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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 computationWed, 26 Nov 2014 16:26:02 +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/26/t141701918384c35v907ca9f1z.htm/, Retrieved Sun, 19 May 2024 14:34:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=259290, Retrieved Sun, 19 May 2024 14:34:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact81

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [Opgave 10 oef 2 c...] [2014-11-26 16:26:02] [1ff8fa16e8926711af5c3a9d39132058] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110,48
111,41
115,5
118,32
118,42
117,5
110,23
109,19
118,41
118,3
116,1
114,11
113,41
114,33
116,61
123,64
123,77
123,39
116,03
114,95
123,4
123,53
114,45
114,26
114,35
112,77
115,31
114,93
116,38
115,07
105
103,43
114,52
115,04
117,16
115
116,22
112,92
116,56
114,32
113,22
111,56
103,87
102,85
112,27
112,76
118,55
122,73
115,44
116,97
119,84
116,37
117,23
115,58
109,82
108,46
116,54
117,49
122,87
127,1
119,81
120,03
128,58
120,4
121,54
118,71
111,57
109,97
120,29
120,61
130,15
136,12

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=259290&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]3 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=259290&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=259290&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1110.48NANA-0.158382NA
2111.41NANA-0.618049NA
3115.5NANA3.33578NA
4118.32NANA1.85287NA
5118.42NANA2.21253NA
6117.5NANA0.346035NA
7110.23108.496114.953-6.456971.73405
8109.19107.376115.197-7.820551.81388
9118.41116.615115.3651.250621.7948
10118.3117.153115.6321.520281.14722
11116.1117.956116.0771.87895-1.85603
12114.11119.202116.5452.65687-5.09228
13113.41116.874117.032-0.158382-3.46412
14114.33116.896117.514-0.618049-2.56612
15116.61121.298117.9623.33578-4.68787
16123.64120.241118.3881.852873.39922
17123.77120.75118.5372.212533.02038
18123.39118.821118.4750.3460354.56938
19116.03112.063118.52-6.456973.96697
20114.95110.674118.494-7.820554.27638
21123.4119.626118.3751.250623.77438
22123.53119.478117.9581.520284.0518
23114.45119.166117.2871.87895-4.71603
24114.26119.289116.6332.65687-5.02937
25114.35115.668115.826-0.158382-1.31787
26112.77114.269114.887-0.618049-1.49862
27115.31117.372114.0373.33578-2.06245
28114.93115.166113.3131.85287-0.235785
29116.38115.285113.0722.212531.09538
30115.07113.562113.2160.3460351.50813
31105106.868113.325-6.45697-1.86762
32103.43105.588113.409-7.82055-2.1582
33114.52114.718113.4671.25062-0.197701
34115.04115.014113.4941.520280.0259653
35117.16115.216113.3371.878951.94438
36115115.716113.0592.65687-0.715618
37116.22112.707112.865-0.1583823.51297
38112.92112.176112.794-0.6180490.743882
39116.56116.012112.6763.335780.547965
40114.32114.34112.4881.85287-0.0203681
41113.22114.663112.452.21253-1.44295
42111.56113.176112.830.346035-1.61645
43103.87106.663113.12-6.45697-2.79303
44102.85105.436113.256-7.82055-2.5857
45112.27114.812113.5621.25062-2.54228
46112.76115.304113.7841.52028-2.54403
