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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 27 Nov 2016 10:10:12 +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/t1480241433me978ra071xcwpt.htm/, Retrieved Tue, 30 Apr 2024 00:35:13 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Tue, 30 Apr 2024 00:35:13 +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	86.37	NA	NA	-0.158134	NA
2	86.84	NA	NA	-0.141363	NA
3	86.73	NA	NA	-0.296155	NA
4	90.99	NA	NA	0.631137	NA
5	92.61	NA	NA	0.27947	NA
6	93.83	NA	NA	0.33572	NA
7	94.2	92.9147	92.105	0.809679	1.28532
8	94.01	93.2175	92.7746	0.442908	0.792509
9	93.47	93.4144	93.4325	-0.0181337	0.0556337
10	93.27	93.5133	93.995	-0.481675	-0.243325
11	94.3	93.6826	94.4154	-0.732821	0.617405
12	94.53	94.0789	94.7496	-0.670634	0.45105
13	94.59	94.8939	95.0521	-0.158134	-0.30395
14	94.69	95.2545	95.3958	-0.141363	-0.56447
15	94.67	95.5213	95.8175	-0.296155	-0.851345
16	96.55	96.9261	96.295	0.631137	-0.376137
17	97.14	97.0028	96.7233	0.27947	0.137196
18	97.32	97.4395	97.1037	0.33572	-0.11947
19	97.97	98.3172	97.5075	0.809679	-0.347179
20	98.49	98.3617	97.9187	0.442908	0.128342
21	99.11	98.3114	98.3296	-0.0181337	0.79855
22	99.09	98.2317	98.7133	-0.481675	0.858342
23	98.76	98.2905	99.0233	-0.732821	0.469488
24	99.2	98.6323	99.3029	-0.670634	0.567717
25	99.61	99.3902	99.5483	-0.158134	0.2198
26	99.54	99.587	99.7283	-0.141363	-0.0469705
27	99.68	99.5534	99.8496	-0.296155	0.126571
28	100.75	100.561	99.9296	0.631137	0.18928
29	100.38	100.264	99.9846	0.27947	0.115946
30	100.79	100.336	100	0.33572	0.453863
31	100.39	100.808	99.9983	0.809679	-0.418012
32	100.39	100.458	100.015	0.442908	-0.067908
33	100.12	100.014	100.032	-0.0181337	0.105634
34	100	99.5625	100.044	-0.481675	0.437509
35	99.17	99.3484	100.081	-0.732821	-0.178429
36	99.17	99.4606	100.131	-0.670634	-0.290616
37	99.59	100.058	100.216	-0.158134	-0.468116
38	99.96	100.182	100.324	-0.141363	-0.222387
39	99.68	100.127	100.423	-0.296155	-0.446762
40	101.03	101.157	100.526	0.631137	-0.12697
41	100.99	100.951	100.672	0.27947	0.0388628
42	101.38	101.22	100.884	0.33572	0.160113
43	101.84	101.997	101.188	0.809679	-0.157179
44	101.52	102.01	101.567	0.442908	-0.489991
45	101.37	101.967	101.985	-0.0181337	-0.596866
46	101.22	101.91	102.391	-0.481675	-0.689575
47	101.45	101.996	102.728	-0.732821	-0.545512
48	101.99	102.355	103.026	-0.670634	-0.3652
49	104.05	103.135	103.293	-0.158134	0.915217
50	104.61	103.413	103.555	-0.141363	1.19678
51	105.06	103.526	103.822	-0.296155	1.53449
52	105.4	104.723	104.092	0.631137	0.67678
53	104.71	104.639	104.36	0.27947	0.0709462
54	104.8	104.932	104.596	0.33572	-0.131554
55	104.83	NA	NA	0.809679	NA
56	104.81	NA	NA	0.442908	NA
57	104.49	NA	NA	-0.0181337	NA
58	104.59	NA	NA	-0.481675	NA
59	104.5	NA	NA	-0.732821	NA
60	104.61	NA	NA	-0.670634	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 86.37 & NA & NA & -0.158134 & NA \tabularnewline
2 & 86.84 & NA & NA & -0.141363 & NA \tabularnewline
