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R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSat, 07 Dec 2013 03:56:09 -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/2013/Dec/07/t13864066174fnkt51c3kp5fnb.htm/, Retrieved Fri, 29 Mar 2024 15:40:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231362, Retrieved Fri, 29 Mar 2024 15:40:43 +0000
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Estimated Impact95
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-       [Classical Decomposition] [] [2013-12-07 08:56:09] [0e26096743f9a17ea293f422aff92055] [Current]
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Dataseries X:
-1
-2
-5
-4
-6
-2
-2
-2
-2
2
1
-8
-1
1
-1
2
2
1
-1
-2
-2
-1
-8
-4
-6
-3
-3
-7
-9
-11
-13
-11
-9
-17
-22
-25
-20
-24
-24
-22
-19
-18
-17
-11
-11
-12
-10
-15
-15
-15
-13
-8
-13
-9
-7
-4
-4
-2
0
-2
-3
1
-2
-1
1
-3
-4
-9
-9
-7
-14
-12
-16
-20
-12
-12
-10
-10
-13
-16
-14
-17
-24
-25
-23
-17
-24
-20
-19
-18
-16
-12




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231362&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231362&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231362&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1-1NANA-2.12492NA
2-2NANA-0.98206NA
3-5NANA-0.495949NA
4-4NANA0.885995NA
5-6NANA1.19155NA
6-2NANA1.14988NA
7-2-2.00587-2.583330.5774640.00586971
8-2-1.37492-2.458331.08342-0.625083
9-2-0.309441-2.166671.85723-1.69056
102-0.113013-1.751.636992.11301
111-2.64277-1.16667-1.476113.64277
12-8-4.01182-0.708333-3.30349-3.98818
13-1-2.66658-0.541667-2.124921.66658
141-1.48206-0.5-0.982062.48206
15-1-0.995949-0.5-0.495949-0.00405093
1620.260995-0.6250.8859951.739
1720.0665509-1.1251.191551.93345
181-0.183449-1.333331.149881.18345
19-1-0.797536-1.3750.577464-0.202464
20-2-0.666584-1.751.08342-1.33342
21-2-0.142774-21.85723-1.85723
22-1-0.821346-2.458331.63699-0.178654
23-8-4.76777-3.29167-1.47611-3.23223
24-4-7.55349-4.25-3.303493.55349
25-6-7.37492-5.25-2.124921.37492
26-3-7.10706-6.125-0.982064.10706
27-3-7.28762-6.79167-0.4959494.28762
28-7-6.864-7.750.885995-0.135995
29-9-7.80845-91.19155-1.19155
30-11-9.30845-10.45831.14988-1.69155
31-13-11.3392-11.91670.577464-1.6608
32-11-12.2916-13.3751.083421.29158
33-9-13.2678-15.1251.857234.26777
34-17-14.988-16.6251.63699-2.01199
35-22-19.1428-17.6667-1.47611-2.85723
36-25-21.6785-18.375-3.30349-3.32151
37-20-20.9583-18.8333-2.124920.958251
38-24-19.9821-19-0.98206-4.01794
39-24-19.5793-19.0833-0.495949-4.42072
40-22-18.0723-18.95830.885995-3.92766
41-19-17.0584-18.251.19155-1.94155
42-18-16.1834-17.33331.14988-1.81655
43-17-16.1309-16.70830.577464-0.86913
44-11-15.0416-16.1251.083424.04158
45-11-13.4344-15.29171.857232.43444
46-12-12.613-14.251.636990.613013