47118.55115.915114.0361.878952.6348
48122.73117.028114.3712.656875.7023
49115.44114.628114.786-0.1583820.812132
50116.97114.65115.268-0.6180492.32013
51119.84119.015115.683.335780.824632
52116.37117.907116.0551.85287-1.53745
53117.23118.644116.4322.21253-1.4142
54115.58117.14116.7940.346035-1.55978
55109.82110.701117.158-6.45697-0.880951
56108.46109.647117.467-7.82055-1.18695
57116.54119.21117.9591.25062-2.66978
58117.49120.012118.4911.52028-2.52153
59122.87120.718118.8391.878952.1523
60127.1121.806119.1492.656875.29438
61119.81119.194119.352-0.1583820.616299
62120.03118.87119.488-0.6180491.16013
63128.58123.043119.7073.335785.53713
64120.4121.846119.9931.85287-1.4462
65121.54122.639120.4272.21253-1.0992
66118.71121.452121.1060.346035-2.74187
67111.57NANA-6.45697NA
68109.97NANA-7.82055NA
69120.29NANA1.25062NA
70120.61NANA1.52028NA
71130.15NANA1.87895NA
72136.12NANA2.65687NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 110.48 & NA & NA & -0.158382 & NA \tabularnewline
2 & 111.41 & NA & NA & -0.618049 & NA \tabularnewline
3 & 115.5 & NA & NA & 3.33578 & NA \tabularnewline
4 & 118.32 & NA & NA & 1.85287 & NA \tabularnewline
5 & 118.42 & NA & NA & 2.21253 & NA \tabularnewline
6 & 117.5 & NA & NA & 0.346035 & NA \tabularnewline
7 & 110.23 & 108.496 & 114.953 & -6.45697 & 1.73405 \tabularnewline
8 & 109.19 & 107.376 & 115.197 & -7.82055 & 1.81388 \tabularnewline
9 & 118.41 & 116.615 & 115.365 & 1.25062 & 1.7948 \tabularnewline
10 & 118.3 & 117.153 & 115.632 & 1.52028 & 1.14722 \tabularnewline
11 & 116.1 & 117.956 & 116.077 & 1.87895 & -1.85603 \tabularnewline
12 & 114.11 & 119.202 & 116.545 & 2.65687 & -5.09228 \tabularnewline
13 & 113.41 & 116.874 & 117.032 & -0.158382 & -3.46412 \tabularnewline
14 & 114.33 & 116.896 & 117.514 & -0.618049 & -2.56612 \tabularnewline
15 & 116.61 & 121.298 & 117.962 & 3.33578 & -4.68787 \tabularnewline
16 & 123.64 & 120.241 & 118.388 & 1.85287 & 3.39922 \tabularnewline
17 & 123.77 & 120.75 & 118.537 & 2.21253 & 3.02038 \tabularnewline
18 & 123.39 & 118.821 & 118.475 & 0.346035 & 4.56938 \tabularnewline
19 & 116.03 & 112.063 & 118.52 & -6.45697 & 3.96697 \tabularnewline
20 & 114.95 & 110.674 & 118.494 & -7.82055 & 4.27638 \tabularnewline
21 & 123.4 & 119.626 & 118.375 & 1.25062 & 3.77438 \tabularnewline
22 & 123.53 & 119.478 & 117.958 & 1.52028 & 4.0518 \tabularnewline
23 & 114.45 & 119.166 & 117.287 & 1.87895 & -4.71603 \tabularnewline
24 & 114.26 & 119.289 & 116.633 & 2.65687 & -5.02937 \tabularnewline
25 & 114.35 & 115.668 & 115.826 & -0.158382 & -1.31787 \tabularnewline
26 & 112.77 & 114.269 & 114.887 & -0.618049 & -1.49862 \tabularnewline
27 & 115.31 & 117.372 & 114.037 & 3.33578 & -2.06245 \tabularnewline
28 & 114.93 & 115.166 & 113.313 & 1.85287 & -0.235785 \tabularnewline
29 & 116.38 & 115.285 & 113.072 & 2.21253 & 1.09538 \tabularnewline
30 & 115.07 & 113.562 & 113.216 & 0.346035 & 1.50813 \tabularnewline
31 & 105 & 106.868 & 113.325 & -6.45697 & -1.86762 \tabularnewline
32 & 103.43 & 105.588 & 113.409 & -7.82055 & -2.1582 \tabularnewline
