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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationMon, 12 May 2014 08:11:02 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/May/12/t13998966935rtwh28mymvo0js.htm/, Retrieved Thu, 16 May 2024 00:39:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=234820, Retrieved Thu, 16 May 2024 00:39:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [Buitenlandse reiz...] [2014-05-12 12:11:02] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
 107,00 
 116,14 
 117,18 
 102,28 
 109,43 
 114,28 
 117,39 
 116,66 
 114,29 
 114,18 
 114,12 
 122,62 
 115,70 
 127,91 
 119,55 
 115,08 
 116,63 
 121,38 
 123,41 
 120,70 
 119,40 
 116,83 
 116,40 
 121,67 
 116,54 
 129,61 
 119,93 
 117,64 
 121,01 
 124,20 
 125,23 
 123,24 
 121,58 
 120,89 
 117,77 
 110,91 
 124,23 
 127,70 
 129,45 
 120,13 
 122,02 
 126,59 
 126,34 
 125,15 
 125,02 
 124,40 
 127,55 
 126,63 
 130,18 
 136,95 
 136,81 
 129,59 
 133,37 
 140,02 
 139,67 
 139,99 
 134,57 
 134,41 
 134,99 
 135,70

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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=234820&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=234820&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234820&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1107NANA-1.46028NA
2116.14NANA6.94462NA
3117.18NANA2.38285NA
4102.28NANA-3.86413NA
5109.43NANA-1.64476NA
6114.28NANA2.7916NA
7117.39117.05114.162.889720.340278
8116.66115.789115.0130.7764930.87059
9114.29114.592115.602-1.00976-0.302326
10114.18113.738116.234-2.496220.442049
11114.12113.922117.068-3.145070.197569
12122.62115.498117.663-2.165077.12174
13115.7116.75118.21-1.46028-1.04972
14127.91125.574118.6296.944622.33622
15119.55121.393119.012.38285-1.84326
16115.08115.47119.334-3.86413-0.389618
17116.63117.894119.539-1.64476-1.26441
18121.38122.386119.5952.7916-1.00618
19123.41122.48119.592.889720.930278
20120.7120.472119.6960.7764930.227674
21119.4118.773119.782-1.009760.627257
22116.83117.409119.905-2.49622-0.578785
23116.4117.049120.194-3.14507-0.649097
24121.67118.329120.494-2.165073.3409
25116.54119.227120.688-1.46028-2.68722
26129.61127.814120.8696.944621.79622
27119.93123.449121.0662.38285-3.51868
28117.64117.462121.326-3.864130.178299
29121.01119.907121.552-1.644761.10267
30124.2123.952121.1612.79160.247569
31125.23123.923121.0332.889721.30736
32123.24122.05121.2740.7764931.18976
33121.58120.581121.591-1.009760.998924
34120.89119.595122.091-2.496221.29497
35117.77119.092122.237-3.14507-1.32201
36110.91120.214122.379-2.16507-9.30368
37124.23121.064122.525-1.460283.16569
38127.7129.595122.656.94462-1.89503
39129.45125.256122.8732.382854.19382
40120.13119.299123.163-3.864130.831215
41122.02122.072123.717-1.64476-0.0519097
42126.59127.571124.7792.7916-0.980764
43126.34128.572125.6822.88972-2.23181
44125.15127.092126.3150.776493-1.94191
45125.02125.998127.008-1.00976-0.977743
46124.4125.212127.708-2.49622-0.812118
47127.55125.43128.575-3.145072.11965
48126.63127.443129.608-2.16507-0.812847
49130.18129.263130.723-1.460280.917361