47-10-14.8928-13.4167-1.476114.89277
48-15-16.0952-12.7917-3.303491.09516
49-15-14.1249-12-2.12492-0.875083
50-15-12.2737-11.2917-0.98206-2.72627
51-13-11.2043-10.7083-0.495949-1.79572
52-8-9.114-100.8859951.114
53-13-7.97512-9.166671.19155-5.02488
54-9-7.05845-8.208331.14988-1.94155
55-7-6.5892-7.166670.577464-0.410797
56-4-4.91658-61.083420.916584
57-4-3.01777-4.8751.85723-0.982226
58-2-2.48801-4.1251.636990.488013
590-4.72611-3.25-1.476114.72611
60-2-5.72016-2.41667-3.303493.72016
61-3-4.16658-2.04167-2.124921.16658
621-3.10706-2.125-0.982064.10706
63-2-3.03762-2.54167-0.4959491.03762
64-1-2.07234-2.958330.8859951.07234
651-2.55845-3.751.191553.55845
66-3-3.60012-4.751.149880.600116
67-4-5.13087-5.708330.5774641.13087
68-9-6.04158-7.1251.08342-2.95842
69-9-6.55944-8.416671.85723-2.44056
70-7-7.65468-9.291671.636990.654679
71-14-11.6844-10.2083-1.47611-2.31556
72-12-14.2618-10.9583-3.303492.26182
73-16-13.7499-11.625-2.12492-2.25008
74-20-13.2737-12.2917-0.98206-6.72627
75-12-13.2876-12.7917-0.4959491.28762
76-12-12.5307-13.41670.8859950.530671
77-10-13.0584-14.251.191553.05845
78-10-14.0584-15.20831.149884.05845
79-13-15.4642-16.04170.5774642.4642
80-16-15.1249-16.20831.08342-0.875083
81-14-14.7261-16.58331.857230.726108
82-17-15.7797-17.41671.63699-1.22032
83-24-19.6011-18.125-1.47611-4.39889
84-25-22.1368-18.8333-3.30349-2.86318
85-23-21.4166-19.2917-2.12492-1.58342
86-17-20.2321-19.25-0.982063.23206
87-24NANA-0.495949NA
88-20NANA0.885995NA
89-19NANA1.19155NA
90-18NANA1.14988NA
91-16NANA0.577464NA
92-12NANA1.08342NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & -1 & NA & NA & -2.12492 & NA \tabularnewline
2 & -2 & NA & NA & -0.98206 & NA \tabularnewline
3 & -5 & NA & NA & -0.495949 & NA \tabularnewline
4 & -4 & NA & NA & 0.885995 & NA \tabularnewline
5 & -6 & NA & NA & 1.19155 & NA \tabularnewline
6 & -2 & NA & NA & 1.14988 & NA \tabularnewline
7 & -2 & -2.00587 & -2.58333 & 0.577464 & 0.00586971 \tabularnewline
8 & -2 & -1.37492 & -2.45833 & 1.08342 & -0.625083 \tabularnewline
9 & -2 & -0.309441 & -2.16667 & 1.85723 & -1.69056 \tabularnewline
10 & 2 & -0.113013 & -1.75 & 1.63699 & 2.11301 \tabularnewline
11 & 1 & -2.64277 & -1.16667 & -1.47611 & 3.64277 \tabularnewline
12 & -8 & -4.01182 & -0.708333 & -3.30349 & -3.98818 \tabularnewline
13 & -1 & -2.66658 & -0.541667 & -2.12492 & 1.66658 \tabularnewline
14 & 1 & -1.48206 & -0.5 & -0.98206 & 2.48206 \tabularnewline
15 & -1 & -0.995949 & -0.5 & -0.495949 & -0.00405093 \tabularnewline
16 & 2 & 0.260995 & -0.625 & 0.885995 & 1.739 \tabularnewline
17 & 2 & 0.0665509 & -1.125 & 1.19155 & 1.93345 \tabularnewline
18 & 1 & -0.183449 & -1.33333 & 1.14988 & 1.18345 \tabularnewline
19 & -1 & -0.797536 & -1.375 & 0.577464 & -0.202464 \tabularnewline