3 & 86.73 & NA & NA & -0.296155 & NA \tabularnewline
4 & 90.99 & NA & NA & 0.631137 & NA \tabularnewline
5 & 92.61 & NA & NA & 0.27947 & NA \tabularnewline
6 & 93.83 & NA & NA & 0.33572 & NA \tabularnewline
7 & 94.2 & 92.9147 & 92.105 & 0.809679 & 1.28532 \tabularnewline
8 & 94.01 & 93.2175 & 92.7746 & 0.442908 & 0.792509 \tabularnewline
9 & 93.47 & 93.4144 & 93.4325 & -0.0181337 & 0.0556337 \tabularnewline
10 & 93.27 & 93.5133 & 93.995 & -0.481675 & -0.243325 \tabularnewline
11 & 94.3 & 93.6826 & 94.4154 & -0.732821 & 0.617405 \tabularnewline
12 & 94.53 & 94.0789 & 94.7496 & -0.670634 & 0.45105 \tabularnewline
13 & 94.59 & 94.8939 & 95.0521 & -0.158134 & -0.30395 \tabularnewline
14 & 94.69 & 95.2545 & 95.3958 & -0.141363 & -0.56447 \tabularnewline
15 & 94.67 & 95.5213 & 95.8175 & -0.296155 & -0.851345 \tabularnewline
16 & 96.55 & 96.9261 & 96.295 & 0.631137 & -0.376137 \tabularnewline
17 & 97.14 & 97.0028 & 96.7233 & 0.27947 & 0.137196 \tabularnewline
18 & 97.32 & 97.4395 & 97.1037 & 0.33572 & -0.11947 \tabularnewline
19 & 97.97 & 98.3172 & 97.5075 & 0.809679 & -0.347179 \tabularnewline
20 & 98.49 & 98.3617 & 97.9187 & 0.442908 & 0.128342 \tabularnewline
21 & 99.11 & 98.3114 & 98.3296 & -0.0181337 & 0.79855 \tabularnewline
22 & 99.09 & 98.2317 & 98.7133 & -0.481675 & 0.858342 \tabularnewline
23 & 98.76 & 98.2905 & 99.0233 & -0.732821 & 0.469488 \tabularnewline
24 & 99.2 & 98.6323 & 99.3029 & -0.670634 & 0.567717 \tabularnewline
25 & 99.61 & 99.3902 & 99.5483 & -0.158134 & 0.2198 \tabularnewline
26 & 99.54 & 99.587 & 99.7283 & -0.141363 & -0.0469705 \tabularnewline
27 & 99.68 & 99.5534 & 99.8496 & -0.296155 & 0.126571 \tabularnewline
28 & 100.75 & 100.561 & 99.9296 & 0.631137 & 0.18928 \tabularnewline
29 & 100.38 & 100.264 & 99.9846 & 0.27947 & 0.115946 \tabularnewline
30 & 100.79 & 100.336 & 100 & 0.33572 & 0.453863 \tabularnewline
31 & 100.39 & 100.808 & 99.9983 & 0.809679 & -0.418012 \tabularnewline
32 & 100.39 & 100.458 & 100.015 & 0.442908 & -0.067908 \tabularnewline
33 & 100.12 & 100.014 & 100.032 & -0.0181337 & 0.105634 \tabularnewline
34 & 100 & 99.5625 & 100.044 & -0.481675 & 0.437509 \tabularnewline
35 & 99.17 & 99.3484 & 100.081 & -0.732821 & -0.178429 \tabularnewline
36 & 99.17 & 99.4606 & 100.131 & -0.670634 & -0.290616 \tabularnewline
37 & 99.59 & 100.058 & 100.216 & -0.158134 & -0.468116 \tabularnewline
38 & 99.96 & 100.182 & 100.324 & -0.141363 & -0.222387 \tabularnewline
39 & 99.68 & 100.127 & 100.423 & -0.296155 & -0.446762 \tabularnewline
40 & 101.03 & 101.157 & 100.526 & 0.631137 & -0.12697 \tabularnewline
41 & 100.99 & 100.951 & 100.672 & 0.27947 & 0.0388628 \tabularnewline
42 & 101.38 & 101.22 & 100.884 & 0.33572 & 0.160113 \tabularnewline
43 & 101.84 & 101.997 & 101.188 & 0.809679 & -0.157179 \tabularnewline
44 & 101.52 & 102.01 & 101.567 & 0.442908 & -0.489991 \tabularnewline
45 & 101.37 & 101.967 & 101.985 & -0.0181337 & -0.596866 \tabularnewline
46 & 101.22 & 101.91 & 102.391 & -0.481675 & -0.689575 \tabularnewline
