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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 computationSat, 29 Nov 2014 13:34:55 +0000
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/Nov/29/t14172682897bz9jf1elljt062.htm/, Retrieved Sun, 19 May 2024 23:30:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=261112, Retrieved Sun, 19 May 2024 23:30:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact93

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2014-11-29 13:34:55] [af43fcfc4e3257f4a3dbe682dec77e63] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109,03
110,43
111,01
111,01
110,76
111,13
111,07
111,09
110,96
110,64
110,62
110,59
111,33
113,94
114,61
114,64
114,62
114,71
114,72
114,66
114,76
114,68
114,75
114,74
116,36
117,53
118,82
119,83
119,97
121,29
120,94
121,02
120,98
121,02
120,89
120,76
123,28
123,98
125,91
125,84
125,98
127,24
127,23
127,82
127,59
127,74
127,44
127,35
128,54
129,3
130,67
130,76
131,34
130,69
130,96
130,68
130,61
130,59
130,44
129,04
131,46
132,77
134,48
134,52
136,11
136,12
136,03
135,84
137,75
137,45
136,84
136,79
140,12
140,68
140,35
140,42
140,19
140,14
140,13
139,45
139,59
139,44
139,53
139,28

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'George Udny Yule' @ yule.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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261112&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261112&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261112&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 time2 seconds
R Server'George Udny Yule' @ yule.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1109.03NANA-0.389664NA
2110.43NANA0.396586NA
3111.01NANA1.10749NA
4111.01NANA0.903669NA
5110.76NANA0.869572NA
6111.13NANA0.799572NA
7111.07111.177110.7910.385822-0.106655
8111.09111.019111.033-0.01348380.0705671
9110.96111.159111.329-0.170637-0.19853
10110.64110.963111.63-0.666956-0.323461
11110.62110.677111.942-1.26557-0.0569329
12110.59110.296112.252-1.95640.2939
13111.33112.164112.554-0.389664-0.834086
14113.94113.251112.8550.3965860.688831
15114.61114.269113.1621.107490.340845
16114.64114.392113.4880.9036690.247998
17114.62114.698113.8290.869572-0.0783218
18114.71114.973114.1740.799572-0.263322
19114.72114.942114.5560.385822-0.222072
20114.66114.902114.915-0.0134838-0.241933
21114.76115.07115.24-0.170637-0.30978
22114.68114.965115.632-0.666956-0.285127
23114.75114.806116.071-1.26557-0.0556829
24114.74114.612116.568-1.95640.128067
25116.36116.712117.102-0.389664-0.352002
26117.53118.022117.6260.396586-0.492419
27118.82119.257118.151.10749-0.437488
28119.83119.577118.6730.9036690.252998
29119.97120.063119.1930.869572-0.0929051
30121.29120.5119.70.7995720.790428
