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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 computationTue, 02 Jun 2009 09:41:09 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Jun/02/t1243957311gdbuzyrjdjpwxrl.htm/, Retrieved Fri, 10 May 2024 09:19:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=41285, Retrieved Fri, 10 May 2024 09:19:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [decomposition - s...] [2009-06-02 15:41:09] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   PD    [Classical Decomposition] [classical decompo...] [2009-06-05 08:03:27] [74be16979710d4c4e7c6647856088456]
- RMPD    [Exponential Smoothing] [exponential smoot...] [2009-06-05 08:29:38] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106,07
106,07
106,07
106,07
106,07
106,2
107,5
108,31
108,53
108,61
108,62
108,62
108,62
108,62
110,1
110,74
110,77
110,77
110,78
110,78
110,78
110,84
110,84
110,84
110,84
110,84
111,01
112,66
114,04
114,16
114,2
114,2
114,23
114,23
114,23
114,23
114,23
114,23
115,97
116,96
117,08
117,08
117,08
117,63
119,12
119,47
119,5
119,52
119,49
119,49
119,5
119,5
119,56
122,35
122,92
122,92
123,04
123,04
123,04
123,06
123,33
128,21
129,57
129,79
131,66
135,01
136,01
136,31
136,37
136,4
136,4
136,4
137,34
142,18
143,79
144,08
144,08
144,09
144,09
144,11
144,11
144,15
144,15
144,16
144,2
144,38
144,38
144,28
144,46
144,53
144,53
145,34
147,98
150,42
150,53
150,64

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=41285&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=41285&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=41285&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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1106.07NANA0.990278534403955NA
2106.07NANA0.997355460812724NA
3106.07NANA1.00119351888841NA
4106.07NANA1.00198095896971NA
5106.07NANA1.00232897386216NA
6106.2NANA1.00559762619841NA
7107.5107.950904746405107.3345833333331.005742058094720.995823057273446
8108.31107.994496194066107.5470833333331.004160158015131.00292148041847
9108.53108.122844611257107.821251.002797172275941.00376567403685
10108.61108.161526436307108.183750.9997945757686081.00414633168067
11108.62108.160014732477108.5741666666670.9961855389094411.00425282179058
12108.62108.152521354594108.9604166666670.9925854238007931.00432240172999
13108.62108.225065328672109.28750.9902785344039551.00364919780948
14108.62109.237434669390109.5270833333330.9973554608127240.994347773991041
15110.1109.854707368133109.723751.001193518888411.00223288230194
16110.74110.128144692427109.9104166666671.001980958969711.00555584868229
17110.77110.352243651499110.0958333333331.002328973862161.00378566248114
18110.77110.898144215182110.2808333333331.005597626198410.998844487289766
19110.78111.100134565815110.4658333333331.005742058094720.997118504247851
20110.78111.111158284506110.6508333333331.004160158015130.997019576704817
21110.78111.091124241194110.781251.002797172275940.997199378048252
22110.84110.876385290592110.8991666666670.9997945757686080.999671839134214
23110.84110.691571233230111.1154166666670.9961855389094411.00134092203332
24110.84110.566985397990111.3929166666670.9925854238007931.00246922353022
25110.84110.591005793786111.6766666666670.9902785344039551.00225148694893
26110.84111.665579651694111.9616666666670.9973554608127240.992606677417795
27111.01112.381886675393112.2479166666671.001193518888410.98779263530825
28112.66112.755839757325112.5329166666671.001980958969710.999150024002913
29114.04113.078160823332112.8154166666671.002328973862161.00850596763924
30114.16113.730996527985113.0979166666671.005597626198411.00377208927304
31114.2114.031453605970113.3804166666671.005742058094721.00147806932825
32114.2114.135772360461113.6629166666671.004160158015131.00056273014333
33114.23114.329741275490114.0108333333331.002797172275940.999127599919516
34114.23114.373166819343114.3966666666670.9997945757686080.998748248183344
