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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 computationSun, 11 Dec 2011 07:51:53 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/11/t1323607965xbvfb6o3ela49er.htm/, Retrieved Mon, 29 Apr 2024 06:44:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=153699, Retrieved Mon, 29 Apr 2024 06:44:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsKDGP2W92
Estimated Impact84

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [Decompositie gem....] [2011-12-11 12:51:53] [a207ad521877ea2910ca3b39cbf26b1e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
104,77
111,04
104,5
102,42
103,05
105,84
107,92
108,22
104,26
105,19
103,25
108,16
105,27
113,99
109,84
104,33
105,48
109,68
111,54
110,63
107,8
108,02
105,59
111,64
107
116,14
117,18
102,28
109,43
114,28
117,39
116,66
114,29
114,18
114,12
122,62
115,7
127,91
119,55
115,08
116,63
121,38
123,41
120,7
119,4
116,83
116,4
121,67
116,54
129,61
119,93
117,64
121,01
124,2
125,23
123,24
121,58
120,89
117,77
110,91

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'Herman Ole Andreas Wold' @ wold.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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153699&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153699&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153699&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'Herman Ole Andreas Wold' @ wold.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1104.77NANA-3.04796006944445NA
2111.04NANA7.40026909722221NA
3104.5NANA1.77589409722223NA
4102.42NANA-5.36056423611111NA
5103.05NANA-2.37035590277777NA
6105.84NANA1.6972482638889NA
7107.92108.713706597222105.7391666666672.97453993055556-0.793706597222211
8108.22107.528914930556105.8829166666671.645998263888890.691085069444455
9104.26104.905164930556106.228333333333-1.32316840277777-0.645164930555566
10105.19104.505477430556106.530416666667-2.024939236111120.684522569444454
11103.25103.125685763889106.71125-3.585564236111110.124314236111118
12108.16109.191102430556106.97252.21860243055555-1.03110243055555
13105.27104.235373263889107.283333333333-3.047960069444451.03462673611111
14113.99114.934852430556107.5345833333337.40026909722221-0.944852430555571
15109.84109.558394097222107.78251.775894097222230.281605902777784
16104.33102.687352430556108.047916666667-5.360564236111111.64264756944446
17105.48105.892977430556108.263333333333-2.37035590277777-0.412977430555543
18109.68110.203081597222108.5058333333331.6972482638889-0.523081597222216
19111.54111.697456597222108.7229166666672.97453993055556-0.157456597222236
20110.63110.530581597222108.8845833333331.645998263888890.099418402777772
21107.8107.956831597222109.28-1.32316840277777-0.156831597222236
22108.02107.475477430556109.500416666667-2.024939236111120.544522569444453
23105.59105.994019097222109.579583333333-3.58556423611111-0.404019097222218
24111.64112.154435763889109.9358333333332.21860243055555-0.514435763888869
25107107.323289930556110.37125-3.04796006944445-0.323289930555532
26116.14118.266519097222110.866257.40026909722221-2.12651909722221
27117.18113.163810763889111.3879166666671.775894097222234.01618923611113