20 & -2 & -0.666584 & -1.75 & 1.08342 & -1.33342 \tabularnewline
21 & -2 & -0.142774 & -2 & 1.85723 & -1.85723 \tabularnewline
22 & -1 & -0.821346 & -2.45833 & 1.63699 & -0.178654 \tabularnewline
23 & -8 & -4.76777 & -3.29167 & -1.47611 & -3.23223 \tabularnewline
24 & -4 & -7.55349 & -4.25 & -3.30349 & 3.55349 \tabularnewline
25 & -6 & -7.37492 & -5.25 & -2.12492 & 1.37492 \tabularnewline
26 & -3 & -7.10706 & -6.125 & -0.98206 & 4.10706 \tabularnewline
27 & -3 & -7.28762 & -6.79167 & -0.495949 & 4.28762 \tabularnewline
28 & -7 & -6.864 & -7.75 & 0.885995 & -0.135995 \tabularnewline
29 & -9 & -7.80845 & -9 & 1.19155 & -1.19155 \tabularnewline
30 & -11 & -9.30845 & -10.4583 & 1.14988 & -1.69155 \tabularnewline
31 & -13 & -11.3392 & -11.9167 & 0.577464 & -1.6608 \tabularnewline
32 & -11 & -12.2916 & -13.375 & 1.08342 & 1.29158 \tabularnewline
33 & -9 & -13.2678 & -15.125 & 1.85723 & 4.26777 \tabularnewline
34 & -17 & -14.988 & -16.625 & 1.63699 & -2.01199 \tabularnewline
35 & -22 & -19.1428 & -17.6667 & -1.47611 & -2.85723 \tabularnewline
36 & -25 & -21.6785 & -18.375 & -3.30349 & -3.32151 \tabularnewline
37 & -20 & -20.9583 & -18.8333 & -2.12492 & 0.958251 \tabularnewline
38 & -24 & -19.9821 & -19 & -0.98206 & -4.01794 \tabularnewline
39 & -24 & -19.5793 & -19.0833 & -0.495949 & -4.42072 \tabularnewline
40 & -22 & -18.0723 & -18.9583 & 0.885995 & -3.92766 \tabularnewline
41 & -19 & -17.0584 & -18.25 & 1.19155 & -1.94155 \tabularnewline
42 & -18 & -16.1834 & -17.3333 & 1.14988 & -1.81655 \tabularnewline
43 & -17 & -16.1309 & -16.7083 & 0.577464 & -0.86913 \tabularnewline
44 & -11 & -15.0416 & -16.125 & 1.08342 & 4.04158 \tabularnewline
45 & -11 & -13.4344 & -15.2917 & 1.85723 & 2.43444 \tabularnewline
46 & -12 & -12.613 & -14.25 & 1.63699 & 0.613013 \tabularnewline
47 & -10 & -14.8928 & -13.4167 & -1.47611 & 4.89277 \tabularnewline
48 & -15 & -16.0952 & -12.7917 & -3.30349 & 1.09516 \tabularnewline
49 & -15 & -14.1249 & -12 & -2.12492 & -0.875083 \tabularnewline
50 & -15 & -12.2737 & -11.2917 & -0.98206 & -2.72627 \tabularnewline
51 & -13 & -11.2043 & -10.7083 & -0.495949 & -1.79572 \tabularnewline
52 & -8 & -9.114 & -10 & 0.885995 & 1.114 \tabularnewline
53 & -13 & -7.97512 & -9.16667 & 1.19155 & -5.02488 \tabularnewline
54 & -9 & -7.05845 & -8.20833 & 1.14988 & -1.94155 \tabularnewline
55 & -7 & -6.5892 & -7.16667 & 0.577464 & -0.410797 \tabularnewline
56 & -4 & -4.91658 & -6 & 1.08342 & 0.916584 \tabularnewline
57 & -4 & -3.01777 & -4.875 & 1.85723 & -0.982226 \tabularnewline
58 & -2 & -2.48801 & -4.125 & 1.63699 & 0.488013 \tabularnewline
59 & 0 & -4.72611 & -3.25 & -1.47611 & 4.72611 \tabularnewline
60 & -2 & -5.72016 & -2.41667 & -3.30349 & 3.72016 \tabularnewline