31120.94120.625120.2390.3858220.315012
32121.02120.783120.796-0.01348380.237234
33120.98121.19121.36-0.170637-0.20978
34121.02121.239121.906-0.666956-0.219294
35120.89121.142122.407-1.26557-0.251516
36120.76120.949122.905-1.9564-0.189016
37123.28123.026123.415-0.3896640.254248
38123.98124.357123.9610.396586-0.377419
39125.91125.627124.521.107490.282928
40125.84125.979125.0750.903669-0.138669
41125.98126.497125.6280.869572-0.517488
42127.24126.975126.1750.7995720.265012
43127.23127.055126.6690.3858220.175012
44127.82127.097127.11-0.01348380.723484
45127.59127.359127.53-0.1706370.230637
46127.74127.266127.933-0.6669560.473623
47127.44127.096128.362-1.265570.3439
48127.35126.772128.729-1.95640.57765
49128.54128.638129.028-0.389664-0.0982523
50129.3129.699129.3020.396586-0.399086
51130.67130.655129.5481.107490.0150116
52130.76130.696129.7920.9036690.0642477
53131.34130.905130.0360.8695720.434595
54130.69131.031130.2310.799572-0.340822
55130.96130.809130.4230.3858220.150845
56130.68130.676130.69-0.01348380.00390046
57130.61130.822130.993-0.170637-0.21228
58130.59130.641131.308-0.666956-0.0513773
59130.44130.398131.664-1.265570.0418171
60129.04130.132132.089-1.9564-1.09235
61131.46132.137132.526-0.389664-0.676586
62132.77133.349132.9530.396586-0.579086
63134.48134.572133.4651.10749-0.0924884
64134.52134.952134.0480.903669-0.432002
65136.11135.47134.6010.8695720.639595
66136.12135.99135.190.7995720.130012
67136.03136.26135.8740.385822-0.229988
68135.84136.551136.565-0.0134838-0.7111
69137.75136.968137.139-0.1706370.781887
70137.45136.962137.629-0.6669560.487789
71136.84136.779138.045-1.265570.0605671
72136.79136.426138.382-1.95640.3639
73140.12138.331138.721-0.3896641.78883
74140.68139.439139.0420.3965861.24133
75140.35140.377139.2691.10749-0.0266551
76140.42140.332139.4290.9036690.087581
77140.19140.493139.6240.869572-0.303322
78140.14140.639139.840.799572-0.499155
79140.13NANA0.385822NA
80139.45NANA-0.0134838NA
81139.59NANA-0.170637NA
82139.44NANA-0.666956NA
83139.53NANA-1.26557NA
84139.28NANA-1.9564NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 109.03 & NA & NA & -0.389664 & NA \tabularnewline
2 & 110.43 & NA & NA & 0.396586 & NA \tabularnewline
3 & 111.01 & NA & NA & 1.10749 & NA \tabularnewline
4 & 111.01 & NA & NA & 0.903669 & NA \tabularnewline
5 & 110.76 & NA & NA & 0.869572 & NA \tabularnewline
6 & 111.13 & NA & NA & 0.799572 & NA \tabularnewline
7 & 111.07 & 111.177 & 110.791 & 0.385822 & -0.106655 \tabularnewline
8 & 111.09 & 111.019 & 111.033 & -0.0134838 & 0.0705671 \tabularnewline
9 & 110.96 & 111.159 & 111.329 & -0.170637 & -0.19853 \tabularnewline
10 & 110.64 & 110.963 & 111.63 & -0.666956 & -0.323461 \tabularnewline
