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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationThu, 06 Dec 2012 10:44:21 -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/2012/Dec/06/t1354808690j3zoa9qsfw3ad0m.htm/, Retrieved Fri, 29 Mar 2024 07:30:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197145, Retrieved Fri, 29 Mar 2024 07:30:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact118
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-12 13:32:37] [76963dc1903f0f612b6153510a3818cf]
- R  D  [Univariate Explorative Data Analysis] [Run Sequence gebo...] [2008-12-17 12:14:40] [76963dc1903f0f612b6153510a3818cf]
-         [Univariate Explorative Data Analysis] [Run Sequence Plot...] [2008-12-22 18:19:51] [1ce0d16c8f4225c977b42c8fa93bc163]
- RMP       [Univariate Data Series] [Identifying Integ...] [2009-11-22 12:08:06] [b98453cac15ba1066b407e146608df68]
- RMPD        [Classical Decomposition] [Klassieke ontledi...] [2011-12-01 08:39:12] [6d29560f77ba0d0db0a9caaa1b5e377d]
- R  D          [Classical Decomposition] [] [2012-11-12 20:23:55] [c0a25563b5321cce5982f113c9f242b0]
- R  D              [Classical Decomposition] [Decompositie van ...] [2012-12-06 15:44:21] [d0e911d4b52239734cc89860ccddde4d] [Current]
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Dataseries X:
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197145&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197145&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197145&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 time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
19081NANA-557.577893518518NA
29084NANA-28.9612268518526NA
39743NANA137.122106481482NA
48587NANA-692.952893518518NA
59731NANA221.313773148148NA
69563NANA-213.352893518519NA
799989510.451273148159512.16666666667-1.71539351851867487.54872685185
894379458.06932870379512.29166666667-54.2223379629624-21.0693287037029
91003810071.70543981489503.66666666667568.038773148148-33.7054398148157
1099189869.797106481489497.70833333333372.08877314814948.202893518519
1192529588.705439814829498.0833333333390.622106481481-336.705439814816
1297379627.680439814819468.08333333333159.597106481481109.319560185186
1390358855.047106481489412.625-557.577893518518179.952893518519
1491339328.538773148159357.5-28.9612268518526-195.538773148148
1594879465.497106481489328.375137.12210648148221.5028935185182
1687008619.547106481489312.5-692.95289351851880.452893518519
1796279524.480439814819303.16666666667221.313773148148102.519560185186
1889479087.063773148159300.41666666667-213.352893518519-140.063773148147
1992839276.742939814819278.45833333333-1.715393518518676.25706018518576
2088299211.152662037049265.375-54.2223379629624-382.152662037035
2199479840.122106481489272.08333333333568.038773148148106.87789351852
2296289641.130439814819269.04166666666372.088773148149-13.1304398148131
2393189334.872106481489244.2590.622106481481-16.8721064814818
2496059407.222106481489247.625159.597106481481197.77789351852
2586408705.172106481489262.75-557.577893518518-65.1721064814792
2692149245.830439814819274.79166666667-28.9612268518526-31.8304398148139
2795679439.913773148159302.79166666667137.122106481482127.086226851852
2885478611.047106481489304-692.952893518518-64.0471064814792
2991859531.272106481489309.95833333333221.313773148148-346.272106481481
