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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Tue, 02 May 2017 14:31:55 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/May/02/t1493732290ytinsdzmhb4nsem.htm/, Retrieved Fri, 17 May 2024 08:26:10 +0200

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Fri, 17 May 2024 08:26:10 +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	4 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.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	4 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	92.42	NA	NA	-0.834948	NA
2	92.64	NA	NA	0.175677	NA
3	94.44	NA	NA	0.769115	NA
4	93.59	NA	NA	0.520885	NA
5	93.39	NA	NA	0.82724	NA
6	93.33	NA	NA	0.786615	NA
7	93.72	94.7447	95.0788	-0.33401	-1.02474
8	95.43	95.4286	95.5771	-0.14849	0.00140625
9	97.06	96.1735	96.1112	0.0622396	0.88651
10	97.7	96.233	96.6279	-0.394948	1.46703
11	97.59	96.8589	97.1412	-0.282344	0.731094
12	96.97	96.5151	97.6621	-1.14703	0.454948
13	97.75	97.3455	98.1804	-0.834948	0.404531
14	99.27	98.8061	98.6304	0.175677	0.463906
15	100.63	99.7662	98.9971	0.769115	0.863802
16	99.8	99.8105	99.2896	0.520885	-0.0104688
17	99.5	100.388	99.5608	0.82724	-0.888073
18	99.72	100.64	99.8537	0.786615	-0.920365
19	99.77	99.8256	100.16	-0.33401	-0.0555729
20	100.18	100.298	100.446	-0.14849	-0.11776
21	101.11	100.744	100.682	0.0622396	0.365677
22	100.67	100.595	100.99	-0.394948	0.0749479
23	101.13	101.119	101.401	-0.282344	0.0110938
24	100.46	100.671	101.818	-1.14703	-0.211302
25	101.6	101.407	102.242	-0.834948	0.193281
26	102.3	102.834	102.659	0.175677	-0.534427
27	103.26	103.777	103.008	0.769115	-0.516615
28	104.56	103.832	103.311	0.520885	0.728281
29	104.61	104.435	103.608	0.82724	0.174844
30	104.62	104.704	103.917	0.786615	-0.0836979
31	105.03	103.874	104.208	-0.33401	1.15609
32	104.93	104.321	104.47	-0.14849	0.608906
33	104.73	104.786	104.724	0.0622396	-0.0564062
34	104.33	104.534	104.929	-0.394948	-0.204219
35	104.6	104.821	105.103	-0.282344	-0.220573
36	104.41	104.123	105.27	-1.14703	0.287031
37	104.63	104.588	105.422	-0.834948	0.0424479
38	105.55	105.756	105.58	0.175677	-0.205677
39	106.12	106.525	105.755	0.769115	-0.404531
40	106.62	106.468	105.947	0.520885	0.152031
41	106.72	107.022	106.195	0.82724	-0.301823
42	106.52	107.272	106.485	0.786615	-0.752031
43	106.79	106.461	106.795	-0.33401	0.329427
44	106.95	107.037	107.186	-0.14849	-0.0873437
45	106.92	107.711	107.648	0.0622396	-0.790573
46	106.74	107.673	108.067	-0.394948	-0.932552
47	108.13	108.246	108.529	-0.282344	-0.116406
48	107.86	107.985	109.132	-1.14703	-0.125469
49	108.6	108.835	109.67	-0.834948	-0.235052
50	110.97	110.289	110.113	0.175677	0.681406
51	111.8	111.337	110.568	0.769115	0.462552
52	111	111.465	110.944	0.520885	-0.464635
53	113.41	111.99	111.162	0.82724	1.42026
54	114.32	112.159	111.372	0.786615	2.1613
55	111.89	NA	NA	-0.33401	NA
56	112.48	NA	NA	-0.14849	NA
57	112.32	NA	NA	0.0622396	NA
58	110.35	NA	NA	-0.394948	NA
59	109.77	NA	NA	-0.282344	NA
