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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 29 Nov 2015 14:27:24 +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/2015/Nov/29/t1448807261yp6ehdbauh6xenf.htm/, Retrieved Wed, 15 May 2024 23:30:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284456, Retrieved Wed, 15 May 2024 23:30:50 +0000

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-       [Classical Decomposition] [] [2015-11-29 14:27:24] [4b4e0ace64f044c9dde59b15676ee69f] [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	'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284456&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284456&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284456&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	'Sir Maurice George Kendall' @ kendall.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	104.93	NA	NA	-0.987708	NA
2	105.68	NA	NA	-0.423958	NA
3	106.93	NA	NA	0.539375	NA
4	107.29	NA	NA	0.764688	NA
5	107.25	NA	NA	0.473229	NA
6	106.74	NA	NA	-0.157396	NA
7	106.44	107	106.804	0.195625	-0.559792
8	106.6	107.055	106.968	0.0866667	-0.455
9	107.26	107.427	107.134	0.293438	-0.167187
10	107.35	107.531	107.307	0.223958	-0.181042
11	107.22	107.159	107.495	-0.336042	0.0610417
12	106.99	107.015	107.687	-0.671875	-0.0252083
13	106.87	106.926	107.913	-0.987708	-0.055625
14	107.68	107.752	108.176	-0.423958	-0.071875
15	108.9	108.967	108.428	0.539375	-0.0672917
16	109.48	109.432	108.667	0.764688	0.0482292
17	109.57	109.369	108.896	0.473229	0.200937
18	109.03	108.956	109.114	-0.157396	0.0736458
19	109.58	109.528	109.332	0.195625	0.0522917
20	109.76	109.64	109.554	0.0866667	0.119583
21	110.15	110.071	109.778	0.293438	0.0786458
22	110.2	110.222	109.998	0.223958	-0.0222917
23	109.86	109.867	110.203	-0.336042	-0.006875
24	109.58	109.725	110.397	-0.671875	-0.145208
25	109.52	109.611	110.599	-0.987708	-0.0910417
26	110.35	110.383	110.807	-0.423958	-0.0327083
27	111.61	111.556	111.016	0.539375	0.054375
28	112.06	112.019	111.254	0.764688	0.0411458
29	111.9	111.986	111.512	0.473229	-0.0857292
30	111.36	111.613	111.771	-0.157396	-0.253437
31	112.09	112.231	112.035	0.195625	-0.140625
32	112.24	112.392	112.305	0.0866667	-0.152083
33	112.7	112.873	112.579	0.293438	-0.172604
34	113.36	113.06	112.836	0.223958	0.299792
35	112.9	112.751	113.087	-0.336042	0.149375
36	112.74	112.678	113.35	-0.671875	0.0622917
37	112.7	112.639	113.627	-0.987708	0.060625
38	113.66	113.478	113.902	-0.423958	0.181875
39	114.87	114.688	114.148	0.539375	0.182292
40	114.97	115.102	114.337	0.764688	-0.131771
41	115	114.953	114.48	0.473229	0.0467708
42	114.57	114.47	114.628	-0.157396	0.0998958
43	115.54	114.971	114.775	0.195625	0.568958
44	115.39	114.982	114.895	0.0866667	0.408333
45	115.46	115.278	114.985	0.293438	0.181979
46	115.13	115.306	115.082	0.223958	-0.175625
47	114.56	114.843	115.179	-0.336042	-0.282708
48	114.62	114.591	115.263	-0.671875	0.0289583
49	114.37	114.363	115.351	-0.987708	0.006875
50	114.86	115.016	115.44	-0.423958	-0.156458
51	115.82	116.069	115.529	0.539375	-0.248542
52	116.35	116.387	115.622	0.764688	-0.0367708
53	115.95	116.191	115.718	0.473229	-0.241146
