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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSat, 26 Nov 2016 17:17:23 +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/2016/Nov/26/t1480180671nq7w5q8wdh57de7.htm/, Retrieved Sat, 04 May 2024 02:18:41 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sat, 04 May 2024 02:18:41 +0200
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact0
Dataseries X:
91,16
91,17
91,17
91,38
92,68
92,72
92,79
92,81
92,81
92,81
92,81
92,81
92,81
92,82
92,82
92,88
93,38
93,89
94,1
94,18
94,3
94,31
94,36
94,38
94,38
94,5
94,57
94,89
96,71
97,57
97,88
97,97
98,4
98,51
98,46
98,46
98,48
98,6
98,6
98,71
99,13
99,2
99,3
100,18
101,37
101,77
102,28
102,38
102,35
103,23
105,37
106,62
107
107,24
107,31
107,35
107,42
107,58
107,64
107,64
107,68
108,51
110,37
111,31
111,57
111,66
111,69
111,9
111,95
112,04
112,13
112,14




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

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

As an alternative you can also use a 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'Sir Maurice George Kendall' @ kendall.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
191.16NANA-0.701215NA
291.17NANA-0.625799NA
391.17NANA-0.130382NA
491.38NANA0.0858681NA
592.68NANA0.440618NA
692.72NANA0.472535NA
792.7992.630492.32870.3016180.159632
892.8192.707792.46620.2414510.102299
992.8192.902792.60370.298951-0.0927014
1092.8192.843992.7350.108868-0.0338681
1192.8192.72692.8267-0.1006320.0839653
1292.8192.512792.9046-0.3918820.297299
1392.8192.306793.0079-0.7012150.503299
1492.8292.493893.1196-0.6257990.326215
1592.8293.108493.2387-0.130382-0.288368
1692.8893.449293.36330.0858681-0.569201
1793.3893.93193.49040.440618-0.551035
1893.8994.09393.62040.472535-0.202951
1994.194.052993.75120.3016180.0471319
2094.1894.128193.88670.2414510.0518819
2194.394.328594.02960.298951-0.0285347
2294.3194.295194.18620.1088680.0148819
2394.3694.308194.4088-0.1006320.0518819
2494.3894.30994.7008-0.3918820.0710486
2594.3894.310595.0117-0.7012150.0695486
2694.594.701395.3271-0.625799-0.201285
2794.5795.525595.6558-0.130382-0.955451
2894.8996.087596.00170.0858681-1.19753
2996.7196.788196.34750.440618-0.0781181
3097.5797.160996.68830.4725350.409132
3197.8897.330897.02920.3016180.549215
3297.9797.612397.37080.2414510.357715
3398.498.008597.70960.2989510.391465
3498.5198.145598.03670.1088680.364465
3598.4698.19698.2967-0.1006320.263965
3698.4698.073598.4654-0.3918820.386465
3798.4897.891398.5925-0.7012150.588715
3898.698.11898.7437-0.6257990.482049
3998.698.829298.9596-0.130382-0.229201
4098.7199.30599.21920.0858681-0.595035
4199.1399.954899.51420.440618-0.824785
4299.2100.30999.83670.472535-1.1092
4399.3100.463100.1610.301618-1.16287
44100.18100.757100.5150.241451-0.576868
45101.37101.289100.990.2989510.0806319
46101.77101.711101.6020.1088680.0590486
47102.28102.159102.26-0.1006320.121049
48102.38102.531102.922-0.391882-0.150618
49102.35102.89103.591-0.701215-0.540035
50103.23103.598104.224-0.625799-0.367951
51105.37104.644104.775-0.1303820.725799
