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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Sun, 24 Apr 2016 19:51:54 +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/2016/Apr/24/t1461524021i2rlrsqag4miu9p.htm/, Retrieved Tue, 30 Apr 2024 18:49:28 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294655, Retrieved Tue, 30 Apr 2024 18:49:28 +0000

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Estimated Impact

119

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-       [Classical Decomposition] [Classical Decompo...] [2016-04-24 18:51:54] [214f5f03d61b6cc2dcf3be3cf135b694] [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	'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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294655&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294655&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294655&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	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	78.21	NA	NA	-0.573403	NA
2	75.5	NA	NA	3.88326	NA
3	79.87	NA	NA	5.50451	NA
4	85.76	NA	NA	6.2291	NA
5	77.02	NA	NA	0.496701	NA
6	75.47	NA	NA	-2.23924	NA
7	75.29	78.0922	81.1967	-3.10444	-2.80222
8	77.52	81.437	83.1729	-1.7359	-3.91701
9	78.44	83.9755	85.8171	-1.84163	-5.53545
10	83.5	86.5514	88.7779	-2.22653	-3.05139
11	86.29	89.3387	91.8542	-2.51549	-3.04868
12	92.14	93.1356	95.0125	-1.87694	-0.995556
13	96.91	97.7741	98.3475	-0.573403	-0.864097
14	104.23	105.317	101.433	3.88326	-1.0866
15	114.6	109.595	104.091	5.50451	5.00465
16	122.09	112.682	106.453	6.2291	9.40757
17	114.52	109.01	108.513	0.496701	5.50997
18	113.77	107.936	110.175	-2.23924	5.83424
19	117.03	108.322	111.426	-3.10444	8.70819
20	109.84	110.906	112.642	-1.7359	-1.06618
21	109.9	111.833	113.674	-1.84163	-1.93253
22	108.74	111.8	114.026	-2.22653	-3.05972
23	110.49	111.272	113.788	-2.51549	-0.782014
24	107.82	110.991	112.867	-1.87694	-3.17056
25	111.26	110.955	111.528	-0.573403	0.305486
26	119.06	114.933	111.05	3.88326	4.12674
27	124.54	116.803	111.299	5.50451	7.73674
28	120.6	117.772	111.543	6.2291	2.82757
29	110.28	112.115	111.619	0.496701	-1.83545
30	95.93	109.397	111.636	-2.23924	-13.4666
31	102.72	108.631	111.736	-3.10444	-5.91139
32	112.68	109.92	111.656	-1.7359	2.76007
33	113.03	109.069	110.91	-1.84163	3.96122
34	111.48	107.606	109.832	-2.22653	3.87444
35	109.56	106.567	109.083	-2.51549	2.99257
36	109.16	107.222	109.099	-1.87694	1.93819
37	112.32	109.027	109.601	-0.573403	3.29257
38	116.08	113.585	109.702	3.88326	2.49465
39	109.63	115.039	109.535	5.50451	-5.4091
40	109.63	115.604	109.375	6.2291	-5.9741
41	103.27	109.719	109.222	0.496701	-6.4492
42	103.32	106.975	109.214	-2.23924	-3.65493
43	107.38	105.96	109.065	-3.10444	1.41986
44	110.45	106.812	108.548	-1.7359	3.63757
45	111.24	106.319	108.16	-1.84163	4.92122
46	109.44	105.789	108.015	-2.22653	3.65111
47	107.94	105.687	108.203	-2.51549	2.25257
48	110.58	106.938	108.815	-1.87694	3.64236
49	107.31	108.63	109.203	-0.573403	-1.31951
50	108.7	112.82	108.937	3.88326	-4.12035
51	107.7	113.618	108.113	5.50451	-5.91785