33 & 114.52 & 114.718 & 113.467 & 1.25062 & -0.197701 \tabularnewline
34 & 115.04 & 115.014 & 113.494 & 1.52028 & 0.0259653 \tabularnewline
35 & 117.16 & 115.216 & 113.337 & 1.87895 & 1.94438 \tabularnewline
36 & 115 & 115.716 & 113.059 & 2.65687 & -0.715618 \tabularnewline
37 & 116.22 & 112.707 & 112.865 & -0.158382 & 3.51297 \tabularnewline
38 & 112.92 & 112.176 & 112.794 & -0.618049 & 0.743882 \tabularnewline
39 & 116.56 & 116.012 & 112.676 & 3.33578 & 0.547965 \tabularnewline
40 & 114.32 & 114.34 & 112.488 & 1.85287 & -0.0203681 \tabularnewline
41 & 113.22 & 114.663 & 112.45 & 2.21253 & -1.44295 \tabularnewline
42 & 111.56 & 113.176 & 112.83 & 0.346035 & -1.61645 \tabularnewline
43 & 103.87 & 106.663 & 113.12 & -6.45697 & -2.79303 \tabularnewline
44 & 102.85 & 105.436 & 113.256 & -7.82055 & -2.5857 \tabularnewline
45 & 112.27 & 114.812 & 113.562 & 1.25062 & -2.54228 \tabularnewline
46 & 112.76 & 115.304 & 113.784 & 1.52028 & -2.54403 \tabularnewline
47 & 118.55 & 115.915 & 114.036 & 1.87895 & 2.6348 \tabularnewline
48 & 122.73 & 117.028 & 114.371 & 2.65687 & 5.7023 \tabularnewline
49 & 115.44 & 114.628 & 114.786 & -0.158382 & 0.812132 \tabularnewline
50 & 116.97 & 114.65 & 115.268 & -0.618049 & 2.32013 \tabularnewline
51 & 119.84 & 119.015 & 115.68 & 3.33578 & 0.824632 \tabularnewline
52 & 116.37 & 117.907 & 116.055 & 1.85287 & -1.53745 \tabularnewline
53 & 117.23 & 118.644 & 116.432 & 2.21253 & -1.4142 \tabularnewline
54 & 115.58 & 117.14 & 116.794 & 0.346035 & -1.55978 \tabularnewline
55 & 109.82 & 110.701 & 117.158 & -6.45697 & -0.880951 \tabularnewline
56 & 108.46 & 109.647 & 117.467 & -7.82055 & -1.18695 \tabularnewline
57 & 116.54 & 119.21 & 117.959 & 1.25062 & -2.66978 \tabularnewline
58 & 117.49 & 120.012 & 118.491 & 1.52028 & -2.52153 \tabularnewline
59 & 122.87 & 120.718 & 118.839 & 1.87895 & 2.1523 \tabularnewline
60 & 127.1 & 121.806 & 119.149 & 2.65687 & 5.29438 \tabularnewline
61 & 119.81 & 119.194 & 119.352 & -0.158382 & 0.616299 \tabularnewline
62 & 120.03 & 118.87 & 119.488 & -0.618049 & 1.16013 \tabularnewline
63 & 128.58 & 123.043 & 119.707 & 3.33578 & 5.53713 \tabularnewline
64 & 120.4 & 121.846 & 119.993 & 1.85287 & -1.4462 \tabularnewline
65 & 121.54 & 122.639 & 120.427 & 2.21253 & -1.0992 \tabularnewline
66 & 118.71 & 121.452 & 121.106 & 0.346035 & -2.74187 \tabularnewline
67 & 111.57 & NA & NA & -6.45697 & NA \tabularnewline
68 & 109.97 & NA & NA & -7.82055 & NA \tabularnewline
69 & 120.29 & NA & NA & 1.25062 & NA \tabularnewline
70 & 120.61 & NA & NA & 1.52028 & NA \tabularnewline
71 & 130.15 & NA & NA & 1.87895 & NA \tabularnewline
72 & 136.12 & NA & NA & 2.65687 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=259290&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]110.48[/C][C]NA[/C][C]NA[/C][C]-0.158382[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]111.41[/C][C]NA[/C][C]NA[/C][C]-0.618049[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]115.5[/C][C]NA[/C][C]NA[/C][C]3.33578[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]118.32[/C][C]NA[/C][C]NA[/C][C]1.85287[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]118.42[/C][C]NA[/C][C]NA[/C][C]2.21253[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]117.5[/C][C]NA[/C][C]NA[/C][C]0.346035[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]110.23[/C][C]108.496[/C][C]114.953[/C][C]-6.45697[/C][C]1.73405[/C][/ROW]