50136.95138.841131.8976.94462-1.89128
51136.81135.296132.9132.382851.51424
52129.59129.864133.728-3.86413-0.273785
53133.37132.81134.455-1.644760.559757
54140.02137.935135.1432.79162.08549
55139.67NANA2.88972NA
56139.99NANA0.776493NA
57134.57NANA-1.00976NA
58134.41NANA-2.49622NA
59134.99NANA-3.14507NA
60135.7NANA-2.16507NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 107 & NA & NA & -1.46028 & NA \tabularnewline
2 & 116.14 & NA & NA & 6.94462 & NA \tabularnewline
3 & 117.18 & NA & NA & 2.38285 & NA \tabularnewline
4 & 102.28 & NA & NA & -3.86413 & NA \tabularnewline
5 & 109.43 & NA & NA & -1.64476 & NA \tabularnewline
6 & 114.28 & NA & NA & 2.7916 & NA \tabularnewline
7 & 117.39 & 117.05 & 114.16 & 2.88972 & 0.340278 \tabularnewline
8 & 116.66 & 115.789 & 115.013 & 0.776493 & 0.87059 \tabularnewline
9 & 114.29 & 114.592 & 115.602 & -1.00976 & -0.302326 \tabularnewline
10 & 114.18 & 113.738 & 116.234 & -2.49622 & 0.442049 \tabularnewline
11 & 114.12 & 113.922 & 117.068 & -3.14507 & 0.197569 \tabularnewline
12 & 122.62 & 115.498 & 117.663 & -2.16507 & 7.12174 \tabularnewline
13 & 115.7 & 116.75 & 118.21 & -1.46028 & -1.04972 \tabularnewline
14 & 127.91 & 125.574 & 118.629 & 6.94462 & 2.33622 \tabularnewline
15 & 119.55 & 121.393 & 119.01 & 2.38285 & -1.84326 \tabularnewline
16 & 115.08 & 115.47 & 119.334 & -3.86413 & -0.389618 \tabularnewline
17 & 116.63 & 117.894 & 119.539 & -1.64476 & -1.26441 \tabularnewline
18 & 121.38 & 122.386 & 119.595 & 2.7916 & -1.00618 \tabularnewline
19 & 123.41 & 122.48 & 119.59 & 2.88972 & 0.930278 \tabularnewline
20 & 120.7 & 120.472 & 119.696 & 0.776493 & 0.227674 \tabularnewline
21 & 119.4 & 118.773 & 119.782 & -1.00976 & 0.627257 \tabularnewline
22 & 116.83 & 117.409 & 119.905 & -2.49622 & -0.578785 \tabularnewline
23 & 116.4 & 117.049 & 120.194 & -3.14507 & -0.649097 \tabularnewline
24 & 121.67 & 118.329 & 120.494 & -2.16507 & 3.3409 \tabularnewline
25 & 116.54 & 119.227 & 120.688 & -1.46028 & -2.68722 \tabularnewline
26 & 129.61 & 127.814 & 120.869 & 6.94462 & 1.79622 \tabularnewline
27 & 119.93 & 123.449 & 121.066 & 2.38285 & -3.51868 \tabularnewline
28 & 117.64 & 117.462 & 121.326 & -3.86413 & 0.178299 \tabularnewline
29 & 121.01 & 119.907 & 121.552 & -1.64476 & 1.10267 \tabularnewline
30 & 124.2 & 123.952 & 121.161 & 2.7916 & 0.247569 \tabularnewline
31 & 125.23 & 123.923 & 121.033 & 2.88972 & 1.30736 \tabularnewline
32 & 123.24 & 122.05 & 121.274 & 0.776493 & 1.18976 \tabularnewline
33 & 121.58 & 120.581 & 121.591 & -1.00976 & 0.998924 \tabularnewline
34 & 120.89 & 119.595 & 122.091 & -2.49622 & 1.29497 \tabularnewline
35 & 117.77 & 119.092 & 122.237 & -3.14507 & -1.32201 \tabularnewline
36 & 110.91 & 120.214 & 122.379 & -2.16507 & -9.30368 \tabularnewline
37 & 124.23 & 121.064 & 122.525 & -1.46028 & 3.16569 \tabularnewline
38 & 127.7 & 129.595 & 122.65 & 6.94462 & -1.89503 \tabularnewline
39 & 129.45 & 125.256 & 122.873 & 2.38285 & 4.19382 \tabularnewline
40 & 120.13 & 119.299 & 123.163 & -3.86413 & 0.831215 \tabularnewline
41 & 122.02 & 122.072 & 123.717 & -1.64476 & -0.0519097 \tabularnewline