52	108.08	112.927	106.698	6.2291	-4.8466
53	109.32	105.131	104.634	0.496701	4.18913
54	111.95	99.2483	101.487	-2.23924	12.7017
55	108.07	NA	NA	-3.10444	NA
56	103.38	NA	NA	-1.7359	NA
57	98.54	NA	NA	-1.84163	NA
58	88.16	NA	NA	-2.22653	NA
59	79.7	NA	NA	-2.51549	NA
60	63.3	NA	NA	-1.87694	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 78.21 & NA & NA & -0.573403 & NA \tabularnewline
2 & 75.5 & NA & NA & 3.88326 & NA \tabularnewline
3 & 79.87 & NA & NA & 5.50451 & NA \tabularnewline
4 & 85.76 & NA & NA & 6.2291 & NA \tabularnewline
5 & 77.02 & NA & NA & 0.496701 & NA \tabularnewline
6 & 75.47 & NA & NA & -2.23924 & NA \tabularnewline
7 & 75.29 & 78.0922 & 81.1967 & -3.10444 & -2.80222 \tabularnewline
8 & 77.52 & 81.437 & 83.1729 & -1.7359 & -3.91701 \tabularnewline
9 & 78.44 & 83.9755 & 85.8171 & -1.84163 & -5.53545 \tabularnewline
10 & 83.5 & 86.5514 & 88.7779 & -2.22653 & -3.05139 \tabularnewline
11 & 86.29 & 89.3387 & 91.8542 & -2.51549 & -3.04868 \tabularnewline
12 & 92.14 & 93.1356 & 95.0125 & -1.87694 & -0.995556 \tabularnewline
13 & 96.91 & 97.7741 & 98.3475 & -0.573403 & -0.864097 \tabularnewline
14 & 104.23 & 105.317 & 101.433 & 3.88326 & -1.0866 \tabularnewline
15 & 114.6 & 109.595 & 104.091 & 5.50451 & 5.00465 \tabularnewline
16 & 122.09 & 112.682 & 106.453 & 6.2291 & 9.40757 \tabularnewline
17 & 114.52 & 109.01 & 108.513 & 0.496701 & 5.50997 \tabularnewline
18 & 113.77 & 107.936 & 110.175 & -2.23924 & 5.83424 \tabularnewline
19 & 117.03 & 108.322 & 111.426 & -3.10444 & 8.70819 \tabularnewline
20 & 109.84 & 110.906 & 112.642 & -1.7359 & -1.06618 \tabularnewline
21 & 109.9 & 111.833 & 113.674 & -1.84163 & -1.93253 \tabularnewline
22 & 108.74 & 111.8 & 114.026 & -2.22653 & -3.05972 \tabularnewline
23 & 110.49 & 111.272 & 113.788 & -2.51549 & -0.782014 \tabularnewline
24 & 107.82 & 110.991 & 112.867 & -1.87694 & -3.17056 \tabularnewline
25 & 111.26 & 110.955 & 111.528 & -0.573403 & 0.305486 \tabularnewline
26 & 119.06 & 114.933 & 111.05 & 3.88326 & 4.12674 \tabularnewline
27 & 124.54 & 116.803 & 111.299 & 5.50451 & 7.73674 \tabularnewline
28 & 120.6 & 117.772 & 111.543 & 6.2291 & 2.82757 \tabularnewline
29 & 110.28 & 112.115 & 111.619 & 0.496701 & -1.83545 \tabularnewline
30 & 95.93 & 109.397 & 111.636 & -2.23924 & -13.4666 \tabularnewline
31 & 102.72 & 108.631 & 111.736 & -3.10444 & -5.91139 \tabularnewline
32 & 112.68 & 109.92 & 111.656 & -1.7359 & 2.76007 \tabularnewline
33 & 113.03 & 109.069 & 110.91 & -1.84163 & 3.96122 \tabularnewline
34 & 111.48 & 107.606 & 109.832 & -2.22653 & 3.87444 \tabularnewline
35 & 109.56 & 106.567 & 109.083 & -2.51549 & 2.99257 \tabularnewline
36 & 109.16 & 107.222 & 109.099 & -1.87694 & 1.93819 \tabularnewline
37 & 112.32 & 109.027 & 109.601 & -0.573403 & 3.29257 \tabularnewline
38 & 116.08 & 113.585 & 109.702 & 3.88326 & 2.49465 \tabularnewline
39 & 109.63 & 115.039 & 109.535 & 5.50451 & -5.4091 \tabularnewline
40 & 109.63 & 115.604 & 109.375 & 6.2291 & -5.9741 \tabularnewline
41 & 103.27 & 109.719 & 109.222 & 0.496701 & -6.4492 \tabularnewline
42 & 103.32 & 106.975 & 109.214 & -2.23924 & -3.65493 \tabularnewline
