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R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 11 Dec 2013 14:21:14 -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/11/t1386789686bcobt11f1tropae.htm/, Retrieved Fri, 29 Mar 2024 14:41:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=232143, Retrieved Fri, 29 Mar 2024 14:41:14 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact57
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-11 19:21:14] [c3373c0d5a698012d591c0e2feefe9b5] [Current]
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Dataseries X:
2,57
2,58
2,58
2,58
2,57
2,57
2,57
2,57
2,59
2,62
2,66
2,67
2,67
2,69
2,69
2,69
2,69
2,71
2,71
2,71
2,74
2,77
2,82
2,82
2,82
2,8
2,82
2,82
2,82
2,82
2,82
2,82
2,85
2,93
2,94
2,94
2,95
2,95
2,96
2,96
2,97
2,97
2,97
2,97
2,98
3,03
3,03
3,03
3,03
3,04
3,05
3,06
3,06
3,07
3,07
3,07
3,04
3,06
3,09
3,09
3,09
3,09
3,1
3,1
3,1
3,1
3,1
3,11
3,11
3,17
3,19
3,19
3,19
3,19
3,19
3,19
3,19
3,19
3,19
3,19
3,25
3,23
3,24
3,24




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232143&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]6 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=232143&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232143&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 time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
12.57NANA0.0141493NA
22.58NANA0.00720486NA
32.58NANA0.00664931NA
42.58NANA-0.000503472NA
52.57NANA-0.00710069NA
62.57NANA-0.0100868NA
72.572.578732.59833-0.0196007-0.00873264
82.572.580612.60708-0.0264757-0.0106076
92.592.59132.61625-0.0249479-0.00130208
102.622.6372.625420.0115799-0.0169965
112.662.663042.6350.0280382-0.00303819
122.672.666932.645830.02109370.00307292
132.672.671652.65750.0141493-0.00164931
142.692.676372.669170.007204860.0136285
152.692.68792.681250.006649310.00210069
162.692.693252.69375-0.000503472-0.00324653
172.692.699572.70667-0.00710069-0.00956597
182.712.70952.71958-0.01008680.000503472
192.712.712482.73208-0.0196007-0.00248264
202.712.716442.74292-0.0264757-0.00644097
212.742.727972.75292-0.02494790.0120313
222.772.775332.763750.0115799-0.00532986
232.822.802622.774580.02803820.0173785
242.822.805682.784580.02109370.0143229
252.822.80792.793750.01414930.0121007
262.82.810122.802920.00720486-0.0101215
272.822.818732.812080.006649310.00126736
282.822.822832.82333-0.000503472-0.00282986
292.822.82792.835-0.00710069-0.00789931
302.822.834912.845-0.0100868-0.0149132
312.822.835822.85542-0.0196007-0.015816
322.822.840612.86708-0.0264757-0.0206076
332.852.854222.87917-0.0249479-0.00421875
342.932.902412.890830.01157990.0275868
352.942.930952.902920.02803820.00904514
362.942.936512.915420.02109370.00348958
372.952.942072.927920.01414930.00793403
382.952.947622.940420.007204860.00237847
392.962.958732.952080.006649310.00126736
402.962.961162.96167-0.000503472-0.00116319
412.972.962482.96958-0.007100690.00751736
422.972.9672.97708-0.01008680.00300347
432.972.964572.98417-0.01960070.00543403
442.972.964772.99125-0.02647570.00522569
452.982.97382.99875-0.02494790.00619792