42 & 126.59 & 127.571 & 124.779 & 2.7916 & -0.980764 \tabularnewline
43 & 126.34 & 128.572 & 125.682 & 2.88972 & -2.23181 \tabularnewline
44 & 125.15 & 127.092 & 126.315 & 0.776493 & -1.94191 \tabularnewline
45 & 125.02 & 125.998 & 127.008 & -1.00976 & -0.977743 \tabularnewline
46 & 124.4 & 125.212 & 127.708 & -2.49622 & -0.812118 \tabularnewline
47 & 127.55 & 125.43 & 128.575 & -3.14507 & 2.11965 \tabularnewline
48 & 126.63 & 127.443 & 129.608 & -2.16507 & -0.812847 \tabularnewline
49 & 130.18 & 129.263 & 130.723 & -1.46028 & 0.917361 \tabularnewline
50 & 136.95 & 138.841 & 131.897 & 6.94462 & -1.89128 \tabularnewline
51 & 136.81 & 135.296 & 132.913 & 2.38285 & 1.51424 \tabularnewline
52 & 129.59 & 129.864 & 133.728 & -3.86413 & -0.273785 \tabularnewline
53 & 133.37 & 132.81 & 134.455 & -1.64476 & 0.559757 \tabularnewline
54 & 140.02 & 137.935 & 135.143 & 2.7916 & 2.08549 \tabularnewline
55 & 139.67 & NA & NA & 2.88972 & NA \tabularnewline
56 & 139.99 & NA & NA & 0.776493 & NA \tabularnewline
57 & 134.57 & NA & NA & -1.00976 & NA \tabularnewline
58 & 134.41 & NA & NA & -2.49622 & NA \tabularnewline
59 & 134.99 & NA & NA & -3.14507 & NA \tabularnewline
60 & 135.7 & NA & NA & -2.16507 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=234820&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]107[/C][C]NA[/C][C]NA[/C][C]-1.46028[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]116.14[/C][C]NA[/C][C]NA[/C][C]6.94462[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]117.18[/C][C]NA[/C][C]NA[/C][C]2.38285[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]102.28[/C][C]NA[/C][C]NA[/C][C]-3.86413[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]109.43[/C][C]NA[/C][C]NA[/C][C]-1.64476[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]114.28[/C][C]NA[/C][C]NA[/C][C]2.7916[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]117.39[/C][C]117.05[/C][C]114.16[/C][C]2.88972[/C][C]0.340278[/C][/ROW]
[ROW][C]8[/C][C]116.66[/C][C]115.789[/C][C]115.013[/C][C]0.776493[/C][C]0.87059[/C][/ROW]
[ROW][C]9[/C][C]114.29[/C][C]114.592[/C][C]115.602[/C][C]-1.00976[/C][C]-0.302326[/C][/ROW]
[ROW][C]10[/C][C]114.18[/C][C]113.738[/C][C]116.234[/C][C]-2.49622[/C][C]0.442049[/C][/ROW]
[ROW][C]11[/C][C]114.12[/C][C]113.922[/C][C]117.068[/C][C]-3.14507[/C][C]0.197569[/C][/ROW]
[ROW][C]12[/C][C]122.62[/C][C]115.498[/C][C]117.663[/C][C]-2.16507[/C][C]7.12174[/C][/ROW]
[ROW][C]13[/C][C]115.7[/C][C]116.75[/C][C]118.21[/C][C]-1.46028[/C][C]-1.04972[/C][/ROW]
[ROW][C]14[/C][C]127.91[/C][C]125.574[/C][C]118.629[/C][C]6.94462[/C][C]2.33622[/C][/ROW]
[ROW][C]15[/C][C]119.55[/C][C]121.393[/C][C]119.01[/C][C]2.38285[/C][C]-1.84326[/C][/ROW]
[ROW][C]16[/C][C]115.08[/C][C]115.47[/C][C]119.334[/C][C]-3.86413[/C][C]-0.389618[/C][/ROW]
[ROW][C]17[/C][C]116.63[/C][C]117.894[/C][C]119.539[/C][C]-1.64476[/C][C]-1.26441[/C][/ROW]
[ROW][C]18[/C][C]121.38[/C][C]122.386[/C][C]119.595[/C][C]2.7916[/C][C]-1.00618[/C][/ROW]
[ROW][C]19[/C][C]123.41[/C][C]122.48[/C][C]119.59[/C][C]2.88972[/C][C]0.930278[/C][/ROW]