3094709103.313773148159316.66666666667-213.352893518519366.686226851851
3191239311.742939814829313.45833333333-1.71539351851867-188.742939814816
3292789277.44432870379331.66666666667-54.22233796296240.555671296297078
33101709918.788773148159350.75568.038773148148251.211226851852
3494349747.505439814819375.41666666667372.088773148149-313.505439814815
3596559505.997106481489415.37590.622106481481149.002893518518
3694299584.263773148159424.66666666667159.597106481481-155.263773148146
3787398853.838773148159411.41666666667-557.577893518518-114.838773148147
3895529401.455439814819430.41666666667-28.9612268518526150.544560185186
3996879594.705439814819457.58333333333137.12210648148292.2945601851861
4090198798.505439814819491.45833333333-692.952893518518220.494560185185
4196729749.397106481489528.08333333333221.313773148148-77.3971064814796
4292069332.855439814819546.20833333333-213.352893518519-126.855439814814
4390699577.117939814819578.83333333333-1.71539351851867-508.117939814814
4497889564.19432870379618.41666666667-54.2223379629624223.805671296295
451031210203.45543981489635.41666666667568.038773148148108.544560185186
461010510009.33877314819637.25372.08877314814995.6612268518529
4798639750.705439814819660.0833333333390.622106481481112.294560185186
4896569862.097106481489702.5159.597106481481-206.09710648148
4992959194.755439814819752.33333333333-557.577893518518100.244560185187
5099469756.747106481489785.70833333333-28.9612268518526189.252893518518
5197019913.955439814819776.83333333333137.122106481482-212.955439814814
5290499084.297106481489777.25-692.952893518518-35.2971064814792
531019010020.98043981489799.66666666667221.313773148148169.019560185186
5497069614.063773148159827.41666666667-213.35289351851991.9362268518507
5597659843.492939814819845.20833333333-1.71539351851867-78.4929398148142
5698939784.527662037049838.75-54.2223379629624108.472337962965
57999410418.03877314819850568.038773148148-424.038773148148
581043310241.33877314819869.25372.088773148149191.661226851853
59100739971.830439814829881.2083333333390.622106481481101.169560185184
601011210048.84710648159889.25159.59710648148163.1528935185197
6192669357.297106481489914.875-557.577893518518-91.297106481481
6298209923.538773148159952.5-28.9612268518526-103.53877314815
631009710116.03877314819978.91666666667137.122106481482-19.0387731481478
6491159307.7137731481510000.6666666667-692.952893518518-192.713773148147
651041110249.980439814810028.6666666667221.313773148148161.019560185186
6696789860.8137731481510074.1666666667-213.352893518519-182.813773148147
671040810115.784606481510117.5-1.71539351851867292.215393518518
681015310071.944328703710126.1666666667-54.222337962962481.0556712962989
6910368NANA568.038773148148NA
7010581NANA372.088773148149NA
7110597NANA90.622106481481NA
7210680NANA159.597106481481NA
739738NANA-557.577893518518NA
749556NANA-28.9612268518526NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 9081 & NA & NA & -557.577893518518 & NA \tabularnewline
2 & 9084 & NA & NA & -28.9612268518526 & NA \tabularnewline
3 & 9743 & NA & NA & 137.122106481482 & NA \tabularnewline
4 & 8587 & NA & NA & -692.952893518518 & NA \tabularnewline
5 & 9731 & NA & NA & 221.313773148148 & NA \tabularnewline
6 & 9563 & NA & NA & -213.352893518519 & NA \tabularnewline
7 & 9998 & 9510.45127314815 & 9512.16666666667 & -1.71539351851867 & 487.54872685185 \tabularnewline