35114.23114.264971776760114.70250.9961855389094410.999693941404646
36114.23114.098521620421114.9508333333330.9925854238007931.00115232325285
37114.23114.072660074328115.19250.9902785344039551.0013792956662
38114.23115.150090292908115.4554166666670.9973554608127240.992009643322312
39115.97115.940295307109115.8020833333331.001193518888411.00025620680724
40116.96116.454401972122116.2241666666671.001980958969711.00434159653320
41117.08116.933786276122116.6620833333331.002328973862161.00125039758426
42117.08117.757577022888117.1020833333331.005597626198410.994246000639465
43117.08118.216597745216117.5416666666671.005742058094720.990385463912047
44117.63118.470815442625117.981.004160158015130.992902763102595
45119.12118.677284849462118.346251.002797172275941.00373041185683
46119.47118.574803524010118.5991666666670.9997945757686081.00754963490881
47119.5118.355143568599118.8083333333330.9961855389094411.00967306022266
48119.52118.247942269168119.131250.9925854238007931.01075754644369
49119.49118.431536089929119.5941666666670.9902785344039551.00893734848856
50119.49119.740418801299120.0579166666670.9973554608127240.997908652702189
51119.5120.585416070785120.4416666666671.001193518888410.990998778242402
52119.5120.992958224189120.753751.001980958969710.98766078418033
53119.56121.331922286014121.051.002328973862160.985396075058982
54122.35122.024243951045121.3451.005597626198411.00266960104326
55122.92122.351035722367121.65251.005742058094721.00465026122806
56122.92122.684104105630122.1758333333331.004160158015131.00192279102570
57123.04123.302686806585122.958751.002797172275940.997869577594878
58123.04123.781650358399123.8070833333330.9997945757686080.99400839820562
59123.04124.264184123564124.740.9961855389094410.99014853610316
60123.06124.839123060465125.7716666666670.9925854238007930.98574867383838
61123.33125.611468080414126.8445833333330.9902785344039550.981837103607824
62128.21127.609553387111127.9479166666670.9973554608127241.00470534217032
63129.57129.215287039637129.061251.001193518888411.00274513154356
64129.79130.431201365617130.1733333333331.001980958969710.995083987888606
65131.66131.592429881783131.2866666666671.002328973862161.00051348028361
66135.01133.140287710647132.3991666666671.005597626198411.01404317447035
67136.01134.305537260396133.538751.005742058094721.01269093422634
68136.31135.264975685362134.7045833333331.004160158015131.00772575686605
69136.37136.259244104545135.8791666666671.002797172275941.00081283215816
70136.4137.038926433090137.0670833333330.9997945757686080.995337628149018
71136.4137.652917766507138.180.9961855389094410.990897993396465
72136.4138.044644969615139.0758333333330.9925854238007930.988086137133554
73137.34138.431861556441139.7908333333330.9902785344039550.99211264268092
74142.18140.081067859799140.45250.9973554608127241.0149836960288
75143.79141.268405515155141.11.001193518888411.01784967045993
76144.08142.026208521228141.7454166666671.001980958969711.01446065131328
77144.08142.722875499450142.391251.002328973862161.00950880856205
78144.09143.838170457354143.03751.005597626198411.00175078382772
79144.09144.471494171779143.6466666666671.005742058094720.997359381004772
80144.11144.623329957997144.0241666666671.004160158015130.996450572959795
81144.11144.543602244009144.1404166666671.002797172275940.997000197606273
82144.15144.143716637146144.1733333333330.9997945757686081.00004359095908
83144.15143.647464246894144.19750.9961855389094411.00349839626993
84144.16143.162249983828144.2316666666670.9925854238007931.00696936529207
85144.2142.865833694235144.2683333333330.9902785344039551.00933859601884
86144.38143.956209389832144.3379166666670.9973554608127241.00294388558829
87144.38144.722940319286144.5504166666671.001193518888410.997630366557438
88144.28145.260102066303144.9729166666671.001980958969710.99325277862014
89144.46145.838865696944145.51.002328973862160.990545279611475