[ROW][C]20[/C][C]120.7[/C][C]120.472[/C][C]119.696[/C][C]0.776493[/C][C]0.227674[/C][/ROW]
[ROW][C]21[/C][C]119.4[/C][C]118.773[/C][C]119.782[/C][C]-1.00976[/C][C]0.627257[/C][/ROW]
[ROW][C]22[/C][C]116.83[/C][C]117.409[/C][C]119.905[/C][C]-2.49622[/C][C]-0.578785[/C][/ROW]
[ROW][C]23[/C][C]116.4[/C][C]117.049[/C][C]120.194[/C][C]-3.14507[/C][C]-0.649097[/C][/ROW]
[ROW][C]24[/C][C]121.67[/C][C]118.329[/C][C]120.494[/C][C]-2.16507[/C][C]3.3409[/C][/ROW]
[ROW][C]25[/C][C]116.54[/C][C]119.227[/C][C]120.688[/C][C]-1.46028[/C][C]-2.68722[/C][/ROW]
[ROW][C]26[/C][C]129.61[/C][C]127.814[/C][C]120.869[/C][C]6.94462[/C][C]1.79622[/C][/ROW]
[ROW][C]27[/C][C]119.93[/C][C]123.449[/C][C]121.066[/C][C]2.38285[/C][C]-3.51868[/C][/ROW]
[ROW][C]28[/C][C]117.64[/C][C]117.462[/C][C]121.326[/C][C]-3.86413[/C][C]0.178299[/C][/ROW]
[ROW][C]29[/C][C]121.01[/C][C]119.907[/C][C]121.552[/C][C]-1.64476[/C][C]1.10267[/C][/ROW]
[ROW][C]30[/C][C]124.2[/C][C]123.952[/C][C]121.161[/C][C]2.7916[/C][C]0.247569[/C][/ROW]
[ROW][C]31[/C][C]125.23[/C][C]123.923[/C][C]121.033[/C][C]2.88972[/C][C]1.30736[/C][/ROW]
[ROW][C]32[/C][C]123.24[/C][C]122.05[/C][C]121.274[/C][C]0.776493[/C][C]1.18976[/C][/ROW]
[ROW][C]33[/C][C]121.58[/C][C]120.581[/C][C]121.591[/C][C]-1.00976[/C][C]0.998924[/C][/ROW]
[ROW][C]34[/C][C]120.89[/C][C]119.595[/C][C]122.091[/C][C]-2.49622[/C][C]1.29497[/C][/ROW]
[ROW][C]35[/C][C]117.77[/C][C]119.092[/C][C]122.237[/C][C]-3.14507[/C][C]-1.32201[/C][/ROW]
[ROW][C]36[/C][C]110.91[/C][C]120.214[/C][C]122.379[/C][C]-2.16507[/C][C]-9.30368[/C][/ROW]
[ROW][C]37[/C][C]124.23[/C][C]121.064[/C][C]122.525[/C][C]-1.46028[/C][C]3.16569[/C][/ROW]
[ROW][C]38[/C][C]127.7[/C][C]129.595[/C][C]122.65[/C][C]6.94462[/C][C]-1.89503[/C][/ROW]
[ROW][C]39[/C][C]129.45[/C][C]125.256[/C][C]122.873[/C][C]2.38285[/C][C]4.19382[/C][/ROW]
[ROW][C]40[/C][C]120.13[/C][C]119.299[/C][C]123.163[/C][C]-3.86413[/C][C]0.831215[/C][/ROW]
[ROW][C]41[/C][C]122.02[/C][C]122.072[/C][C]123.717[/C][C]-1.64476[/C][C]-0.0519097[/C][/ROW]
[ROW][C]42[/C][C]126.59[/C][C]127.571[/C][C]124.779[/C][C]2.7916[/C][C]-0.980764[/C][/ROW]
[ROW][C]43[/C][C]126.34[/C][C]128.572[/C][C]125.682[/C][C]2.88972[/C][C]-2.23181[/C][/ROW]
[ROW][C]44[/C][C]125.15[/C][C]127.092[/C][C]126.315[/C][C]0.776493[/C][C]-1.94191[/C][/ROW]
[ROW][C]45[/C][C]125.02[/C][C]125.998[/C][C]127.008[/C][C]-1.00976[/C][C]-0.977743[/C][/ROW]
[ROW][C]46[/C][C]124.4[/C][C]125.212[/C][C]127.708[/C][C]-2.49622[/C][C]-0.812118[/C][/ROW]
[ROW][C]47[/C][C]127.55[/C][C]125.43[/C][C]128.575[/C][C]-3.14507[/C][C]2.11965[/C][/ROW]
[ROW][C]48[/C][C]126.63[/C][C]127.443[/C][C]129.608[/C][C]-2.16507[/C][C]-0.812847[/C][/ROW]
[ROW][C]49[/C][C]130.18[/C][C]129.263[/C][C]130.723[/C][C]-1.46028[/C][C]0.917361[/C][/ROW]
[ROW][C]50[/C][C]136.95[/C][C]138.841[/C][C]131.897[/C][C]6.94462[/C][C]-1.89128[/C][/ROW]
[ROW][C]51[/C][C]136.81[/C][C]135.296[/C][C]132.913[/C][C]2.38285[/C][C]1.51424[/C][/ROW]
[ROW][C]52[/C][C]129.59[/C][C]129.864[/C][C]133.728[/C][C]-3.86413[/C][C]-0.273785[/C][/ROW]
[ROW][C]53[/C][C]133.37[/C][C]132.81[/C][C]134.455[/C][C]-1.64476[/C][C]0.559757[/C][/ROW]