43 & 107.38 & 105.96 & 109.065 & -3.10444 & 1.41986 \tabularnewline
44 & 110.45 & 106.812 & 108.548 & -1.7359 & 3.63757 \tabularnewline
45 & 111.24 & 106.319 & 108.16 & -1.84163 & 4.92122 \tabularnewline
46 & 109.44 & 105.789 & 108.015 & -2.22653 & 3.65111 \tabularnewline
47 & 107.94 & 105.687 & 108.203 & -2.51549 & 2.25257 \tabularnewline
48 & 110.58 & 106.938 & 108.815 & -1.87694 & 3.64236 \tabularnewline
49 & 107.31 & 108.63 & 109.203 & -0.573403 & -1.31951 \tabularnewline
50 & 108.7 & 112.82 & 108.937 & 3.88326 & -4.12035 \tabularnewline
51 & 107.7 & 113.618 & 108.113 & 5.50451 & -5.91785 \tabularnewline
52 & 108.08 & 112.927 & 106.698 & 6.2291 & -4.8466 \tabularnewline
53 & 109.32 & 105.131 & 104.634 & 0.496701 & 4.18913 \tabularnewline
54 & 111.95 & 99.2483 & 101.487 & -2.23924 & 12.7017 \tabularnewline
55 & 108.07 & NA & NA & -3.10444 & NA \tabularnewline
56 & 103.38 & NA & NA & -1.7359 & NA \tabularnewline
57 & 98.54 & NA & NA & -1.84163 & NA \tabularnewline
58 & 88.16 & NA & NA & -2.22653 & NA \tabularnewline
59 & 79.7 & NA & NA & -2.51549 & NA \tabularnewline
60 & 63.3 & NA & NA & -1.87694 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294655&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]78.21[/C][C]NA[/C][C]NA[/C][C]-0.573403[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]75.5[/C][C]NA[/C][C]NA[/C][C]3.88326[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]79.87[/C][C]NA[/C][C]NA[/C][C]5.50451[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]85.76[/C][C]NA[/C][C]NA[/C][C]6.2291[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]77.02[/C][C]NA[/C][C]NA[/C][C]0.496701[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]75.47[/C][C]NA[/C][C]NA[/C][C]-2.23924[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]75.29[/C][C]78.0922[/C][C]81.1967[/C][C]-3.10444[/C][C]-2.80222[/C][/ROW]
[ROW][C]8[/C][C]77.52[/C][C]81.437[/C][C]83.1729[/C][C]-1.7359[/C][C]-3.91701[/C][/ROW]
[ROW][C]9[/C][C]78.44[/C][C]83.9755[/C][C]85.8171[/C][C]-1.84163[/C][C]-5.53545[/C][/ROW]
[ROW][C]10[/C][C]83.5[/C][C]86.5514[/C][C]88.7779[/C][C]-2.22653[/C][C]-3.05139[/C][/ROW]
[ROW][C]11[/C][C]86.29[/C][C]89.3387[/C][C]91.8542[/C][C]-2.51549[/C][C]-3.04868[/C][/ROW]
[ROW][C]12[/C][C]92.14[/C][C]93.1356[/C][C]95.0125[/C][C]-1.87694[/C][C]-0.995556[/C][/ROW]
[ROW][C]13[/C][C]96.91[/C][C]97.7741[/C][C]98.3475[/C][C]-0.573403[/C][C]-0.864097[/C][/ROW]
[ROW][C]14[/C][C]104.23[/C][C]105.317[/C][C]101.433[/C][C]3.88326[/C][C]-1.0866[/C][/ROW]
[ROW][C]15[/C][C]114.6[/C][C]109.595[/C][C]104.091[/C][C]5.50451[/C][C]5.00465[/C][/ROW]
[ROW][C]16[/C][C]122.09[/C][C]112.682[/C][C]106.453[/C][C]6.2291[/C][C]9.40757[/C][/ROW]
[ROW][C]17[/C][C]114.52[/C][C]109.01[/C][C]108.513[/C][C]0.496701[/C][C]5.50997[/C][/ROW]
[ROW][C]18[/C][C]113.77[/C][C]107.936[/C][C]110.175[/C][C]-2.23924[/C][C]5.83424[/C][/ROW]
[ROW][C]19[/C][C]117.03[/C][C]108.322[/C][C]111.426[/C][C]-3.10444[/C][C]8.70819[/C][/ROW]
[ROW][C]20[/C][C]109.84[/C][C]110.906[/C][C]112.642[/C][C]-1.7359[/C][C]-1.06618[/C][/ROW]
[ROW][C]21[/C][C]109.9[/C][C]111.833[/C][C]113.674[/C][C]-1.84163[/C][C]-1.93253[/C][/ROW]