90144.53146.853287339906146.0358333333331.005597626198410.98417953467716
91144.53NANA1.00574205809472NA
92145.34NANA1.00416015801513NA
93147.98NANA1.00279717227594NA
94150.42NANA0.999794575768608NA
95150.53NANA0.996185538909441NA
96150.64NANA0.992585423800793NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 106.07 & NA & NA & 0.990278534403955 & NA \tabularnewline
2 & 106.07 & NA & NA & 0.997355460812724 & NA \tabularnewline
3 & 106.07 & NA & NA & 1.00119351888841 & NA \tabularnewline
4 & 106.07 & NA & NA & 1.00198095896971 & NA \tabularnewline
5 & 106.07 & NA & NA & 1.00232897386216 & NA \tabularnewline
6 & 106.2 & NA & NA & 1.00559762619841 & NA \tabularnewline
7 & 107.5 & 107.950904746405 & 107.334583333333 & 1.00574205809472 & 0.995823057273446 \tabularnewline
8 & 108.31 & 107.994496194066 & 107.547083333333 & 1.00416015801513 & 1.00292148041847 \tabularnewline
9 & 108.53 & 108.122844611257 & 107.82125 & 1.00279717227594 & 1.00376567403685 \tabularnewline
10 & 108.61 & 108.161526436307 & 108.18375 & 0.999794575768608 & 1.00414633168067 \tabularnewline
11 & 108.62 & 108.160014732477 & 108.574166666667 & 0.996185538909441 & 1.00425282179058 \tabularnewline
12 & 108.62 & 108.152521354594 & 108.960416666667 & 0.992585423800793 & 1.00432240172999 \tabularnewline
13 & 108.62 & 108.225065328672 & 109.2875 & 0.990278534403955 & 1.00364919780948 \tabularnewline
14 & 108.62 & 109.237434669390 & 109.527083333333 & 0.997355460812724 & 0.994347773991041 \tabularnewline
15 & 110.1 & 109.854707368133 & 109.72375 & 1.00119351888841 & 1.00223288230194 \tabularnewline
16 & 110.74 & 110.128144692427 & 109.910416666667 & 1.00198095896971 & 1.00555584868229 \tabularnewline
17 & 110.77 & 110.352243651499 & 110.095833333333 & 1.00232897386216 & 1.00378566248114 \tabularnewline
18 & 110.77 & 110.898144215182 & 110.280833333333 & 1.00559762619841 & 0.998844487289766 \tabularnewline
19 & 110.78 & 111.100134565815 & 110.465833333333 & 1.00574205809472 & 0.997118504247851 \tabularnewline
20 & 110.78 & 111.111158284506 & 110.650833333333 & 1.00416015801513 & 0.997019576704817 \tabularnewline
21 & 110.78 & 111.091124241194 & 110.78125 & 1.00279717227594 & 0.997199378048252 \tabularnewline
22 & 110.84 & 110.876385290592 & 110.899166666667 & 0.999794575768608 & 0.999671839134214 \tabularnewline
23 & 110.84 & 110.691571233230 & 111.115416666667 & 0.996185538909441 & 1.00134092203332 \tabularnewline
24 & 110.84 & 110.566985397990 & 111.392916666667 & 0.992585423800793 & 1.00246922353022 \tabularnewline
25 & 110.84 & 110.591005793786 & 111.676666666667 & 0.990278534403955 & 1.00225148694893 \tabularnewline
26 & 110.84 & 111.665579651694 & 111.961666666667 & 0.997355460812724 & 0.992606677417795 \tabularnewline
27 & 111.01 & 112.381886675393 & 112.247916666667 & 1.00119351888841 & 0.98779263530825 \tabularnewline
28 & 112.66 & 112.755839757325 & 112.532916666667 & 1.00198095896971 & 0.999150024002913 \tabularnewline
29 & 114.04 & 113.078160823332 & 112.815416666667 & 1.00232897386216 & 1.00850596763924 \tabularnewline
30 & 114.16 & 113.730996527985 & 113.097916666667 & 1.00559762619841 & 1.00377208927304 \tabularnewline
31 & 114.2 & 114.031453605970 & 113.380416666667 & 1.00574205809472 & 1.00147806932825 \tabularnewline
32 & 114.2 & 114.135772360461 & 113.662916666667 & 1.00416015801513 & 1.00056273014333 \tabularnewline
33 & 114.23 & 114.329741275490 & 114.010833333333 & 1.00279717227594 & 0.999127599919516 \tabularnewline
34 & 114.23 & 114.373166819343 & 114.396666666667 & 0.999794575768608 & 0.998748248183344 \tabularnewline
35 & 114.23 & 114.264971776760 & 114.7025 & 0.996185538909441 & 0.999693941404646 \tabularnewline
36 & 114.23 & 114.098521620421 & 114.950833333333 & 0.992585423800793 & 1.00115232325285 \tabularnewline
37 & 114.23 & 114.072660074328 & 115.1925 & 0.990278534403955 & 1.0013792956662 \tabularnewline