47 & 101.45 & 101.996 & 102.728 & -0.732821 & -0.545512 \tabularnewline
48 & 101.99 & 102.355 & 103.026 & -0.670634 & -0.3652 \tabularnewline
49 & 104.05 & 103.135 & 103.293 & -0.158134 & 0.915217 \tabularnewline
50 & 104.61 & 103.413 & 103.555 & -0.141363 & 1.19678 \tabularnewline
51 & 105.06 & 103.526 & 103.822 & -0.296155 & 1.53449 \tabularnewline
52 & 105.4 & 104.723 & 104.092 & 0.631137 & 0.67678 \tabularnewline
53 & 104.71 & 104.639 & 104.36 & 0.27947 & 0.0709462 \tabularnewline
54 & 104.8 & 104.932 & 104.596 & 0.33572 & -0.131554 \tabularnewline
55 & 104.83 & NA & NA & 0.809679 & NA \tabularnewline
56 & 104.81 & NA & NA & 0.442908 & NA \tabularnewline
57 & 104.49 & NA & NA & -0.0181337 & NA \tabularnewline
58 & 104.59 & NA & NA & -0.481675 & NA \tabularnewline
59 & 104.5 & NA & NA & -0.732821 & NA \tabularnewline
60 & 104.61 & NA & NA & -0.670634 & 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]86.37[/C][C]NA[/C][C]NA[/C][C]-0.158134[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]86.84[/C][C]NA[/C][C]NA[/C][C]-0.141363[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]86.73[/C][C]NA[/C][C]NA[/C][C]-0.296155[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]90.99[/C][C]NA[/C][C]NA[/C][C]0.631137[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]92.61[/C][C]NA[/C][C]NA[/C][C]0.27947[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]93.83[/C][C]NA[/C][C]NA[/C][C]0.33572[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]94.2[/C][C]92.9147[/C][C]92.105[/C][C]0.809679[/C][C]1.28532[/C][/ROW]
[ROW][C]8[/C][C]94.01[/C][C]93.2175[/C][C]92.7746[/C][C]0.442908[/C][C]0.792509[/C][/ROW]
[ROW][C]9[/C][C]93.47[/C][C]93.4144[/C][C]93.4325[/C][C]-0.0181337[/C][C]0.0556337[/C][/ROW]
[ROW][C]10[/C][C]93.27[/C][C]93.5133[/C][C]93.995[/C][C]-0.481675[/C][C]-0.243325[/C][/ROW]
[ROW][C]11[/C][C]94.3[/C][C]93.6826[/C][C]94.4154[/C][C]-0.732821[/C][C]0.617405[/C][/ROW]
[ROW][C]12[/C][C]94.53[/C][C]94.0789[/C][C]94.7496[/C][C]-0.670634[/C][C]0.45105[/C][/ROW]
[ROW][C]13[/C][C]94.59[/C][C]94.8939[/C][C]95.0521[/C][C]-0.158134[/C][C]-0.30395[/C][/ROW]
[ROW][C]14[/C][C]94.69[/C][C]95.2545[/C][C]95.3958[/C][C]-0.141363[/C][C]-0.56447[/C][/ROW]
[ROW][C]15[/C][C]94.67[/C][C]95.5213[/C][C]95.8175[/C][C]-0.296155[/C][C]-0.851345[/C][/ROW]
[ROW][C]16[/C][C]96.55[/C][C]96.9261[/C][C]96.295[/C][C]0.631137[/C][C]-0.376137[/C][/ROW]
[ROW][C]17[/C][C]97.14[/C][C]97.0028[/C][C]96.7233[/C][C]0.27947[/C][C]0.137196[/C][/ROW]
[ROW][C]18[/C][C]97.32[/C][C]97.4395[/C][C]97.1037[/C][C]0.33572[/C][C]-0.11947[/C][/ROW]
[ROW][C]19[/C][C]97.97[/C][C]98.3172[/C][C]97.5075[/C][C]0.809679[/C][C]-0.347179[/C][/ROW]
[ROW][C]20[/C][C]98.49[/C][C]98.3617[/C][C]97.9187[/C][C]0.442908[/C][C]0.128342[/C][/ROW]
[ROW][C]21[/C][C]99.11[/C][C]98.3114[/C][C]98.3296[/C][C]-0.0181337[/C][C]0.79855[/C][/ROW]
[ROW][C]22[/C][C]99.09[/C][C]98.2317[/C][C]98.7133[/C][C]-0.481675[/C][C]0.858342[/C][/ROW]
[ROW][C]23[/C][C]98.76[/C][C]98.2905[/C][C]99.0233[/C][C]-0.732821[/C][C]0.469488[/C][/ROW]
