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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Tue, 26 Apr 2016 17:01:06 +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/26/t1461686490zssviybg4xz93sn.htm/, Retrieved Fri, 03 May 2024 17:51:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294911, Retrieved Fri, 03 May 2024 17:51:57 +0000

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-       [Classical Decomposition] [Moerman Nicola�] [2016-04-26 16:01:06] [ab100cc47aff291ae023e643a55282f8] [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=294911&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=294911&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294911&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	96.4	NA	NA	-0.00529514	NA
2	96.9	NA	NA	0.801997	NA
3	98.1	NA	NA	1.09991	NA
4	99.2	NA	NA	0.856163	NA
5	100	NA	NA	0.334288	NA
6	100.3	NA	NA	0.0561632	NA
7	100.3	100.247	100.383	-0.136545	0.0532118
8	100.8	100.783	101.208	-0.425087	0.0167535
9	101.3	101.791	102.104	-0.313628	-0.490538
10	101.4	102.267	103.029	-0.762587	-0.86658
11	101.9	102.975	103.908	-0.93342	-1.07491
12	103.4	104.132	104.704	-0.571962	-0.732205
13	105.6	105.474	105.479	-0.00529514	0.126128
14	107.5	107.002	106.2	0.801997	0.498003
15	109	107.971	106.871	1.09991	1.02925
16	110.5	108.385	107.529	0.856163	2.11467
17	109.8	108.48	108.146	0.334288	1.31988
18	109.6	108.739	108.683	0.0561632	0.860503
19	109.6	108.993	109.129	-0.136545	0.607378
20	108.8	109.104	109.529	-0.425087	-0.30408
21	109.4	109.586	109.9	-0.313628	-0.186372
22	109.1	109.442	110.204	-0.762587	-0.34158
23	109	109.542	110.475	-0.93342	-0.54158
24	109.2	110.153	110.725	-0.571962	-0.953038
25	110.5	110.936	110.942	-0.00529514	-0.436372
26	112.2	112.035	111.233	0.801997	0.16467
27	113.2	112.7	111.6	1.09991	0.500087
28	113.6	112.835	111.979	0.856163	0.76467
29	113.2	112.705	112.371	0.334288	0.494878
30	112.2	112.819	112.762	0.0561632	-0.618663
31	112.2	112.968	113.104	-0.136545	-0.767622
32	113.2	112.912	113.337	-0.425087	0.287587
33	113.8	113.157	113.471	-0.313628	0.642795
34	113.8	112.721	113.483	-0.762587	1.07925
35	113.7	112.479	113.412	-0.93342	1.22092
36	113.9	112.803	113.375	-0.571962	1.09696
37	114	113.386	113.392	-0.00529514	0.613628
38	114.3	114.156	113.354	0.801997	0.143837
39	114.3	114.312	113.212	1.09991	-0.0124132
40	112.8	113.864	113.008	0.856163	-1.0645
41	112.3	113.105	112.771	0.334288	-0.805122
42	112.2	112.589	112.533	0.0561632	-0.389497
43	112.6	112.134	112.271	-0.136545	0.465712
44	111.9	111.542	111.967	-0.425087	0.35842
45	111.7	111.307	111.621	-0.313628	0.392795
46	111	110.512	111.275	-0.762587	0.487587
47	110.8	110.046	110.979	-0.93342	0.754253
48	111.1	110.153	110.725	-0.571962	0.946962
49	110.5	110.445	110.45	-0.00529514	0.0552951
50	110.5	110.948	110.146	0.801997	-0.44783
51	109.8	110.958	109.858	1.09991	-1.15825
52	109	110.456	109.6	0.856163	-1.45616
53	109	109.651	109.317	0.334288	-0.650955
54	109.4	108.894	108.837	0.0561632	0.506337
55	108.8	NA	NA	-0.136545	NA
56	108.4	NA	NA	-0.425087	NA
57	108.3	NA	NA	-0.313628	NA