38 & 114.23 & 115.150090292908 & 115.455416666667 & 0.997355460812724 & 0.992009643322312 \tabularnewline
39 & 115.97 & 115.940295307109 & 115.802083333333 & 1.00119351888841 & 1.00025620680724 \tabularnewline
40 & 116.96 & 116.454401972122 & 116.224166666667 & 1.00198095896971 & 1.00434159653320 \tabularnewline
41 & 117.08 & 116.933786276122 & 116.662083333333 & 1.00232897386216 & 1.00125039758426 \tabularnewline
42 & 117.08 & 117.757577022888 & 117.102083333333 & 1.00559762619841 & 0.994246000639465 \tabularnewline
43 & 117.08 & 118.216597745216 & 117.541666666667 & 1.00574205809472 & 0.990385463912047 \tabularnewline
44 & 117.63 & 118.470815442625 & 117.98 & 1.00416015801513 & 0.992902763102595 \tabularnewline
45 & 119.12 & 118.677284849462 & 118.34625 & 1.00279717227594 & 1.00373041185683 \tabularnewline
46 & 119.47 & 118.574803524010 & 118.599166666667 & 0.999794575768608 & 1.00754963490881 \tabularnewline
47 & 119.5 & 118.355143568599 & 118.808333333333 & 0.996185538909441 & 1.00967306022266 \tabularnewline
48 & 119.52 & 118.247942269168 & 119.13125 & 0.992585423800793 & 1.01075754644369 \tabularnewline
49 & 119.49 & 118.431536089929 & 119.594166666667 & 0.990278534403955 & 1.00893734848856 \tabularnewline
50 & 119.49 & 119.740418801299 & 120.057916666667 & 0.997355460812724 & 0.997908652702189 \tabularnewline
51 & 119.5 & 120.585416070785 & 120.441666666667 & 1.00119351888841 & 0.990998778242402 \tabularnewline
52 & 119.5 & 120.992958224189 & 120.75375 & 1.00198095896971 & 0.98766078418033 \tabularnewline
53 & 119.56 & 121.331922286014 & 121.05 & 1.00232897386216 & 0.985396075058982 \tabularnewline
54 & 122.35 & 122.024243951045 & 121.345 & 1.00559762619841 & 1.00266960104326 \tabularnewline
55 & 122.92 & 122.351035722367 & 121.6525 & 1.00574205809472 & 1.00465026122806 \tabularnewline
56 & 122.92 & 122.684104105630 & 122.175833333333 & 1.00416015801513 & 1.00192279102570 \tabularnewline
57 & 123.04 & 123.302686806585 & 122.95875 & 1.00279717227594 & 0.997869577594878 \tabularnewline
58 & 123.04 & 123.781650358399 & 123.807083333333 & 0.999794575768608 & 0.99400839820562 \tabularnewline
59 & 123.04 & 124.264184123564 & 124.74 & 0.996185538909441 & 0.99014853610316 \tabularnewline
60 & 123.06 & 124.839123060465 & 125.771666666667 & 0.992585423800793 & 0.98574867383838 \tabularnewline
61 & 123.33 & 125.611468080414 & 126.844583333333 & 0.990278534403955 & 0.981837103607824 \tabularnewline
62 & 128.21 & 127.609553387111 & 127.947916666667 & 0.997355460812724 & 1.00470534217032 \tabularnewline
63 & 129.57 & 129.215287039637 & 129.06125 & 1.00119351888841 & 1.00274513154356 \tabularnewline
64 & 129.79 & 130.431201365617 & 130.173333333333 & 1.00198095896971 & 0.995083987888606 \tabularnewline
65 & 131.66 & 131.592429881783 & 131.286666666667 & 1.00232897386216 & 1.00051348028361 \tabularnewline
66 & 135.01 & 133.140287710647 & 132.399166666667 & 1.00559762619841 & 1.01404317447035 \tabularnewline
67 & 136.01 & 134.305537260396 & 133.53875 & 1.00574205809472 & 1.01269093422634 \tabularnewline
68 & 136.31 & 135.264975685362 & 134.704583333333 & 1.00416015801513 & 1.00772575686605 \tabularnewline
69 & 136.37 & 136.259244104545 & 135.879166666667 & 1.00279717227594 & 1.00081283215816 \tabularnewline
70 & 136.4 & 137.038926433090 & 137.067083333333 & 0.999794575768608 & 0.995337628149018 \tabularnewline
71 & 136.4 & 137.652917766507 & 138.18 & 0.996185538909441 & 0.990897993396465 \tabularnewline
72 & 136.4 & 138.044644969615 & 139.075833333333 & 0.992585423800793 & 0.988086137133554 \tabularnewline
73 & 137.34 & 138.431861556441 & 139.790833333333 & 0.990278534403955 & 0.99211264268092 \tabularnewline
74 & 142.18 & 140.081067859799 & 140.4525 & 0.997355460812724 & 1.0149836960288 \tabularnewline
75 & 143.79 & 141.268405515155 & 141.1 & 1.00119351888841 & 1.01784967045993 \tabularnewline