[ROW][C]24[/C][C]99.2[/C][C]98.6323[/C][C]99.3029[/C][C]-0.670634[/C][C]0.567717[/C][/ROW]
[ROW][C]25[/C][C]99.61[/C][C]99.3902[/C][C]99.5483[/C][C]-0.158134[/C][C]0.2198[/C][/ROW]
[ROW][C]26[/C][C]99.54[/C][C]99.587[/C][C]99.7283[/C][C]-0.141363[/C][C]-0.0469705[/C][/ROW]
[ROW][C]27[/C][C]99.68[/C][C]99.5534[/C][C]99.8496[/C][C]-0.296155[/C][C]0.126571[/C][/ROW]
[ROW][C]28[/C][C]100.75[/C][C]100.561[/C][C]99.9296[/C][C]0.631137[/C][C]0.18928[/C][/ROW]
[ROW][C]29[/C][C]100.38[/C][C]100.264[/C][C]99.9846[/C][C]0.27947[/C][C]0.115946[/C][/ROW]
[ROW][C]30[/C][C]100.79[/C][C]100.336[/C][C]100[/C][C]0.33572[/C][C]0.453863[/C][/ROW]
[ROW][C]31[/C][C]100.39[/C][C]100.808[/C][C]99.9983[/C][C]0.809679[/C][C]-0.418012[/C][/ROW]
[ROW][C]32[/C][C]100.39[/C][C]100.458[/C][C]100.015[/C][C]0.442908[/C][C]-0.067908[/C][/ROW]
[ROW][C]33[/C][C]100.12[/C][C]100.014[/C][C]100.032[/C][C]-0.0181337[/C][C]0.105634[/C][/ROW]
[ROW][C]34[/C][C]100[/C][C]99.5625[/C][C]100.044[/C][C]-0.481675[/C][C]0.437509[/C][/ROW]
[ROW][C]35[/C][C]99.17[/C][C]99.3484[/C][C]100.081[/C][C]-0.732821[/C][C]-0.178429[/C][/ROW]
[ROW][C]36[/C][C]99.17[/C][C]99.4606[/C][C]100.131[/C][C]-0.670634[/C][C]-0.290616[/C][/ROW]
[ROW][C]37[/C][C]99.59[/C][C]100.058[/C][C]100.216[/C][C]-0.158134[/C][C]-0.468116[/C][/ROW]
[ROW][C]38[/C][C]99.96[/C][C]100.182[/C][C]100.324[/C][C]-0.141363[/C][C]-0.222387[/C][/ROW]
[ROW][C]39[/C][C]99.68[/C][C]100.127[/C][C]100.423[/C][C]-0.296155[/C][C]-0.446762[/C][/ROW]
[ROW][C]40[/C][C]101.03[/C][C]101.157[/C][C]100.526[/C][C]0.631137[/C][C]-0.12697[/C][/ROW]
[ROW][C]41[/C][C]100.99[/C][C]100.951[/C][C]100.672[/C][C]0.27947[/C][C]0.0388628[/C][/ROW]
[ROW][C]42[/C][C]101.38[/C][C]101.22[/C][C]100.884[/C][C]0.33572[/C][C]0.160113[/C][/ROW]
[ROW][C]43[/C][C]101.84[/C][C]101.997[/C][C]101.188[/C][C]0.809679[/C][C]-0.157179[/C][/ROW]
[ROW][C]44[/C][C]101.52[/C][C]102.01[/C][C]101.567[/C][C]0.442908[/C][C]-0.489991[/C][/ROW]
[ROW][C]45[/C][C]101.37[/C][C]101.967[/C][C]101.985[/C][C]-0.0181337[/C][C]-0.596866[/C][/ROW]
[ROW][C]46[/C][C]101.22[/C][C]101.91[/C][C]102.391[/C][C]-0.481675[/C][C]-0.689575[/C][/ROW]
[ROW][C]47[/C][C]101.45[/C][C]101.996[/C][C]102.728[/C][C]-0.732821[/C][C]-0.545512[/C][/ROW]
[ROW][C]48[/C][C]101.99[/C][C]102.355[/C][C]103.026[/C][C]-0.670634[/C][C]-0.3652[/C][/ROW]
[ROW][C]49[/C][C]104.05[/C][C]103.135[/C][C]103.293[/C][C]-0.158134[/C][C]0.915217[/C][/ROW]
[ROW][C]50[/C][C]104.61[/C][C]103.413[/C][C]103.555[/C][C]-0.141363[/C][C]1.19678[/C][/ROW]
[ROW][C]51[/C][C]105.06[/C][C]103.526[/C][C]103.822[/C][C]-0.296155[/C][C]1.53449[/C][/ROW]
[ROW][C]52[/C][C]105.4[/C][C]104.723[/C][C]104.092[/C][C]0.631137[/C][C]0.67678[/C][/ROW]
[ROW][C]53[/C][C]104.71[/C][C]104.639[/C][C]104.36[/C][C]0.27947[/C][C]0.0709462[/C][/ROW]
[ROW][C]54[/C][C]104.8[/C][C]104.932[/C][C]104.596[/C][C]0.33572[/C][C]-0.131554[/C][/ROW]