[ROW][C]22[/C][C]108.74[/C][C]111.8[/C][C]114.026[/C][C]-2.22653[/C][C]-3.05972[/C][/ROW]
[ROW][C]23[/C][C]110.49[/C][C]111.272[/C][C]113.788[/C][C]-2.51549[/C][C]-0.782014[/C][/ROW]
[ROW][C]24[/C][C]107.82[/C][C]110.991[/C][C]112.867[/C][C]-1.87694[/C][C]-3.17056[/C][/ROW]
[ROW][C]25[/C][C]111.26[/C][C]110.955[/C][C]111.528[/C][C]-0.573403[/C][C]0.305486[/C][/ROW]
[ROW][C]26[/C][C]119.06[/C][C]114.933[/C][C]111.05[/C][C]3.88326[/C][C]4.12674[/C][/ROW]
[ROW][C]27[/C][C]124.54[/C][C]116.803[/C][C]111.299[/C][C]5.50451[/C][C]7.73674[/C][/ROW]
[ROW][C]28[/C][C]120.6[/C][C]117.772[/C][C]111.543[/C][C]6.2291[/C][C]2.82757[/C][/ROW]
[ROW][C]29[/C][C]110.28[/C][C]112.115[/C][C]111.619[/C][C]0.496701[/C][C]-1.83545[/C][/ROW]
[ROW][C]30[/C][C]95.93[/C][C]109.397[/C][C]111.636[/C][C]-2.23924[/C][C]-13.4666[/C][/ROW]
[ROW][C]31[/C][C]102.72[/C][C]108.631[/C][C]111.736[/C][C]-3.10444[/C][C]-5.91139[/C][/ROW]
[ROW][C]32[/C][C]112.68[/C][C]109.92[/C][C]111.656[/C][C]-1.7359[/C][C]2.76007[/C][/ROW]
[ROW][C]33[/C][C]113.03[/C][C]109.069[/C][C]110.91[/C][C]-1.84163[/C][C]3.96122[/C][/ROW]
[ROW][C]34[/C][C]111.48[/C][C]107.606[/C][C]109.832[/C][C]-2.22653[/C][C]3.87444[/C][/ROW]
[ROW][C]35[/C][C]109.56[/C][C]106.567[/C][C]109.083[/C][C]-2.51549[/C][C]2.99257[/C][/ROW]
[ROW][C]36[/C][C]109.16[/C][C]107.222[/C][C]109.099[/C][C]-1.87694[/C][C]1.93819[/C][/ROW]
[ROW][C]37[/C][C]112.32[/C][C]109.027[/C][C]109.601[/C][C]-0.573403[/C][C]3.29257[/C][/ROW]
[ROW][C]38[/C][C]116.08[/C][C]113.585[/C][C]109.702[/C][C]3.88326[/C][C]2.49465[/C][/ROW]
[ROW][C]39[/C][C]109.63[/C][C]115.039[/C][C]109.535[/C][C]5.50451[/C][C]-5.4091[/C][/ROW]
[ROW][C]40[/C][C]109.63[/C][C]115.604[/C][C]109.375[/C][C]6.2291[/C][C]-5.9741[/C][/ROW]
[ROW][C]41[/C][C]103.27[/C][C]109.719[/C][C]109.222[/C][C]0.496701[/C][C]-6.4492[/C][/ROW]
[ROW][C]42[/C][C]103.32[/C][C]106.975[/C][C]109.214[/C][C]-2.23924[/C][C]-3.65493[/C][/ROW]
[ROW][C]43[/C][C]107.38[/C][C]105.96[/C][C]109.065[/C][C]-3.10444[/C][C]1.41986[/C][/ROW]
[ROW][C]44[/C][C]110.45[/C][C]106.812[/C][C]108.548[/C][C]-1.7359[/C][C]3.63757[/C][/ROW]
[ROW][C]45[/C][C]111.24[/C][C]106.319[/C][C]108.16[/C][C]-1.84163[/C][C]4.92122[/C][/ROW]
[ROW][C]46[/C][C]109.44[/C][C]105.789[/C][C]108.015[/C][C]-2.22653[/C][C]3.65111[/C][/ROW]
[ROW][C]47[/C][C]107.94[/C][C]105.687[/C][C]108.203[/C][C]-2.51549[/C][C]2.25257[/C][/ROW]
[ROW][C]48[/C][C]110.58[/C][C]106.938[/C][C]108.815[/C][C]-1.87694[/C][C]3.64236[/C][/ROW]
[ROW][C]49[/C][C]107.31[/C][C]108.63[/C][C]109.203[/C][C]-0.573403[/C][C]-1.31951[/C][/ROW]
[ROW][C]50[/C][C]108.7[/C][C]112.82[/C][C]108.937[/C][C]3.88326[/C][C]-4.12035[/C][/ROW]
[ROW][C]51[/C][C]107.7[/C][C]113.618[/C][C]108.113[/C][C]5.50451[/C][C]-5.91785[/C][/ROW]
[ROW][C]52[/C][C]108.08[/C][C]112.927[/C][C]106.698[/C][C]6.2291[/C][C]-4.8466[/C][/ROW]
[ROW][C]53[/C][C]109.32[/C][C]105.131[/C][C]104.634[/C][C]0.496701[/C][C]4.18913[/C][/ROW]
[ROW][C]54[/C][C]111.95[/C][C]99.2483[/C][C]101.487[/C][C]-2.23924[/C][C]12.7017[/C][/ROW]