76 & 144.08 & 142.026208521228 & 141.745416666667 & 1.00198095896971 & 1.01446065131328 \tabularnewline
77 & 144.08 & 142.722875499450 & 142.39125 & 1.00232897386216 & 1.00950880856205 \tabularnewline
78 & 144.09 & 143.838170457354 & 143.0375 & 1.00559762619841 & 1.00175078382772 \tabularnewline
79 & 144.09 & 144.471494171779 & 143.646666666667 & 1.00574205809472 & 0.997359381004772 \tabularnewline
80 & 144.11 & 144.623329957997 & 144.024166666667 & 1.00416015801513 & 0.996450572959795 \tabularnewline
81 & 144.11 & 144.543602244009 & 144.140416666667 & 1.00279717227594 & 0.997000197606273 \tabularnewline
82 & 144.15 & 144.143716637146 & 144.173333333333 & 0.999794575768608 & 1.00004359095908 \tabularnewline
83 & 144.15 & 143.647464246894 & 144.1975 & 0.996185538909441 & 1.00349839626993 \tabularnewline
84 & 144.16 & 143.162249983828 & 144.231666666667 & 0.992585423800793 & 1.00696936529207 \tabularnewline
85 & 144.2 & 142.865833694235 & 144.268333333333 & 0.990278534403955 & 1.00933859601884 \tabularnewline
86 & 144.38 & 143.956209389832 & 144.337916666667 & 0.997355460812724 & 1.00294388558829 \tabularnewline
87 & 144.38 & 144.722940319286 & 144.550416666667 & 1.00119351888841 & 0.997630366557438 \tabularnewline
88 & 144.28 & 145.260102066303 & 144.972916666667 & 1.00198095896971 & 0.99325277862014 \tabularnewline
89 & 144.46 & 145.838865696944 & 145.5 & 1.00232897386216 & 0.990545279611475 \tabularnewline
90 & 144.53 & 146.853287339906 & 146.035833333333 & 1.00559762619841 & 0.98417953467716 \tabularnewline
91 & 144.53 & NA & NA & 1.00574205809472 & NA \tabularnewline
92 & 145.34 & NA & NA & 1.00416015801513 & NA \tabularnewline
93 & 147.98 & NA & NA & 1.00279717227594 & NA \tabularnewline
94 & 150.42 & NA & NA & 0.999794575768608 & NA \tabularnewline
95 & 150.53 & NA & NA & 0.996185538909441 & NA \tabularnewline
96 & 150.64 & NA & NA & 0.992585423800793 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=41285&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]106.07[/C][C]NA[/C][C]NA[/C][C]0.990278534403955[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]106.07[/C][C]NA[/C][C]NA[/C][C]0.997355460812724[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]106.07[/C][C]NA[/C][C]NA[/C][C]1.00119351888841[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]106.07[/C][C]NA[/C][C]NA[/C][C]1.00198095896971[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]106.07[/C][C]NA[/C][C]NA[/C][C]1.00232897386216[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]106.2[/C][C]NA[/C][C]NA[/C][C]1.00559762619841[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]107.5[/C][C]107.950904746405[/C][C]107.334583333333[/C][C]1.00574205809472[/C][C]0.995823057273446[/C][/ROW]
[ROW][C]8[/C][C]108.31[/C][C]107.994496194066[/C][C]107.547083333333[/C][C]1.00416015801513[/C][C]1.00292148041847[/C][/ROW]
[ROW][C]9[/C][C]108.53[/C][C]108.122844611257[/C][C]107.82125[/C][C]1.00279717227594[/C][C]1.00376567403685[/C][/ROW]
[ROW][C]10[/C][C]108.61[/C][C]108.161526436307[/C][C]108.18375[/C][C]0.999794575768608[/C][C]1.00414633168067[/C][/ROW]
[ROW][C]11[/C][C]108.62[/C][C]108.160014732477[/C][C]108.574166666667[/C][C]0.996185538909441[/C][C]1.00425282179058[/C][/ROW]
[ROW][C]12[/C][C]108.62[/C][C]108.152521354594[/C][C]108.960416666667[/C][C]0.992585423800793[/C][C]1.00432240172999[/C][/ROW]
[ROW][C]13[/C][C]108.62[/C][C]108.225065328672[/C][C]109.2875[/C][C]0.990278534403955[/C][C]1.00364919780948[/C][/ROW]
[ROW][C]14[/C][C]108.62[/C][C]109.237434669390[/C][C]109.527083333333[/C][C]0.997355460812724[/C][C]0.994347773991041[/C][/ROW]
[ROW][C]15[/C][C]110.1[/C][C]109.854707368133[/C][C]109.72375[/C][C]1.00119351888841[/C][C]1.00223288230194[/C][/ROW]
[ROW][C]16[/C][C]110.74[/C][C]110.128144692427[/C][C]109.910416666667[/C][C]1.00198095896971[/C][C]1.00555584868229[/C][/ROW]