58	108.2	NA	NA	-0.762587	NA
59	106.8	NA	NA	-0.93342	NA
60	103.6	NA	NA	-0.571962	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 96.4 & NA & NA & -0.00529514 & NA \tabularnewline
2 & 96.9 & NA & NA & 0.801997 & NA \tabularnewline
3 & 98.1 & NA & NA & 1.09991 & NA \tabularnewline
4 & 99.2 & NA & NA & 0.856163 & NA \tabularnewline
5 & 100 & NA & NA & 0.334288 & NA \tabularnewline
6 & 100.3 & NA & NA & 0.0561632 & NA \tabularnewline
7 & 100.3 & 100.247 & 100.383 & -0.136545 & 0.0532118 \tabularnewline
8 & 100.8 & 100.783 & 101.208 & -0.425087 & 0.0167535 \tabularnewline
9 & 101.3 & 101.791 & 102.104 & -0.313628 & -0.490538 \tabularnewline
10 & 101.4 & 102.267 & 103.029 & -0.762587 & -0.86658 \tabularnewline
11 & 101.9 & 102.975 & 103.908 & -0.93342 & -1.07491 \tabularnewline
12 & 103.4 & 104.132 & 104.704 & -0.571962 & -0.732205 \tabularnewline
13 & 105.6 & 105.474 & 105.479 & -0.00529514 & 0.126128 \tabularnewline
14 & 107.5 & 107.002 & 106.2 & 0.801997 & 0.498003 \tabularnewline
15 & 109 & 107.971 & 106.871 & 1.09991 & 1.02925 \tabularnewline
16 & 110.5 & 108.385 & 107.529 & 0.856163 & 2.11467 \tabularnewline
17 & 109.8 & 108.48 & 108.146 & 0.334288 & 1.31988 \tabularnewline
18 & 109.6 & 108.739 & 108.683 & 0.0561632 & 0.860503 \tabularnewline
19 & 109.6 & 108.993 & 109.129 & -0.136545 & 0.607378 \tabularnewline
20 & 108.8 & 109.104 & 109.529 & -0.425087 & -0.30408 \tabularnewline
21 & 109.4 & 109.586 & 109.9 & -0.313628 & -0.186372 \tabularnewline
22 & 109.1 & 109.442 & 110.204 & -0.762587 & -0.34158 \tabularnewline
23 & 109 & 109.542 & 110.475 & -0.93342 & -0.54158 \tabularnewline
24 & 109.2 & 110.153 & 110.725 & -0.571962 & -0.953038 \tabularnewline
25 & 110.5 & 110.936 & 110.942 & -0.00529514 & -0.436372 \tabularnewline
26 & 112.2 & 112.035 & 111.233 & 0.801997 & 0.16467 \tabularnewline
27 & 113.2 & 112.7 & 111.6 & 1.09991 & 0.500087 \tabularnewline
28 & 113.6 & 112.835 & 111.979 & 0.856163 & 0.76467 \tabularnewline
29 & 113.2 & 112.705 & 112.371 & 0.334288 & 0.494878 \tabularnewline
30 & 112.2 & 112.819 & 112.762 & 0.0561632 & -0.618663 \tabularnewline
31 & 112.2 & 112.968 & 113.104 & -0.136545 & -0.767622 \tabularnewline
32 & 113.2 & 112.912 & 113.337 & -0.425087 & 0.287587 \tabularnewline
33 & 113.8 & 113.157 & 113.471 & -0.313628 & 0.642795 \tabularnewline
34 & 113.8 & 112.721 & 113.483 & -0.762587 & 1.07925 \tabularnewline
35 & 113.7 & 112.479 & 113.412 & -0.93342 & 1.22092 \tabularnewline
36 & 113.9 & 112.803 & 113.375 & -0.571962 & 1.09696 \tabularnewline
37 & 114 & 113.386 & 113.392 & -0.00529514 & 0.613628 \tabularnewline
38 & 114.3 & 114.156 & 113.354 & 0.801997 & 0.143837 \tabularnewline
39 & 114.3 & 114.312 & 113.212 & 1.09991 & -0.0124132 \tabularnewline
40 & 112.8 & 113.864 & 113.008 & 0.856163 & -1.0645 \tabularnewline
41 & 112.3 & 113.105 & 112.771 & 0.334288 & -0.805122 \tabularnewline
42 & 112.2 & 112.589 & 112.533 & 0.0561632 & -0.389497 \tabularnewline
43 & 112.6 & 112.134 & 112.271 & -0.136545 & 0.465712 \tabularnewline
44 & 111.9 & 111.542 & 111.967 & -0.425087 & 0.35842 \tabularnewline