28102.28106.554435763889111.915-5.36056423611111-4.27443576388887
29109.43110.156727430556112.527083333333-2.37035590277777-0.726727430555542
30114.28115.037248263889113.341.6972482638889-0.757248263888883
31117.39117.134539930556114.162.974539930555560.255460069444453
32116.66116.658914930556115.0129166666671.645998263888890.00108506944445708
33114.29114.278914930556115.602083333333-1.323168402777770.0110850694444622
34114.18114.209227430556116.234166666667-2.02493923611112-0.0292274305555367
35114.12113.481935763889117.0675-3.585564236111110.63806423611112
36122.62119.881935763889117.6633333333332.218602430555552.73806423611111
37115.7115.162039930556118.21-3.047960069444450.537960069444452
38127.91126.029435763889118.6291666666677.400269097222211.88056423611111
39119.55120.786310763889119.0104166666671.77589409722223-1.23631076388889
40115.08113.973185763889119.33375-5.360564236111111.10681423611112
41116.63117.168810763889119.539166666667-2.37035590277777-0.53881076388889
42121.38121.291831597222119.5945833333331.69724826388890.0881684027777681
43123.41122.564539930556119.592.974539930555560.845460069444428
44120.7121.341831597222119.6958333333331.64599826388889-0.641831597222222
45119.4118.459331597222119.7825-1.323168402777770.940668402777774
46116.83117.880060763889119.905-2.02493923611112-1.05006076388889
47116.4116.608602430556120.194166666667-3.58556423611111-0.208602430555544
48121.67122.712769097222120.4941666666672.21860243055555-1.04276909722222
49116.54117.639539930556120.6875-3.04796006944445-1.09953993055555
50129.61128.269435763889120.8691666666677.400269097222211.34056423611111
51119.93122.841727430556121.0658333333331.77589409722223-2.91172743055554
52117.64115.965269097222121.325833333333-5.360564236111111.67473090277777
53121.01119.181727430556121.552083333333-2.370355902777771.82827256944445
54124.2122.858081597222121.1608333333331.69724826388891.34191840277779
55125.23NANA2.97453993055556NA
56123.24NANA1.64599826388889NA
57121.58NANA-1.32316840277777NA
58120.89NANA-2.02493923611112NA
59117.77NANA-3.58556423611111NA
60110.91NANA2.21860243055555NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 104.77 & NA & NA & -3.04796006944445 & NA \tabularnewline
2 & 111.04 & NA & NA & 7.40026909722221 & NA \tabularnewline
3 & 104.5 & NA & NA & 1.77589409722223 & NA \tabularnewline
4 & 102.42 & NA & NA & -5.36056423611111 & NA \tabularnewline
5 & 103.05 & NA & NA & -2.37035590277777 & NA \tabularnewline
6 & 105.84 & NA & NA & 1.6972482638889 & NA \tabularnewline
7 & 107.92 & 108.713706597222 & 105.739166666667 & 2.97453993055556 & -0.793706597222211 \tabularnewline
8 & 108.22 & 107.528914930556 & 105.882916666667 & 1.64599826388889 & 0.691085069444455 \tabularnewline
9 & 104.26 & 104.905164930556 & 106.228333333333 & -1.32316840277777 & -0.645164930555566 \tabularnewline
10 & 105.19 & 104.505477430556 & 106.530416666667 & -2.02493923611112 & 0.684522569444454 \tabularnewline
11 & 103.25 & 103.125685763889 & 106.71125 & -3.58556423611111 & 0.124314236111118 \tabularnewline
12 & 108.16 & 109.191102430556 & 106.9725 & 2.21860243055555 & -1.03110243055555 \tabularnewline