[ROW][C]8[/C][C]109.19[/C][C]107.376[/C][C]115.197[/C][C]-7.82055[/C][C]1.81388[/C][/ROW]
[ROW][C]9[/C][C]118.41[/C][C]116.615[/C][C]115.365[/C][C]1.25062[/C][C]1.7948[/C][/ROW]
[ROW][C]10[/C][C]118.3[/C][C]117.153[/C][C]115.632[/C][C]1.52028[/C][C]1.14722[/C][/ROW]
[ROW][C]11[/C][C]116.1[/C][C]117.956[/C][C]116.077[/C][C]1.87895[/C][C]-1.85603[/C][/ROW]
[ROW][C]12[/C][C]114.11[/C][C]119.202[/C][C]116.545[/C][C]2.65687[/C][C]-5.09228[/C][/ROW]
[ROW][C]13[/C][C]113.41[/C][C]116.874[/C][C]117.032[/C][C]-0.158382[/C][C]-3.46412[/C][/ROW]
[ROW][C]14[/C][C]114.33[/C][C]116.896[/C][C]117.514[/C][C]-0.618049[/C][C]-2.56612[/C][/ROW]
[ROW][C]15[/C][C]116.61[/C][C]121.298[/C][C]117.962[/C][C]3.33578[/C][C]-4.68787[/C][/ROW]
[ROW][C]16[/C][C]123.64[/C][C]120.241[/C][C]118.388[/C][C]1.85287[/C][C]3.39922[/C][/ROW]
[ROW][C]17[/C][C]123.77[/C][C]120.75[/C][C]118.537[/C][C]2.21253[/C][C]3.02038[/C][/ROW]
[ROW][C]18[/C][C]123.39[/C][C]118.821[/C][C]118.475[/C][C]0.346035[/C][C]4.56938[/C][/ROW]
[ROW][C]19[/C][C]116.03[/C][C]112.063[/C][C]118.52[/C][C]-6.45697[/C][C]3.96697[/C][/ROW]
[ROW][C]20[/C][C]114.95[/C][C]110.674[/C][C]118.494[/C][C]-7.82055[/C][C]4.27638[/C][/ROW]
[ROW][C]21[/C][C]123.4[/C][C]119.626[/C][C]118.375[/C][C]1.25062[/C][C]3.77438[/C][/ROW]
[ROW][C]22[/C][C]123.53[/C][C]119.478[/C][C]117.958[/C][C]1.52028[/C][C]4.0518[/C][/ROW]
[ROW][C]23[/C][C]114.45[/C][C]119.166[/C][C]117.287[/C][C]1.87895[/C][C]-4.71603[/C][/ROW]
[ROW][C]24[/C][C]114.26[/C][C]119.289[/C][C]116.633[/C][C]2.65687[/C][C]-5.02937[/C][/ROW]
[ROW][C]25[/C][C]114.35[/C][C]115.668[/C][C]115.826[/C][C]-0.158382[/C][C]-1.31787[/C][/ROW]
[ROW][C]26[/C][C]112.77[/C][C]114.269[/C][C]114.887[/C][C]-0.618049[/C][C]-1.49862[/C][/ROW]
[ROW][C]27[/C][C]115.31[/C][C]117.372[/C][C]114.037[/C][C]3.33578[/C][C]-2.06245[/C][/ROW]
[ROW][C]28[/C][C]114.93[/C][C]115.166[/C][C]113.313[/C][C]1.85287[/C][C]-0.235785[/C][/ROW]
[ROW][C]29[/C][C]116.38[/C][C]115.285[/C][C]113.072[/C][C]2.21253[/C][C]1.09538[/C][/ROW]
[ROW][C]30[/C][C]115.07[/C][C]113.562[/C][C]113.216[/C][C]0.346035[/C][C]1.50813[/C][/ROW]
[ROW][C]31[/C][C]105[/C][C]106.868[/C][C]113.325[/C][C]-6.45697[/C][C]-1.86762[/C][/ROW]
[ROW][C]32[/C][C]103.43[/C][C]105.588[/C][C]113.409[/C][C]-7.82055[/C][C]-2.1582[/C][/ROW]
[ROW][C]33[/C][C]114.52[/C][C]114.718[/C][C]113.467[/C][C]1.25062[/C][C]-0.197701[/C][/ROW]
[ROW][C]34[/C][C]115.04[/C][C]115.014[/C][C]113.494[/C][C]1.52028[/C][C]0.0259653[/C][/ROW]
[ROW][C]35[/C][C]117.16[/C][C]115.216[/C][C]113.337[/C][C]1.87895[/C][C]1.94438[/C][/ROW]
[ROW][C]36[/C][C]115[/C][C]115.716[/C][C]113.059[/C][C]2.65687[/C][C]-0.715618[/C][/ROW]
[ROW][C]37[/C][C]116.22[/C][C]112.707[/C][C]112.865[/C][C]-0.158382[/C][C]3.51297[/C][/ROW]