45 & 111.7 & 111.307 & 111.621 & -0.313628 & 0.392795 \tabularnewline
46 & 111 & 110.512 & 111.275 & -0.762587 & 0.487587 \tabularnewline
47 & 110.8 & 110.046 & 110.979 & -0.93342 & 0.754253 \tabularnewline
48 & 111.1 & 110.153 & 110.725 & -0.571962 & 0.946962 \tabularnewline
49 & 110.5 & 110.445 & 110.45 & -0.00529514 & 0.0552951 \tabularnewline
50 & 110.5 & 110.948 & 110.146 & 0.801997 & -0.44783 \tabularnewline
51 & 109.8 & 110.958 & 109.858 & 1.09991 & -1.15825 \tabularnewline
52 & 109 & 110.456 & 109.6 & 0.856163 & -1.45616 \tabularnewline
53 & 109 & 109.651 & 109.317 & 0.334288 & -0.650955 \tabularnewline
54 & 109.4 & 108.894 & 108.837 & 0.0561632 & 0.506337 \tabularnewline
55 & 108.8 & NA & NA & -0.136545 & NA \tabularnewline
56 & 108.4 & NA & NA & -0.425087 & NA \tabularnewline
57 & 108.3 & NA & NA & -0.313628 & NA \tabularnewline
58 & 108.2 & NA & NA & -0.762587 & NA \tabularnewline
59 & 106.8 & NA & NA & -0.93342 & NA \tabularnewline
60 & 103.6 & NA & NA & -0.571962 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294911&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]96.4[/C][C]NA[/C][C]NA[/C][C]-0.00529514[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]96.9[/C][C]NA[/C][C]NA[/C][C]0.801997[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]98.1[/C][C]NA[/C][C]NA[/C][C]1.09991[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]99.2[/C][C]NA[/C][C]NA[/C][C]0.856163[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]100[/C][C]NA[/C][C]NA[/C][C]0.334288[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]100.3[/C][C]NA[/C][C]NA[/C][C]0.0561632[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]100.3[/C][C]100.247[/C][C]100.383[/C][C]-0.136545[/C][C]0.0532118[/C][/ROW]
[ROW][C]8[/C][C]100.8[/C][C]100.783[/C][C]101.208[/C][C]-0.425087[/C][C]0.0167535[/C][/ROW]
[ROW][C]9[/C][C]101.3[/C][C]101.791[/C][C]102.104[/C][C]-0.313628[/C][C]-0.490538[/C][/ROW]
[ROW][C]10[/C][C]101.4[/C][C]102.267[/C][C]103.029[/C][C]-0.762587[/C][C]-0.86658[/C][/ROW]
[ROW][C]11[/C][C]101.9[/C][C]102.975[/C][C]103.908[/C][C]-0.93342[/C][C]-1.07491[/C][/ROW]
[ROW][C]12[/C][C]103.4[/C][C]104.132[/C][C]104.704[/C][C]-0.571962[/C][C]-0.732205[/C][/ROW]
[ROW][C]13[/C][C]105.6[/C][C]105.474[/C][C]105.479[/C][C]-0.00529514[/C][C]0.126128[/C][/ROW]
[ROW][C]14[/C][C]107.5[/C][C]107.002[/C][C]106.2[/C][C]0.801997[/C][C]0.498003[/C][/ROW]
[ROW][C]15[/C][C]109[/C][C]107.971[/C][C]106.871[/C][C]1.09991[/C][C]1.02925[/C][/ROW]
[ROW][C]16[/C][C]110.5[/C][C]108.385[/C][C]107.529[/C][C]0.856163[/C][C]2.11467[/C][/ROW]
[ROW][C]17[/C][C]109.8[/C][C]108.48[/C][C]108.146[/C][C]0.334288[/C][C]1.31988[/C][/ROW]
[ROW][C]18[/C][C]109.6[/C][C]108.739[/C][C]108.683[/C][C]0.0561632[/C][C]0.860503[/C][/ROW]
[ROW][C]19[/C][C]109.6[/C][C]108.993[/C][C]109.129[/C][C]-0.136545[/C][C]0.607378[/C][/ROW]
[ROW][C]20[/C][C]108.8[/C][C]109.104[/C][C]109.529[/C][C]-0.425087[/C][C]-0.30408[/C][/ROW]
[ROW][C]21[/C][C]109.4[/C][C]109.586[/C][C]109.9[/C][C]-0.313628[/C][C]-0.186372[/C][/ROW]