8 & 9437 & 9458.0693287037 & 9512.29166666667 & -54.2223379629624 & -21.0693287037029 \tabularnewline
9 & 10038 & 10071.7054398148 & 9503.66666666667 & 568.038773148148 & -33.7054398148157 \tabularnewline
10 & 9918 & 9869.79710648148 & 9497.70833333333 & 372.088773148149 & 48.202893518519 \tabularnewline
11 & 9252 & 9588.70543981482 & 9498.08333333333 & 90.622106481481 & -336.705439814816 \tabularnewline
12 & 9737 & 9627.68043981481 & 9468.08333333333 & 159.597106481481 & 109.319560185186 \tabularnewline
13 & 9035 & 8855.04710648148 & 9412.625 & -557.577893518518 & 179.952893518519 \tabularnewline
14 & 9133 & 9328.53877314815 & 9357.5 & -28.9612268518526 & -195.538773148148 \tabularnewline
15 & 9487 & 9465.49710648148 & 9328.375 & 137.122106481482 & 21.5028935185182 \tabularnewline
16 & 8700 & 8619.54710648148 & 9312.5 & -692.952893518518 & 80.452893518519 \tabularnewline
17 & 9627 & 9524.48043981481 & 9303.16666666667 & 221.313773148148 & 102.519560185186 \tabularnewline
18 & 8947 & 9087.06377314815 & 9300.41666666667 & -213.352893518519 & -140.063773148147 \tabularnewline
19 & 9283 & 9276.74293981481 & 9278.45833333333 & -1.71539351851867 & 6.25706018518576 \tabularnewline
20 & 8829 & 9211.15266203704 & 9265.375 & -54.2223379629624 & -382.152662037035 \tabularnewline
21 & 9947 & 9840.12210648148 & 9272.08333333333 & 568.038773148148 & 106.87789351852 \tabularnewline
22 & 9628 & 9641.13043981481 & 9269.04166666666 & 372.088773148149 & -13.1304398148131 \tabularnewline
23 & 9318 & 9334.87210648148 & 9244.25 & 90.622106481481 & -16.8721064814818 \tabularnewline
24 & 9605 & 9407.22210648148 & 9247.625 & 159.597106481481 & 197.77789351852 \tabularnewline
25 & 8640 & 8705.17210648148 & 9262.75 & -557.577893518518 & -65.1721064814792 \tabularnewline
26 & 9214 & 9245.83043981481 & 9274.79166666667 & -28.9612268518526 & -31.8304398148139 \tabularnewline
27 & 9567 & 9439.91377314815 & 9302.79166666667 & 137.122106481482 & 127.086226851852 \tabularnewline
28 & 8547 & 8611.04710648148 & 9304 & -692.952893518518 & -64.0471064814792 \tabularnewline
29 & 9185 & 9531.27210648148 & 9309.95833333333 & 221.313773148148 & -346.272106481481 \tabularnewline
30 & 9470 & 9103.31377314815 & 9316.66666666667 & -213.352893518519 & 366.686226851851 \tabularnewline
31 & 9123 & 9311.74293981482 & 9313.45833333333 & -1.71539351851867 & -188.742939814816 \tabularnewline
32 & 9278 & 9277.4443287037 & 9331.66666666667 & -54.2223379629624 & 0.555671296297078 \tabularnewline
33 & 10170 & 9918.78877314815 & 9350.75 & 568.038773148148 & 251.211226851852 \tabularnewline
34 & 9434 & 9747.50543981481 & 9375.41666666667 & 372.088773148149 & -313.505439814815 \tabularnewline
35 & 9655 & 9505.99710648148 & 9415.375 & 90.622106481481 & 149.002893518518 \tabularnewline
36 & 9429 & 9584.26377314815 & 9424.66666666667 & 159.597106481481 & -155.263773148146 \tabularnewline
37 & 8739 & 8853.83877314815 & 9411.41666666667 & -557.577893518518 & -114.838773148147 \tabularnewline
38 & 9552 & 9401.45543981481 & 9430.41666666667 & -28.9612268518526 & 150.544560185186 \tabularnewline
39 & 9687 & 9594.70543981481 & 9457.58333333333 & 137.122106481482 & 92.2945601851861 \tabularnewline
40 & 9019 & 8798.50543981481 & 9491.45833333333 & -692.952893518518 & 220.494560185185 \tabularnewline
41 & 9672 & 9749.39710648148 & 9528.08333333333 & 221.313773148148 & -77.3971064814796 \tabularnewline