52106.62105.355105.2690.08586811.26538
53107106.175105.7340.4406180.825215
54107.24106.649106.1770.4725350.590799
55107.31106.92106.6180.3016180.390465
56107.35107.301107.060.2414510.0485486
57107.42107.787107.4880.298951-0.367285
58107.58108.001107.8920.108868-0.420951
59107.64108.177108.278-0.100632-0.537285
60107.64108.261108.652-0.391882-0.620618
61107.68108.318109.019-0.701215-0.637951
62108.51108.765109.391-0.625799-0.255451
63110.37109.639109.77-0.1303820.730799
64111.31110.23110.1440.08586811.07997
65111.57110.958110.5170.4406180.612299
66111.66111.364110.8920.4725350.295799
67111.69NANA0.301618NA
68111.9NANA0.241451NA
69111.95NANA0.298951NA
70112.04NANA0.108868NA
71112.13NANA-0.100632NA
72112.14NANA-0.391882NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 91.16 & NA & NA & -0.701215 & NA \tabularnewline
2 & 91.17 & NA & NA & -0.625799 & NA \tabularnewline
3 & 91.17 & NA & NA & -0.130382 & NA \tabularnewline
4 & 91.38 & NA & NA & 0.0858681 & NA \tabularnewline
5 & 92.68 & NA & NA & 0.440618 & NA \tabularnewline
6 & 92.72 & NA & NA & 0.472535 & NA \tabularnewline
7 & 92.79 & 92.6304 & 92.3287 & 0.301618 & 0.159632 \tabularnewline
8 & 92.81 & 92.7077 & 92.4662 & 0.241451 & 0.102299 \tabularnewline
9 & 92.81 & 92.9027 & 92.6037 & 0.298951 & -0.0927014 \tabularnewline
10 & 92.81 & 92.8439 & 92.735 & 0.108868 & -0.0338681 \tabularnewline
11 & 92.81 & 92.726 & 92.8267 & -0.100632 & 0.0839653 \tabularnewline
12 & 92.81 & 92.5127 & 92.9046 & -0.391882 & 0.297299 \tabularnewline
13 & 92.81 & 92.3067 & 93.0079 & -0.701215 & 0.503299 \tabularnewline
14 & 92.82 & 92.4938 & 93.1196 & -0.625799 & 0.326215 \tabularnewline
15 & 92.82 & 93.1084 & 93.2387 & -0.130382 & -0.288368 \tabularnewline
16 & 92.88 & 93.4492 & 93.3633 & 0.0858681 & -0.569201 \tabularnewline
17 & 93.38 & 93.931 & 93.4904 & 0.440618 & -0.551035 \tabularnewline
18 & 93.89 & 94.093 & 93.6204 & 0.472535 & -0.202951 \tabularnewline
19 & 94.1 & 94.0529 & 93.7512 & 0.301618 & 0.0471319 \tabularnewline
20 & 94.18 & 94.1281 & 93.8867 & 0.241451 & 0.0518819 \tabularnewline
21 & 94.3 & 94.3285 & 94.0296 & 0.298951 & -0.0285347 \tabularnewline
22 & 94.31 & 94.2951 & 94.1862 & 0.108868 & 0.0148819 \tabularnewline
23 & 94.36 & 94.3081 & 94.4088 & -0.100632 & 0.0518819 \tabularnewline
24 & 94.38 & 94.309 & 94.7008 & -0.391882 & 0.0710486 \tabularnewline
25 & 94.38 & 94.3105 & 95.0117 & -0.701215 & 0.0695486 \tabularnewline
26 & 94.5 & 94.7013 & 95.3271 & -0.625799 & -0.201285 \tabularnewline
27 & 94.57 & 95.5255 & 95.6558 & -0.130382 & -0.955451 \tabularnewline
28 & 94.89 & 96.0875 & 96.0017 & 0.0858681 & -1.19753 \tabularnewline
29 & 96.71 & 96.7881 & 96.3475 & 0.440618 & -0.0781181 \tabularnewline
30 & 97.57 & 97.1609 & 96.6883 & 0.472535 & 0.409132 \tabularnewline
31 & 97.88 & 97.3308 & 97.0292 & 0.301618 & 0.549215 \tabularnewline
32 & 97.97 & 97.6123 & 97.3708 & 0.241451 & 0.357715 \tabularnewline
33 & 98.4 & 98.0085 & 97.7096 & 0.298951 & 0.391465 \tabularnewline
34 & 98.51 & 98.1455 & 98.0367 & 0.108868 & 0.364465 \tabularnewline