60	111.25	NA	NA	-1.14703	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 92.42 & NA & NA & -0.834948 & NA \tabularnewline
2 & 92.64 & NA & NA & 0.175677 & NA \tabularnewline
3 & 94.44 & NA & NA & 0.769115 & NA \tabularnewline
4 & 93.59 & NA & NA & 0.520885 & NA \tabularnewline
5 & 93.39 & NA & NA & 0.82724 & NA \tabularnewline
6 & 93.33 & NA & NA & 0.786615 & NA \tabularnewline
7 & 93.72 & 94.7447 & 95.0788 & -0.33401 & -1.02474 \tabularnewline
8 & 95.43 & 95.4286 & 95.5771 & -0.14849 & 0.00140625 \tabularnewline
9 & 97.06 & 96.1735 & 96.1112 & 0.0622396 & 0.88651 \tabularnewline
10 & 97.7 & 96.233 & 96.6279 & -0.394948 & 1.46703 \tabularnewline
11 & 97.59 & 96.8589 & 97.1412 & -0.282344 & 0.731094 \tabularnewline
12 & 96.97 & 96.5151 & 97.6621 & -1.14703 & 0.454948 \tabularnewline
13 & 97.75 & 97.3455 & 98.1804 & -0.834948 & 0.404531 \tabularnewline
14 & 99.27 & 98.8061 & 98.6304 & 0.175677 & 0.463906 \tabularnewline
15 & 100.63 & 99.7662 & 98.9971 & 0.769115 & 0.863802 \tabularnewline
16 & 99.8 & 99.8105 & 99.2896 & 0.520885 & -0.0104688 \tabularnewline
17 & 99.5 & 100.388 & 99.5608 & 0.82724 & -0.888073 \tabularnewline
18 & 99.72 & 100.64 & 99.8537 & 0.786615 & -0.920365 \tabularnewline
19 & 99.77 & 99.8256 & 100.16 & -0.33401 & -0.0555729 \tabularnewline
20 & 100.18 & 100.298 & 100.446 & -0.14849 & -0.11776 \tabularnewline
21 & 101.11 & 100.744 & 100.682 & 0.0622396 & 0.365677 \tabularnewline
22 & 100.67 & 100.595 & 100.99 & -0.394948 & 0.0749479 \tabularnewline
23 & 101.13 & 101.119 & 101.401 & -0.282344 & 0.0110938 \tabularnewline
24 & 100.46 & 100.671 & 101.818 & -1.14703 & -0.211302 \tabularnewline
25 & 101.6 & 101.407 & 102.242 & -0.834948 & 0.193281 \tabularnewline
26 & 102.3 & 102.834 & 102.659 & 0.175677 & -0.534427 \tabularnewline
27 & 103.26 & 103.777 & 103.008 & 0.769115 & -0.516615 \tabularnewline
28 & 104.56 & 103.832 & 103.311 & 0.520885 & 0.728281 \tabularnewline
29 & 104.61 & 104.435 & 103.608 & 0.82724 & 0.174844 \tabularnewline
30 & 104.62 & 104.704 & 103.917 & 0.786615 & -0.0836979 \tabularnewline
31 & 105.03 & 103.874 & 104.208 & -0.33401 & 1.15609 \tabularnewline
32 & 104.93 & 104.321 & 104.47 & -0.14849 & 0.608906 \tabularnewline
33 & 104.73 & 104.786 & 104.724 & 0.0622396 & -0.0564062 \tabularnewline
34 & 104.33 & 104.534 & 104.929 & -0.394948 & -0.204219 \tabularnewline
35 & 104.6 & 104.821 & 105.103 & -0.282344 & -0.220573 \tabularnewline
36 & 104.41 & 104.123 & 105.27 & -1.14703 & 0.287031 \tabularnewline
37 & 104.63 & 104.588 & 105.422 & -0.834948 & 0.0424479 \tabularnewline
38 & 105.55 & 105.756 & 105.58 & 0.175677 & -0.205677 \tabularnewline
39 & 106.12 & 106.525 & 105.755 & 0.769115 & -0.404531 \tabularnewline
40 & 106.62 & 106.468 & 105.947 & 0.520885 & 0.152031 \tabularnewline
41 & 106.72 & 107.022 & 106.195 & 0.82724 & -0.301823 \tabularnewline
42 & 106.52 & 107.272 & 106.485 & 0.786615 & -0.752031 \tabularnewline
43 & 106.79 & 106.461 & 106.795 & -0.33401 & 0.329427 \tabularnewline
44 & 106.95 & 107.037 & 107.186 & -0.14849 & -0.0873437 \tabularnewline