[ROW][C]38[/C][C]112.92[/C][C]112.176[/C][C]112.794[/C][C]-0.618049[/C][C]0.743882[/C][/ROW]
[ROW][C]39[/C][C]116.56[/C][C]116.012[/C][C]112.676[/C][C]3.33578[/C][C]0.547965[/C][/ROW]
[ROW][C]40[/C][C]114.32[/C][C]114.34[/C][C]112.488[/C][C]1.85287[/C][C]-0.0203681[/C][/ROW]
[ROW][C]41[/C][C]113.22[/C][C]114.663[/C][C]112.45[/C][C]2.21253[/C][C]-1.44295[/C][/ROW]
[ROW][C]42[/C][C]111.56[/C][C]113.176[/C][C]112.83[/C][C]0.346035[/C][C]-1.61645[/C][/ROW]
[ROW][C]43[/C][C]103.87[/C][C]106.663[/C][C]113.12[/C][C]-6.45697[/C][C]-2.79303[/C][/ROW]
[ROW][C]44[/C][C]102.85[/C][C]105.436[/C][C]113.256[/C][C]-7.82055[/C][C]-2.5857[/C][/ROW]
[ROW][C]45[/C][C]112.27[/C][C]114.812[/C][C]113.562[/C][C]1.25062[/C][C]-2.54228[/C][/ROW]
[ROW][C]46[/C][C]112.76[/C][C]115.304[/C][C]113.784[/C][C]1.52028[/C][C]-2.54403[/C][/ROW]
[ROW][C]47[/C][C]118.55[/C][C]115.915[/C][C]114.036[/C][C]1.87895[/C][C]2.6348[/C][/ROW]
[ROW][C]48[/C][C]122.73[/C][C]117.028[/C][C]114.371[/C][C]2.65687[/C][C]5.7023[/C][/ROW]
[ROW][C]49[/C][C]115.44[/C][C]114.628[/C][C]114.786[/C][C]-0.158382[/C][C]0.812132[/C][/ROW]
[ROW][C]50[/C][C]116.97[/C][C]114.65[/C][C]115.268[/C][C]-0.618049[/C][C]2.32013[/C][/ROW]
[ROW][C]51[/C][C]119.84[/C][C]119.015[/C][C]115.68[/C][C]3.33578[/C][C]0.824632[/C][/ROW]
[ROW][C]52[/C][C]116.37[/C][C]117.907[/C][C]116.055[/C][C]1.85287[/C][C]-1.53745[/C][/ROW]
[ROW][C]53[/C][C]117.23[/C][C]118.644[/C][C]116.432[/C][C]2.21253[/C][C]-1.4142[/C][/ROW]
[ROW][C]54[/C][C]115.58[/C][C]117.14[/C][C]116.794[/C][C]0.346035[/C][C]-1.55978[/C][/ROW]
[ROW][C]55[/C][C]109.82[/C][C]110.701[/C][C]117.158[/C][C]-6.45697[/C][C]-0.880951[/C][/ROW]
[ROW][C]56[/C][C]108.46[/C][C]109.647[/C][C]117.467[/C][C]-7.82055[/C][C]-1.18695[/C][/ROW]
[ROW][C]57[/C][C]116.54[/C][C]119.21[/C][C]117.959[/C][C]1.25062[/C][C]-2.66978[/C][/ROW]
[ROW][C]58[/C][C]117.49[/C][C]120.012[/C][C]118.491[/C][C]1.52028[/C][C]-2.52153[/C][/ROW]
[ROW][C]59[/C][C]122.87[/C][C]120.718[/C][C]118.839[/C][C]1.87895[/C][C]2.1523[/C][/ROW]
[ROW][C]60[/C][C]127.1[/C][C]121.806[/C][C]119.149[/C][C]2.65687[/C][C]5.29438[/C][/ROW]
[ROW][C]61[/C][C]119.81[/C][C]119.194[/C][C]119.352[/C][C]-0.158382[/C][C]0.616299[/C][/ROW]
[ROW][C]62[/C][C]120.03[/C][C]118.87[/C][C]119.488[/C][C]-0.618049[/C][C]1.16013[/C][/ROW]
[ROW][C]63[/C][C]128.58[/C][C]123.043[/C][C]119.707[/C][C]3.33578[/C][C]5.53713[/C][/ROW]
[ROW][C]64[/C][C]120.4[/C][C]121.846[/C][C]119.993[/C][C]1.85287[/C][C]-1.4462[/C][/ROW]
[ROW][C]65[/C][C]121.54[/C][C]122.639[/C][C]120.427[/C][C]2.21253[/C][C]-1.0992[/C][/ROW]
[ROW][C]66[/C][C]118.71[/C][C]121.452[/C][C]121.106[/C][C]0.346035[/C][C]-2.74187[/C][/ROW]
[ROW][C]67[/C][C]111.57[/C][C]NA[/C][C]NA[/C][C]-6.45697[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]109.97[/C][C]NA[/C][C]NA[/C][C]-7.82055[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]120.29[/C][C]NA[/C][C]NA[/C][C]1.25062[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]120.61[/C][C]NA[/C][C]NA[/C][C]1.52028[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]130.15[/C][C]NA[/C][C]NA[/C][C]1.87895[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]136.12[/C][C]NA[/C][C]NA[/C][C]2.65687[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=259290&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=259290&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