61 & -3 & -4.16658 & -2.04167 & -2.12492 & 1.16658 \tabularnewline
62 & 1 & -3.10706 & -2.125 & -0.98206 & 4.10706 \tabularnewline
63 & -2 & -3.03762 & -2.54167 & -0.495949 & 1.03762 \tabularnewline
64 & -1 & -2.07234 & -2.95833 & 0.885995 & 1.07234 \tabularnewline
65 & 1 & -2.55845 & -3.75 & 1.19155 & 3.55845 \tabularnewline
66 & -3 & -3.60012 & -4.75 & 1.14988 & 0.600116 \tabularnewline
67 & -4 & -5.13087 & -5.70833 & 0.577464 & 1.13087 \tabularnewline
68 & -9 & -6.04158 & -7.125 & 1.08342 & -2.95842 \tabularnewline
69 & -9 & -6.55944 & -8.41667 & 1.85723 & -2.44056 \tabularnewline
70 & -7 & -7.65468 & -9.29167 & 1.63699 & 0.654679 \tabularnewline
71 & -14 & -11.6844 & -10.2083 & -1.47611 & -2.31556 \tabularnewline
72 & -12 & -14.2618 & -10.9583 & -3.30349 & 2.26182 \tabularnewline
73 & -16 & -13.7499 & -11.625 & -2.12492 & -2.25008 \tabularnewline
74 & -20 & -13.2737 & -12.2917 & -0.98206 & -6.72627 \tabularnewline
75 & -12 & -13.2876 & -12.7917 & -0.495949 & 1.28762 \tabularnewline
76 & -12 & -12.5307 & -13.4167 & 0.885995 & 0.530671 \tabularnewline
77 & -10 & -13.0584 & -14.25 & 1.19155 & 3.05845 \tabularnewline
78 & -10 & -14.0584 & -15.2083 & 1.14988 & 4.05845 \tabularnewline
79 & -13 & -15.4642 & -16.0417 & 0.577464 & 2.4642 \tabularnewline
80 & -16 & -15.1249 & -16.2083 & 1.08342 & -0.875083 \tabularnewline
81 & -14 & -14.7261 & -16.5833 & 1.85723 & 0.726108 \tabularnewline
82 & -17 & -15.7797 & -17.4167 & 1.63699 & -1.22032 \tabularnewline
83 & -24 & -19.6011 & -18.125 & -1.47611 & -4.39889 \tabularnewline
84 & -25 & -22.1368 & -18.8333 & -3.30349 & -2.86318 \tabularnewline
85 & -23 & -21.4166 & -19.2917 & -2.12492 & -1.58342 \tabularnewline
86 & -17 & -20.2321 & -19.25 & -0.98206 & 3.23206 \tabularnewline
87 & -24 & NA & NA & -0.495949 & NA \tabularnewline
88 & -20 & NA & NA & 0.885995 & NA \tabularnewline
89 & -19 & NA & NA & 1.19155 & NA \tabularnewline
90 & -18 & NA & NA & 1.14988 & NA \tabularnewline
91 & -16 & NA & NA & 0.577464 & NA \tabularnewline
92 & -12 & NA & NA & 1.08342 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231362&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]-1[/C][C]NA[/C][C]NA[/C][C]-2.12492[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]-2[/C][C]NA[/C][C]NA[/C][C]-0.98206[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]-5[/C][C]NA[/C][C]NA[/C][C]-0.495949[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]-4[/C][C]NA[/C][C]NA[/C][C]0.885995[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]-6[/C][C]NA[/C][C]NA[/C][C]1.19155[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]-2[/C][C]NA[/C][C]NA[/C][C]1.14988[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]-2[/C][C]-2.00587[/C][C]-2.58333[/C][C]0.577464[/C][C]0.00586971[/C][/ROW]