42 & 9206 & 9332.85543981481 & 9546.20833333333 & -213.352893518519 & -126.855439814814 \tabularnewline
43 & 9069 & 9577.11793981481 & 9578.83333333333 & -1.71539351851867 & -508.117939814814 \tabularnewline
44 & 9788 & 9564.1943287037 & 9618.41666666667 & -54.2223379629624 & 223.805671296295 \tabularnewline
45 & 10312 & 10203.4554398148 & 9635.41666666667 & 568.038773148148 & 108.544560185186 \tabularnewline
46 & 10105 & 10009.3387731481 & 9637.25 & 372.088773148149 & 95.6612268518529 \tabularnewline
47 & 9863 & 9750.70543981481 & 9660.08333333333 & 90.622106481481 & 112.294560185186 \tabularnewline
48 & 9656 & 9862.09710648148 & 9702.5 & 159.597106481481 & -206.09710648148 \tabularnewline
49 & 9295 & 9194.75543981481 & 9752.33333333333 & -557.577893518518 & 100.244560185187 \tabularnewline
50 & 9946 & 9756.74710648148 & 9785.70833333333 & -28.9612268518526 & 189.252893518518 \tabularnewline
51 & 9701 & 9913.95543981481 & 9776.83333333333 & 137.122106481482 & -212.955439814814 \tabularnewline
52 & 9049 & 9084.29710648148 & 9777.25 & -692.952893518518 & -35.2971064814792 \tabularnewline
53 & 10190 & 10020.9804398148 & 9799.66666666667 & 221.313773148148 & 169.019560185186 \tabularnewline
54 & 9706 & 9614.06377314815 & 9827.41666666667 & -213.352893518519 & 91.9362268518507 \tabularnewline
55 & 9765 & 9843.49293981481 & 9845.20833333333 & -1.71539351851867 & -78.4929398148142 \tabularnewline
56 & 9893 & 9784.52766203704 & 9838.75 & -54.2223379629624 & 108.472337962965 \tabularnewline
57 & 9994 & 10418.0387731481 & 9850 & 568.038773148148 & -424.038773148148 \tabularnewline
58 & 10433 & 10241.3387731481 & 9869.25 & 372.088773148149 & 191.661226851853 \tabularnewline
59 & 10073 & 9971.83043981482 & 9881.20833333333 & 90.622106481481 & 101.169560185184 \tabularnewline
60 & 10112 & 10048.8471064815 & 9889.25 & 159.597106481481 & 63.1528935185197 \tabularnewline
61 & 9266 & 9357.29710648148 & 9914.875 & -557.577893518518 & -91.297106481481 \tabularnewline
62 & 9820 & 9923.53877314815 & 9952.5 & -28.9612268518526 & -103.53877314815 \tabularnewline
63 & 10097 & 10116.0387731481 & 9978.91666666667 & 137.122106481482 & -19.0387731481478 \tabularnewline
64 & 9115 & 9307.71377314815 & 10000.6666666667 & -692.952893518518 & -192.713773148147 \tabularnewline
65 & 10411 & 10249.9804398148 & 10028.6666666667 & 221.313773148148 & 161.019560185186 \tabularnewline
66 & 9678 & 9860.81377314815 & 10074.1666666667 & -213.352893518519 & -182.813773148147 \tabularnewline
67 & 10408 & 10115.7846064815 & 10117.5 & -1.71539351851867 & 292.215393518518 \tabularnewline
68 & 10153 & 10071.9443287037 & 10126.1666666667 & -54.2223379629624 & 81.0556712962989 \tabularnewline
69 & 10368 & NA & NA & 568.038773148148 & NA \tabularnewline
70 & 10581 & NA & NA & 372.088773148149 & NA \tabularnewline
71 & 10597 & NA & NA & 90.622106481481 & NA \tabularnewline
72 & 10680 & NA & NA & 159.597106481481 & NA \tabularnewline
73 & 9738 & NA & NA & -557.577893518518 & NA \tabularnewline