13 & 105.27 & 104.235373263889 & 107.283333333333 & -3.04796006944445 & 1.03462673611111 \tabularnewline
14 & 113.99 & 114.934852430556 & 107.534583333333 & 7.40026909722221 & -0.944852430555571 \tabularnewline
15 & 109.84 & 109.558394097222 & 107.7825 & 1.77589409722223 & 0.281605902777784 \tabularnewline
16 & 104.33 & 102.687352430556 & 108.047916666667 & -5.36056423611111 & 1.64264756944446 \tabularnewline
17 & 105.48 & 105.892977430556 & 108.263333333333 & -2.37035590277777 & -0.412977430555543 \tabularnewline
18 & 109.68 & 110.203081597222 & 108.505833333333 & 1.6972482638889 & -0.523081597222216 \tabularnewline
19 & 111.54 & 111.697456597222 & 108.722916666667 & 2.97453993055556 & -0.157456597222236 \tabularnewline
20 & 110.63 & 110.530581597222 & 108.884583333333 & 1.64599826388889 & 0.099418402777772 \tabularnewline
21 & 107.8 & 107.956831597222 & 109.28 & -1.32316840277777 & -0.156831597222236 \tabularnewline
22 & 108.02 & 107.475477430556 & 109.500416666667 & -2.02493923611112 & 0.544522569444453 \tabularnewline
23 & 105.59 & 105.994019097222 & 109.579583333333 & -3.58556423611111 & -0.404019097222218 \tabularnewline
24 & 111.64 & 112.154435763889 & 109.935833333333 & 2.21860243055555 & -0.514435763888869 \tabularnewline
25 & 107 & 107.323289930556 & 110.37125 & -3.04796006944445 & -0.323289930555532 \tabularnewline
26 & 116.14 & 118.266519097222 & 110.86625 & 7.40026909722221 & -2.12651909722221 \tabularnewline
27 & 117.18 & 113.163810763889 & 111.387916666667 & 1.77589409722223 & 4.01618923611113 \tabularnewline
28 & 102.28 & 106.554435763889 & 111.915 & -5.36056423611111 & -4.27443576388887 \tabularnewline
29 & 109.43 & 110.156727430556 & 112.527083333333 & -2.37035590277777 & -0.726727430555542 \tabularnewline
30 & 114.28 & 115.037248263889 & 113.34 & 1.6972482638889 & -0.757248263888883 \tabularnewline
31 & 117.39 & 117.134539930556 & 114.16 & 2.97453993055556 & 0.255460069444453 \tabularnewline
32 & 116.66 & 116.658914930556 & 115.012916666667 & 1.64599826388889 & 0.00108506944445708 \tabularnewline
33 & 114.29 & 114.278914930556 & 115.602083333333 & -1.32316840277777 & 0.0110850694444622 \tabularnewline
34 & 114.18 & 114.209227430556 & 116.234166666667 & -2.02493923611112 & -0.0292274305555367 \tabularnewline
35 & 114.12 & 113.481935763889 & 117.0675 & -3.58556423611111 & 0.63806423611112 \tabularnewline
36 & 122.62 & 119.881935763889 & 117.663333333333 & 2.21860243055555 & 2.73806423611111 \tabularnewline
37 & 115.7 & 115.162039930556 & 118.21 & -3.04796006944445 & 0.537960069444452 \tabularnewline
38 & 127.91 & 126.029435763889 & 118.629166666667 & 7.40026909722221 & 1.88056423611111 \tabularnewline
39 & 119.55 & 120.786310763889 & 119.010416666667 & 1.77589409722223 & -1.23631076388889 \tabularnewline
40 & 115.08 & 113.973185763889 & 119.33375 & -5.36056423611111 & 1.10681423611112 \tabularnewline
41 & 116.63 & 117.168810763889 & 119.539166666667 & -2.37035590277777 & -0.53881076388889 \tabularnewline
42 & 121.38 & 121.291831597222 & 119.594583333333 & 1.6972482638889 & 0.0881684027777681 \tabularnewline
43 & 123.41 & 122.564539930556 & 119.59 & 2.97453993055556 & 0.845460069444428 \tabularnewline
44 & 120.7 & 121.341831597222 & 119.695833333333 & 1.64599826388889 & -0.641831597222222 \tabularnewline