35 & 98.46 & 98.196 & 98.2967 & -0.100632 & 0.263965 \tabularnewline
36 & 98.46 & 98.0735 & 98.4654 & -0.391882 & 0.386465 \tabularnewline
37 & 98.48 & 97.8913 & 98.5925 & -0.701215 & 0.588715 \tabularnewline
38 & 98.6 & 98.118 & 98.7437 & -0.625799 & 0.482049 \tabularnewline
39 & 98.6 & 98.8292 & 98.9596 & -0.130382 & -0.229201 \tabularnewline
40 & 98.71 & 99.305 & 99.2192 & 0.0858681 & -0.595035 \tabularnewline
41 & 99.13 & 99.9548 & 99.5142 & 0.440618 & -0.824785 \tabularnewline
42 & 99.2 & 100.309 & 99.8367 & 0.472535 & -1.1092 \tabularnewline
43 & 99.3 & 100.463 & 100.161 & 0.301618 & -1.16287 \tabularnewline
44 & 100.18 & 100.757 & 100.515 & 0.241451 & -0.576868 \tabularnewline
45 & 101.37 & 101.289 & 100.99 & 0.298951 & 0.0806319 \tabularnewline
46 & 101.77 & 101.711 & 101.602 & 0.108868 & 0.0590486 \tabularnewline
47 & 102.28 & 102.159 & 102.26 & -0.100632 & 0.121049 \tabularnewline
48 & 102.38 & 102.531 & 102.922 & -0.391882 & -0.150618 \tabularnewline
49 & 102.35 & 102.89 & 103.591 & -0.701215 & -0.540035 \tabularnewline
50 & 103.23 & 103.598 & 104.224 & -0.625799 & -0.367951 \tabularnewline
51 & 105.37 & 104.644 & 104.775 & -0.130382 & 0.725799 \tabularnewline
52 & 106.62 & 105.355 & 105.269 & 0.0858681 & 1.26538 \tabularnewline
53 & 107 & 106.175 & 105.734 & 0.440618 & 0.825215 \tabularnewline
54 & 107.24 & 106.649 & 106.177 & 0.472535 & 0.590799 \tabularnewline
55 & 107.31 & 106.92 & 106.618 & 0.301618 & 0.390465 \tabularnewline
56 & 107.35 & 107.301 & 107.06 & 0.241451 & 0.0485486 \tabularnewline
57 & 107.42 & 107.787 & 107.488 & 0.298951 & -0.367285 \tabularnewline
58 & 107.58 & 108.001 & 107.892 & 0.108868 & -0.420951 \tabularnewline
59 & 107.64 & 108.177 & 108.278 & -0.100632 & -0.537285 \tabularnewline
60 & 107.64 & 108.261 & 108.652 & -0.391882 & -0.620618 \tabularnewline
61 & 107.68 & 108.318 & 109.019 & -0.701215 & -0.637951 \tabularnewline
62 & 108.51 & 108.765 & 109.391 & -0.625799 & -0.255451 \tabularnewline
63 & 110.37 & 109.639 & 109.77 & -0.130382 & 0.730799 \tabularnewline
64 & 111.31 & 110.23 & 110.144 & 0.0858681 & 1.07997 \tabularnewline
65 & 111.57 & 110.958 & 110.517 & 0.440618 & 0.612299 \tabularnewline
66 & 111.66 & 111.364 & 110.892 & 0.472535 & 0.295799 \tabularnewline
67 & 111.69 & NA & NA & 0.301618 & NA \tabularnewline
68 & 111.9 & NA & NA & 0.241451 & NA \tabularnewline
69 & 111.95 & NA & NA & 0.298951 & NA \tabularnewline
70 & 112.04 & NA & NA & 0.108868 & NA \tabularnewline
71 & 112.13 & NA & NA & -0.100632 & NA \tabularnewline
72 & 112.14 & NA & NA & -0.391882 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]91.16[/C][C]NA[/C][C]NA[/C][C]-0.701215[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]91.17[/C][C]NA[/C][C]NA[/C][C]-0.625799[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]91.17[/C][C]NA[/C][C]NA[/C][C]-0.130382[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]91.38[/C][C]NA[/C][C]NA[/C][C]0.0858681[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]92.68[/C][C]NA[/C][C]NA[/C][C]0.440618[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]92.72[/C][C]NA[/C][C]NA[/C][C]0.472535[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]92.79[/C][C]92.6304[/C][C]92.3287[/C][C]0.301618[/C][C]0.159632[/C][/ROW]
