/C][C]-4.20582[/C][/ROW]
[ROW][C]31[/C][C]125.1[/C][C]126.919[/C][C]127.375[/C][C]-0.455642[/C][C]-1.81936[/C][/ROW]
[ROW][C]32[/C][C]128.5[/C][C]126.258[/C][C]127.4[/C][C]-1.1421[/C][C]2.2421[/C][/ROW]
[ROW][C]33[/C][C]129.5[/C][C]126.844[/C][C]127.121[/C][C]-0.276476[/C][C]2.65564[/C][/ROW]
[ROW][C]34[/C][C]128.5[/C][C]124.678[/C][C]126.558[/C][C]-1.88064[/C][C]3.82231[/C][/ROW]
[ROW][C]35[/C][C]127.2[/C][C]124.141[/C][C]126.012[/C][C]-1.87127[/C][C]3.05877[/C][/ROW]
[ROW][C]36[/C][C]126.2[/C][C]124.957[/C][C]125.779[/C][C]-0.822309[/C][C]1.24314[/C][/ROW]
[ROW][C]37[/C][C]125.9[/C][C]125.716[/C][C]125.696[/C][C]0.0203993[/C][C]0.183767[/C][/ROW]
[ROW][C]38[/C][C]127.3[/C][C]127.326[/C][C]125.338[/C][C]1.98811[/C][C]-0.0256076[/C][/ROW]
[ROW][C]39[/C][C]125.7[/C][C]127.983[/C][C]124.729[/C][C]3.25373[/C][C]-2.2829[/C][/ROW]
[ROW][C]40[/C][C]122.5[/C][C]126.353[/C][C]124.008[/C][C]2.34436[/C][C]-3.85269[/C][/ROW]
[ROW][C]41[/C][C]121.3[/C][C]123.161[/C][C]123.254[/C][C]-0.0931424[/C][C]-1.86102[/C][/ROW]
[ROW][C]42[/C][C]121.5[/C][C]121.568[/C][C]122.633[/C][C]-1.06502[/C][C]-0.068316[/C][/ROW]
[ROW][C]43[/C][C]123.4[/C][C]121.603[/C][C]122.058[/C][C]-0.455642[/C][C]1.79731[/C][/ROW]
[ROW][C]44[/C][C]121.6[/C][C]120.258[/C][C]121.4[/C][C]-1.1421[/C][C]1.3421[/C][/ROW]
[ROW][C]45[/C][C]121.8[/C][C]120.428[/C][C]120.704[/C][C]-0.276476[/C][C]1.37231[/C][/ROW]
[ROW][C]46[/C][C]118.9[/C][C]118.207[/C][C]120.088[/C][C]-1.88064[/C][C]0.693142[/C][/ROW]
[ROW][C]47[/C][C]118.7[/C][C]117.704[/C][C]119.575[/C][C]-1.87127[/C][C]0.996267[/C][/ROW]
[ROW][C]48[/C][C]119.8[/C][C]118.303[/C][C]119.125[/C][C]-0.822309[/C][C]1.49731[/C][/ROW]
[ROW][C]49[/C][C]118.5[/C][C]118.583[/C][C]118.562[/C][C]0.0203993[/C][C]-0.0828993[/C][/ROW]
[ROW][C]50[/C][C]118.9[/C][C]119.876[/C][C]117.887[/C][C]1.98811[/C][C]-0.975608[/C][/ROW]
[ROW][C]51[/C][C]117.4[/C][C]120.508[/C][C]117.254[/C][C]3.25373[/C][C]-3.1079[/C][/ROW]
[ROW][C]52[/C][C]116[/C][C]118.999[/C][C]116.654[/C][C]2.34436[/C][C]-2.99852[/C][/ROW]
[ROW][C]53[/C][C]115.5[/C][C]115.828[/C][C]115.921[/C][C]-0.0931424[/C][C]-0.327691[/C][/ROW]
[ROW][C]54[/C][C]116.5[/C][C]113.489[/C][C]114.554[/C][C]-1.06502[/C][C]3.01085[/C][/ROW]
[ROW][C]55[/C][C]114.9[/C][C]NA[/C][C]NA[/C][C]-0.455642[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]113.9[/C][C]NA[/C][C]NA[/C][C]-1.1421[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]114.3[/C][C]NA[/C][C]NA[/C][C]-0.276476[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]112[/C][C]NA[/C][C]NA[/C][C]-1.88064[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]108[/C][C]NA[/C][C]NA[/C][C]-1.87127[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]97.7[/C][C]NA[/C][C]NA[/C][C]-0.822309[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=287336&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=287336&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	94.9	NA	NA	0.0203993	NA