45 & 119.4 & 118.459331597222 & 119.7825 & -1.32316840277777 & 0.940668402777774 \tabularnewline
46 & 116.83 & 117.880060763889 & 119.905 & -2.02493923611112 & -1.05006076388889 \tabularnewline
47 & 116.4 & 116.608602430556 & 120.194166666667 & -3.58556423611111 & -0.208602430555544 \tabularnewline
48 & 121.67 & 122.712769097222 & 120.494166666667 & 2.21860243055555 & -1.04276909722222 \tabularnewline
49 & 116.54 & 117.639539930556 & 120.6875 & -3.04796006944445 & -1.09953993055555 \tabularnewline
50 & 129.61 & 128.269435763889 & 120.869166666667 & 7.40026909722221 & 1.34056423611111 \tabularnewline
51 & 119.93 & 122.841727430556 & 121.065833333333 & 1.77589409722223 & -2.91172743055554 \tabularnewline
52 & 117.64 & 115.965269097222 & 121.325833333333 & -5.36056423611111 & 1.67473090277777 \tabularnewline
53 & 121.01 & 119.181727430556 & 121.552083333333 & -2.37035590277777 & 1.82827256944445 \tabularnewline
54 & 124.2 & 122.858081597222 & 121.160833333333 & 1.6972482638889 & 1.34191840277779 \tabularnewline
55 & 125.23 & NA & NA & 2.97453993055556 & NA \tabularnewline
56 & 123.24 & NA & NA & 1.64599826388889 & NA \tabularnewline
57 & 121.58 & NA & NA & -1.32316840277777 & NA \tabularnewline
58 & 120.89 & NA & NA & -2.02493923611112 & NA \tabularnewline
59 & 117.77 & NA & NA & -3.58556423611111 & NA \tabularnewline
60 & 110.91 & NA & NA & 2.21860243055555 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=153699&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]104.77[/C][C]NA[/C][C]NA[/C][C]-3.04796006944445[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]111.04[/C][C]NA[/C][C]NA[/C][C]7.40026909722221[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]104.5[/C][C]NA[/C][C]NA[/C][C]1.77589409722223[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]102.42[/C][C]NA[/C][C]NA[/C][C]-5.36056423611111[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]103.05[/C][C]NA[/C][C]NA[/C][C]-2.37035590277777[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]105.84[/C][C]NA[/C][C]NA[/C][C]1.6972482638889[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]107.92[/C][C]108.713706597222[/C][C]105.739166666667[/C][C]2.97453993055556[/C][C]-0.793706597222211[/C][/ROW]
[ROW][C]8[/C][C]108.22[/C][C]107.528914930556[/C][C]105.882916666667[/C][C]1.64599826388889[/C][C]0.691085069444455[/C][/ROW]
[ROW][C]9[/C][C]104.26[/C][C]104.905164930556[/C][C]106.228333333333[/C][C]-1.32316840277777[/C][C]-0.645164930555566[/C][/ROW]
[ROW][C]10[/C][C]105.19[/C][C]104.505477430556[/C][C]106.530416666667[/C][C]-2.02493923611112[/C][C]0.684522569444454[/C][/ROW]
[ROW][C]11[/C][C]103.25[/C][C]103.125685763889[/C][C]106.71125[/C][C]-3.58556423611111[/C][C]0.124314236111118[/C][/ROW]
[ROW][C]12[/C][C]108.16[/C][C]109.191102430556[/C][C]106.9725[/C][C]2.21860243055555[/C][C]-1.03110243055555[/C][/ROW]
[ROW][C]13[/C][C]105.27[/C][C]104.235373263889[/C][C]107.283333333333[/C][C]-3.04796006944445[/C][C]1.03462673611111[/C][/ROW]
[ROW][C]14[/C][C]113.99[/C][C]114.934852430556[/C][C]107.534583333333[/C][C]7.40026909722221[/C][C]-0.944852430555571[/C][/ROW]
[ROW][C]15[/C][C]109.84[/C][C]109.558394097222[/C][C]107.7825[/C][C]1.77589409722223[/C][C]0.281605902777784[/C][/ROW]