11 & 110.62 & 110.677 & 111.942 & -1.26557 & -0.0569329 \tabularnewline
12 & 110.59 & 110.296 & 112.252 & -1.9564 & 0.2939 \tabularnewline
13 & 111.33 & 112.164 & 112.554 & -0.389664 & -0.834086 \tabularnewline
14 & 113.94 & 113.251 & 112.855 & 0.396586 & 0.688831 \tabularnewline
15 & 114.61 & 114.269 & 113.162 & 1.10749 & 0.340845 \tabularnewline
16 & 114.64 & 114.392 & 113.488 & 0.903669 & 0.247998 \tabularnewline
17 & 114.62 & 114.698 & 113.829 & 0.869572 & -0.0783218 \tabularnewline
18 & 114.71 & 114.973 & 114.174 & 0.799572 & -0.263322 \tabularnewline
19 & 114.72 & 114.942 & 114.556 & 0.385822 & -0.222072 \tabularnewline
20 & 114.66 & 114.902 & 114.915 & -0.0134838 & -0.241933 \tabularnewline
21 & 114.76 & 115.07 & 115.24 & -0.170637 & -0.30978 \tabularnewline
22 & 114.68 & 114.965 & 115.632 & -0.666956 & -0.285127 \tabularnewline
23 & 114.75 & 114.806 & 116.071 & -1.26557 & -0.0556829 \tabularnewline
24 & 114.74 & 114.612 & 116.568 & -1.9564 & 0.128067 \tabularnewline
25 & 116.36 & 116.712 & 117.102 & -0.389664 & -0.352002 \tabularnewline
26 & 117.53 & 118.022 & 117.626 & 0.396586 & -0.492419 \tabularnewline
27 & 118.82 & 119.257 & 118.15 & 1.10749 & -0.437488 \tabularnewline
28 & 119.83 & 119.577 & 118.673 & 0.903669 & 0.252998 \tabularnewline
29 & 119.97 & 120.063 & 119.193 & 0.869572 & -0.0929051 \tabularnewline
30 & 121.29 & 120.5 & 119.7 & 0.799572 & 0.790428 \tabularnewline
31 & 120.94 & 120.625 & 120.239 & 0.385822 & 0.315012 \tabularnewline
32 & 121.02 & 120.783 & 120.796 & -0.0134838 & 0.237234 \tabularnewline
33 & 120.98 & 121.19 & 121.36 & -0.170637 & -0.20978 \tabularnewline
34 & 121.02 & 121.239 & 121.906 & -0.666956 & -0.219294 \tabularnewline
35 & 120.89 & 121.142 & 122.407 & -1.26557 & -0.251516 \tabularnewline
36 & 120.76 & 120.949 & 122.905 & -1.9564 & -0.189016 \tabularnewline
37 & 123.28 & 123.026 & 123.415 & -0.389664 & 0.254248 \tabularnewline
38 & 123.98 & 124.357 & 123.961 & 0.396586 & -0.377419 \tabularnewline
39 & 125.91 & 125.627 & 124.52 & 1.10749 & 0.282928 \tabularnewline
40 & 125.84 & 125.979 & 125.075 & 0.903669 & -0.138669 \tabularnewline
41 & 125.98 & 126.497 & 125.628 & 0.869572 & -0.517488 \tabularnewline
42 & 127.24 & 126.975 & 126.175 & 0.799572 & 0.265012 \tabularnewline
43 & 127.23 & 127.055 & 126.669 & 0.385822 & 0.175012 \tabularnewline
44 & 127.82 & 127.097 & 127.11 & -0.0134838 & 0.723484 \tabularnewline
45 & 127.59 & 127.359 & 127.53 & -0.170637 & 0.230637 \tabularnewline
46 & 127.74 & 127.266 & 127.933 & -0.666956 & 0.473623 \tabularnewline
47 & 127.44 & 127.096 & 128.362 & -1.26557 & 0.3439 \tabularnewline
48 & 127.35 & 126.772 & 128.729 & -1.9564 & 0.57765 \tabularnewline
49 & 128.54 & 128.638 & 129.028 & -0.389664 & -0.0982523 \tabularnewline