54	115.64	115.639	115.797	-0.157396	0.000729167
55	116.58	NA	NA	0.195625	NA
56	116.5	NA	NA	0.0866667	NA
57	116.48	NA	NA	0.293438	NA
58	116.34	NA	NA	0.223958	NA
59	115.65	NA	NA	-0.336042	NA
60	115.42	NA	NA	-0.671875	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 104.93 & NA & NA & -0.987708 & NA \tabularnewline
2 & 105.68 & NA & NA & -0.423958 & NA \tabularnewline
3 & 106.93 & NA & NA & 0.539375 & NA \tabularnewline
4 & 107.29 & NA & NA & 0.764688 & NA \tabularnewline
5 & 107.25 & NA & NA & 0.473229 & NA \tabularnewline
6 & 106.74 & NA & NA & -0.157396 & NA \tabularnewline
7 & 106.44 & 107 & 106.804 & 0.195625 & -0.559792 \tabularnewline
8 & 106.6 & 107.055 & 106.968 & 0.0866667 & -0.455 \tabularnewline
9 & 107.26 & 107.427 & 107.134 & 0.293438 & -0.167187 \tabularnewline
10 & 107.35 & 107.531 & 107.307 & 0.223958 & -0.181042 \tabularnewline
11 & 107.22 & 107.159 & 107.495 & -0.336042 & 0.0610417 \tabularnewline
12 & 106.99 & 107.015 & 107.687 & -0.671875 & -0.0252083 \tabularnewline
13 & 106.87 & 106.926 & 107.913 & -0.987708 & -0.055625 \tabularnewline
14 & 107.68 & 107.752 & 108.176 & -0.423958 & -0.071875 \tabularnewline
15 & 108.9 & 108.967 & 108.428 & 0.539375 & -0.0672917 \tabularnewline
16 & 109.48 & 109.432 & 108.667 & 0.764688 & 0.0482292 \tabularnewline
17 & 109.57 & 109.369 & 108.896 & 0.473229 & 0.200937 \tabularnewline
18 & 109.03 & 108.956 & 109.114 & -0.157396 & 0.0736458 \tabularnewline
19 & 109.58 & 109.528 & 109.332 & 0.195625 & 0.0522917 \tabularnewline
20 & 109.76 & 109.64 & 109.554 & 0.0866667 & 0.119583 \tabularnewline
21 & 110.15 & 110.071 & 109.778 & 0.293438 & 0.0786458 \tabularnewline
22 & 110.2 & 110.222 & 109.998 & 0.223958 & -0.0222917 \tabularnewline
23 & 109.86 & 109.867 & 110.203 & -0.336042 & -0.006875 \tabularnewline
24 & 109.58 & 109.725 & 110.397 & -0.671875 & -0.145208 \tabularnewline
25 & 109.52 & 109.611 & 110.599 & -0.987708 & -0.0910417 \tabularnewline
26 & 110.35 & 110.383 & 110.807 & -0.423958 & -0.0327083 \tabularnewline
27 & 111.61 & 111.556 & 111.016 & 0.539375 & 0.054375 \tabularnewline
28 & 112.06 & 112.019 & 111.254 & 0.764688 & 0.0411458 \tabularnewline
29 & 111.9 & 111.986 & 111.512 & 0.473229 & -0.0857292 \tabularnewline
30 & 111.36 & 111.613 & 111.771 & -0.157396 & -0.253437 \tabularnewline
31 & 112.09 & 112.231 & 112.035 & 0.195625 & -0.140625 \tabularnewline
32 & 112.24 & 112.392 & 112.305 & 0.0866667 & -0.152083 \tabularnewline
33 & 112.7 & 112.873 & 112.579 & 0.293438 & -0.172604 \tabularnewline
34 & 113.36 & 113.06 & 112.836 & 0.223958 & 0.299792 \tabularnewline
35 & 112.9 & 112.751 & 113.087 & -0.336042 & 0.149375 \tabularnewline
36 & 112.74 & 112.678 & 113.35 & -0.671875 & 0.0622917 \tabularnewline
37 & 112.7 & 112.639 & 113.627 & -0.987708 & 0.060625 \tabularnewline
38 & 113.66 & 113.478 & 113.902 & -0.423958 & 0.181875 \tabularnewline
39 & 114.87 & 114.688 & 114.148 & 0.539375 & 0.182292 \tabularnewline
40 & 114.97 & 115.102 & 114.337 & 0.764688 & -0.131771 \tabularnewline
41 & 115 & 114.953 & 114.48 & 0.473229 & 0.0467708 \tabularnewline
42 & 114.57 & 114.47 & 114.628 & -0.157396 & 0.0998958 \tabularnewline