74 & 9556 & NA & NA & -28.9612268518526 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197145&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]9081[/C][C]NA[/C][C]NA[/C][C]-557.577893518518[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]9084[/C][C]NA[/C][C]NA[/C][C]-28.9612268518526[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]9743[/C][C]NA[/C][C]NA[/C][C]137.122106481482[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]8587[/C][C]NA[/C][C]NA[/C][C]-692.952893518518[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]9731[/C][C]NA[/C][C]NA[/C][C]221.313773148148[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]9563[/C][C]NA[/C][C]NA[/C][C]-213.352893518519[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]9998[/C][C]9510.45127314815[/C][C]9512.16666666667[/C][C]-1.71539351851867[/C][C]487.54872685185[/C][/ROW]
[ROW][C]8[/C][C]9437[/C][C]9458.0693287037[/C][C]9512.29166666667[/C][C]-54.2223379629624[/C][C]-21.0693287037029[/C][/ROW]
[ROW][C]9[/C][C]10038[/C][C]10071.7054398148[/C][C]9503.66666666667[/C][C]568.038773148148[/C][C]-33.7054398148157[/C][/ROW]
[ROW][C]10[/C][C]9918[/C][C]9869.79710648148[/C][C]9497.70833333333[/C][C]372.088773148149[/C][C]48.202893518519[/C][/ROW]
[ROW][C]11[/C][C]9252[/C][C]9588.70543981482[/C][C]9498.08333333333[/C][C]90.622106481481[/C][C]-336.705439814816[/C][/ROW]
[ROW][C]12[/C][C]9737[/C][C]9627.68043981481[/C][C]9468.08333333333[/C][C]159.597106481481[/C][C]109.319560185186[/C][/ROW]
[ROW][C]13[/C][C]9035[/C][C]8855.04710648148[/C][C]9412.625[/C][C]-557.577893518518[/C][C]179.952893518519[/C][/ROW]
[ROW][C]14[/C][C]9133[/C][C]9328.53877314815[/C][C]9357.5[/C][C]-28.9612268518526[/C][C]-195.538773148148[/C][/ROW]
[ROW][C]15[/C][C]9487[/C][C]9465.49710648148[/C][C]9328.375[/C][C]137.122106481482[/C][C]21.5028935185182[/C][/ROW]
[ROW][C]16[/C][C]8700[/C][C]8619.54710648148[/C][C]9312.5[/C][C]-692.952893518518[/C][C]80.452893518519[/C][/ROW]
[ROW][C]17[/C][C]9627[/C][C]9524.48043981481[/C][C]9303.16666666667[/C][C]221.313773148148[/C][C]102.519560185186[/C][/ROW]
[ROW][C]18[/C][C]8947[/C][C]9087.06377314815[/C][C]9300.41666666667[/C][C]-213.352893518519[/C][C]-140.063773148147[/C][/ROW]
[ROW][C]19[/C][C]9283[/C][C]9276.74293981481[/C][C]9278.45833333333[/C][C]-1.71539351851867[/C][C]6.25706018518576[/C][/ROW]
[ROW][C]20[/C][C]8829[/C][C]9211.15266203704[/C][C]9265.375[/C][C]-54.2223379629624[/C][C]-382.152662037035[/C][/ROW]
[ROW][C]21[/C][C]9947[/C][C]9840.12210648148[/C][C]9272.08333333333[/C][C]568.038773148148[/C][C]106.87789351852[/C][/ROW]
[ROW][C]22[/C][C]9628[/C][C]9641.13043981481[/C][C]9269.04166666666[/C][C]372.088773148149[/C][C]-13.1304398148131[/C][/ROW]
[ROW][C]23[/C][C]9318[/C][C]9334.87210648148[/C][C]9244.25[/C][C]90.622106481481[/C][C]-16.8721064814818[/C][/ROW]
[ROW][C]24[/C][C]9605[/C][C]9407.22210648148[/C][C]9247.625[/C][C]159.597106481481[/C][C]197.77789351852[/C][/ROW]
[ROW][C]25[/C][C]8640[/C][C]8705.17210648148[/C][C]9262.75[/C][C]-557.577893518518[/C][C]-65.1721064814792[/C][/ROW]
[ROW][C]26[/C][C]9214[/C][C]9245.83043981481[/C][C]9274.79166666667[/C][C]-28.9612268518526[/C][C]-31.8304398148139[/C][/ROW]
[ROW][C]27[/C][C]9567[/C][C]9439.91377314815[/C][C]9302.79166666667[/C][C]137.122106481482[/C][C]127.086226851852[/C][/ROW]
[ROW][C]28[/C][C]8547[/C][C]8611.04710648148[/C][C]9304[/C][C]-692.952893518518[/C][C]-64.0471064814792[/C][/ROW]
[ROW][C]29[/C][C]9185[/C][C]9531.27210648148[/C][C]9309.95833333333[/C][C]221.313773148148[/C][C]-346.272106481481[/C][/ROW]
[ROW][C]30[/C][C]9470[/C][C]9103.31377314815[/C][C]9316.66666666667[/C][C]-213.352893518519[/C][C]366.686226851851[/C][/ROW]