50 & 129.3 & 129.699 & 129.302 & 0.396586 & -0.399086 \tabularnewline
51 & 130.67 & 130.655 & 129.548 & 1.10749 & 0.0150116 \tabularnewline
52 & 130.76 & 130.696 & 129.792 & 0.903669 & 0.0642477 \tabularnewline
53 & 131.34 & 130.905 & 130.036 & 0.869572 & 0.434595 \tabularnewline
54 & 130.69 & 131.031 & 130.231 & 0.799572 & -0.340822 \tabularnewline
55 & 130.96 & 130.809 & 130.423 & 0.385822 & 0.150845 \tabularnewline
56 & 130.68 & 130.676 & 130.69 & -0.0134838 & 0.00390046 \tabularnewline
57 & 130.61 & 130.822 & 130.993 & -0.170637 & -0.21228 \tabularnewline
58 & 130.59 & 130.641 & 131.308 & -0.666956 & -0.0513773 \tabularnewline
59 & 130.44 & 130.398 & 131.664 & -1.26557 & 0.0418171 \tabularnewline
60 & 129.04 & 130.132 & 132.089 & -1.9564 & -1.09235 \tabularnewline
61 & 131.46 & 132.137 & 132.526 & -0.389664 & -0.676586 \tabularnewline
62 & 132.77 & 133.349 & 132.953 & 0.396586 & -0.579086 \tabularnewline
63 & 134.48 & 134.572 & 133.465 & 1.10749 & -0.0924884 \tabularnewline
64 & 134.52 & 134.952 & 134.048 & 0.903669 & -0.432002 \tabularnewline
65 & 136.11 & 135.47 & 134.601 & 0.869572 & 0.639595 \tabularnewline
66 & 136.12 & 135.99 & 135.19 & 0.799572 & 0.130012 \tabularnewline
67 & 136.03 & 136.26 & 135.874 & 0.385822 & -0.229988 \tabularnewline
68 & 135.84 & 136.551 & 136.565 & -0.0134838 & -0.7111 \tabularnewline
69 & 137.75 & 136.968 & 137.139 & -0.170637 & 0.781887 \tabularnewline
70 & 137.45 & 136.962 & 137.629 & -0.666956 & 0.487789 \tabularnewline
71 & 136.84 & 136.779 & 138.045 & -1.26557 & 0.0605671 \tabularnewline
72 & 136.79 & 136.426 & 138.382 & -1.9564 & 0.3639 \tabularnewline
73 & 140.12 & 138.331 & 138.721 & -0.389664 & 1.78883 \tabularnewline
74 & 140.68 & 139.439 & 139.042 & 0.396586 & 1.24133 \tabularnewline
75 & 140.35 & 140.377 & 139.269 & 1.10749 & -0.0266551 \tabularnewline
76 & 140.42 & 140.332 & 139.429 & 0.903669 & 0.087581 \tabularnewline
77 & 140.19 & 140.493 & 139.624 & 0.869572 & -0.303322 \tabularnewline
78 & 140.14 & 140.639 & 139.84 & 0.799572 & -0.499155 \tabularnewline
79 & 140.13 & NA & NA & 0.385822 & NA \tabularnewline
80 & 139.45 & NA & NA & -0.0134838 & NA \tabularnewline
81 & 139.59 & NA & NA & -0.170637 & NA \tabularnewline
82 & 139.44 & NA & NA & -0.666956 & NA \tabularnewline
83 & 139.53 & NA & NA & -1.26557 & NA \tabularnewline