[ROW][C]16[/C][C]104.33[/C][C]102.687352430556[/C][C]108.047916666667[/C][C]-5.36056423611111[/C][C]1.64264756944446[/C][/ROW]
[ROW][C]17[/C][C]105.48[/C][C]105.892977430556[/C][C]108.263333333333[/C][C]-2.37035590277777[/C][C]-0.412977430555543[/C][/ROW]
[ROW][C]18[/C][C]109.68[/C][C]110.203081597222[/C][C]108.505833333333[/C][C]1.6972482638889[/C][C]-0.523081597222216[/C][/ROW]
[ROW][C]19[/C][C]111.54[/C][C]111.697456597222[/C][C]108.722916666667[/C][C]2.97453993055556[/C][C]-0.157456597222236[/C][/ROW]
[ROW][C]20[/C][C]110.63[/C][C]110.530581597222[/C][C]108.884583333333[/C][C]1.64599826388889[/C][C]0.099418402777772[/C][/ROW]
[ROW][C]21[/C][C]107.8[/C][C]107.956831597222[/C][C]109.28[/C][C]-1.32316840277777[/C][C]-0.156831597222236[/C][/ROW]
[ROW][C]22[/C][C]108.02[/C][C]107.475477430556[/C][C]109.500416666667[/C][C]-2.02493923611112[/C][C]0.544522569444453[/C][/ROW]
[ROW][C]23[/C][C]105.59[/C][C]105.994019097222[/C][C]109.579583333333[/C][C]-3.58556423611111[/C][C]-0.404019097222218[/C][/ROW]
[ROW][C]24[/C][C]111.64[/C][C]112.154435763889[/C][C]109.935833333333[/C][C]2.21860243055555[/C][C]-0.514435763888869[/C][/ROW]
[ROW][C]25[/C][C]107[/C][C]107.323289930556[/C][C]110.37125[/C][C]-3.04796006944445[/C][C]-0.323289930555532[/C][/ROW]
[ROW][C]26[/C][C]116.14[/C][C]118.266519097222[/C][C]110.86625[/C][C]7.40026909722221[/C][C]-2.12651909722221[/C][/ROW]
[ROW][C]27[/C][C]117.18[/C][C]113.163810763889[/C][C]111.387916666667[/C][C]1.77589409722223[/C][C]4.01618923611113[/C][/ROW]
[ROW][C]28[/C][C]102.28[/C][C]106.554435763889[/C][C]111.915[/C][C]-5.36056423611111[/C][C]-4.27443576388887[/C][/ROW]
[ROW][C]29[/C][C]109.43[/C][C]110.156727430556[/C][C]112.527083333333[/C][C]-2.37035590277777[/C][C]-0.726727430555542[/C][/ROW]
[ROW][C]30[/C][C]114.28[/C][C]115.037248263889[/C][C]113.34[/C][C]1.6972482638889[/C][C]-0.757248263888883[/C][/ROW]
[ROW][C]31[/C][C]117.39[/C][C]117.134539930556[/C][C]114.16[/C][C]2.97453993055556[/C][C]0.255460069444453[/C][/ROW]
[ROW][C]32[/C][C]116.66[/C][C]116.658914930556[/C][C]115.012916666667[/C][C]1.64599826388889[/C][C]0.00108506944445708[/C][/ROW]
[ROW][C]33[/C][C]114.29[/C][C]114.278914930556[/C][C]115.602083333333[/C][C]-1.32316840277777[/C][C]0.0110850694444622[/C][/ROW]
[ROW][C]34[/C][C]114.18[/C][C]114.209227430556[/C][C]116.234166666667[/C][C]-2.02493923611112[/C][C]-0.0292274305555367[/C][/ROW]
[ROW][C]35[/C][C]114.12[/C][C]113.481935763889[/C][C]117.0675[/C][C]-3.58556423611111[/C][C]0.63806423611112[/C][/ROW]
[ROW][C]36[/C][C]122.62[/C][C]119.881935763889[/C][C]117.663333333333[/C][C]2.21860243055555[/C][C]2.73806423611111[/C][/ROW]
[ROW][C]37[/C][C]115.7[/C][C]115.162039930556[/C][C]118.21[/C][C]-3.04796006944445[/C][C]0.537960069444452[/C][/ROW]
[ROW][C]38[/C][C]127.91[/C][C]126.029435763889[/C][C]118.629166666667[/C][C]7.40026909722221[/C][C]1.88056423611111[/C][/ROW]
[ROW][C]39[/C][C]119.55[/C][C]120.786310763889[/C][C]119.010416666667[/C][C]1.77589409722223[/C][C]-1.23631076388889[/C][/ROW]
[ROW][C]40[/C][C]115.08[/C][C]113.973185763889[/C][C]119.33375[/C][C]-5.36056423611111[/C][C]1.10681423611112[/C][/ROW]
