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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 27 Nov 2016 13:52:15 +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/27/t1480254897j51jdutilrgn36n.htm/, Retrieved Mon, 29 Apr 2024 23:43:15 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Mon, 29 Apr 2024 23:43:15 +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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.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]'Gertrude Mary Cox' @ cox.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	'Gertrude Mary Cox' @ cox.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	102.8	NA	NA	-0.564123	NA
2	103.66	NA	NA	-0.0172483	NA
3	103.55	NA	NA	0.0358767	NA
4	103.87	NA	NA	0.144627	NA
5	104.03	NA	NA	0.302543	NA
6	104.02	NA	NA	0.393585	NA
7	104.02	103.912	103.649	0.26296	0.10829
8	102.97	103.147	103.739	-0.592144	-0.177023
9	103.18	103.419	103.836	-0.416936	-0.239314
10	103.53	103.882	103.927	-0.0448524	-0.352231
11	103.78	104.192	104.002	0.190252	-0.411918
12	103.85	104.375	104.07	0.30546	-0.525043
13	103.85	103.573	104.137	-0.564123	0.27704
14	104.78	104.184	104.202	-0.0172483	0.595582
15	104.76	104.296	104.26	0.0358767	0.464123
16	104.84	104.467	104.322	0.144627	0.372873
17	104.85	104.688	104.385	0.302543	0.162457
18	104.83	104.829	104.435	0.393585	0.000998264
19	104.83	104.542	104.279	0.26296	0.28829
20	103.71	103.325	103.917	-0.592144	0.384644
21	103.84	103.135	103.552	-0.416936	0.705269
22	104.37	103.136	103.181	-0.0448524	1.23402
23	104.44	102.998	102.807	0.190252	1.44225
24	104.4	102.743	102.437	0.30546	1.65746
25	99.54	101.505	102.069	-0.564123	-1.96504
26	100.42	101.677	101.694	-0.0172483	-1.25692
27	100.34	101.355	101.319	0.0358767	-1.01504
28	100.36	101.088	100.943	0.144627	-0.727543
29	100.37	100.865	100.562	0.302543	-0.495043
30	100.42	100.581	100.187	0.393585	-0.161085
31	100.41	100.303	100.04	0.26296	0.107457
32	99.13	99.487	100.079	-0.592144	-0.357023
33	99.42	99.6685	100.085	-0.416936	-0.248481
34	99.76	100.056	100.101	-0.0448524	-0.296398
35	99.92	100.317	100.127	0.190252	-0.396918
36	99.92	100.456	100.151	0.30546	-0.536293
37	100.47	99.5784	100.142	-0.564123	0.891623
38	100.44	100.133	100.15	-0.0172483	0.306832
39	100.47	100.215	100.179	0.0358767	0.255373
40	100.61	100.312	100.167	0.144627	0.29829
41	100.73	100.437	100.135	0.302543	0.292873
42	100.64	100.494	100.1	0.393585	0.145998
43	99.99	100.324	100.061	0.26296	-0.333793
44	99.74	99.4204	100.012	-0.592144	0.319644
45	99.49	99.5372	99.9542	-0.416936	-0.0472309
46	99.41	99.8251	99.87	-0.0448524	-0.415148
47	99.49	99.9532	99.7629	0.190252	-0.463168
48	99.53	99.9559	99.6504	0.30546	-0.425877
49	99.91	98.9434	99.5075	-0.564123	0.966623
50	99.84	99.3153	99.3325	-0.0172483	0.524748
51	99.67	99.2042	99.1683	0.0358767	0.46579
52	99.39	99.1634	99.0188	0.144627	0.226623
53	99.38	99.17	98.8675	0.302543	0.209957
54	99.29	99.1057	98.7121	0.393585	0.184332
55	97.91	NA	NA	0.26296	NA
56	97.62	NA	NA	-0.592144	NA
57	97.67	NA	NA	-0.416936	NA
58	97.64	NA	NA	-0.0448524	NA