84 & 139.28 & NA & NA & -1.9564 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=261112&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]109.03[/C][C]NA[/C][C]NA[/C][C]-0.389664[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]110.43[/C][C]NA[/C][C]NA[/C][C]0.396586[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]111.01[/C][C]NA[/C][C]NA[/C][C]1.10749[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]111.01[/C][C]NA[/C][C]NA[/C][C]0.903669[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]110.76[/C][C]NA[/C][C]NA[/C][C]0.869572[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]111.13[/C][C]NA[/C][C]NA[/C][C]0.799572[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]111.07[/C][C]111.177[/C][C]110.791[/C][C]0.385822[/C][C]-0.106655[/C][/ROW]
[ROW][C]8[/C][C]111.09[/C][C]111.019[/C][C]111.033[/C][C]-0.0134838[/C][C]0.0705671[/C][/ROW]
[ROW][C]9[/C][C]110.96[/C][C]111.159[/C][C]111.329[/C][C]-0.170637[/C][C]-0.19853[/C][/ROW]
[ROW][C]10[/C][C]110.64[/C][C]110.963[/C][C]111.63[/C][C]-0.666956[/C][C]-0.323461[/C][/ROW]
[ROW][C]11[/C][C]110.62[/C][C]110.677[/C][C]111.942[/C][C]-1.26557[/C][C]-0.0569329[/C][/ROW]
[ROW][C]12[/C][C]110.59[/C][C]110.296[/C][C]112.252[/C][C]-1.9564[/C][C]0.2939[/C][/ROW]
[ROW][C]13[/C][C]111.33[/C][C]112.164[/C][C]112.554[/C][C]-0.389664[/C][C]-0.834086[/C][/ROW]
[ROW][C]14[/C][C]113.94[/C][C]113.251[/C][C]112.855[/C][C]0.396586[/C][C]0.688831[/C][/ROW]
[ROW][C]15[/C][C]114.61[/C][C]114.269[/C][C]113.162[/C][C]1.10749[/C][C]0.340845[/C][/ROW]
[ROW][C]16[/C][C]114.64[/C][C]114.392[/C][C]113.488[/C][C]0.903669[/C][C]0.247998[/C][/ROW]
[ROW][C]17[/C][C]114.62[/C][C]114.698[/C][C]113.829[/C][C]0.869572[/C][C]-0.0783218[/C][/ROW]
[ROW][C]18[/C][C]114.71[/C][C]114.973[/C][C]114.174[/C][C]0.799572[/C][C]-0.263322[/C][/ROW]
[ROW][C]19[/C][C]114.72[/C][C]114.942[/C][C]114.556[/C][C]0.385822[/C][C]-0.222072[/C][/ROW]
[ROW][C]20[/C][C]114.66[/C][C]114.902[/C][C]114.915[/C][C]-0.0134838[/C][C]-0.241933[/C][/ROW]
[ROW][C]21[/C][C]114.76[/C][C]115.07[/C][C]115.24[/C][C]-0.170637[/C][C]-0.30978[/C][/ROW]
[ROW][C]22[/C][C]114.68[/C][C]114.965[/C][C]115.632[/C][C]-0.666956[/C][C]-0.285127[/C][/ROW]
[ROW][C]23[/C][C]114.75[/C][C]114.806[/C][C]116.071[/C][C]-1.26557[/C][C]-0.0556829[/C][/ROW]
[ROW][C]24[/C][C]114.74[/C][C]114.612[/C][C]116.568[/C][C]-1.9564[/C][C]0.128067[/C][/ROW]
[ROW][C]25[/C][C]116.36[/C][C]116.712[/C][C]117.102[/C][C]-0.389664[/C][C]-0.352002[/C][/ROW]
[ROW][C]26[/C][C]117.53[/C][C]118.022[/C][C]117.626[/C][C]0.396586[/C][C]-0.492419[/C][/ROW]
[ROW][C]27[/C][C]118.82[/C][C]119.257[/C][C]118.15[/C][C]1.10749[/C][C]-0.437488[/C][/ROW]
[ROW][C]28[/C][C]119.83[/C][C]119.577[/C][C]118.673[/C][C]0.903669[/C][C]0.252998[/C][/ROW]
[ROW][C]29[/C][C]119.97[/C][C]120.063[/C][C]119.193[/C][C]0.869572[/C][C]-0.0929051[/C][/ROW]
[ROW][C]30[/C][C]121.29[/C][C]120.5[/C][C]119.7[/C][C]0.799572[/C][C]0.790428[/C][/ROW]
[ROW][C]31[/C][C]120.94[/C][C]120.625[/C][C]120.239[/C][C]0.385822[/C][C]0.315012[/C][/ROW]
[ROW][C]32[/C][C]121.02[/C][C]120.783[/C][C]120.796[/C][C]-0.0134838[/C][C]0.237234[/C][/ROW]