[ROW][C]54[/C][C]140.02[/C][C]137.935[/C][C]135.143[/C][C]2.7916[/C][C]2.08549[/C][/ROW]
[ROW][C]55[/C][C]139.67[/C][C]NA[/C][C]NA[/C][C]2.88972[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]139.99[/C][C]NA[/C][C]NA[/C][C]0.776493[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]134.57[/C][C]NA[/C][C]NA[/C][C]-1.00976[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]134.41[/C][C]NA[/C][C]NA[/C][C]-2.49622[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]134.99[/C][C]NA[/C][C]NA[/C][C]-3.14507[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]135.7[/C][C]NA[/C][C]NA[/C][C]-2.16507[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=234820&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=234820&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1107NANA-1.46028NA
2116.14NANA6.94462NA
3117.18NANA2.38285NA
4102.28NANA-3.86413NA
5109.43NANA-1.64476NA
6114.28NANA2.7916NA
7117.39117.05114.162.889720.340278
8116.66115.789115.0130.7764930.87059
9114.29114.592115.602-1.00976-0.302326
10114.18113.738116.234-2.496220.442049
11114.12113.922117.068-3.145070.197569
12122.62115.498117.663-2.165077.12174
13115.7116.75118.21-1.46028-1.04972
14127.91125.574118.6296.944622.33622
15119.55121.393119.012.38285-1.84326
16115.08115.47119.334-3.86413-0.389618
17116.63117.894119.539-1.64476-1.26441
18121.38122.386119.5952.7916-1.00618
19123.41122.48119.592.889720.930278
20120.7120.472119.6960.7764930.227674
21119.4118.773119.782-1.009760.627257
22116.83117.409119.905-2.49622-0.578785
23116.4117.049120.194-3.14507-0.649097
24121.67118.329120.494-2.165073.3409
25116.54119.227120.688-1.46028-2.68722
26129.61127.814120.8696.944621.79622
27119.93123.449121.0662.38285-3.51868
28117.64117.462121.326-3.864130.178299
29121.01119.907121.552-1.644761.10267
30124.2123.952121.1612.79160.247569
31125.23123.923121.0332.889721.30736
32123.24122.05121.2740.7764931.18976
33121.58120.581121.591-1.009760.998924
34120.89119.595122.091-2.496221.29497
35117.77119.092122.237-3.14507-1.32201
36110.91120.214122.379-2.16507-9.30368
37124.23121.064122.525-1.460283.16569
38127.7129.595122.656.94462-1.89503
39129.45125.256122.8732.382854.19382
40120.13119.299123.163-3.864130.831215
41122.02122.072123.717-1.64476-0.0519097
42126.59127.571124.7792.7916-0.980764
43126.34128.572125.6822.88972-2.23181
44125.15127.092126.3150.776493-1.94191
45125.02125.998127.008-1.00976-0.977743
46124.4125.212127.708-2.49622-0.812118
47127.55125.43128.575-3.145072.11965
48126.63127.443129.608-2.16507-0.812847
49130.18129.263130.723-1.460280.917361
50136.95138.841131.8976.94462-1.89128
51136.81135.296132.9132.382851.51424
52129.59129.864133.728-3.86413-0.273785
53133.37132.81134.455-1.644760.559757
54140.02137.935135.1432.79162.08549
55139.67NANA2.88972NA
56139.99NANA0.776493NA
57134.57NANA-1.00976NA
58134.41NANA-2.49622NA
59134.99NANA-3.14507NA
60135.7NANA-2.16507NA


Figures (Output of Computation)

Input Parameters & R Code

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 <- 'multiplicative'
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')