59	97.63	NA	NA	0.190252	NA
60	97.66	NA	NA	0.30546	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 102.8 & NA & NA & -0.564123 & NA \tabularnewline
2 & 103.66 & NA & NA & -0.0172483 & NA \tabularnewline
3 & 103.55 & NA & NA & 0.0358767 & NA \tabularnewline
4 & 103.87 & NA & NA & 0.144627 & NA \tabularnewline
5 & 104.03 & NA & NA & 0.302543 & NA \tabularnewline
6 & 104.02 & NA & NA & 0.393585 & NA \tabularnewline
7 & 104.02 & 103.912 & 103.649 & 0.26296 & 0.10829 \tabularnewline
8 & 102.97 & 103.147 & 103.739 & -0.592144 & -0.177023 \tabularnewline
9 & 103.18 & 103.419 & 103.836 & -0.416936 & -0.239314 \tabularnewline
10 & 103.53 & 103.882 & 103.927 & -0.0448524 & -0.352231 \tabularnewline
11 & 103.78 & 104.192 & 104.002 & 0.190252 & -0.411918 \tabularnewline
12 & 103.85 & 104.375 & 104.07 & 0.30546 & -0.525043 \tabularnewline
13 & 103.85 & 103.573 & 104.137 & -0.564123 & 0.27704 \tabularnewline
14 & 104.78 & 104.184 & 104.202 & -0.0172483 & 0.595582 \tabularnewline
15 & 104.76 & 104.296 & 104.26 & 0.0358767 & 0.464123 \tabularnewline
16 & 104.84 & 104.467 & 104.322 & 0.144627 & 0.372873 \tabularnewline
17 & 104.85 & 104.688 & 104.385 & 0.302543 & 0.162457 \tabularnewline
18 & 104.83 & 104.829 & 104.435 & 0.393585 & 0.000998264 \tabularnewline
19 & 104.83 & 104.542 & 104.279 & 0.26296 & 0.28829 \tabularnewline
20 & 103.71 & 103.325 & 103.917 & -0.592144 & 0.384644 \tabularnewline
21 & 103.84 & 103.135 & 103.552 & -0.416936 & 0.705269 \tabularnewline
22 & 104.37 & 103.136 & 103.181 & -0.0448524 & 1.23402 \tabularnewline
23 & 104.44 & 102.998 & 102.807 & 0.190252 & 1.44225 \tabularnewline
24 & 104.4 & 102.743 & 102.437 & 0.30546 & 1.65746 \tabularnewline
25 & 99.54 & 101.505 & 102.069 & -0.564123 & -1.96504 \tabularnewline
26 & 100.42 & 101.677 & 101.694 & -0.0172483 & -1.25692 \tabularnewline
27 & 100.34 & 101.355 & 101.319 & 0.0358767 & -1.01504 \tabularnewline
28 & 100.36 & 101.088 & 100.943 & 0.144627 & -0.727543 \tabularnewline
29 & 100.37 & 100.865 & 100.562 & 0.302543 & -0.495043 \tabularnewline
30 & 100.42 & 100.581 & 100.187 & 0.393585 & -0.161085 \tabularnewline
31 & 100.41 & 100.303 & 100.04 & 0.26296 & 0.107457 \tabularnewline
32 & 99.13 & 99.487 & 100.079 & -0.592144 & -0.357023 \tabularnewline
33 & 99.42 & 99.6685 & 100.085 & -0.416936 & -0.248481 \tabularnewline
34 & 99.76 & 100.056 & 100.101 & -0.0448524 & -0.296398 \tabularnewline
35 & 99.92 & 100.317 & 100.127 & 0.190252 & -0.396918 \tabularnewline
36 & 99.92 & 100.456 & 100.151 & 0.30546 & -0.536293 \tabularnewline
37 & 100.47 & 99.5784 & 100.142 & -0.564123 & 0.891623 \tabularnewline
38 & 100.44 & 100.133 & 100.15 & -0.0172483 & 0.306832 \tabularnewline
39 & 100.47 & 100.215 & 100.179 & 0.0358767 & 0.255373 \tabularnewline
40 & 100.61 & 100.312 & 100.167 & 0.144627 & 0.29829 \tabularnewline
41 & 100.73 & 100.437 & 100.135 & 0.302543 & 0.292873 \tabularnewline
42 & 100.64 & 100.494 & 100.1 & 0.393585 & 0.145998 \tabularnewline
43 & 99.99 & 100.324 & 100.061 & 0.26296 & -0.333793 \tabularnewline