[ROW][C]33[/C][C]120.98[/C][C]121.19[/C][C]121.36[/C][C]-0.170637[/C][C]-0.20978[/C][/ROW]
[ROW][C]34[/C][C]121.02[/C][C]121.239[/C][C]121.906[/C][C]-0.666956[/C][C]-0.219294[/C][/ROW]
[ROW][C]35[/C][C]120.89[/C][C]121.142[/C][C]122.407[/C][C]-1.26557[/C][C]-0.251516[/C][/ROW]
[ROW][C]36[/C][C]120.76[/C][C]120.949[/C][C]122.905[/C][C]-1.9564[/C][C]-0.189016[/C][/ROW]
[ROW][C]37[/C][C]123.28[/C][C]123.026[/C][C]123.415[/C][C]-0.389664[/C][C]0.254248[/C][/ROW]
[ROW][C]38[/C][C]123.98[/C][C]124.357[/C][C]123.961[/C][C]0.396586[/C][C]-0.377419[/C][/ROW]
[ROW][C]39[/C][C]125.91[/C][C]125.627[/C][C]124.52[/C][C]1.10749[/C][C]0.282928[/C][/ROW]
[ROW][C]40[/C][C]125.84[/C][C]125.979[/C][C]125.075[/C][C]0.903669[/C][C]-0.138669[/C][/ROW]
[ROW][C]41[/C][C]125.98[/C][C]126.497[/C][C]125.628[/C][C]0.869572[/C][C]-0.517488[/C][/ROW]
[ROW][C]42[/C][C]127.24[/C][C]126.975[/C][C]126.175[/C][C]0.799572[/C][C]0.265012[/C][/ROW]
[ROW][C]43[/C][C]127.23[/C][C]127.055[/C][C]126.669[/C][C]0.385822[/C][C]0.175012[/C][/ROW]
[ROW][C]44[/C][C]127.82[/C][C]127.097[/C][C]127.11[/C][C]-0.0134838[/C][C]0.723484[/C][/ROW]
[ROW][C]45[/C][C]127.59[/C][C]127.359[/C][C]127.53[/C][C]-0.170637[/C][C]0.230637[/C][/ROW]
[ROW][C]46[/C][C]127.74[/C][C]127.266[/C][C]127.933[/C][C]-0.666956[/C][C]0.473623[/C][/ROW]
[ROW][C]47[/C][C]127.44[/C][C]127.096[/C][C]128.362[/C][C]-1.26557[/C][C]0.3439[/C][/ROW]
[ROW][C]48[/C][C]127.35[/C][C]126.772[/C][C]128.729[/C][C]-1.9564[/C][C]0.57765[/C][/ROW]
[ROW][C]49[/C][C]128.54[/C][C]128.638[/C][C]129.028[/C][C]-0.389664[/C][C]-0.0982523[/C][/ROW]
[ROW][C]50[/C][C]129.3[/C][C]129.699[/C][C]129.302[/C][C]0.396586[/C][C]-0.399086[/C][/ROW]
[ROW][C]51[/C][C]130.67[/C][C]130.655[/C][C]129.548[/C][C]1.10749[/C][C]0.0150116[/C][/ROW]
[ROW][C]52[/C][C]130.76[/C][C]130.696[/C][C]129.792[/C][C]0.903669[/C][C]0.0642477[/C][/ROW]
[ROW][C]53[/C][C]131.34[/C][C]130.905[/C][C]130.036[/C][C]0.869572[/C][C]0.434595[/C][/ROW]
[ROW][C]54[/C][C]130.69[/C][C]131.031[/C][C]130.231[/C][C]0.799572[/C][C]-0.340822[/C][/ROW]
[ROW][C]55[/C][C]130.96[/C][C]130.809[/C][C]130.423[/C][C]0.385822[/C][C]0.150845[/C][/ROW]
[ROW][C]56[/C][C]130.68[/C][C]130.676[/C][C]130.69[/C][C]-0.0134838[/C][C]0.00390046[/C][/ROW]
[ROW][C]57[/C][C]130.61[/C][C]130.822[/C][C]130.993[/C][C]-0.170637[/C][C]-0.21228[/C][/ROW]
[ROW][C]58[/C][C]130.59[/C][C]130.641[/C][C]131.308[/C][C]-0.666956[/C][C]-0.0513773[/C][/ROW]
[ROW][C]59[/C][C]130.44[/C][C]130.398[/C][C]131.664[/C][C]-1.26557[/C][C]0.0418171[/C][/ROW]
[ROW][C]60[/C][C]129.04[/C][C]130.132[/C][C]132.089[/C][C]-1.9564[/C][C]-1.09235[/C][/ROW]
[ROW][C]61[/C][C]131.46[/C][C]132.137[/C][C]132.526[/C][C]-0.389664[/C][C]-0.676586[/C][/ROW]