[ROW][C]55[/C][C]104.83[/C][C]NA[/C][C]NA[/C][C]0.809679[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]104.81[/C][C]NA[/C][C]NA[/C][C]0.442908[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]104.49[/C][C]NA[/C][C]NA[/C][C]-0.0181337[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]104.59[/C][C]NA[/C][C]NA[/C][C]-0.481675[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]104.5[/C][C]NA[/C][C]NA[/C][C]-0.732821[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]104.61[/C][C]NA[/C][C]NA[/C][C]-0.670634[/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	86.37	NA	NA	-0.158134	NA
2	86.84	NA	NA	-0.141363	NA
3	86.73	NA	NA	-0.296155	NA
4	90.99	NA	NA	0.631137	NA
5	92.61	NA	NA	0.27947	NA
6	93.83	NA	NA	0.33572	NA
7	94.2	92.9147	92.105	0.809679	1.28532
8	94.01	93.2175	92.7746	0.442908	0.792509
9	93.47	93.4144	93.4325	-0.0181337	0.0556337
10	93.27	93.5133	93.995	-0.481675	-0.243325
11	94.3	93.6826	94.4154	-0.732821	0.617405
12	94.53	94.0789	94.7496	-0.670634	0.45105
13	94.59	94.8939	95.0521	-0.158134	-0.30395
14	94.69	95.2545	95.3958	-0.141363	-0.56447
15	94.67	95.5213	95.8175	-0.296155	-0.851345
16	96.55	96.9261	96.295	0.631137	-0.376137
17	97.14	97.0028	96.7233	0.27947	0.137196
18	97.32	97.4395	97.1037	0.33572	-0.11947
19	97.97	98.3172	97.5075	0.809679	-0.347179
20	98.49	98.3617	97.9187	0.442908	0.128342
21	99.11	98.3114	98.3296	-0.0181337	0.79855
22	99.09	98.2317	98.7133	-0.481675	0.858342
23	98.76	98.2905	99.0233	-0.732821	0.469488
24	99.2	98.6323	99.3029	-0.670634	0.567717
25	99.61	99.3902	99.5483	-0.158134	0.2198
26	99.54	99.587	99.7283	-0.141363	-0.0469705
27	99.68	99.5534	99.8496	-0.296155	0.126571
28	100.75	100.561	99.9296	0.631137	0.18928
29	100.38	100.264	99.9846	0.27947	0.115946
30	100.79	100.336	100	0.33572	0.453863
31	100.39	100.808	99.9983	0.809679	-0.418012
32	100.39	100.458	100.015	0.442908	-0.067908
33	100.12	100.014	100.032	-0.0181337	0.105634
34	100	99.5625	100.044	-0.481675	0.437509
35	99.17	99.3484	100.081	-0.732821	-0.178429
36	99.17	99.4606	100.131	-0.670634	-0.290616
37	99.59	100.058	100.216	-0.158134	-0.468116
38	99.96	100.182	100.324	-0.141363	-0.222387
39	99.68	100.127	100.423	-0.296155	-0.446762
40	101.03	101.157	100.526	0.631137	-0.12697
41	100.99	100.951	100.672	0.27947	0.0388628
42	101.38	101.22	100.884	0.33572	0.160113
43	101.84	101.997	101.188	0.809679	-0.157179
44	101.52	102.01	101.567	0.442908	-0.489991
45	101.37	101.967	101.985	-0.0181337	-0.596866
46	101.22	101.91	102.391	-0.481675	-0.689575
47	101.45	101.996	102.728	-0.732821	-0.545512
48	101.99	102.355	103.026	-0.670634	-0.3652
49	104.05	103.135	103.293	-0.158134	0.915217
50	104.61	103.413	103.555	-0.141363	1.19678
51	105.06	103.526	103.822	-0.296155	1.53449
52	105.4	104.723	104.092	0.631137	0.67678
53	104.71	104.639	104.36	0.27947	0.0709462
54	104.8	104.932	104.596	0.33572	-0.131554
55	104.83	NA	NA	0.809679	NA
56	104.81	NA	NA	0.442908	NA
57	104.49	NA	NA	-0.0181337	NA
58	104.59	NA	NA	-0.481675	NA
59	104.5	NA	NA	-0.732821	NA
60	104.61	NA	NA	-0.670634	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