44 & 99.74 & 99.4204 & 100.012 & -0.592144 & 0.319644 \tabularnewline
45 & 99.49 & 99.5372 & 99.9542 & -0.416936 & -0.0472309 \tabularnewline
46 & 99.41 & 99.8251 & 99.87 & -0.0448524 & -0.415148 \tabularnewline
47 & 99.49 & 99.9532 & 99.7629 & 0.190252 & -0.463168 \tabularnewline
48 & 99.53 & 99.9559 & 99.6504 & 0.30546 & -0.425877 \tabularnewline
49 & 99.91 & 98.9434 & 99.5075 & -0.564123 & 0.966623 \tabularnewline
50 & 99.84 & 99.3153 & 99.3325 & -0.0172483 & 0.524748 \tabularnewline
51 & 99.67 & 99.2042 & 99.1683 & 0.0358767 & 0.46579 \tabularnewline
52 & 99.39 & 99.1634 & 99.0188 & 0.144627 & 0.226623 \tabularnewline
53 & 99.38 & 99.17 & 98.8675 & 0.302543 & 0.209957 \tabularnewline
54 & 99.29 & 99.1057 & 98.7121 & 0.393585 & 0.184332 \tabularnewline
55 & 97.91 & NA & NA & 0.26296 & NA \tabularnewline
56 & 97.62 & NA & NA & -0.592144 & NA \tabularnewline
57 & 97.67 & NA & NA & -0.416936 & NA \tabularnewline
58 & 97.64 & NA & NA & -0.0448524 & NA \tabularnewline
59 & 97.63 & NA & NA & 0.190252 & NA \tabularnewline
60 & 97.66 & NA & NA & 0.30546 & 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]102.8[/C][C]NA[/C][C]NA[/C][C]-0.564123[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]103.66[/C][C]NA[/C][C]NA[/C][C]-0.0172483[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]103.55[/C][C]NA[/C][C]NA[/C][C]0.0358767[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]103.87[/C][C]NA[/C][C]NA[/C][C]0.144627[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]104.03[/C][C]NA[/C][C]NA[/C][C]0.302543[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]104.02[/C][C]NA[/C][C]NA[/C][C]0.393585[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]104.02[/C][C]103.912[/C][C]103.649[/C][C]0.26296[/C][C]0.10829[/C][/ROW]
[ROW][C]8[/C][C]102.97[/C][C]103.147[/C][C]103.739[/C][C]-0.592144[/C][C]-0.177023[/C][/ROW]
[ROW][C]9[/C][C]103.18[/C][C]103.419[/C][C]103.836[/C][C]-0.416936[/C][C]-0.239314[/C][/ROW]
[ROW][C]10[/C][C]103.53[/C][C]103.882[/C][C]103.927[/C][C]-0.0448524[/C][C]-0.352231[/C][/ROW]
[ROW][C]11[/C][C]103.78[/C][C]104.192[/C][C]104.002[/C][C]0.190252[/C][C]-0.411918[/C][/ROW]
[ROW][C]12[/C][C]103.85[/C][C]104.375[/C][C]104.07[/C][C]0.30546[/C][C]-0.525043[/C][/ROW]
[ROW][C]13[/C][C]103.85[/C][C]103.573[/C][C]104.137[/C][C]-0.564123[/C][C]0.27704[/C][/ROW]
[ROW][C]14[/C][C]104.78[/C][C]104.184[/C][C]104.202[/C][C]-0.0172483[/C][C]0.595582[/C][/ROW]
[ROW][C]15[/C][C]104.76[/C][C]104.296[/C][C]104.26[/C][C]0.0358767[/C][C]0.464123[/C][/ROW]
[ROW][C]16[/C][C]104.84[/C][C]104.467[/C][C]104.322[/C][C]0.144627[/C][C]0.372873[/C][/ROW]
[ROW][C]17[/C][C]104.85[/C][C]104.688[/C][C]104.385[/C][C]0.302543[/C][C]0.162457[/C][/ROW]
[ROW][C]18[/C][C]104.83[/C][C]104.829[/C][C]104.435[/C][C]0.393585[/C][C]0.000998264[/C][/ROW]
[ROW][C]19[/C][C]104.83[/C][C]104.542[/C][C]104.279[/C][C]0.26296[/C][C]0.28829[/C][/ROW]
[ROW][C]20[/C][C]103.71[/C][C]103.325[/C][C]103.917[/C][C]-0.592144[/C][C]0.384644[/C][/ROW]
[ROW][C]21[/C][C]103.84[/C][C]103.135[/C][C]103.552[/C][C]-0.416936[/C][C]0.705269[/C][/ROW]