[ROW][C]17[/C][C]110.77[/C][C]110.352243651499[/C][C]110.095833333333[/C][C]1.00232897386216[/C][C]1.00378566248114[/C][/ROW]
[ROW][C]18[/C][C]110.77[/C][C]110.898144215182[/C][C]110.280833333333[/C][C]1.00559762619841[/C][C]0.998844487289766[/C][/ROW]
[ROW][C]19[/C][C]110.78[/C][C]111.100134565815[/C][C]110.465833333333[/C][C]1.00574205809472[/C][C]0.997118504247851[/C][/ROW]
[ROW][C]20[/C][C]110.78[/C][C]111.111158284506[/C][C]110.650833333333[/C][C]1.00416015801513[/C][C]0.997019576704817[/C][/ROW]
[ROW][C]21[/C][C]110.78[/C][C]111.091124241194[/C][C]110.78125[/C][C]1.00279717227594[/C][C]0.997199378048252[/C][/ROW]
[ROW][C]22[/C][C]110.84[/C][C]110.876385290592[/C][C]110.899166666667[/C][C]0.999794575768608[/C][C]0.999671839134214[/C][/ROW]
[ROW][C]23[/C][C]110.84[/C][C]110.691571233230[/C][C]111.115416666667[/C][C]0.996185538909441[/C][C]1.00134092203332[/C][/ROW]
[ROW][C]24[/C][C]110.84[/C][C]110.566985397990[/C][C]111.392916666667[/C][C]0.992585423800793[/C][C]1.00246922353022[/C][/ROW]
[ROW][C]25[/C][C]110.84[/C][C]110.591005793786[/C][C]111.676666666667[/C][C]0.990278534403955[/C][C]1.00225148694893[/C][/ROW]
[ROW][C]26[/C][C]110.84[/C][C]111.665579651694[/C][C]111.961666666667[/C][C]0.997355460812724[/C][C]0.992606677417795[/C][/ROW]
[ROW][C]27[/C][C]111.01[/C][C]112.381886675393[/C][C]112.247916666667[/C][C]1.00119351888841[/C][C]0.98779263530825[/C][/ROW]
[ROW][C]28[/C][C]112.66[/C][C]112.755839757325[/C][C]112.532916666667[/C][C]1.00198095896971[/C][C]0.999150024002913[/C][/ROW]
[ROW][C]29[/C][C]114.04[/C][C]113.078160823332[/C][C]112.815416666667[/C][C]1.00232897386216[/C][C]1.00850596763924[/C][/ROW]
[ROW][C]30[/C][C]114.16[/C][C]113.730996527985[/C][C]113.097916666667[/C][C]1.00559762619841[/C][C]1.00377208927304[/C][/ROW]
[ROW][C]31[/C][C]114.2[/C][C]114.031453605970[/C][C]113.380416666667[/C][C]1.00574205809472[/C][C]1.00147806932825[/C][/ROW]
[ROW][C]32[/C][C]114.2[/C][C]114.135772360461[/C][C]113.662916666667[/C][C]1.00416015801513[/C][C]1.00056273014333[/C][/ROW]
[ROW][C]33[/C][C]114.23[/C][C]114.329741275490[/C][C]114.010833333333[/C][C]1.00279717227594[/C][C]0.999127599919516[/C][/ROW]
[ROW][C]34[/C][C]114.23[/C][C]114.373166819343[/C][C]114.396666666667[/C][C]0.999794575768608[/C][C]0.998748248183344[/C][/ROW]
[ROW][C]35[/C][C]114.23[/C][C]114.264971776760[/C][C]114.7025[/C][C]0.996185538909441[/C][C]0.999693941404646[/C][/ROW]
[ROW][C]36[/C][C]114.23[/C][C]114.098521620421[/C][C]114.950833333333[/C][C]0.992585423800793[/C][C]1.00115232325285[/C][/ROW]
[ROW][C]37[/C][C]114.23[/C][C]114.072660074328[/C][C]115.1925[/C][C]0.990278534403955[/C][C]1.0013792956662[/C][/ROW]
[ROW][C]38[/C][C]114.23[/C][C]115.150090292908[/C][C]115.455416666667[/C][C]0.997355460812724[/C][C]0.992009643322312[/C][/ROW]
[ROW][C]39[/C][C]115.97[/C][C]115.940295307109[/C][C]115.802083333333[/C][C]1.00119351888841[/C][C]1.00025620680724[/C][/ROW]
[ROW][C]40[/C][C]116.96[/C][C]116.454401972122[/C][C]116.224166666667[/C][C]1.00198095896971[/C][C]1.00434159653320[/C][/ROW]
[ROW][C]41[/C][C]117.08[/C][C]116.933786276122[/C][C]116.662083333333[/C][C]1.00232897386216[/C][C]1.00125039758426[/C][/ROW]
[ROW][C]42[/C][C]117.08[/C][C]117.757577022888[/C][C]117.102083333333[/C][C]1.00559762619841[/C][C]0.994246000639465[/C][/ROW]
[ROW][C]43[/C][C]117.08[/C][C]118.216597745216[/C][C]117.541666666667[/C][C]1.00574205809472[/C][C]0.990385463912047[/C][/ROW]
[ROW][C]44[/C][C]117.63[/C][C]118.470815442625[/C][C]117.98[/C][C]1.00416015801513[/C][C]0.992902763102595[/C][/ROW]
[ROW][C]45[/C][C]119.12[/C][C]118.677284849462[/C][C]118.34625[/C][C]1.00279717227594[/C][C]1.00373041185683[/C][/ROW]
[ROW][C]46[/C][C]119.47[/C][C]118.574803524010[/C][C]118.599166666667[/C][C]0.999794575768608[/C][C]1.00754963490881[/C][/ROW]
[ROW][C]47[/C][C]119.5[/C][C]118.355143568599[/C][C]118.808333333333[/C][C]0.996185538909441[/C][C]1.00967306022266[/C][/ROW]