[ROW][C]8[/C][C]-2[/C][C]-1.37492[/C][C]-2.45833[/C][C]1.08342[/C][C]-0.625083[/C][/ROW]
[ROW][C]9[/C][C]-2[/C][C]-0.309441[/C][C]-2.16667[/C][C]1.85723[/C][C]-1.69056[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]-0.113013[/C][C]-1.75[/C][C]1.63699[/C][C]2.11301[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]-2.64277[/C][C]-1.16667[/C][C]-1.47611[/C][C]3.64277[/C][/ROW]
[ROW][C]12[/C][C]-8[/C][C]-4.01182[/C][C]-0.708333[/C][C]-3.30349[/C][C]-3.98818[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]-2.66658[/C][C]-0.541667[/C][C]-2.12492[/C][C]1.66658[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]-1.48206[/C][C]-0.5[/C][C]-0.98206[/C][C]2.48206[/C][/ROW]
[ROW][C]15[/C][C]-1[/C][C]-0.995949[/C][C]-0.5[/C][C]-0.495949[/C][C]-0.00405093[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]0.260995[/C][C]-0.625[/C][C]0.885995[/C][C]1.739[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]0.0665509[/C][C]-1.125[/C][C]1.19155[/C][C]1.93345[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]-0.183449[/C][C]-1.33333[/C][C]1.14988[/C][C]1.18345[/C][/ROW]
[ROW][C]19[/C][C]-1[/C][C]-0.797536[/C][C]-1.375[/C][C]0.577464[/C][C]-0.202464[/C][/ROW]
[ROW][C]20[/C][C]-2[/C][C]-0.666584[/C][C]-1.75[/C][C]1.08342[/C][C]-1.33342[/C][/ROW]
[ROW][C]21[/C][C]-2[/C][C]-0.142774[/C][C]-2[/C][C]1.85723[/C][C]-1.85723[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]-0.821346[/C][C]-2.45833[/C][C]1.63699[/C][C]-0.178654[/C][/ROW]
[ROW][C]23[/C][C]-8[/C][C]-4.76777[/C][C]-3.29167[/C][C]-1.47611[/C][C]-3.23223[/C][/ROW]
[ROW][C]24[/C][C]-4[/C][C]-7.55349[/C][C]-4.25[/C][C]-3.30349[/C][C]3.55349[/C][/ROW]
[ROW][C]25[/C][C]-6[/C][C]-7.37492[/C][C]-5.25[/C][C]-2.12492[/C][C]1.37492[/C][/ROW]
[ROW][C]26[/C][C]-3[/C][C]-7.10706[/C][C]-6.125[/C][C]-0.98206[/C][C]4.10706[/C][/ROW]
[ROW][C]27[/C][C]-3[/C][C]-7.28762[/C][C]-6.79167[/C][C]-0.495949[/C][C]4.28762[/C][/ROW]
[ROW][C]28[/C][C]-7[/C][C]-6.864[/C][C]-7.75[/C][C]0.885995[/C][C]-0.135995[/C][/ROW]
[ROW][C]29[/C][C]-9[/C][C]-7.80845[/C][C]-9[/C][C]1.19155[/C][C]-1.19155[/C][/ROW]
[ROW][C]30[/C][C]-11[/C][C]-9.30845[/C][C]-10.4583[/C][C]1.14988[/C][C]-1.69155[/C][/ROW]
[ROW][C]31[/C][C]-13[/C][C]-11.3392[/C][C]-11.9167[/C][C]0.577464[/C][C]-1.6608[/C][/ROW]
[ROW][C]32[/C][C]-11[/C][C]-12.2916[/C][C]-13.375[/C][C]1.08342[/C][C]1.29158[/C][/ROW]
[ROW][C]33[/C][C]-9[/C][C]-13.2678[/C][C]-15.125[/C][C]1.85723[/C][C]4.26777[/C][/ROW]
[ROW][C]34[/C][C]-17[/C][C]-14.988[/C][C]-16.625[/C][C]1.63699[/C][C]-2.01199[/C][/ROW]
[ROW][C]35[/C][C]-22[/C][C]-19.1428[/C][C]-17.6667[/C][C]-1.47611[/C][C]-2.85723[/C][/ROW]
[ROW][C]36[/C][C]-25[/C][C]-21.6785[/C][C]-18.375[/C][C]-3.30349[/C][C]-3.32151[/C][/ROW]