[ROW][C]22[/C][C]104.37[/C][C]103.136[/C][C]103.181[/C][C]-0.0448524[/C][C]1.23402[/C][/ROW]
[ROW][C]23[/C][C]104.44[/C][C]102.998[/C][C]102.807[/C][C]0.190252[/C][C]1.44225[/C][/ROW]
[ROW][C]24[/C][C]104.4[/C][C]102.743[/C][C]102.437[/C][C]0.30546[/C][C]1.65746[/C][/ROW]
[ROW][C]25[/C][C]99.54[/C][C]101.505[/C][C]102.069[/C][C]-0.564123[/C][C]-1.96504[/C][/ROW]
[ROW][C]26[/C][C]100.42[/C][C]101.677[/C][C]101.694[/C][C]-0.0172483[/C][C]-1.25692[/C][/ROW]
[ROW][C]27[/C][C]100.34[/C][C]101.355[/C][C]101.319[/C][C]0.0358767[/C][C]-1.01504[/C][/ROW]
[ROW][C]28[/C][C]100.36[/C][C]101.088[/C][C]100.943[/C][C]0.144627[/C][C]-0.727543[/C][/ROW]
[ROW][C]29[/C][C]100.37[/C][C]100.865[/C][C]100.562[/C][C]0.302543[/C][C]-0.495043[/C][/ROW]
[ROW][C]30[/C][C]100.42[/C][C]100.581[/C][C]100.187[/C][C]0.393585[/C][C]-0.161085[/C][/ROW]
[ROW][C]31[/C][C]100.41[/C][C]100.303[/C][C]100.04[/C][C]0.26296[/C][C]0.107457[/C][/ROW]
[ROW][C]32[/C][C]99.13[/C][C]99.487[/C][C]100.079[/C][C]-0.592144[/C][C]-0.357023[/C][/ROW]
[ROW][C]33[/C][C]99.42[/C][C]99.6685[/C][C]100.085[/C][C]-0.416936[/C][C]-0.248481[/C][/ROW]
[ROW][C]34[/C][C]99.76[/C][C]100.056[/C][C]100.101[/C][C]-0.0448524[/C][C]-0.296398[/C][/ROW]
[ROW][C]35[/C][C]99.92[/C][C]100.317[/C][C]100.127[/C][C]0.190252[/C][C]-0.396918[/C][/ROW]
[ROW][C]36[/C][C]99.92[/C][C]100.456[/C][C]100.151[/C][C]0.30546[/C][C]-0.536293[/C][/ROW]
[ROW][C]37[/C][C]100.47[/C][C]99.5784[/C][C]100.142[/C][C]-0.564123[/C][C]0.891623[/C][/ROW]
[ROW][C]38[/C][C]100.44[/C][C]100.133[/C][C]100.15[/C][C]-0.0172483[/C][C]0.306832[/C][/ROW]
[ROW][C]39[/C][C]100.47[/C][C]100.215[/C][C]100.179[/C][C]0.0358767[/C][C]0.255373[/C][/ROW]
[ROW][C]40[/C][C]100.61[/C][C]100.312[/C][C]100.167[/C][C]0.144627[/C][C]0.29829[/C][/ROW]
[ROW][C]41[/C][C]100.73[/C][C]100.437[/C][C]100.135[/C][C]0.302543[/C][C]0.292873[/C][/ROW]
[ROW][C]42[/C][C]100.64[/C][C]100.494[/C][C]100.1[/C][C]0.393585[/C][C]0.145998[/C][/ROW]
[ROW][C]43[/C][C]99.99[/C][C]100.324[/C][C]100.061[/C][C]0.26296[/C][C]-0.333793[/C][/ROW]
[ROW][C]44[/C][C]99.74[/C][C]99.4204[/C][C]100.012[/C][C]-0.592144[/C][C]0.319644[/C][/ROW]
[ROW][C]45[/C][C]99.49[/C][C]99.5372[/C][C]99.9542[/C][C]-0.416936[/C][C]-0.0472309[/C][/ROW]
[ROW][C]46[/C][C]99.41[/C][C]99.8251[/C][C]99.87[/C][C]-0.0448524[/C][C]-0.415148[/C][/ROW]
[ROW][C]47[/C][C]99.49[/C][C]99.9532[/C][C]99.7629[/C][C]0.190252[/C][C]-0.463168[/C][/ROW]
[ROW][C]48[/C][C]99.53[/C][C]99.9559[/C][C]99.6504[/C][C]0.30546[/C][C]-0.425877[/C][/ROW]
[ROW][C]49[/C][C]99.91[/C][C]98.9434[/C][C]99.5075[/C][C]-0.564123[/C][C]0.966623[/C][/ROW]
[ROW][C]50[/C][C]99.84[/C][C]99.3153[/C][C]99.3325[/C][C]-0.0172483[/C][C]0.524748[/C][/ROW]
[ROW][C]51[/C][C]99.67[/C][C]99.2042[/C][C]99.1683[/C][C]0.0358767[/C][C]0.46579[/C][/ROW]
[ROW][C]52[/C][C]99.39[/C][C]99.1634[/C][C]99.0188[/C][C]0.144627[/C][C]0.226623[/C][/ROW]
[ROW][C]53[/C][C]99.38[/C][C]99.17[/C][C]98.8675[/C][C]0.302543[/C][C]0.209957[/C][/ROW]
[ROW][C]54[/C][C]99.29[/C][C]99.1057[/C][C]98.7121[/C][C]0.393585[/C][C]0.184332[/C][/ROW]