[ROW][C]48[/C][C]119.52[/C][C]118.247942269168[/C][C]119.13125[/C][C]0.992585423800793[/C][C]1.01075754644369[/C][/ROW]
[ROW][C]49[/C][C]119.49[/C][C]118.431536089929[/C][C]119.594166666667[/C][C]0.990278534403955[/C][C]1.00893734848856[/C][/ROW]
[ROW][C]50[/C][C]119.49[/C][C]119.740418801299[/C][C]120.057916666667[/C][C]0.997355460812724[/C][C]0.997908652702189[/C][/ROW]
[ROW][C]51[/C][C]119.5[/C][C]120.585416070785[/C][C]120.441666666667[/C][C]1.00119351888841[/C][C]0.990998778242402[/C][/ROW]
[ROW][C]52[/C][C]119.5[/C][C]120.992958224189[/C][C]120.75375[/C][C]1.00198095896971[/C][C]0.98766078418033[/C][/ROW]
[ROW][C]53[/C][C]119.56[/C][C]121.331922286014[/C][C]121.05[/C][C]1.00232897386216[/C][C]0.985396075058982[/C][/ROW]
[ROW][C]54[/C][C]122.35[/C][C]122.024243951045[/C][C]121.345[/C][C]1.00559762619841[/C][C]1.00266960104326[/C][/ROW]
[ROW][C]55[/C][C]122.92[/C][C]122.351035722367[/C][C]121.6525[/C][C]1.00574205809472[/C][C]1.00465026122806[/C][/ROW]
[ROW][C]56[/C][C]122.92[/C][C]122.684104105630[/C][C]122.175833333333[/C][C]1.00416015801513[/C][C]1.00192279102570[/C][/ROW]
[ROW][C]57[/C][C]123.04[/C][C]123.302686806585[/C][C]122.95875[/C][C]1.00279717227594[/C][C]0.997869577594878[/C][/ROW]
[ROW][C]58[/C][C]123.04[/C][C]123.781650358399[/C][C]123.807083333333[/C][C]0.999794575768608[/C][C]0.99400839820562[/C][/ROW]
[ROW][C]59[/C][C]123.04[/C][C]124.264184123564[/C][C]124.74[/C][C]0.996185538909441[/C][C]0.99014853610316[/C][/ROW]
[ROW][C]60[/C][C]123.06[/C][C]124.839123060465[/C][C]125.771666666667[/C][C]0.992585423800793[/C][C]0.98574867383838[/C][/ROW]
[ROW][C]61[/C][C]123.33[/C][C]125.611468080414[/C][C]126.844583333333[/C][C]0.990278534403955[/C][C]0.981837103607824[/C][/ROW]
[ROW][C]62[/C][C]128.21[/C][C]127.609553387111[/C][C]127.947916666667[/C][C]0.997355460812724[/C][C]1.00470534217032[/C][/ROW]
[ROW][C]63[/C][C]129.57[/C][C]129.215287039637[/C][C]129.06125[/C][C]1.00119351888841[/C][C]1.00274513154356[/C][/ROW]
[ROW][C]64[/C][C]129.79[/C][C]130.431201365617[/C][C]130.173333333333[/C][C]1.00198095896971[/C][C]0.995083987888606[/C][/ROW]
[ROW][C]65[/C][C]131.66[/C][C]131.592429881783[/C][C]131.286666666667[/C][C]1.00232897386216[/C][C]1.00051348028361[/C][/ROW]
[ROW][C]66[/C][C]135.01[/C][C]133.140287710647[/C][C]132.399166666667[/C][C]1.00559762619841[/C][C]1.01404317447035[/C][/ROW]
[ROW][C]67[/C][C]136.01[/C][C]134.305537260396[/C][C]133.53875[/C][C]1.00574205809472[/C][C]1.01269093422634[/C][/ROW]
[ROW][C]68[/C][C]136.31[/C][C]135.264975685362[/C][C]134.704583333333[/C][C]1.00416015801513[/C][C]1.00772575686605[/C][/ROW]
[ROW][C]69[/C][C]136.37[/C][C]136.259244104545[/C][C]135.879166666667[/C][C]1.00279717227594[/C][C]1.00081283215816[/C][/ROW]
[ROW][C]70[/C][C]136.4[/C][C]137.038926433090[/C][C]137.067083333333[/C][C]0.999794575768608[/C][C]0.995337628149018[/C][/ROW]
[ROW][C]71[/C][C]136.4[/C][C]137.652917766507[/C][C]138.18[/C][C]0.996185538909441[/C][C]0.990897993396465[/C][/ROW]
[ROW][C]72[/C][C]136.4[/C][C]138.044644969615[/C][C]139.075833333333[/C][C]0.992585423800793[/C][C]0.988086137133554[/C][/ROW]
[ROW][C]73[/C][C]137.34[/C][C]138.431861556441[/C][C]139.790833333333[/C][C]0.990278534403955[/C][C]0.99211264268092[/C][/ROW]
[ROW][C]74[/C][C]142.18[/C][C]140.081067859799[/C][C]140.4525[/C][C]0.997355460812724[/C][C]1.0149836960288[/C][/ROW]
[ROW][C]75[/C][C]143.79[/C][C]141.268405515155[/C][C]141.1[/C][C]1.00119351888841[/C][C]1.01784967045993[/C][/ROW]
[ROW][C]76[/C][C]144.08[/C][C]142.026208521228[/C][C]141.745416666667[/C][C]1.00198095896971[/C][C]1.01446065131328[/C][/ROW]
[ROW][C]77[/C][C]144.08[/C][C]142.722875499450[/C][C]142.39125[/C][C]1.00232897386216[/C][C]1.00950880856205[/C][/ROW]
[ROW][C]78[/C][C]144.09[/C][C]143.838170457354[/C][C]143.0375[/C][C]1.00559762619841[/C][C]1.00175078382772[/C][/ROW]
[ROW][C]79[/C][C]144.09[/C][C]144.471494171779[/C][C]143.646666666667[/C][C]1.00574205809472[/C][C]0.997359381004772[/C][/ROW]