2	95.8	NA	NA	1.98811	NA
3	98.8	NA	NA	3.25373	NA
4	100.6	NA	NA	2.34436	NA
5	100.6	NA	NA	-0.0931424	NA
6	100.1	NA	NA	-1.06502	NA
7	99.4	100.136	100.592	-0.455642	-0.736024
8	99.9	100.75	101.892	-1.1421	-0.849566
9	101	103.149	103.425	-0.276476	-2.14852
10	100.4	103.265	105.146	-1.88064	-2.86519
11	101.6	104.9	106.771	-1.87127	-3.29957
12	106.8	107.428	108.25	-0.822309	-0.627691
13	109.3	109.837	109.817	0.0203993	-0.537066
14	112.6	113.313	111.325	1.98811	-0.713108
15	118.8	116.02	112.767	3.25373	2.7796
16	121.9	116.632	114.288	2.34436	5.26814
17	118.3	115.736	115.829	-0.0931424	2.56398
18	117.9	116.114	117.179	-1.06502	1.78585
19	119.2	117.919	118.375	-0.455642	1.28064
20	116.3	118.512	119.654	-1.1421	-2.21207
21	119.2	120.557	120.833	-0.276476	-1.35686
22	118.7	119.828	121.708	-1.88064	-1.12769
23	120.3	120.533	122.404	-1.87127	-0.232899
24	120.5	122.09	122.913	-0.822309	-1.59019
25	124.3	123.341	123.321	0.0203993	0.958767
26	128.3	126.063	124.075	1.98811	2.23689
27	131.4	128.266	125.012	3.25373	3.13377
28	130.3	128.194	125.85	2.34436	2.10564
29	126.6	126.453	126.546	-0.0931424	0.147309
30	121.8	126.006	127.071	-1.06502	-4.20582
31	125.1	126.919	127.375	-0.455642	-1.81936
32	128.5	126.258	127.4	-1.1421	2.2421
33	129.5	126.844	127.121	-0.276476	2.65564
34	128.5	124.678	126.558	-1.88064	3.82231
35	127.2	124.141	126.012	-1.87127	3.05877
36	126.2	124.957	125.779	-0.822309	1.24314
37	125.9	125.716	125.696	0.0203993	0.183767
38	127.3	127.326	125.338	1.98811	-0.0256076
39	125.7	127.983	124.729	3.25373	-2.2829
40	122.5	126.353	124.008	2.34436	-3.85269
41	121.3	123.161	123.254	-0.0931424	-1.86102
42	121.5	121.568	122.633	-1.06502	-0.068316
43	123.4	121.603	122.058	-0.455642	1.79731
44	121.6	120.258	121.4	-1.1421	1.3421
45	121.8	120.428	120.704	-0.276476	1.37231
46	118.9	118.207	120.088	-1.88064	0.693142
47	118.7	117.704	119.575	-1.87127	0.996267
48	119.8	118.303	119.125	-0.822309	1.49731
49	118.5	118.583	118.562	0.0203993	-0.0828993
50	118.9	119.876	117.887	1.98811	-0.975608
51	117.4	120.508	117.254	3.25373	-3.1079
52	116	118.999	116.654	2.34436	-2.99852
53	115.5	115.828	115.921	-0.0931424	-0.327691
54	116.5	113.489	114.554	-1.06502	3.01085
55	114.9	NA	NA	-0.455642	NA
56	113.9	NA	NA	-1.1421	NA
57	114.3	NA	NA	-0.276476	NA
58	112	NA	NA	-1.88064	NA
59	108	NA	NA	-1.87127	NA
60	97.7	NA	NA	-0.822309	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