1110.48NANA-0.158382NA
2111.41NANA-0.618049NA
3115.5NANA3.33578NA
4118.32NANA1.85287NA
5118.42NANA2.21253NA
6117.5NANA0.346035NA
7110.23108.496114.953-6.456971.73405
8109.19107.376115.197-7.820551.81388
9118.41116.615115.3651.250621.7948
10118.3117.153115.6321.520281.14722
11116.1117.956116.0771.87895-1.85603
12114.11119.202116.5452.65687-5.09228
13113.41116.874117.032-0.158382-3.46412
14114.33116.896117.514-0.618049-2.56612
15116.61121.298117.9623.33578-4.68787
16123.64120.241118.3881.852873.39922
17123.77120.75118.5372.212533.02038
18123.39118.821118.4750.3460354.56938
19116.03112.063118.52-6.456973.96697
20114.95110.674118.494-7.820554.27638
21123.4119.626118.3751.250623.77438
22123.53119.478117.9581.520284.0518
23114.45119.166117.2871.87895-4.71603
24114.26119.289116.6332.65687-5.02937
25114.35115.668115.826-0.158382-1.31787
26112.77114.269114.887-0.618049-1.49862
27115.31117.372114.0373.33578-2.06245
28114.93115.166113.3131.85287-0.235785
29116.38115.285113.0722.212531.09538
30115.07113.562113.2160.3460351.50813
31105106.868113.325-6.45697-1.86762
32103.43105.588113.409-7.82055-2.1582
33114.52114.718113.4671.25062-0.197701
34115.04115.014113.4941.520280.0259653
35117.16115.216113.3371.878951.94438
36115115.716113.0592.65687-0.715618
37116.22112.707112.865-0.1583823.51297
38112.92112.176112.794-0.6180490.743882
39116.56116.012112.6763.335780.547965
40114.32114.34112.4881.85287-0.0203681
41113.22114.663112.452.21253-1.44295
42111.56113.176112.830.346035-1.61645
43103.87106.663113.12-6.45697-2.79303
44102.85105.436113.256-7.82055-2.5857
45112.27114.812113.5621.25062-2.54228
46112.76115.304113.7841.52028-2.54403
47118.55115.915114.0361.878952.6348
48122.73117.028114.3712.656875.7023
49115.44114.628114.786-0.1583820.812132
50116.97114.65115.268-0.6180492.32013
51119.84119.015115.683.335780.824632
52116.37117.907116.0551.85287-1.53745
53117.23118.644116.4322.21253-1.4142
54115.58117.14116.7940.346035-1.55978
55109.82110.701117.158-6.45697-0.880951
56108.46109.647117.467-7.82055-1.18695
57116.54119.21117.9591.25062-2.66978
58117.49120.012118.4911.52028-2.52153
59122.87120.718118.8391.878952.1523
60127.1121.806119.1492.656875.29438
61119.81119.194119.352-0.1583820.616299
62120.03118.87119.488-0.6180491.16013
63128.58123.043119.7073.335785.53713
64120.4121.846119.9931.85287-1.4462
65121.54122.639120.4272.21253-1.0992
66118.71121.452121.1060.346035-2.74187
67111.57NANA-6.45697NA
68109.97NANA-7.82055NA
69120.29NANA1.25062NA
70120.61NANA1.52028NA
71130.15NANA1.87895NA
72136.12NANA2.65687NA


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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$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')