[ROW][C]22[/C][C]109.1[/C][C]109.442[/C][C]110.204[/C][C]-0.762587[/C][C]-0.34158[/C][/ROW]
[ROW][C]23[/C][C]109[/C][C]109.542[/C][C]110.475[/C][C]-0.93342[/C][C]-0.54158[/C][/ROW]
[ROW][C]24[/C][C]109.2[/C][C]110.153[/C][C]110.725[/C][C]-0.571962[/C][C]-0.953038[/C][/ROW]
[ROW][C]25[/C][C]110.5[/C][C]110.936[/C][C]110.942[/C][C]-0.00529514[/C][C]-0.436372[/C][/ROW]
[ROW][C]26[/C][C]112.2[/C][C]112.035[/C][C]111.233[/C][C]0.801997[/C][C]0.16467[/C][/ROW]
[ROW][C]27[/C][C]113.2[/C][C]112.7[/C][C]111.6[/C][C]1.09991[/C][C]0.500087[/C][/ROW]
[ROW][C]28[/C][C]113.6[/C][C]112.835[/C][C]111.979[/C][C]0.856163[/C][C]0.76467[/C][/ROW]
[ROW][C]29[/C][C]113.2[/C][C]112.705[/C][C]112.371[/C][C]0.334288[/C][C]0.494878[/C][/ROW]
[ROW][C]30[/C][C]112.2[/C][C]112.819[/C][C]112.762[/C][C]0.0561632[/C][C]-0.618663[/C][/ROW]
[ROW][C]31[/C][C]112.2[/C][C]112.968[/C][C]113.104[/C][C]-0.136545[/C][C]-0.767622[/C][/ROW]
[ROW][C]32[/C][C]113.2[/C][C]112.912[/C][C]113.337[/C][C]-0.425087[/C][C]0.287587[/C][/ROW]
[ROW][C]33[/C][C]113.8[/C][C]113.157[/C][C]113.471[/C][C]-0.313628[/C][C]0.642795[/C][/ROW]
[ROW][C]34[/C][C]113.8[/C][C]112.721[/C][C]113.483[/C][C]-0.762587[/C][C]1.07925[/C][/ROW]
[ROW][C]35[/C][C]113.7[/C][C]112.479[/C][C]113.412[/C][C]-0.93342[/C][C]1.22092[/C][/ROW]
[ROW][C]36[/C][C]113.9[/C][C]112.803[/C][C]113.375[/C][C]-0.571962[/C][C]1.09696[/C][/ROW]
[ROW][C]37[/C][C]114[/C][C]113.386[/C][C]113.392[/C][C]-0.00529514[/C][C]0.613628[/C][/ROW]
[ROW][C]38[/C][C]114.3[/C][C]114.156[/C][C]113.354[/C][C]0.801997[/C][C]0.143837[/C][/ROW]
[ROW][C]39[/C][C]114.3[/C][C]114.312[/C][C]113.212[/C][C]1.09991[/C][C]-0.0124132[/C][/ROW]
[ROW][C]40[/C][C]112.8[/C][C]113.864[/C][C]113.008[/C][C]0.856163[/C][C]-1.0645[/C][/ROW]
[ROW][C]41[/C][C]112.3[/C][C]113.105[/C][C]112.771[/C][C]0.334288[/C][C]-0.805122[/C][/ROW]
[ROW][C]42[/C][C]112.2[/C][C]112.589[/C][C]112.533[/C][C]0.0561632[/C][C]-0.389497[/C][/ROW]
[ROW][C]43[/C][C]112.6[/C][C]112.134[/C][C]112.271[/C][C]-0.136545[/C][C]0.465712[/C][/ROW]
[ROW][C]44[/C][C]111.9[/C][C]111.542[/C][C]111.967[/C][C]-0.425087[/C][C]0.35842[/C][/ROW]
[ROW][C]45[/C][C]111.7[/C][C]111.307[/C][C]111.621[/C][C]-0.313628[/C][C]0.392795[/C][/ROW]
[ROW][C]46[/C][C]111[/C][C]110.512[/C][C]111.275[/C][C]-0.762587[/C][C]0.487587[/C][/ROW]
[ROW][C]47[/C][C]110.8[/C][C]110.046[/C][C]110.979[/C][C]-0.93342[/C][C]0.754253[/C][/ROW]
[ROW][C]48[/C][C]111.1[/C][C]110.153[/C][C]110.725[/C][C]-0.571962[/C][C]0.946962[/C][/ROW]
[ROW][C]49[/C][C]110.5[/C][C]110.445[/C][C]110.45[/C][C]-0.00529514[/C][C]0.0552951[/C][/ROW]
[ROW][C]50[/C][C]110.5[/C][C]110.948[/C][C]110.146[/C][C]0.801997[/C][C]-0.44783[/C][/ROW]
[ROW][C]51[/C][C]109.8[/C][C]110.958[/C][C]109.858[/C][C]1.09991[/C][C]-1.15825[/C][/ROW]
[ROW][C]52[/C][C]109[/C][C]110.456[/C][C]109.6[/C][C]0.856163[/C][C]-1.45616[/C][/ROW]
[ROW][C]53[/C][C]109[/C][C]109.651[/C][C]109.317[/C][C]0.334288[/C][C]-0.650955[/C][/ROW]
[ROW][C]54[/C][C]109.4[/C][C]108.894[/C][C]108.837[/C][C]0.0561632[/C][C]0.506337[/C][/ROW]