45 & 106.92 & 107.711 & 107.648 & 0.0622396 & -0.790573 \tabularnewline
46 & 106.74 & 107.673 & 108.067 & -0.394948 & -0.932552 \tabularnewline
47 & 108.13 & 108.246 & 108.529 & -0.282344 & -0.116406 \tabularnewline
48 & 107.86 & 107.985 & 109.132 & -1.14703 & -0.125469 \tabularnewline
49 & 108.6 & 108.835 & 109.67 & -0.834948 & -0.235052 \tabularnewline
50 & 110.97 & 110.289 & 110.113 & 0.175677 & 0.681406 \tabularnewline
51 & 111.8 & 111.337 & 110.568 & 0.769115 & 0.462552 \tabularnewline
52 & 111 & 111.465 & 110.944 & 0.520885 & -0.464635 \tabularnewline
53 & 113.41 & 111.99 & 111.162 & 0.82724 & 1.42026 \tabularnewline
54 & 114.32 & 112.159 & 111.372 & 0.786615 & 2.1613 \tabularnewline
55 & 111.89 & NA & NA & -0.33401 & NA \tabularnewline
56 & 112.48 & NA & NA & -0.14849 & NA \tabularnewline
57 & 112.32 & NA & NA & 0.0622396 & NA \tabularnewline
58 & 110.35 & NA & NA & -0.394948 & NA \tabularnewline
59 & 109.77 & NA & NA & -0.282344 & NA \tabularnewline
60 & 111.25 & NA & NA & -1.14703 & 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]92.42[/C][C]NA[/C][C]NA[/C][C]-0.834948[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]92.64[/C][C]NA[/C][C]NA[/C][C]0.175677[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]94.44[/C][C]NA[/C][C]NA[/C][C]0.769115[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]93.59[/C][C]NA[/C][C]NA[/C][C]0.520885[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]93.39[/C][C]NA[/C][C]NA[/C][C]0.82724[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]93.33[/C][C]NA[/C][C]NA[/C][C]0.786615[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]93.72[/C][C]94.7447[/C][C]95.0788[/C][C]-0.33401[/C][C]-1.02474[/C][/ROW]
[ROW][C]8[/C][C]95.43[/C][C]95.4286[/C][C]95.5771[/C][C]-0.14849[/C][C]0.00140625[/C][/ROW]
[ROW][C]9[/C][C]97.06[/C][C]96.1735[/C][C]96.1112[/C][C]0.0622396[/C][C]0.88651[/C][/ROW]
[ROW][C]10[/C][C]97.7[/C][C]96.233[/C][C]96.6279[/C][C]-0.394948[/C][C]1.46703[/C][/ROW]
[ROW][C]11[/C][C]97.59[/C][C]96.8589[/C][C]97.1412[/C][C]-0.282344[/C][C]0.731094[/C][/ROW]
[ROW][C]12[/C][C]96.97[/C][C]96.5151[/C][C]97.6621[/C][C]-1.14703[/C][C]0.454948[/C][/ROW]
[ROW][C]13[/C][C]97.75[/C][C]97.3455[/C][C]98.1804[/C][C]-0.834948[/C][C]0.404531[/C][/ROW]
[ROW][C]14[/C][C]99.27[/C][C]98.8061[/C][C]98.6304[/C][C]0.175677[/C][C]0.463906[/C][/ROW]
[ROW][C]15[/C][C]100.63[/C][C]99.7662[/C][C]98.9971[/C][C]0.769115[/C][C]0.863802[/C][/ROW]
[ROW][C]16[/C][C]99.8[/C][C]99.8105[/C][C]99.2896[/C][C]0.520885[/C][C]-0.0104688[/C][/ROW]
[ROW][C]17[/C][C]99.5[/C][C]100.388[/C][C]99.5608[/C][C]0.82724[/C][C]-0.888073[/C][/ROW]
[ROW][C]18[/C][C]99.72[/C][C]100.64[/C][C]99.8537[/C][C]0.786615[/C][C]-0.920365[/C][/ROW]
[ROW][C]19[/C][C]99.77[/C][C]99.8256[/C][C]100.16[/C][C]-0.33401[/C][C]-0.0555729[/C][/ROW]
[ROW][C]20[/C][C]100.18[/C][C]100.298[/C][C]100.446[/C][C]-0.14849[/C][C]-0.11776[/C][/ROW]
[ROW][C]21[/C][C]101.11[/C][C]100.744[/C][C]100.682[/C][C]0.0622396[/C][C]0.365677[/C][/ROW]
[ROW][C]22[/C][C]100.67[/C][C]100.595[/C][C]100.99[/C][C]-0.394948[/C][C]0.0749479[/C][/ROW]