[ROW][C]37[/C][C]-20[/C][C]-20.9583[/C][C]-18.8333[/C][C]-2.12492[/C][C]0.958251[/C][/ROW]
[ROW][C]38[/C][C]-24[/C][C]-19.9821[/C][C]-19[/C][C]-0.98206[/C][C]-4.01794[/C][/ROW]
[ROW][C]39[/C][C]-24[/C][C]-19.5793[/C][C]-19.0833[/C][C]-0.495949[/C][C]-4.42072[/C][/ROW]
[ROW][C]40[/C][C]-22[/C][C]-18.0723[/C][C]-18.9583[/C][C]0.885995[/C][C]-3.92766[/C][/ROW]
[ROW][C]41[/C][C]-19[/C][C]-17.0584[/C][C]-18.25[/C][C]1.19155[/C][C]-1.94155[/C][/ROW]
[ROW][C]42[/C][C]-18[/C][C]-16.1834[/C][C]-17.3333[/C][C]1.14988[/C][C]-1.81655[/C][/ROW]
[ROW][C]43[/C][C]-17[/C][C]-16.1309[/C][C]-16.7083[/C][C]0.577464[/C][C]-0.86913[/C][/ROW]
[ROW][C]44[/C][C]-11[/C][C]-15.0416[/C][C]-16.125[/C][C]1.08342[/C][C]4.04158[/C][/ROW]
[ROW][C]45[/C][C]-11[/C][C]-13.4344[/C][C]-15.2917[/C][C]1.85723[/C][C]2.43444[/C][/ROW]
[ROW][C]46[/C][C]-12[/C][C]-12.613[/C][C]-14.25[/C][C]1.63699[/C][C]0.613013[/C][/ROW]
[ROW][C]47[/C][C]-10[/C][C]-14.8928[/C][C]-13.4167[/C][C]-1.47611[/C][C]4.89277[/C][/ROW]
[ROW][C]48[/C][C]-15[/C][C]-16.0952[/C][C]-12.7917[/C][C]-3.30349[/C][C]1.09516[/C][/ROW]
[ROW][C]49[/C][C]-15[/C][C]-14.1249[/C][C]-12[/C][C]-2.12492[/C][C]-0.875083[/C][/ROW]
[ROW][C]50[/C][C]-15[/C][C]-12.2737[/C][C]-11.2917[/C][C]-0.98206[/C][C]-2.72627[/C][/ROW]
[ROW][C]51[/C][C]-13[/C][C]-11.2043[/C][C]-10.7083[/C][C]-0.495949[/C][C]-1.79572[/C][/ROW]
[ROW][C]52[/C][C]-8[/C][C]-9.114[/C][C]-10[/C][C]0.885995[/C][C]1.114[/C][/ROW]
[ROW][C]53[/C][C]-13[/C][C]-7.97512[/C][C]-9.16667[/C][C]1.19155[/C][C]-5.02488[/C][/ROW]
[ROW][C]54[/C][C]-9[/C][C]-7.05845[/C][C]-8.20833[/C][C]1.14988[/C][C]-1.94155[/C][/ROW]
[ROW][C]55[/C][C]-7[/C][C]-6.5892[/C][C]-7.16667[/C][C]0.577464[/C][C]-0.410797[/C][/ROW]
[ROW][C]56[/C][C]-4[/C][C]-4.91658[/C][C]-6[/C][C]1.08342[/C][C]0.916584[/C][/ROW]
[ROW][C]57[/C][C]-4[/C][C]-3.01777[/C][C]-4.875[/C][C]1.85723[/C][C]-0.982226[/C][/ROW]
[ROW][C]58[/C][C]-2[/C][C]-2.48801[/C][C]-4.125[/C][C]1.63699[/C][C]0.488013[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-4.72611[/C][C]-3.25[/C][C]-1.47611[/C][C]4.72611[/C][/ROW]
[ROW][C]60[/C][C]-2[/C][C]-5.72016[/C][C]-2.41667[/C][C]-3.30349[/C][C]3.72016[/C][/ROW]
[ROW][C]61[/C][C]-3[/C][C]-4.16658[/C][C]-2.04167[/C][C]-2.12492[/C][C]1.16658[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]-3.10706[/C][C]-2.125[/C][C]-0.98206[/C][C]4.10706[/C][/ROW]
[ROW][C]63[/C][C]-2[/C][C]-3.03762[/C][C]-2.54167[/C][C]-0.495949[/C][C]1.03762[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]-2.07234[/C][C]-2.95833[/C][C]0.885995[/C][C]1.07234[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]-2.55845[/C][C]-3.75[/C][C]1.19155[/C][C]3.55845[/C][/ROW]
[ROW][C]66[/C][C]-3[/C][C]-3.60012[/C][C]-4.75[/C][C]1.14988[/C][C]0.600116[/C][/ROW]