[ROW][C]31[/C][C]9123[/C][C]9311.74293981482[/C][C]9313.45833333333[/C][C]-1.71539351851867[/C][C]-188.742939814816[/C][/ROW]
[ROW][C]32[/C][C]9278[/C][C]9277.4443287037[/C][C]9331.66666666667[/C][C]-54.2223379629624[/C][C]0.555671296297078[/C][/ROW]
[ROW][C]33[/C][C]10170[/C][C]9918.78877314815[/C][C]9350.75[/C][C]568.038773148148[/C][C]251.211226851852[/C][/ROW]
[ROW][C]34[/C][C]9434[/C][C]9747.50543981481[/C][C]9375.41666666667[/C][C]372.088773148149[/C][C]-313.505439814815[/C][/ROW]
[ROW][C]35[/C][C]9655[/C][C]9505.99710648148[/C][C]9415.375[/C][C]90.622106481481[/C][C]149.002893518518[/C][/ROW]
[ROW][C]36[/C][C]9429[/C][C]9584.26377314815[/C][C]9424.66666666667[/C][C]159.597106481481[/C][C]-155.263773148146[/C][/ROW]
[ROW][C]37[/C][C]8739[/C][C]8853.83877314815[/C][C]9411.41666666667[/C][C]-557.577893518518[/C][C]-114.838773148147[/C][/ROW]
[ROW][C]38[/C][C]9552[/C][C]9401.45543981481[/C][C]9430.41666666667[/C][C]-28.9612268518526[/C][C]150.544560185186[/C][/ROW]
[ROW][C]39[/C][C]9687[/C][C]9594.70543981481[/C][C]9457.58333333333[/C][C]137.122106481482[/C][C]92.2945601851861[/C][/ROW]
[ROW][C]40[/C][C]9019[/C][C]8798.50543981481[/C][C]9491.45833333333[/C][C]-692.952893518518[/C][C]220.494560185185[/C][/ROW]
[ROW][C]41[/C][C]9672[/C][C]9749.39710648148[/C][C]9528.08333333333[/C][C]221.313773148148[/C][C]-77.3971064814796[/C][/ROW]
[ROW][C]42[/C][C]9206[/C][C]9332.85543981481[/C][C]9546.20833333333[/C][C]-213.352893518519[/C][C]-126.855439814814[/C][/ROW]
[ROW][C]43[/C][C]9069[/C][C]9577.11793981481[/C][C]9578.83333333333[/C][C]-1.71539351851867[/C][C]-508.117939814814[/C][/ROW]
[ROW][C]44[/C][C]9788[/C][C]9564.1943287037[/C][C]9618.41666666667[/C][C]-54.2223379629624[/C][C]223.805671296295[/C][/ROW]
[ROW][C]45[/C][C]10312[/C][C]10203.4554398148[/C][C]9635.41666666667[/C][C]568.038773148148[/C][C]108.544560185186[/C][/ROW]
[ROW][C]46[/C][C]10105[/C][C]10009.3387731481[/C][C]9637.25[/C][C]372.088773148149[/C][C]95.6612268518529[/C][/ROW]
[ROW][C]47[/C][C]9863[/C][C]9750.70543981481[/C][C]9660.08333333333[/C][C]90.622106481481[/C][C]112.294560185186[/C][/ROW]
[ROW][C]48[/C][C]9656[/C][C]9862.09710648148[/C][C]9702.5[/C][C]159.597106481481[/C][C]-206.09710648148[/C][/ROW]
[ROW][C]49[/C][C]9295[/C][C]9194.75543981481[/C][C]9752.33333333333[/C][C]-557.577893518518[/C][C]100.244560185187[/C][/ROW]
[ROW][C]50[/C][C]9946[/C][C]9756.74710648148[/C][C]9785.70833333333[/C][C]-28.9612268518526[/C][C]189.252893518518[/C][/ROW]
[ROW][C]51[/C][C]9701[/C][C]9913.95543981481[/C][C]9776.83333333333[/C][C]137.122106481482[/C][C]-212.955439814814[/C][/ROW]
[ROW][C]52[/C][C]9049[/C][C]9084.29710648148[/C][C]9777.25[/C][C]-692.952893518518[/C][C]-35.2971064814792[/C][/ROW]
[ROW][C]53[/C][C]10190[/C][C]10020.9804398148[/C][C]9799.66666666667[/C][C]221.313773148148[/C][C]169.019560185186[/C][/ROW]
[ROW][C]54[/C][C]9706[/C][C]9614.06377314815[/C][C]9827.41666666667[/C][C]-213.352893518519[/C][C]91.9362268518507[/C][/ROW]
[ROW][C]55[/C][C]9765[/C][C]9843.49293981481[/C][C]9845.20833333333[/C][C]-1.71539351851867[/C][C]-78.4929398148142[/C][/ROW]
[ROW][C]56[/C][C]9893[/C][C]9784.52766203704[/C][C]9838.75[/C][C]-54.2223379629624[/C][C]108.472337962965[/C][/ROW]
[ROW][C]57[/C][C]9994[/C][C]10418.0387731481[/C][C]9850[/C][C]568.038773148148[/C][C]-424.038773148148[/C][/ROW]
[ROW][C]58[/C][C]10433[/C][C]10241.3387731481[/C][C]9869.25[/C][C]372.088773148149[/C][C]191.661226851853[/C][/ROW]