[ROW][C]23[/C][C]101.13[/C][C]101.119[/C][C]101.401[/C][C]-0.282344[/C][C]0.0110938[/C][/ROW]
[ROW][C]24[/C][C]100.46[/C][C]100.671[/C][C]101.818[/C][C]-1.14703[/C][C]-0.211302[/C][/ROW]
[ROW][C]25[/C][C]101.6[/C][C]101.407[/C][C]102.242[/C][C]-0.834948[/C][C]0.193281[/C][/ROW]
[ROW][C]26[/C][C]102.3[/C][C]102.834[/C][C]102.659[/C][C]0.175677[/C][C]-0.534427[/C][/ROW]
[ROW][C]27[/C][C]103.26[/C][C]103.777[/C][C]103.008[/C][C]0.769115[/C][C]-0.516615[/C][/ROW]
[ROW][C]28[/C][C]104.56[/C][C]103.832[/C][C]103.311[/C][C]0.520885[/C][C]0.728281[/C][/ROW]
[ROW][C]29[/C][C]104.61[/C][C]104.435[/C][C]103.608[/C][C]0.82724[/C][C]0.174844[/C][/ROW]
[ROW][C]30[/C][C]104.62[/C][C]104.704[/C][C]103.917[/C][C]0.786615[/C][C]-0.0836979[/C][/ROW]
[ROW][C]31[/C][C]105.03[/C][C]103.874[/C][C]104.208[/C][C]-0.33401[/C][C]1.15609[/C][/ROW]
[ROW][C]32[/C][C]104.93[/C][C]104.321[/C][C]104.47[/C][C]-0.14849[/C][C]0.608906[/C][/ROW]
[ROW][C]33[/C][C]104.73[/C][C]104.786[/C][C]104.724[/C][C]0.0622396[/C][C]-0.0564062[/C][/ROW]
[ROW][C]34[/C][C]104.33[/C][C]104.534[/C][C]104.929[/C][C]-0.394948[/C][C]-0.204219[/C][/ROW]
[ROW][C]35[/C][C]104.6[/C][C]104.821[/C][C]105.103[/C][C]-0.282344[/C][C]-0.220573[/C][/ROW]
[ROW][C]36[/C][C]104.41[/C][C]104.123[/C][C]105.27[/C][C]-1.14703[/C][C]0.287031[/C][/ROW]
[ROW][C]37[/C][C]104.63[/C][C]104.588[/C][C]105.422[/C][C]-0.834948[/C][C]0.0424479[/C][/ROW]
[ROW][C]38[/C][C]105.55[/C][C]105.756[/C][C]105.58[/C][C]0.175677[/C][C]-0.205677[/C][/ROW]
[ROW][C]39[/C][C]106.12[/C][C]106.525[/C][C]105.755[/C][C]0.769115[/C][C]-0.404531[/C][/ROW]
[ROW][C]40[/C][C]106.62[/C][C]106.468[/C][C]105.947[/C][C]0.520885[/C][C]0.152031[/C][/ROW]
[ROW][C]41[/C][C]106.72[/C][C]107.022[/C][C]106.195[/C][C]0.82724[/C][C]-0.301823[/C][/ROW]
[ROW][C]42[/C][C]106.52[/C][C]107.272[/C][C]106.485[/C][C]0.786615[/C][C]-0.752031[/C][/ROW]
[ROW][C]43[/C][C]106.79[/C][C]106.461[/C][C]106.795[/C][C]-0.33401[/C][C]0.329427[/C][/ROW]
[ROW][C]44[/C][C]106.95[/C][C]107.037[/C][C]107.186[/C][C]-0.14849[/C][C]-0.0873437[/C][/ROW]
[ROW][C]45[/C][C]106.92[/C][C]107.711[/C][C]107.648[/C][C]0.0622396[/C][C]-0.790573[/C][/ROW]
[ROW][C]46[/C][C]106.74[/C][C]107.673[/C][C]108.067[/C][C]-0.394948[/C][C]-0.932552[/C][/ROW]
[ROW][C]47[/C][C]108.13[/C][C]108.246[/C][C]108.529[/C][C]-0.282344[/C][C]-0.116406[/C][/ROW]
[ROW][C]48[/C][C]107.86[/C][C]107.985[/C][C]109.132[/C][C]-1.14703[/C][C]-0.125469[/C][/ROW]
[ROW][C]49[/C][C]108.6[/C][C]108.835[/C][C]109.67[/C][C]-0.834948[/C][C]-0.235052[/C][/ROW]
[ROW][C]50[/C][C]110.97[/C][C]110.289[/C][C]110.113[/C][C]0.175677[/C][C]0.681406[/C][/ROW]
[ROW][C]51[/C][C]111.8[/C][C]111.337[/C][C]110.568[/C][C]0.769115[/C][C]0.462552[/C][/ROW]
[ROW][C]52[/C][C]111[/C][C]111.465[/C][C]110.944[/C][C]0.520885[/C][C]-0.464635[/C][/ROW]
[ROW][C]53[/C][C]113.41[/C][C]111.99[/C][C]111.162[/C][C]0.82724[/C][C]1.42026[/C][/ROW]
[ROW][C]54[/C][C]114.32[/C][C]112.159[/C][C]111.372[/C][C]0.786615[/C][C]2.1613[/C][/ROW]