[ROW][C]55[/C][C]108.07[/C][C]NA[/C][C]NA[/C][C]-3.10444[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]103.38[/C][C]NA[/C][C]NA[/C][C]-1.7359[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]98.54[/C][C]NA[/C][C]NA[/C][C]-1.84163[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]88.16[/C][C]NA[/C][C]NA[/C][C]-2.22653[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]79.7[/C][C]NA[/C][C]NA[/C][C]-2.51549[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]63.3[/C][C]NA[/C][C]NA[/C][C]-1.87694[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294655&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294655&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	78.21	NA	NA	-0.573403	NA
2	75.5	NA	NA	3.88326	NA
3	79.87	NA	NA	5.50451	NA
4	85.76	NA	NA	6.2291	NA
5	77.02	NA	NA	0.496701	NA
6	75.47	NA	NA	-2.23924	NA
7	75.29	78.0922	81.1967	-3.10444	-2.80222
8	77.52	81.437	83.1729	-1.7359	-3.91701
9	78.44	83.9755	85.8171	-1.84163	-5.53545
10	83.5	86.5514	88.7779	-2.22653	-3.05139
11	86.29	89.3387	91.8542	-2.51549	-3.04868
12	92.14	93.1356	95.0125	-1.87694	-0.995556
13	96.91	97.7741	98.3475	-0.573403	-0.864097
14	104.23	105.317	101.433	3.88326	-1.0866
15	114.6	109.595	104.091	5.50451	5.00465
16	122.09	112.682	106.453	6.2291	9.40757
17	114.52	109.01	108.513	0.496701	5.50997
18	113.77	107.936	110.175	-2.23924	5.83424
19	117.03	108.322	111.426	-3.10444	8.70819
20	109.84	110.906	112.642	-1.7359	-1.06618
21	109.9	111.833	113.674	-1.84163	-1.93253
22	108.74	111.8	114.026	-2.22653	-3.05972
23	110.49	111.272	113.788	-2.51549	-0.782014
24	107.82	110.991	112.867	-1.87694	-3.17056
25	111.26	110.955	111.528	-0.573403	0.305486
26	119.06	114.933	111.05	3.88326	4.12674
27	124.54	116.803	111.299	5.50451	7.73674
28	120.6	117.772	111.543	6.2291	2.82757
29	110.28	112.115	111.619	0.496701	-1.83545
30	95.93	109.397	111.636	-2.23924	-13.4666
31	102.72	108.631	111.736	-3.10444	-5.91139
32	112.68	109.92	111.656	-1.7359	2.76007
33	113.03	109.069	110.91	-1.84163	3.96122
34	111.48	107.606	109.832	-2.22653	3.87444
35	109.56	106.567	109.083	-2.51549	2.99257
36	109.16	107.222	109.099	-1.87694	1.93819
37	112.32	109.027	109.601	-0.573403	3.29257
38	116.08	113.585	109.702	3.88326	2.49465
39	109.63	115.039	109.535	5.50451	-5.4091
40	109.63	115.604	109.375	6.2291	-5.9741
41	103.27	109.719	109.222	0.496701	-6.4492
42	103.32	106.975	109.214	-2.23924	-3.65493
43	107.38	105.96	109.065	-3.10444	1.41986
44	110.45	106.812	108.548	-1.7359	3.63757
45	111.24	106.319	108.16	-1.84163	4.92122
46	109.44	105.789	108.015	-2.22653	3.65111
47	107.94	105.687	108.203	-2.51549	2.25257
48	110.58	106.938	108.815	-1.87694	3.64236
49	107.31	108.63	109.203	-0.573403	-1.31951
50	108.7	112.82	108.937	3.88326	-4.12035
51	107.7	113.618	108.113	5.50451	-5.91785
52	108.08	112.927	106.698	6.2291	-4.8466
53	109.32	105.131	104.634	0.496701	4.18913
54	111.95	99.2483	101.487	-2.23924	12.7017
55	108.07	NA	NA	-3.10444	NA
56	103.38	NA	NA	-1.7359	NA
57	98.54	NA	NA	-1.84163	NA
58	88.16	NA	NA	-2.22653	NA
59	79.7	NA	NA	-2.51549	NA
60	63.3	NA	NA	-1.87694	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 <- '12'
par1 <- 'additive'
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