[ROW][C]55[/C][C]108.8[/C][C]NA[/C][C]NA[/C][C]-0.136545[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]108.4[/C][C]NA[/C][C]NA[/C][C]-0.425087[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]108.3[/C][C]NA[/C][C]NA[/C][C]-0.313628[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]108.2[/C][C]NA[/C][C]NA[/C][C]-0.762587[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]106.8[/C][C]NA[/C][C]NA[/C][C]-0.93342[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]103.6[/C][C]NA[/C][C]NA[/C][C]-0.571962[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294911&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294911&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	96.4	NA	NA	-0.00529514	NA
2	96.9	NA	NA	0.801997	NA
3	98.1	NA	NA	1.09991	NA
4	99.2	NA	NA	0.856163	NA
5	100	NA	NA	0.334288	NA
6	100.3	NA	NA	0.0561632	NA
7	100.3	100.247	100.383	-0.136545	0.0532118
8	100.8	100.783	101.208	-0.425087	0.0167535
9	101.3	101.791	102.104	-0.313628	-0.490538
10	101.4	102.267	103.029	-0.762587	-0.86658
11	101.9	102.975	103.908	-0.93342	-1.07491
12	103.4	104.132	104.704	-0.571962	-0.732205
13	105.6	105.474	105.479	-0.00529514	0.126128
14	107.5	107.002	106.2	0.801997	0.498003
15	109	107.971	106.871	1.09991	1.02925
16	110.5	108.385	107.529	0.856163	2.11467
17	109.8	108.48	108.146	0.334288	1.31988
18	109.6	108.739	108.683	0.0561632	0.860503
19	109.6	108.993	109.129	-0.136545	0.607378
20	108.8	109.104	109.529	-0.425087	-0.30408
21	109.4	109.586	109.9	-0.313628	-0.186372
22	109.1	109.442	110.204	-0.762587	-0.34158
23	109	109.542	110.475	-0.93342	-0.54158
24	109.2	110.153	110.725	-0.571962	-0.953038
25	110.5	110.936	110.942	-0.00529514	-0.436372
26	112.2	112.035	111.233	0.801997	0.16467
27	113.2	112.7	111.6	1.09991	0.500087
28	113.6	112.835	111.979	0.856163	0.76467
29	113.2	112.705	112.371	0.334288	0.494878
30	112.2	112.819	112.762	0.0561632	-0.618663
31	112.2	112.968	113.104	-0.136545	-0.767622
32	113.2	112.912	113.337	-0.425087	0.287587
33	113.8	113.157	113.471	-0.313628	0.642795
34	113.8	112.721	113.483	-0.762587	1.07925
35	113.7	112.479	113.412	-0.93342	1.22092
36	113.9	112.803	113.375	-0.571962	1.09696
37	114	113.386	113.392	-0.00529514	0.613628
38	114.3	114.156	113.354	0.801997	0.143837
39	114.3	114.312	113.212	1.09991	-0.0124132
40	112.8	113.864	113.008	0.856163	-1.0645
41	112.3	113.105	112.771	0.334288	-0.805122
42	112.2	112.589	112.533	0.0561632	-0.389497
43	112.6	112.134	112.271	-0.136545	0.465712
44	111.9	111.542	111.967	-0.425087	0.35842
45	111.7	111.307	111.621	-0.313628	0.392795
46	111	110.512	111.275	-0.762587	0.487587
47	110.8	110.046	110.979	-0.93342	0.754253
48	111.1	110.153	110.725	-0.571962	0.946962
49	110.5	110.445	110.45	-0.00529514	0.0552951
50	110.5	110.948	110.146	0.801997	-0.44783
51	109.8	110.958	109.858	1.09991	-1.15825
52	109	110.456	109.6	0.856163	-1.45616
53	109	109.651	109.317	0.334288	-0.650955
54	109.4	108.894	108.837	0.0561632	0.506337
55	108.8	NA	NA	-0.136545	NA
56	108.4	NA	NA	-0.425087	NA
57	108.3	NA	NA	-0.313628	NA
58	108.2	NA	NA	-0.762587	NA
59	106.8	NA	NA	-0.93342	NA
60	103.6	NA	NA	-0.571962	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