[ROW][C]41[/C][C]116.63[/C][C]117.168810763889[/C][C]119.539166666667[/C][C]-2.37035590277777[/C][C]-0.53881076388889[/C][/ROW]
[ROW][C]42[/C][C]121.38[/C][C]121.291831597222[/C][C]119.594583333333[/C][C]1.6972482638889[/C][C]0.0881684027777681[/C][/ROW]
[ROW][C]43[/C][C]123.41[/C][C]122.564539930556[/C][C]119.59[/C][C]2.97453993055556[/C][C]0.845460069444428[/C][/ROW]
[ROW][C]44[/C][C]120.7[/C][C]121.341831597222[/C][C]119.695833333333[/C][C]1.64599826388889[/C][C]-0.641831597222222[/C][/ROW]
[ROW][C]45[/C][C]119.4[/C][C]118.459331597222[/C][C]119.7825[/C][C]-1.32316840277777[/C][C]0.940668402777774[/C][/ROW]
[ROW][C]46[/C][C]116.83[/C][C]117.880060763889[/C][C]119.905[/C][C]-2.02493923611112[/C][C]-1.05006076388889[/C][/ROW]
[ROW][C]47[/C][C]116.4[/C][C]116.608602430556[/C][C]120.194166666667[/C][C]-3.58556423611111[/C][C]-0.208602430555544[/C][/ROW]
[ROW][C]48[/C][C]121.67[/C][C]122.712769097222[/C][C]120.494166666667[/C][C]2.21860243055555[/C][C]-1.04276909722222[/C][/ROW]
[ROW][C]49[/C][C]116.54[/C][C]117.639539930556[/C][C]120.6875[/C][C]-3.04796006944445[/C][C]-1.09953993055555[/C][/ROW]
[ROW][C]50[/C][C]129.61[/C][C]128.269435763889[/C][C]120.869166666667[/C][C]7.40026909722221[/C][C]1.34056423611111[/C][/ROW]
[ROW][C]51[/C][C]119.93[/C][C]122.841727430556[/C][C]121.065833333333[/C][C]1.77589409722223[/C][C]-2.91172743055554[/C][/ROW]
[ROW][C]52[/C][C]117.64[/C][C]115.965269097222[/C][C]121.325833333333[/C][C]-5.36056423611111[/C][C]1.67473090277777[/C][/ROW]
[ROW][C]53[/C][C]121.01[/C][C]119.181727430556[/C][C]121.552083333333[/C][C]-2.37035590277777[/C][C]1.82827256944445[/C][/ROW]
[ROW][C]54[/C][C]124.2[/C][C]122.858081597222[/C][C]121.160833333333[/C][C]1.6972482638889[/C][C]1.34191840277779[/C][/ROW]
[ROW][C]55[/C][C]125.23[/C][C]NA[/C][C]NA[/C][C]2.97453993055556[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]123.24[/C][C]NA[/C][C]NA[/C][C]1.64599826388889[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]121.58[/C][C]NA[/C][C]NA[/C][C]-1.32316840277777[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]120.89[/C][C]NA[/C][C]NA[/C][C]-2.02493923611112[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]117.77[/C][C]NA[/C][C]NA[/C][C]-3.58556423611111[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]110.91[/C][C]NA[/C][C]NA[/C][C]2.21860243055555[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=153699&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=153699&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
1104.77NANA-3.04796006944445NA
2111.04NANA7.40026909722221NA
3104.5NANA1.77589409722223NA
4102.42NANA-5.36056423611111NA
5103.05NANA-2.37035590277777NA
6105.84NANA1.6972482638889NA
7107.92108.713706597222105.7391666666672.97453993055556-0.793706597222211
8108.22107.528914930556105.8829166666671.645998263888890.691085069444455
9104.26104.905164930556106.228333333333-1.32316840277777-0.645164930555566
10105.19104.505477430556106.530416666667-2.024939236111120.684522569444454
11103.25103.125685763889106.71125-3.585564236111110.124314236111118
12108.16109.191102430556106.97252.21860243055555-1.03110243055555
13105.27104.235373263889107.283333333333-3.047960069444451.03462673611111
14113.99114.934852430556107.5345833333337.40026909722221-0.944852430555571