[ROW][C]55[/C][C]111.89[/C][C]NA[/C][C]NA[/C][C]-0.33401[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]112.48[/C][C]NA[/C][C]NA[/C][C]-0.14849[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]112.32[/C][C]NA[/C][C]NA[/C][C]0.0622396[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]110.35[/C][C]NA[/C][C]NA[/C][C]-0.394948[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]109.77[/C][C]NA[/C][C]NA[/C][C]-0.282344[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]111.25[/C][C]NA[/C][C]NA[/C][C]-1.14703[/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	92.42	NA	NA	-0.834948	NA
2	92.64	NA	NA	0.175677	NA
3	94.44	NA	NA	0.769115	NA
4	93.59	NA	NA	0.520885	NA
5	93.39	NA	NA	0.82724	NA
6	93.33	NA	NA	0.786615	NA
7	93.72	94.7447	95.0788	-0.33401	-1.02474
8	95.43	95.4286	95.5771	-0.14849	0.00140625
9	97.06	96.1735	96.1112	0.0622396	0.88651
10	97.7	96.233	96.6279	-0.394948	1.46703
11	97.59	96.8589	97.1412	-0.282344	0.731094
12	96.97	96.5151	97.6621	-1.14703	0.454948
13	97.75	97.3455	98.1804	-0.834948	0.404531
14	99.27	98.8061	98.6304	0.175677	0.463906
15	100.63	99.7662	98.9971	0.769115	0.863802
16	99.8	99.8105	99.2896	0.520885	-0.0104688
17	99.5	100.388	99.5608	0.82724	-0.888073
18	99.72	100.64	99.8537	0.786615	-0.920365
19	99.77	99.8256	100.16	-0.33401	-0.0555729
20	100.18	100.298	100.446	-0.14849	-0.11776
21	101.11	100.744	100.682	0.0622396	0.365677
22	100.67	100.595	100.99	-0.394948	0.0749479
23	101.13	101.119	101.401	-0.282344	0.0110938
24	100.46	100.671	101.818	-1.14703	-0.211302
25	101.6	101.407	102.242	-0.834948	0.193281
26	102.3	102.834	102.659	0.175677	-0.534427
27	103.26	103.777	103.008	0.769115	-0.516615
28	104.56	103.832	103.311	0.520885	0.728281
29	104.61	104.435	103.608	0.82724	0.174844
30	104.62	104.704	103.917	0.786615	-0.0836979
31	105.03	103.874	104.208	-0.33401	1.15609
32	104.93	104.321	104.47	-0.14849	0.608906
33	104.73	104.786	104.724	0.0622396	-0.0564062
34	104.33	104.534	104.929	-0.394948	-0.204219
35	104.6	104.821	105.103	-0.282344	-0.220573
36	104.41	104.123	105.27	-1.14703	0.287031
37	104.63	104.588	105.422	-0.834948	0.0424479
38	105.55	105.756	105.58	0.175677	-0.205677
39	106.12	106.525	105.755	0.769115	-0.404531
40	106.62	106.468	105.947	0.520885	0.152031
41	106.72	107.022	106.195	0.82724	-0.301823
42	106.52	107.272	106.485	0.786615	-0.752031
43	106.79	106.461	106.795	-0.33401	0.329427
44	106.95	107.037	107.186	-0.14849	-0.0873437
45	106.92	107.711	107.648	0.0622396	-0.790573
46	106.74	107.673	108.067	-0.394948	-0.932552
47	108.13	108.246	108.529	-0.282344	-0.116406
48	107.86	107.985	109.132	-1.14703	-0.125469
49	108.6	108.835	109.67	-0.834948	-0.235052
50	110.97	110.289	110.113	0.175677	0.681406
51	111.8	111.337	110.568	0.769115	0.462552
52	111	111.465	110.944	0.520885	-0.464635
53	113.41	111.99	111.162	0.82724	1.42026
54	114.32	112.159	111.372	0.786615	2.1613
55	111.89	NA	NA	-0.33401	NA
56	112.48	NA	NA	-0.14849	NA
57	112.32	NA	NA	0.0622396	NA
58	110.35	NA	NA	-0.394948	NA
59	109.77	NA	NA	-0.282344	NA
60	111.25	NA	NA	-1.14703	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