43 & 115.54 & 114.971 & 114.775 & 0.195625 & 0.568958 \tabularnewline
44 & 115.39 & 114.982 & 114.895 & 0.0866667 & 0.408333 \tabularnewline
45 & 115.46 & 115.278 & 114.985 & 0.293438 & 0.181979 \tabularnewline
46 & 115.13 & 115.306 & 115.082 & 0.223958 & -0.175625 \tabularnewline
47 & 114.56 & 114.843 & 115.179 & -0.336042 & -0.282708 \tabularnewline
48 & 114.62 & 114.591 & 115.263 & -0.671875 & 0.0289583 \tabularnewline
49 & 114.37 & 114.363 & 115.351 & -0.987708 & 0.006875 \tabularnewline
50 & 114.86 & 115.016 & 115.44 & -0.423958 & -0.156458 \tabularnewline
51 & 115.82 & 116.069 & 115.529 & 0.539375 & -0.248542 \tabularnewline
52 & 116.35 & 116.387 & 115.622 & 0.764688 & -0.0367708 \tabularnewline
53 & 115.95 & 116.191 & 115.718 & 0.473229 & -0.241146 \tabularnewline
54 & 115.64 & 115.639 & 115.797 & -0.157396 & 0.000729167 \tabularnewline
55 & 116.58 & NA & NA & 0.195625 & NA \tabularnewline
56 & 116.5 & NA & NA & 0.0866667 & NA \tabularnewline
57 & 116.48 & NA & NA & 0.293438 & NA \tabularnewline
58 & 116.34 & NA & NA & 0.223958 & NA \tabularnewline
59 & 115.65 & NA & NA & -0.336042 & NA \tabularnewline
60 & 115.42 & NA & NA & -0.671875 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284456&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]104.93[/C][C]NA[/C][C]NA[/C][C]-0.987708[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]105.68[/C][C]NA[/C][C]NA[/C][C]-0.423958[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]106.93[/C][C]NA[/C][C]NA[/C][C]0.539375[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]107.29[/C][C]NA[/C][C]NA[/C][C]0.764688[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]107.25[/C][C]NA[/C][C]NA[/C][C]0.473229[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]106.74[/C][C]NA[/C][C]NA[/C][C]-0.157396[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]106.44[/C][C]107[/C][C]106.804[/C][C]0.195625[/C][C]-0.559792[/C][/ROW]
[ROW][C]8[/C][C]106.6[/C][C]107.055[/C][C]106.968[/C][C]0.0866667[/C][C]-0.455[/C][/ROW]
[ROW][C]9[/C][C]107.26[/C][C]107.427[/C][C]107.134[/C][C]0.293438[/C][C]-0.167187[/C][/ROW]
[ROW][C]10[/C][C]107.35[/C][C]107.531[/C][C]107.307[/C][C]0.223958[/C][C]-0.181042[/C][/ROW]
[ROW][C]11[/C][C]107.22[/C][C]107.159[/C][C]107.495[/C][C]-0.336042[/C][C]0.0610417[/C][/ROW]
[ROW][C]12[/C][C]106.99[/C][C]107.015[/C][C]107.687[/C][C]-0.671875[/C][C]-0.0252083[/C][/ROW]
[ROW][C]13[/C][C]106.87[/C][C]106.926[/C][C]107.913[/C][C]-0.987708[/C][C]-0.055625[/C][/ROW]
[ROW][C]14[/C][C]107.68[/C][C]107.752[/C][C]108.176[/C][C]-0.423958[/C][C]-0.071875[/C][/ROW]
[ROW][C]15[/C][C]108.9[/C][C]108.967[/C][C]108.428[/C][C]0.539375[/C][C]-0.0672917[/C][/ROW]
[ROW][C]16[/C][C]109.48[/C][C]109.432[/C][C]108.667[/C][C]0.764688[/C][C]0.0482292[/C][/ROW]
[ROW][C]17[/C][C]109.57[/C][C]109.369[/C][C]108.896[/C][C]0.473229[/C][C]0.200937[/C][/ROW]
[ROW][C]18[/C][C]109.03[/C][C]108.956[/C][C]109.114[/C][C]-0.157396[/C][C]0.0736458[/C][/ROW]
[ROW][C]19[/C][C]109.58[/C][C]109.528[/C][C]109.332[/C][C]0.195625[/C][C]0.0522917[/C][/ROW]
[ROW][C]20[/C][C]109.76[/C][C]109.64[/C][C]109.554[/C][C]0.0866667[/C][C]0.119583[/C][/ROW]
[ROW][C]21[/C][C]110.15[/C][C]110.071[/C][C]109.778[/C][C]0.293438[/C][C]0.0786458[/C][/ROW]