[ROW][C]55[/C][C]97.91[/C][C]NA[/C][C]NA[/C][C]0.26296[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]97.62[/C][C]NA[/C][C]NA[/C][C]-0.592144[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]97.67[/C][C]NA[/C][C]NA[/C][C]-0.416936[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]97.64[/C][C]NA[/C][C]NA[/C][C]-0.0448524[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]97.63[/C][C]NA[/C][C]NA[/C][C]0.190252[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]97.66[/C][C]NA[/C][C]NA[/C][C]0.30546[/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	102.8	NA	NA	-0.564123	NA
2	103.66	NA	NA	-0.0172483	NA
3	103.55	NA	NA	0.0358767	NA
4	103.87	NA	NA	0.144627	NA
5	104.03	NA	NA	0.302543	NA
6	104.02	NA	NA	0.393585	NA
7	104.02	103.912	103.649	0.26296	0.10829
8	102.97	103.147	103.739	-0.592144	-0.177023
9	103.18	103.419	103.836	-0.416936	-0.239314
10	103.53	103.882	103.927	-0.0448524	-0.352231
11	103.78	104.192	104.002	0.190252	-0.411918
12	103.85	104.375	104.07	0.30546	-0.525043
13	103.85	103.573	104.137	-0.564123	0.27704
14	104.78	104.184	104.202	-0.0172483	0.595582
15	104.76	104.296	104.26	0.0358767	0.464123
16	104.84	104.467	104.322	0.144627	0.372873
17	104.85	104.688	104.385	0.302543	0.162457
18	104.83	104.829	104.435	0.393585	0.000998264
19	104.83	104.542	104.279	0.26296	0.28829
20	103.71	103.325	103.917	-0.592144	0.384644
21	103.84	103.135	103.552	-0.416936	0.705269
22	104.37	103.136	103.181	-0.0448524	1.23402
23	104.44	102.998	102.807	0.190252	1.44225
24	104.4	102.743	102.437	0.30546	1.65746
25	99.54	101.505	102.069	-0.564123	-1.96504
26	100.42	101.677	101.694	-0.0172483	-1.25692
27	100.34	101.355	101.319	0.0358767	-1.01504
28	100.36	101.088	100.943	0.144627	-0.727543
29	100.37	100.865	100.562	0.302543	-0.495043
30	100.42	100.581	100.187	0.393585	-0.161085
31	100.41	100.303	100.04	0.26296	0.107457
32	99.13	99.487	100.079	-0.592144	-0.357023
33	99.42	99.6685	100.085	-0.416936	-0.248481
34	99.76	100.056	100.101	-0.0448524	-0.296398
35	99.92	100.317	100.127	0.190252	-0.396918
36	99.92	100.456	100.151	0.30546	-0.536293
37	100.47	99.5784	100.142	-0.564123	0.891623
38	100.44	100.133	100.15	-0.0172483	0.306832
39	100.47	100.215	100.179	0.0358767	0.255373
40	100.61	100.312	100.167	0.144627	0.29829
41	100.73	100.437	100.135	0.302543	0.292873
42	100.64	100.494	100.1	0.393585	0.145998
43	99.99	100.324	100.061	0.26296	-0.333793
44	99.74	99.4204	100.012	-0.592144	0.319644
45	99.49	99.5372	99.9542	-0.416936	-0.0472309
46	99.41	99.8251	99.87	-0.0448524	-0.415148
47	99.49	99.9532	99.7629	0.190252	-0.463168
48	99.53	99.9559	99.6504	0.30546	-0.425877
49	99.91	98.9434	99.5075	-0.564123	0.966623
50	99.84	99.3153	99.3325	-0.0172483	0.524748
51	99.67	99.2042	99.1683	0.0358767	0.46579
52	99.39	99.1634	99.0188	0.144627	0.226623
53	99.38	99.17	98.8675	0.302543	0.209957
54	99.29	99.1057	98.7121	0.393585	0.184332
55	97.91	NA	NA	0.26296	NA
56	97.62	NA	NA	-0.592144	NA
57	97.67	NA	NA	-0.416936	NA
58	97.64	NA	NA	-0.0448524	NA
59	97.63	NA	NA	0.190252	NA
60	97.66	NA	NA	0.30546	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