[ROW][C]62[/C][C]132.77[/C][C]133.349[/C][C]132.953[/C][C]0.396586[/C][C]-0.579086[/C][/ROW]
[ROW][C]63[/C][C]134.48[/C][C]134.572[/C][C]133.465[/C][C]1.10749[/C][C]-0.0924884[/C][/ROW]
[ROW][C]64[/C][C]134.52[/C][C]134.952[/C][C]134.048[/C][C]0.903669[/C][C]-0.432002[/C][/ROW]
[ROW][C]65[/C][C]136.11[/C][C]135.47[/C][C]134.601[/C][C]0.869572[/C][C]0.639595[/C][/ROW]
[ROW][C]66[/C][C]136.12[/C][C]135.99[/C][C]135.19[/C][C]0.799572[/C][C]0.130012[/C][/ROW]
[ROW][C]67[/C][C]136.03[/C][C]136.26[/C][C]135.874[/C][C]0.385822[/C][C]-0.229988[/C][/ROW]
[ROW][C]68[/C][C]135.84[/C][C]136.551[/C][C]136.565[/C][C]-0.0134838[/C][C]-0.7111[/C][/ROW]
[ROW][C]69[/C][C]137.75[/C][C]136.968[/C][C]137.139[/C][C]-0.170637[/C][C]0.781887[/C][/ROW]
[ROW][C]70[/C][C]137.45[/C][C]136.962[/C][C]137.629[/C][C]-0.666956[/C][C]0.487789[/C][/ROW]
[ROW][C]71[/C][C]136.84[/C][C]136.779[/C][C]138.045[/C][C]-1.26557[/C][C]0.0605671[/C][/ROW]
[ROW][C]72[/C][C]136.79[/C][C]136.426[/C][C]138.382[/C][C]-1.9564[/C][C]0.3639[/C][/ROW]
[ROW][C]73[/C][C]140.12[/C][C]138.331[/C][C]138.721[/C][C]-0.389664[/C][C]1.78883[/C][/ROW]
[ROW][C]74[/C][C]140.68[/C][C]139.439[/C][C]139.042[/C][C]0.396586[/C][C]1.24133[/C][/ROW]
[ROW][C]75[/C][C]140.35[/C][C]140.377[/C][C]139.269[/C][C]1.10749[/C][C]-0.0266551[/C][/ROW]
[ROW][C]76[/C][C]140.42[/C][C]140.332[/C][C]139.429[/C][C]0.903669[/C][C]0.087581[/C][/ROW]
[ROW][C]77[/C][C]140.19[/C][C]140.493[/C][C]139.624[/C][C]0.869572[/C][C]-0.303322[/C][/ROW]
[ROW][C]78[/C][C]140.14[/C][C]140.639[/C][C]139.84[/C][C]0.799572[/C][C]-0.499155[/C][/ROW]
[ROW][C]79[/C][C]140.13[/C][C]NA[/C][C]NA[/C][C]0.385822[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]139.45[/C][C]NA[/C][C]NA[/C][C]-0.0134838[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]139.59[/C][C]NA[/C][C]NA[/C][C]-0.170637[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]139.44[/C][C]NA[/C][C]NA[/C][C]-0.666956[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]139.53[/C][C]NA[/C][C]NA[/C][C]-1.26557[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]139.28[/C][C]NA[/C][C]NA[/C][C]-1.9564[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=261112&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=261112&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
1109.03NANA-0.389664NA
2110.43NANA0.396586NA
3111.01NANA1.10749NA
4111.01NANA0.903669NA
5110.76NANA0.869572NA
6111.13NANA0.799572NA
7111.07111.177110.7910.385822-0.106655
8111.09111.019111.033-0.01348380.0705671
9110.96111.159111.329-0.170637-0.19853
10110.64110.963111.63-0.666956-0.323461
11110.62110.677111.942-1.26557-0.0569329
12110.59110.296112.252-1.95640.2939
13111.33112.164112.554-0.389664-0.834086
14113.94113.251112.8550.3965860.688831