[ROW][C]8[/C][C]92.81[/C][C]92.7077[/C][C]92.4662[/C][C]0.241451[/C][C]0.102299[/C][/ROW]
[ROW][C]9[/C][C]92.81[/C][C]92.9027[/C][C]92.6037[/C][C]0.298951[/C][C]-0.0927014[/C][/ROW]
[ROW][C]10[/C][C]92.81[/C][C]92.8439[/C][C]92.735[/C][C]0.108868[/C][C]-0.0338681[/C][/ROW]
[ROW][C]11[/C][C]92.81[/C][C]92.726[/C][C]92.8267[/C][C]-0.100632[/C][C]0.0839653[/C][/ROW]
[ROW][C]12[/C][C]92.81[/C][C]92.5127[/C][C]92.9046[/C][C]-0.391882[/C][C]0.297299[/C][/ROW]
[ROW][C]13[/C][C]92.81[/C][C]92.3067[/C][C]93.0079[/C][C]-0.701215[/C][C]0.503299[/C][/ROW]
[ROW][C]14[/C][C]92.82[/C][C]92.4938[/C][C]93.1196[/C][C]-0.625799[/C][C]0.326215[/C][/ROW]
[ROW][C]15[/C][C]92.82[/C][C]93.1084[/C][C]93.2387[/C][C]-0.130382[/C][C]-0.288368[/C][/ROW]
[ROW][C]16[/C][C]92.88[/C][C]93.4492[/C][C]93.3633[/C][C]0.0858681[/C][C]-0.569201[/C][/ROW]
[ROW][C]17[/C][C]93.38[/C][C]93.931[/C][C]93.4904[/C][C]0.440618[/C][C]-0.551035[/C][/ROW]
[ROW][C]18[/C][C]93.89[/C][C]94.093[/C][C]93.6204[/C][C]0.472535[/C][C]-0.202951[/C][/ROW]
[ROW][C]19[/C][C]94.1[/C][C]94.0529[/C][C]93.7512[/C][C]0.301618[/C][C]0.0471319[/C][/ROW]
[ROW][C]20[/C][C]94.18[/C][C]94.1281[/C][C]93.8867[/C][C]0.241451[/C][C]0.0518819[/C][/ROW]
[ROW][C]21[/C][C]94.3[/C][C]94.3285[/C][C]94.0296[/C][C]0.298951[/C][C]-0.0285347[/C][/ROW]
[ROW][C]22[/C][C]94.31[/C][C]94.2951[/C][C]94.1862[/C][C]0.108868[/C][C]0.0148819[/C][/ROW]
[ROW][C]23[/C][C]94.36[/C][C]94.3081[/C][C]94.4088[/C][C]-0.100632[/C][C]0.0518819[/C][/ROW]
[ROW][C]24[/C][C]94.38[/C][C]94.309[/C][C]94.7008[/C][C]-0.391882[/C][C]0.0710486[/C][/ROW]
[ROW][C]25[/C][C]94.38[/C][C]94.3105[/C][C]95.0117[/C][C]-0.701215[/C][C]0.0695486[/C][/ROW]
[ROW][C]26[/C][C]94.5[/C][C]94.7013[/C][C]95.3271[/C][C]-0.625799[/C][C]-0.201285[/C][/ROW]
[ROW][C]27[/C][C]94.57[/C][C]95.5255[/C][C]95.6558[/C][C]-0.130382[/C][C]-0.955451[/C][/ROW]
[ROW][C]28[/C][C]94.89[/C][C]96.0875[/C][C]96.0017[/C][C]0.0858681[/C][C]-1.19753[/C][/ROW]
[ROW][C]29[/C][C]96.71[/C][C]96.7881[/C][C]96.3475[/C][C]0.440618[/C][C]-0.0781181[/C][/ROW]
[ROW][C]30[/C][C]97.57[/C][C]97.1609[/C][C]96.6883[/C][C]0.472535[/C][C]0.409132[/C][/ROW]
[ROW][C]31[/C][C]97.88[/C][C]97.3308[/C][C]97.0292[/C][C]0.301618[/C][C]0.549215[/C][/ROW]
[ROW][C]32[/C][C]97.97[/C][C]97.6123[/C][C]97.3708[/C][C]0.241451[/C][C]0.357715[/C][/ROW]
[ROW][C]33[/C][C]98.4[/C][C]98.0085[/C][C]97.7096[/C][C]0.298951[/C][C]0.391465[/C][/ROW]
[ROW][C]34[/C][C]98.51[/C][C]98.1455[/C][C]98.0367[/C][C]0.108868[/C][C]0.364465[/C][/ROW]
[ROW][C]35[/C][C]98.46[/C][C]98.196[/C][C]98.2967[/C][C]-0.100632[/C][C]0.263965[/C][/ROW]
[ROW][C]36[/C][C]98.46[/C][C]98.0735[/C][C]98.4654[/C][C]-0.391882[/C][C]0.386465[/C][/ROW]
[ROW][C]37[/C][C]98.48[/C][C]97.8913[/C][C]98.5925[/C][C]-0.701215[/C][C]0.588715[/C][/ROW]
[ROW][C]38[/C][C]98.6[/C][C]98.118[/C][C]98.7437[/C][C]-0.625799[/C][C]0.482049[/C][/ROW]