[ROW][C]67[/C][C]-4[/C][C]-5.13087[/C][C]-5.70833[/C][C]0.577464[/C][C]1.13087[/C][/ROW]
[ROW][C]68[/C][C]-9[/C][C]-6.04158[/C][C]-7.125[/C][C]1.08342[/C][C]-2.95842[/C][/ROW]
[ROW][C]69[/C][C]-9[/C][C]-6.55944[/C][C]-8.41667[/C][C]1.85723[/C][C]-2.44056[/C][/ROW]
[ROW][C]70[/C][C]-7[/C][C]-7.65468[/C][C]-9.29167[/C][C]1.63699[/C][C]0.654679[/C][/ROW]
[ROW][C]71[/C][C]-14[/C][C]-11.6844[/C][C]-10.2083[/C][C]-1.47611[/C][C]-2.31556[/C][/ROW]
[ROW][C]72[/C][C]-12[/C][C]-14.2618[/C][C]-10.9583[/C][C]-3.30349[/C][C]2.26182[/C][/ROW]
[ROW][C]73[/C][C]-16[/C][C]-13.7499[/C][C]-11.625[/C][C]-2.12492[/C][C]-2.25008[/C][/ROW]
[ROW][C]74[/C][C]-20[/C][C]-13.2737[/C][C]-12.2917[/C][C]-0.98206[/C][C]-6.72627[/C][/ROW]
[ROW][C]75[/C][C]-12[/C][C]-13.2876[/C][C]-12.7917[/C][C]-0.495949[/C][C]1.28762[/C][/ROW]
[ROW][C]76[/C][C]-12[/C][C]-12.5307[/C][C]-13.4167[/C][C]0.885995[/C][C]0.530671[/C][/ROW]
[ROW][C]77[/C][C]-10[/C][C]-13.0584[/C][C]-14.25[/C][C]1.19155[/C][C]3.05845[/C][/ROW]
[ROW][C]78[/C][C]-10[/C][C]-14.0584[/C][C]-15.2083[/C][C]1.14988[/C][C]4.05845[/C][/ROW]
[ROW][C]79[/C][C]-13[/C][C]-15.4642[/C][C]-16.0417[/C][C]0.577464[/C][C]2.4642[/C][/ROW]
[ROW][C]80[/C][C]-16[/C][C]-15.1249[/C][C]-16.2083[/C][C]1.08342[/C][C]-0.875083[/C][/ROW]
[ROW][C]81[/C][C]-14[/C][C]-14.7261[/C][C]-16.5833[/C][C]1.85723[/C][C]0.726108[/C][/ROW]
[ROW][C]82[/C][C]-17[/C][C]-15.7797[/C][C]-17.4167[/C][C]1.63699[/C][C]-1.22032[/C][/ROW]
[ROW][C]83[/C][C]-24[/C][C]-19.6011[/C][C]-18.125[/C][C]-1.47611[/C][C]-4.39889[/C][/ROW]
[ROW][C]84[/C][C]-25[/C][C]-22.1368[/C][C]-18.8333[/C][C]-3.30349[/C][C]-2.86318[/C][/ROW]
[ROW][C]85[/C][C]-23[/C][C]-21.4166[/C][C]-19.2917[/C][C]-2.12492[/C][C]-1.58342[/C][/ROW]
[ROW][C]86[/C][C]-17[/C][C]-20.2321[/C][C]-19.25[/C][C]-0.98206[/C][C]3.23206[/C][/ROW]
[ROW][C]87[/C][C]-24[/C][C]NA[/C][C]NA[/C][C]-0.495949[/C][C]NA[/C][/ROW]
[ROW][C]88[/C][C]-20[/C][C]NA[/C][C]NA[/C][C]0.885995[/C][C]NA[/C][/ROW]
[ROW][C]89[/C][C]-19[/C][C]NA[/C][C]NA[/C][C]1.19155[/C][C]NA[/C][/ROW]
[ROW][C]90[/C][C]-18[/C][C]NA[/C][C]NA[/C][C]1.14988[/C][C]NA[/C][/ROW]
[ROW][C]91[/C][C]-16[/C][C]NA[/C][C]NA[/C][C]0.577464[/C][C]NA[/C][/ROW]
[ROW][C]92[/C][C]-12[/C][C]NA[/C][C]NA[/C][C]1.08342[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231362&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231362&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
1-1NANA-2.12492NA
2-2NANA-0.98206NA
3-5NANA-0.495949NA
4-4NANA0.885995NA
5-6NANA1.19155NA
6-2NANA1.14988NA
7-2-2.00587-2.583330.5774640.00586971
8-2-1.37492-2.458331.08342-0.625083
9-2-0.309441-2.166671.85723-1.69056
102-0.113013-1.751.636992.11301
111-2.64277-1.16667-1.476113.64277
12-8-4.01182-0.708333-3.30349-3.98818
13-1-2.66658-0.541667-2.124921.66658
141-1.48206-0.5-0.982062.48206