[ROW][C]22[/C][C]110.2[/C][C]110.222[/C][C]109.998[/C][C]0.223958[/C][C]-0.0222917[/C][/ROW]
[ROW][C]23[/C][C]109.86[/C][C]109.867[/C][C]110.203[/C][C]-0.336042[/C][C]-0.006875[/C][/ROW]
[ROW][C]24[/C][C]109.58[/C][C]109.725[/C][C]110.397[/C][C]-0.671875[/C][C]-0.145208[/C][/ROW]
[ROW][C]25[/C][C]109.52[/C][C]109.611[/C][C]110.599[/C][C]-0.987708[/C][C]-0.0910417[/C][/ROW]
[ROW][C]26[/C][C]110.35[/C][C]110.383[/C][C]110.807[/C][C]-0.423958[/C][C]-0.0327083[/C][/ROW]
[ROW][C]27[/C][C]111.61[/C][C]111.556[/C][C]111.016[/C][C]0.539375[/C][C]0.054375[/C][/ROW]
[ROW][C]28[/C][C]112.06[/C][C]112.019[/C][C]111.254[/C][C]0.764688[/C][C]0.0411458[/C][/ROW]
[ROW][C]29[/C][C]111.9[/C][C]111.986[/C][C]111.512[/C][C]0.473229[/C][C]-0.0857292[/C][/ROW]
[ROW][C]30[/C][C]111.36[/C][C]111.613[/C][C]111.771[/C][C]-0.157396[/C][C]-0.253437[/C][/ROW]
[ROW][C]31[/C][C]112.09[/C][C]112.231[/C][C]112.035[/C][C]0.195625[/C][C]-0.140625[/C][/ROW]
[ROW][C]32[/C][C]112.24[/C][C]112.392[/C][C]112.305[/C][C]0.0866667[/C][C]-0.152083[/C][/ROW]
[ROW][C]33[/C][C]112.7[/C][C]112.873[/C][C]112.579[/C][C]0.293438[/C][C]-0.172604[/C][/ROW]
[ROW][C]34[/C][C]113.36[/C][C]113.06[/C][C]112.836[/C][C]0.223958[/C][C]0.299792[/C][/ROW]
[ROW][C]35[/C][C]112.9[/C][C]112.751[/C][C]113.087[/C][C]-0.336042[/C][C]0.149375[/C][/ROW]
[ROW][C]36[/C][C]112.74[/C][C]112.678[/C][C]113.35[/C][C]-0.671875[/C][C]0.0622917[/C][/ROW]
[ROW][C]37[/C][C]112.7[/C][C]112.639[/C][C]113.627[/C][C]-0.987708[/C][C]0.060625[/C][/ROW]
[ROW][C]38[/C][C]113.66[/C][C]113.478[/C][C]113.902[/C][C]-0.423958[/C][C]0.181875[/C][/ROW]
[ROW][C]39[/C][C]114.87[/C][C]114.688[/C][C]114.148[/C][C]0.539375[/C][C]0.182292[/C][/ROW]
[ROW][C]40[/C][C]114.97[/C][C]115.102[/C][C]114.337[/C][C]0.764688[/C][C]-0.131771[/C][/ROW]
[ROW][C]41[/C][C]115[/C][C]114.953[/C][C]114.48[/C][C]0.473229[/C][C]0.0467708[/C][/ROW]
[ROW][C]42[/C][C]114.57[/C][C]114.47[/C][C]114.628[/C][C]-0.157396[/C][C]0.0998958[/C][/ROW]
[ROW][C]43[/C][C]115.54[/C][C]114.971[/C][C]114.775[/C][C]0.195625[/C][C]0.568958[/C][/ROW]
[ROW][C]44[/C][C]115.39[/C][C]114.982[/C][C]114.895[/C][C]0.0866667[/C][C]0.408333[/C][/ROW]
[ROW][C]45[/C][C]115.46[/C][C]115.278[/C][C]114.985[/C][C]0.293438[/C][C]0.181979[/C][/ROW]
[ROW][C]46[/C][C]115.13[/C][C]115.306[/C][C]115.082[/C][C]0.223958[/C][C]-0.175625[/C][/ROW]
[ROW][C]47[/C][C]114.56[/C][C]114.843[/C][C]115.179[/C][C]-0.336042[/C][C]-0.282708[/C][/ROW]
[ROW][C]48[/C][C]114.62[/C][C]114.591[/C][C]115.263[/C][C]-0.671875[/C][C]0.0289583[/C][/ROW]
[ROW][C]49[/C][C]114.37[/C][C]114.363[/C][C]115.351[/C][C]-0.987708[/C][C]0.006875[/C][/ROW]
[ROW][C]50[/C][C]114.86[/C][C]115.016[/C][C]115.44[/C][C]-0.423958[/C][C]-0.156458[/C][/ROW]
[ROW][C]51[/C][C]115.82[/C][C]116.069[/C][C]115.529[/C][C]0.539375[/C][C]-0.248542[/C][/ROW]
[ROW][C]52[/C][C]116.35[/C][C]116.387[/C][C]115.622[/C][C]0.764688[/C][C]-0.0367708[/C][/ROW]
[ROW][C]53[/C][C]115.95[/C][C]116.191[/C][C]115.718[/C][C]0.473229[/C][C]-0.241146[/C][/ROW]
[ROW][C]54[/C][C]115.64[/C][C]115.639[/C][C]115.797[/C][C]-0.157396[/C][C]0.000729167[/C][/ROW]