[ROW][C]39[/C][C]98.6[/C][C]98.8292[/C][C]98.9596[/C][C]-0.130382[/C][C]-0.229201[/C][/ROW]
[ROW][C]40[/C][C]98.71[/C][C]99.305[/C][C]99.2192[/C][C]0.0858681[/C][C]-0.595035[/C][/ROW]
[ROW][C]41[/C][C]99.13[/C][C]99.9548[/C][C]99.5142[/C][C]0.440618[/C][C]-0.824785[/C][/ROW]
[ROW][C]42[/C][C]99.2[/C][C]100.309[/C][C]99.8367[/C][C]0.472535[/C][C]-1.1092[/C][/ROW]
[ROW][C]43[/C][C]99.3[/C][C]100.463[/C][C]100.161[/C][C]0.301618[/C][C]-1.16287[/C][/ROW]
[ROW][C]44[/C][C]100.18[/C][C]100.757[/C][C]100.515[/C][C]0.241451[/C][C]-0.576868[/C][/ROW]
[ROW][C]45[/C][C]101.37[/C][C]101.289[/C][C]100.99[/C][C]0.298951[/C][C]0.0806319[/C][/ROW]
[ROW][C]46[/C][C]101.77[/C][C]101.711[/C][C]101.602[/C][C]0.108868[/C][C]0.0590486[/C][/ROW]
[ROW][C]47[/C][C]102.28[/C][C]102.159[/C][C]102.26[/C][C]-0.100632[/C][C]0.121049[/C][/ROW]
[ROW][C]48[/C][C]102.38[/C][C]102.531[/C][C]102.922[/C][C]-0.391882[/C][C]-0.150618[/C][/ROW]
[ROW][C]49[/C][C]102.35[/C][C]102.89[/C][C]103.591[/C][C]-0.701215[/C][C]-0.540035[/C][/ROW]
[ROW][C]50[/C][C]103.23[/C][C]103.598[/C][C]104.224[/C][C]-0.625799[/C][C]-0.367951[/C][/ROW]
[ROW][C]51[/C][C]105.37[/C][C]104.644[/C][C]104.775[/C][C]-0.130382[/C][C]0.725799[/C][/ROW]
[ROW][C]52[/C][C]106.62[/C][C]105.355[/C][C]105.269[/C][C]0.0858681[/C][C]1.26538[/C][/ROW]
[ROW][C]53[/C][C]107[/C][C]106.175[/C][C]105.734[/C][C]0.440618[/C][C]0.825215[/C][/ROW]
[ROW][C]54[/C][C]107.24[/C][C]106.649[/C][C]106.177[/C][C]0.472535[/C][C]0.590799[/C][/ROW]
[ROW][C]55[/C][C]107.31[/C][C]106.92[/C][C]106.618[/C][C]0.301618[/C][C]0.390465[/C][/ROW]
[ROW][C]56[/C][C]107.35[/C][C]107.301[/C][C]107.06[/C][C]0.241451[/C][C]0.0485486[/C][/ROW]
[ROW][C]57[/C][C]107.42[/C][C]107.787[/C][C]107.488[/C][C]0.298951[/C][C]-0.367285[/C][/ROW]
[ROW][C]58[/C][C]107.58[/C][C]108.001[/C][C]107.892[/C][C]0.108868[/C][C]-0.420951[/C][/ROW]
[ROW][C]59[/C][C]107.64[/C][C]108.177[/C][C]108.278[/C][C]-0.100632[/C][C]-0.537285[/C][/ROW]
[ROW][C]60[/C][C]107.64[/C][C]108.261[/C][C]108.652[/C][C]-0.391882[/C][C]-0.620618[/C][/ROW]
[ROW][C]61[/C][C]107.68[/C][C]108.318[/C][C]109.019[/C][C]-0.701215[/C][C]-0.637951[/C][/ROW]
[ROW][C]62[/C][C]108.51[/C][C]108.765[/C][C]109.391[/C][C]-0.625799[/C][C]-0.255451[/C][/ROW]
[ROW][C]63[/C][C]110.37[/C][C]109.639[/C][C]109.77[/C][C]-0.130382[/C][C]0.730799[/C][/ROW]
[ROW][C]64[/C][C]111.31[/C][C]110.23[/C][C]110.144[/C][C]0.0858681[/C][C]1.07997[/C][/ROW]
[ROW][C]65[/C][C]111.57[/C][C]110.958[/C][C]110.517[/C][C]0.440618[/C][C]0.612299[/C][/ROW]
[ROW][C]66[/C][C]111.66[/C][C]111.364[/C][C]110.892[/C][C]0.472535[/C][C]0.295799[/C][/ROW]
[ROW][C]67[/C][C]111.69[/C][C]NA[/C][C]NA[/C][C]0.301618[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]111.9[/C][C]NA[/C][C]NA[/C][C]0.241451[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]111.95[/C][C]NA[/C][C]NA[/C][C]0.298951[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]112.04[/C][C]NA[/C][C]NA[/C][C]0.108868[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]112.13[/C][C]NA[/C][C]NA[/C][C]-0.100632[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]112.14[/C][C]NA[/C][C]NA[/C][C]-0.391882[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