15-1-0.995949-0.5-0.495949-0.00405093
1620.260995-0.6250.8859951.739
1720.0665509-1.1251.191551.93345
181-0.183449-1.333331.149881.18345
19-1-0.797536-1.3750.577464-0.202464
20-2-0.666584-1.751.08342-1.33342
21-2-0.142774-21.85723-1.85723
22-1-0.821346-2.458331.63699-0.178654
23-8-4.76777-3.29167-1.47611-3.23223
24-4-7.55349-4.25-3.303493.55349
25-6-7.37492-5.25-2.124921.37492
26-3-7.10706-6.125-0.982064.10706
27-3-7.28762-6.79167-0.4959494.28762
28-7-6.864-7.750.885995-0.135995
29-9-7.80845-91.19155-1.19155
30-11-9.30845-10.45831.14988-1.69155
31-13-11.3392-11.91670.577464-1.6608
32-11-12.2916-13.3751.083421.29158
33-9-13.2678-15.1251.857234.26777
34-17-14.988-16.6251.63699-2.01199
35-22-19.1428-17.6667-1.47611-2.85723
36-25-21.6785-18.375-3.30349-3.32151
37-20-20.9583-18.8333-2.124920.958251
38-24-19.9821-19-0.98206-4.01794
39-24-19.5793-19.0833-0.495949-4.42072
40-22-18.0723-18.95830.885995-3.92766
41-19-17.0584-18.251.19155-1.94155
42-18-16.1834-17.33331.14988-1.81655
43-17-16.1309-16.70830.577464-0.86913
44-11-15.0416-16.1251.083424.04158
45-11-13.4344-15.29171.857232.43444
46-12-12.613-14.251.636990.613013
47-10-14.8928-13.4167-1.476114.89277
48-15-16.0952-12.7917-3.303491.09516
49-15-14.1249-12-2.12492-0.875083
50-15-12.2737-11.2917-0.98206-2.72627
51-13-11.2043-10.7083-0.495949-1.79572
52-8-9.114-100.8859951.114
53-13-7.97512-9.166671.19155-5.02488
54-9-7.05845-8.208331.14988-1.94155
55-7-6.5892-7.166670.577464-0.410797
56-4-4.91658-61.083420.916584
57-4-3.01777-4.8751.85723-0.982226
58-2-2.48801-4.1251.636990.488013
590-4.72611-3.25-1.476114.72611
60-2-5.72016-2.41667-3.303493.72016
61-3-4.16658-2.04167-2.124921.16658
621-3.10706-2.125-0.982064.10706
63-2-3.03762-2.54167-0.4959491.03762
64-1-2.07234-2.958330.8859951.07234
651-2.55845-3.751.191553.55845
66-3-3.60012-4.751.149880.600116
67-4-5.13087-5.708330.5774641.13087
68-9-6.04158-7.1251.08342-2.95842
69-9-6.55944-8.416671.85723-2.44056
70-7-7.65468-9.291671.636990.654679
71-14-11.6844-10.2083-1.47611-2.31556
72-12-14.2618-10.9583-3.303492.26182
73-16-13.7499-11.625-2.12492-2.25008
74-20-13.2737-12.2917-0.98206-6.72627
75-12-13.2876-12.7917-0.4959491.28762
76-12-12.5307-13.41670.8859950.530671
77-10-13.0584-14.251.191553.05845
78-10-14.0584-15.20831.149884.05845
79-13-15.4642-16.04170.5774642.4642
80-16-15.1249-16.20831.08342-0.875083
81-14-14.7261-16.58331.857230.726108
82-17-15.7797-17.41671.63699-1.22032
83-24-19.6011-18.125-1.47611-4.39889
84-25-22.1368-18.8333-3.30349-2.86318
85-23-21.4166-19.2917-2.12492-1.58342
86-17-20.2321-19.25-0.982063.23206
87-24NANA-0.495949NA
88-20NANA0.885995NA
89-19NANA1.19155NA
90-18NANA1.14988NA
91-16NANA0.577464NA
92-12NANA1.08342NA



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')