15109.84109.558394097222107.78251.775894097222230.281605902777784
16104.33102.687352430556108.047916666667-5.360564236111111.64264756944446
17105.48105.892977430556108.263333333333-2.37035590277777-0.412977430555543
18109.68110.203081597222108.5058333333331.6972482638889-0.523081597222216
19111.54111.697456597222108.7229166666672.97453993055556-0.157456597222236
20110.63110.530581597222108.8845833333331.645998263888890.099418402777772
21107.8107.956831597222109.28-1.32316840277777-0.156831597222236
22108.02107.475477430556109.500416666667-2.024939236111120.544522569444453
23105.59105.994019097222109.579583333333-3.58556423611111-0.404019097222218
24111.64112.154435763889109.9358333333332.21860243055555-0.514435763888869
25107107.323289930556110.37125-3.04796006944445-0.323289930555532
26116.14118.266519097222110.866257.40026909722221-2.12651909722221
27117.18113.163810763889111.3879166666671.775894097222234.01618923611113
28102.28106.554435763889111.915-5.36056423611111-4.27443576388887
29109.43110.156727430556112.527083333333-2.37035590277777-0.726727430555542
30114.28115.037248263889113.341.6972482638889-0.757248263888883
31117.39117.134539930556114.162.974539930555560.255460069444453
32116.66116.658914930556115.0129166666671.645998263888890.00108506944445708
33114.29114.278914930556115.602083333333-1.323168402777770.0110850694444622
34114.18114.209227430556116.234166666667-2.02493923611112-0.0292274305555367
35114.12113.481935763889117.0675-3.585564236111110.63806423611112
36122.62119.881935763889117.6633333333332.218602430555552.73806423611111
37115.7115.162039930556118.21-3.047960069444450.537960069444452
38127.91126.029435763889118.6291666666677.400269097222211.88056423611111
39119.55120.786310763889119.0104166666671.77589409722223-1.23631076388889
40115.08113.973185763889119.33375-5.360564236111111.10681423611112
41116.63117.168810763889119.539166666667-2.37035590277777-0.53881076388889
42121.38121.291831597222119.5945833333331.69724826388890.0881684027777681
43123.41122.564539930556119.592.974539930555560.845460069444428
44120.7121.341831597222119.6958333333331.64599826388889-0.641831597222222
45119.4118.459331597222119.7825-1.323168402777770.940668402777774
46116.83117.880060763889119.905-2.02493923611112-1.05006076388889
47116.4116.608602430556120.194166666667-3.58556423611111-0.208602430555544
48121.67122.712769097222120.4941666666672.21860243055555-1.04276909722222
49116.54117.639539930556120.6875-3.04796006944445-1.09953993055555
50129.61128.269435763889120.8691666666677.400269097222211.34056423611111
51119.93122.841727430556121.0658333333331.77589409722223-2.91172743055554
52117.64115.965269097222121.325833333333-5.360564236111111.67473090277777
53121.01119.181727430556121.552083333333-2.370355902777771.82827256944445
54124.2122.858081597222121.1608333333331.69724826388891.34191840277779
55125.23NANA2.97453993055556NA
56123.24NANA1.64599826388889NA
57121.58NANA-1.32316840277777NA
58120.89NANA-2.02493923611112NA
59117.77NANA-3.58556423611111NA
60110.91NANA2.21860243055555NA


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 <- 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')