[ROW][C]59[/C][C]10073[/C][C]9971.83043981482[/C][C]9881.20833333333[/C][C]90.622106481481[/C][C]101.169560185184[/C][/ROW]
[ROW][C]60[/C][C]10112[/C][C]10048.8471064815[/C][C]9889.25[/C][C]159.597106481481[/C][C]63.1528935185197[/C][/ROW]
[ROW][C]61[/C][C]9266[/C][C]9357.29710648148[/C][C]9914.875[/C][C]-557.577893518518[/C][C]-91.297106481481[/C][/ROW]
[ROW][C]62[/C][C]9820[/C][C]9923.53877314815[/C][C]9952.5[/C][C]-28.9612268518526[/C][C]-103.53877314815[/C][/ROW]
[ROW][C]63[/C][C]10097[/C][C]10116.0387731481[/C][C]9978.91666666667[/C][C]137.122106481482[/C][C]-19.0387731481478[/C][/ROW]
[ROW][C]64[/C][C]9115[/C][C]9307.71377314815[/C][C]10000.6666666667[/C][C]-692.952893518518[/C][C]-192.713773148147[/C][/ROW]
[ROW][C]65[/C][C]10411[/C][C]10249.9804398148[/C][C]10028.6666666667[/C][C]221.313773148148[/C][C]161.019560185186[/C][/ROW]
[ROW][C]66[/C][C]9678[/C][C]9860.81377314815[/C][C]10074.1666666667[/C][C]-213.352893518519[/C][C]-182.813773148147[/C][/ROW]
[ROW][C]67[/C][C]10408[/C][C]10115.7846064815[/C][C]10117.5[/C][C]-1.71539351851867[/C][C]292.215393518518[/C][/ROW]
[ROW][C]68[/C][C]10153[/C][C]10071.9443287037[/C][C]10126.1666666667[/C][C]-54.2223379629624[/C][C]81.0556712962989[/C][/ROW]
[ROW][C]69[/C][C]10368[/C][C]NA[/C][C]NA[/C][C]568.038773148148[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]10581[/C][C]NA[/C][C]NA[/C][C]372.088773148149[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]10597[/C][C]NA[/C][C]NA[/C][C]90.622106481481[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]10680[/C][C]NA[/C][C]NA[/C][C]159.597106481481[/C][C]NA[/C][/ROW]
[ROW][C]73[/C][C]9738[/C][C]NA[/C][C]NA[/C][C]-557.577893518518[/C][C]NA[/C][/ROW]
[ROW][C]74[/C][C]9556[/C][C]NA[/C][C]NA[/C][C]-28.9612268518526[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197145&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197145&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
19081NANA-557.577893518518NA
29084NANA-28.9612268518526NA
39743NANA137.122106481482NA
48587NANA-692.952893518518NA
59731NANA221.313773148148NA
69563NANA-213.352893518519NA
799989510.451273148159512.16666666667-1.71539351851867487.54872685185
894379458.06932870379512.29166666667-54.2223379629624-21.0693287037029
91003810071.70543981489503.66666666667568.038773148148-33.7054398148157
1099189869.797106481489497.70833333333372.08877314814948.202893518519
1192529588.705439814829498.0833333333390.622106481481-336.705439814816
1297379627.680439814819468.08333333333159.597106481481109.319560185186
1390358855.047106481489412.625-557.577893518518179.952893518519
1491339328.538773148159357.5-28.9612268518526-195.538773148148
1594879465.497106481489328.375137.12210648148221.5028935185182
1687008619.547106481489312.5-692.95289351851880.452893518519
1796279524.480439814819303.16666666667221.313773148148102.519560185186
1889479087.063773148159300.41666666667-213.352893518519-140.063773148147
1992839276.742939814819278.45833333333-1.715393518518676.25706018518576
2088299211.152662037049265.375-54.2223379629624-382.152662037035
2199479840.122106481489272.08333333333568.038773148148106.87789351852
2296289641.130439814819269.04166666666372.088773148149-13.1304398148131
2393189334.872106481489244.2590.622106481481-16.8721064814818
2496059407.222106481489247.625159.597106481481197.77789351852