[ROW][C]80[/C][C]144.11[/C][C]144.623329957997[/C][C]144.024166666667[/C][C]1.00416015801513[/C][C]0.996450572959795[/C][/ROW]
[ROW][C]81[/C][C]144.11[/C][C]144.543602244009[/C][C]144.140416666667[/C][C]1.00279717227594[/C][C]0.997000197606273[/C][/ROW]
[ROW][C]82[/C][C]144.15[/C][C]144.143716637146[/C][C]144.173333333333[/C][C]0.999794575768608[/C][C]1.00004359095908[/C][/ROW]
[ROW][C]83[/C][C]144.15[/C][C]143.647464246894[/C][C]144.1975[/C][C]0.996185538909441[/C][C]1.00349839626993[/C][/ROW]
[ROW][C]84[/C][C]144.16[/C][C]143.162249983828[/C][C]144.231666666667[/C][C]0.992585423800793[/C][C]1.00696936529207[/C][/ROW]
[ROW][C]85[/C][C]144.2[/C][C]142.865833694235[/C][C]144.268333333333[/C][C]0.990278534403955[/C][C]1.00933859601884[/C][/ROW]
[ROW][C]86[/C][C]144.38[/C][C]143.956209389832[/C][C]144.337916666667[/C][C]0.997355460812724[/C][C]1.00294388558829[/C][/ROW]
[ROW][C]87[/C][C]144.38[/C][C]144.722940319286[/C][C]144.550416666667[/C][C]1.00119351888841[/C][C]0.997630366557438[/C][/ROW]
[ROW][C]88[/C][C]144.28[/C][C]145.260102066303[/C][C]144.972916666667[/C][C]1.00198095896971[/C][C]0.99325277862014[/C][/ROW]
[ROW][C]89[/C][C]144.46[/C][C]145.838865696944[/C][C]145.5[/C][C]1.00232897386216[/C][C]0.990545279611475[/C][/ROW]
[ROW][C]90[/C][C]144.53[/C][C]146.853287339906[/C][C]146.035833333333[/C][C]1.00559762619841[/C][C]0.98417953467716[/C][/ROW]
[ROW][C]91[/C][C]144.53[/C][C]NA[/C][C]NA[/C][C]1.00574205809472[/C][C]NA[/C][/ROW]
[ROW][C]92[/C][C]145.34[/C][C]NA[/C][C]NA[/C][C]1.00416015801513[/C][C]NA[/C][/ROW]
[ROW][C]93[/C][C]147.98[/C][C]NA[/C][C]NA[/C][C]1.00279717227594[/C][C]NA[/C][/ROW]
[ROW][C]94[/C][C]150.42[/C][C]NA[/C][C]NA[/C][C]0.999794575768608[/C][C]NA[/C][/ROW]
[ROW][C]95[/C][C]150.53[/C][C]NA[/C][C]NA[/C][C]0.996185538909441[/C][C]NA[/C][/ROW]
[ROW][C]96[/C][C]150.64[/C][C]NA[/C][C]NA[/C][C]0.992585423800793[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=41285&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=41285&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
1106.07NANA0.990278534403955NA
2106.07NANA0.997355460812724NA
3106.07NANA1.00119351888841NA
4106.07NANA1.00198095896971NA
5106.07NANA1.00232897386216NA
6106.2NANA1.00559762619841NA
7107.5107.950904746405107.3345833333331.005742058094720.995823057273446
8108.31107.994496194066107.5470833333331.004160158015131.00292148041847
9108.53108.122844611257107.821251.002797172275941.00376567403685
10108.61108.161526436307108.183750.9997945757686081.00414633168067
11108.62108.160014732477108.5741666666670.9961855389094411.00425282179058
12108.62108.152521354594108.9604166666670.9925854238007931.00432240172999
13108.62108.225065328672109.28750.9902785344039551.00364919780948
14108.62109.237434669390109.5270833333330.9973554608127240.994347773991041
15110.1109.854707368133109.723751.001193518888411.00223288230194
16110.74110.128144692427109.9104166666671.001980958969711.00555584868229
17110.77110.352243651499110.0958333333331.002328973862161.00378566248114
18110.77110.898144215182110.2808333333331.005597626198410.998844487289766
19110.78111.100134565815110.4658333333331.005742058094720.997118504247851
20110.78111.111158284506110.6508333333331.004160158015130.997019576704817
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88144.28145.260102066303144.9729166666671.001980958969710.99325277862014
89144.46145.838865696944145.51.002328973862160.990545279611475
90144.53146.853287339906146.0358333333331.005597626198410.98417953467716
91144.53NANA1.00574205809472NA
92145.34NANA1.00416015801513NA
93147.98NANA1.00279717227594NA
94150.42NANA0.999794575768608NA
95150.53NANA0.996185538909441NA
96150.64NANA0.992585423800793NA


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
Parameters (R input):
par1 = multiplicative ; 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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')