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

As an alternative you can also use a QR Code:  

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

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
191.16NANA-0.701215NA
291.17NANA-0.625799NA
391.17NANA-0.130382NA
491.38NANA0.0858681NA
592.68NANA0.440618NA
692.72NANA0.472535NA
792.7992.630492.32870.3016180.159632
892.8192.707792.46620.2414510.102299
992.8192.902792.60370.298951-0.0927014
1092.8192.843992.7350.108868-0.0338681
1192.8192.72692.8267-0.1006320.0839653
1292.8192.512792.9046-0.3918820.297299
1392.8192.306793.0079-0.7012150.503299
1492.8292.493893.1196-0.6257990.326215
1592.8293.108493.2387-0.130382-0.288368
1692.8893.449293.36330.0858681-0.569201
1793.3893.93193.49040.440618-0.551035
1893.8994.09393.62040.472535-0.202951
1994.194.052993.75120.3016180.0471319
2094.1894.128193.88670.2414510.0518819
2194.394.328594.02960.298951-0.0285347
2294.3194.295194.18620.1088680.0148819
2394.3694.308194.4088-0.1006320.0518819
2494.3894.30994.7008-0.3918820.0710486
2594.3894.310595.0117-0.7012150.0695486
2694.594.701395.3271-0.625799-0.201285
2794.5795.525595.6558-0.130382-0.955451
2894.8996.087596.00170.0858681-1.19753
2996.7196.788196.34750.440618-0.0781181
3097.5797.160996.68830.4725350.409132
3197.8897.330897.02920.3016180.549215
3297.9797.612397.37080.2414510.357715
3398.498.008597.70960.2989510.391465
3498.5198.145598.03670.1088680.364465
3598.4698.19698.2967-0.1006320.263965
3698.4698.073598.4654-0.3918820.386465
3798.4897.891398.5925-0.7012150.588715
3898.698.11898.7437-0.6257990.482049
3998.698.829298.9596-0.130382-0.229201
4098.7199.30599.21920.0858681-0.595035
4199.1399.954899.51420.440618-0.824785
4299.2100.30999.83670.472535-1.1092
4399.3100.463100.1610.301618-1.16287
44100.18100.757100.5150.241451-0.576868
45101.37101.289100.990.2989510.0806319
46101.77101.711101.6020.1088680.0590486
47102.28102.159102.26-0.1006320.121049
48102.38102.531102.922-0.391882-0.150618
49102.35102.89103.591-0.701215-0.540035
50103.23103.598104.224-0.625799-0.367951
51105.37104.644104.775-0.1303820.725799
52106.62105.355105.2690.08586811.26538
53107106.175105.7340.4406180.825215
54107.24106.649106.1770.4725350.590799
55107.31106.92106.6180.3016180.390465
56107.35107.301107.060.2414510.0485486
57107.42107.787107.4880.298951-0.367285
58107.58108.001107.8920.108868-0.420951
59107.64108.177108.278-0.100632-0.537285
60107.64108.261108.652-0.391882-0.620618
61107.68108.318109.019-0.701215-0.637951
62108.51108.765109.391-0.625799-0.255451
63110.37109.639109.77-0.1303820.730799
64111.31110.23110.1440.08586811.07997
65111.57110.958110.5170.4406180.612299
66111.66111.364110.8920.4725350.295799
67111.69NANA0.301618NA
68111.9NANA0.241451NA
69111.95NANA0.298951NA
70112.04NANA0.108868NA
71112.13NANA-0.100632NA
72112.14NANA-0.391882NA



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 <- 'additive'
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')