[ROW][C]55[/C][C]116.58[/C][C]NA[/C][C]NA[/C][C]0.195625[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]116.5[/C][C]NA[/C][C]NA[/C][C]0.0866667[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]116.48[/C][C]NA[/C][C]NA[/C][C]0.293438[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]116.34[/C][C]NA[/C][C]NA[/C][C]0.223958[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]115.65[/C][C]NA[/C][C]NA[/C][C]-0.336042[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]115.42[/C][C]NA[/C][C]NA[/C][C]-0.671875[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284456&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284456&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	104.93	NA	NA	-0.987708	NA
2	105.68	NA	NA	-0.423958	NA
3	106.93	NA	NA	0.539375	NA
4	107.29	NA	NA	0.764688	NA
5	107.25	NA	NA	0.473229	NA
6	106.74	NA	NA	-0.157396	NA
7	106.44	107	106.804	0.195625	-0.559792
8	106.6	107.055	106.968	0.0866667	-0.455
9	107.26	107.427	107.134	0.293438	-0.167187
10	107.35	107.531	107.307	0.223958	-0.181042
11	107.22	107.159	107.495	-0.336042	0.0610417
12	106.99	107.015	107.687	-0.671875	-0.0252083
13	106.87	106.926	107.913	-0.987708	-0.055625
14	107.68	107.752	108.176	-0.423958	-0.071875
15	108.9	108.967	108.428	0.539375	-0.0672917
16	109.48	109.432	108.667	0.764688	0.0482292
17	109.57	109.369	108.896	0.473229	0.200937
18	109.03	108.956	109.114	-0.157396	0.0736458
19	109.58	109.528	109.332	0.195625	0.0522917
20	109.76	109.64	109.554	0.0866667	0.119583
21	110.15	110.071	109.778	0.293438	0.0786458
22	110.2	110.222	109.998	0.223958	-0.0222917
23	109.86	109.867	110.203	-0.336042	-0.006875
24	109.58	109.725	110.397	-0.671875	-0.145208
25	109.52	109.611	110.599	-0.987708	-0.0910417
26	110.35	110.383	110.807	-0.423958	-0.0327083
27	111.61	111.556	111.016	0.539375	0.054375
28	112.06	112.019	111.254	0.764688	0.0411458
29	111.9	111.986	111.512	0.473229	-0.0857292
30	111.36	111.613	111.771	-0.157396	-0.253437
31	112.09	112.231	112.035	0.195625	-0.140625
32	112.24	112.392	112.305	0.0866667	-0.152083
33	112.7	112.873	112.579	0.293438	-0.172604
34	113.36	113.06	112.836	0.223958	0.299792
35	112.9	112.751	113.087	-0.336042	0.149375
36	112.74	112.678	113.35	-0.671875	0.0622917
37	112.7	112.639	113.627	-0.987708	0.060625
38	113.66	113.478	113.902	-0.423958	0.181875
39	114.87	114.688	114.148	0.539375	0.182292
40	114.97	115.102	114.337	0.764688	-0.131771
41	115	114.953	114.48	0.473229	0.0467708
42	114.57	114.47	114.628	-0.157396	0.0998958
43	115.54	114.971	114.775	0.195625	0.568958
44	115.39	114.982	114.895	0.0866667	0.408333
45	115.46	115.278	114.985	0.293438	0.181979
46	115.13	115.306	115.082	0.223958	-0.175625
47	114.56	114.843	115.179	-0.336042	-0.282708
48	114.62	114.591	115.263	-0.671875	0.0289583
49	114.37	114.363	115.351	-0.987708	0.006875
50	114.86	115.016	115.44	-0.423958	-0.156458
51	115.82	116.069	115.529	0.539375	-0.248542
52	116.35	116.387	115.622	0.764688	-0.0367708
53	115.95	116.191	115.718	0.473229	-0.241146
54	115.64	115.639	115.797	-0.157396	0.000729167
55	116.58	NA	NA	0.195625	NA
56	116.5	NA	NA	0.0866667	NA
57	116.48	NA	NA	0.293438	NA
58	116.34	NA	NA	0.223958	NA
59	115.65	NA	NA	-0.336042	NA
60	115.42	NA	NA	-0.671875	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