2586408705.172106481489262.75-557.577893518518-65.1721064814792
2692149245.830439814819274.79166666667-28.9612268518526-31.8304398148139
2795679439.913773148159302.79166666667137.122106481482127.086226851852
2885478611.047106481489304-692.952893518518-64.0471064814792
2991859531.272106481489309.95833333333221.313773148148-346.272106481481
3094709103.313773148159316.66666666667-213.352893518519366.686226851851
3191239311.742939814829313.45833333333-1.71539351851867-188.742939814816
3292789277.44432870379331.66666666667-54.22233796296240.555671296297078
33101709918.788773148159350.75568.038773148148251.211226851852
3494349747.505439814819375.41666666667372.088773148149-313.505439814815
3596559505.997106481489415.37590.622106481481149.002893518518
3694299584.263773148159424.66666666667159.597106481481-155.263773148146
3787398853.838773148159411.41666666667-557.577893518518-114.838773148147
3895529401.455439814819430.41666666667-28.9612268518526150.544560185186
3996879594.705439814819457.58333333333137.12210648148292.2945601851861
4090198798.505439814819491.45833333333-692.952893518518220.494560185185
4196729749.397106481489528.08333333333221.313773148148-77.3971064814796
4292069332.855439814819546.20833333333-213.352893518519-126.855439814814
4390699577.117939814819578.83333333333-1.71539351851867-508.117939814814
4497889564.19432870379618.41666666667-54.2223379629624223.805671296295
451031210203.45543981489635.41666666667568.038773148148108.544560185186
461010510009.33877314819637.25372.08877314814995.6612268518529
4798639750.705439814819660.0833333333390.622106481481112.294560185186
4896569862.097106481489702.5159.597106481481-206.09710648148
4992959194.755439814819752.33333333333-557.577893518518100.244560185187
5099469756.747106481489785.70833333333-28.9612268518526189.252893518518
5197019913.955439814819776.83333333333137.122106481482-212.955439814814
5290499084.297106481489777.25-692.952893518518-35.2971064814792
531019010020.98043981489799.66666666667221.313773148148169.019560185186
5497069614.063773148159827.41666666667-213.35289351851991.9362268518507
5597659843.492939814819845.20833333333-1.71539351851867-78.4929398148142
5698939784.527662037049838.75-54.2223379629624108.472337962965
57999410418.03877314819850568.038773148148-424.038773148148
581043310241.33877314819869.25372.088773148149191.661226851853
59100739971.830439814829881.2083333333390.622106481481101.169560185184
601011210048.84710648159889.25159.59710648148163.1528935185197
6192669357.297106481489914.875-557.577893518518-91.297106481481
6298209923.538773148159952.5-28.9612268518526-103.53877314815
631009710116.03877314819978.91666666667137.122106481482-19.0387731481478
6491159307.7137731481510000.6666666667-692.952893518518-192.713773148147
651041110249.980439814810028.6666666667221.313773148148161.019560185186
6696789860.8137731481510074.1666666667-213.352893518519-182.813773148147
671040810115.784606481510117.5-1.71539351851867292.215393518518
681015310071.944328703710126.1666666667-54.222337962962481.0556712962989
6910368NANA568.038773148148NA
7010581NANA372.088773148149NA
7110597NANA90.622106481481NA
7210680NANA159.597106481481NA
739738NANA-557.577893518518NA
749556NANA-28.9612268518526NA



Parameters (Session):
par1 = additive ; par2 = 12 ;
Parameters (R input):
par1 = additive ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')