15114.61114.269113.1621.107490.340845
16114.64114.392113.4880.9036690.247998
17114.62114.698113.8290.869572-0.0783218
18114.71114.973114.1740.799572-0.263322
19114.72114.942114.5560.385822-0.222072
20114.66114.902114.915-0.0134838-0.241933
21114.76115.07115.24-0.170637-0.30978
22114.68114.965115.632-0.666956-0.285127
23114.75114.806116.071-1.26557-0.0556829
24114.74114.612116.568-1.95640.128067
25116.36116.712117.102-0.389664-0.352002
26117.53118.022117.6260.396586-0.492419
27118.82119.257118.151.10749-0.437488
28119.83119.577118.6730.9036690.252998
29119.97120.063119.1930.869572-0.0929051
30121.29120.5119.70.7995720.790428
31120.94120.625120.2390.3858220.315012
32121.02120.783120.796-0.01348380.237234
33120.98121.19121.36-0.170637-0.20978
34121.02121.239121.906-0.666956-0.219294
35120.89121.142122.407-1.26557-0.251516
36120.76120.949122.905-1.9564-0.189016
37123.28123.026123.415-0.3896640.254248
38123.98124.357123.9610.396586-0.377419
39125.91125.627124.521.107490.282928
40125.84125.979125.0750.903669-0.138669
41125.98126.497125.6280.869572-0.517488
42127.24126.975126.1750.7995720.265012
43127.23127.055126.6690.3858220.175012
44127.82127.097127.11-0.01348380.723484
45127.59127.359127.53-0.1706370.230637
46127.74127.266127.933-0.6669560.473623
47127.44127.096128.362-1.265570.3439
48127.35126.772128.729-1.95640.57765
49128.54128.638129.028-0.389664-0.0982523
50129.3129.699129.3020.396586-0.399086
51130.67130.655129.5481.107490.0150116
52130.76130.696129.7920.9036690.0642477
53131.34130.905130.0360.8695720.434595
54130.69131.031130.2310.799572-0.340822
55130.96130.809130.4230.3858220.150845
56130.68130.676130.69-0.01348380.00390046
57130.61130.822130.993-0.170637-0.21228
58130.59130.641131.308-0.666956-0.0513773
59130.44130.398131.664-1.265570.0418171
60129.04130.132132.089-1.9564-1.09235
61131.46132.137132.526-0.389664-0.676586
62132.77133.349132.9530.396586-0.579086
63134.48134.572133.4651.10749-0.0924884
64134.52134.952134.0480.903669-0.432002
65136.11135.47134.6010.8695720.639595
66136.12135.99135.190.7995720.130012
67136.03136.26135.8740.385822-0.229988
68135.84136.551136.565-0.0134838-0.7111
69137.75136.968137.139-0.1706370.781887
70137.45136.962137.629-0.6669560.487789
71136.84136.779138.045-1.265570.0605671
72136.79136.426138.382-1.95640.3639
73140.12138.331138.721-0.3896641.78883
74140.68139.439139.0420.3965861.24133
75140.35140.377139.2691.10749-0.0266551
76140.42140.332139.4290.9036690.087581
77140.19140.493139.6240.869572-0.303322
78140.14140.639139.840.799572-0.499155
79140.13NANA0.385822NA
80139.45NANA-0.0134838NA
81139.59NANA-0.170637NA
82139.44NANA-0.666956NA
83139.53NANA-1.26557NA
84139.28NANA-1.9564NA


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')