463.033.018253.006670.01157990.0117535
473.033.042623.014580.0280382-0.0126215
483.033.043593.02250.0210937-0.0135938
493.033.044983.030830.0141493-0.0149826
503.043.046373.039170.00720486-0.00637153
513.053.052483.045830.00664931-0.00248264
523.063.049083.04958-0.0005034720.0109201
533.063.046233.05333-0.007100690.0137674
543.073.048253.05833-0.01008680.0217535
553.073.043733.06333-0.01960070.0262674
563.073.041443.06792-0.02647570.028559
573.043.047143.07208-0.0249479-0.00713542
583.063.087413.075830.0115799-0.0274132
593.093.10723.079170.0280382-0.0172049
603.093.103183.082080.0210937-0.0131771
613.093.098733.084580.0141493-0.00873264
623.093.09473.08750.00720486-0.00470486
633.13.098733.092080.006649310.00126736
643.13.099083.09958-0.0005034720.000920139
653.13.101233.10833-0.00710069-0.00123264
663.13.106583.11667-0.0100868-0.00657986
673.13.10543.125-0.0196007-0.00539931
683.113.106863.13333-0.02647570.00314236
693.113.11633.14125-0.0249479-0.00630208
703.173.160333.148750.01157990.00967014
713.193.184293.156250.02803820.00571181
723.193.184843.163750.02109370.00515625
733.193.18543.171250.01414930.00460069
743.193.185543.178330.007204860.00446181
753.193.194153.18750.00664931-0.00414931
763.193.195333.19583-0.000503472-0.00532986
773.193.193323.20042-0.00710069-0.00331597
783.193.19453.20458-0.0100868-0.00449653
793.19NANA-0.0196007NA
803.19NANA-0.0264757NA
813.25NANA-0.0249479NA
823.23NANA0.0115799NA
833.24NANA0.0280382NA
843.24NANA0.0210937NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 2.57 & NA & NA & 0.0141493 & NA \tabularnewline
2 & 2.58 & NA & NA & 0.00720486 & NA \tabularnewline
3 & 2.58 & NA & NA & 0.00664931 & NA \tabularnewline
4 & 2.58 & NA & NA & -0.000503472 & NA \tabularnewline
5 & 2.57 & NA & NA & -0.00710069 & NA \tabularnewline
6 & 2.57 & NA & NA & -0.0100868 & NA \tabularnewline
7 & 2.57 & 2.57873 & 2.59833 & -0.0196007 & -0.00873264 \tabularnewline
8 & 2.57 & 2.58061 & 2.60708 & -0.0264757 & -0.0106076 \tabularnewline
9 & 2.59 & 2.5913 & 2.61625 & -0.0249479 & -0.00130208 \tabularnewline
10 & 2.62 & 2.637 & 2.62542 & 0.0115799 & -0.0169965 \tabularnewline
11 & 2.66 & 2.66304 & 2.635 & 0.0280382 & -0.00303819 \tabularnewline
12 & 2.67 & 2.66693 & 2.64583 & 0.0210937 & 0.00307292 \tabularnewline
13 & 2.67 & 2.67165 & 2.6575 & 0.0141493 & -0.00164931 \tabularnewline
14 & 2.69 & 2.67637 & 2.66917 & 0.00720486 & 0.0136285 \tabularnewline
15 & 2.69 & 2.6879 & 2.68125 & 0.00664931 & 0.00210069 \tabularnewline
16 & 2.69 & 2.69325 & 2.69375 & -0.000503472 & -0.00324653 \tabularnewline
17 & 2.69 & 2.69957 & 2.70667 & -0.00710069 & -0.00956597 \tabularnewline
18 & 2.71 & 2.7095 & 2.71958 & -0.0100868 & 0.000503472 \tabularnewline
19 & 2.71 & 2.71248 & 2.73208 & -0.0196007 & -0.00248264 \tabularnewline
20 & 2.71 & 2.71644 & 2.74292 & -0.0264757 & -0.00644097 \tabularnewline
21 & 2.74 & 2.72797 & 2.75292 & -0.0249479 & 0.0120313 \tabularnewline
22 & 2.77 & 2.77533 & 2.76375 & 0.0115799 & -0.00532986 \tabularnewline
23 & 2.82 & 2.80262 & 2.77458 & 0.0280382 & 0.0173785 \tabularnewline
24 & 2.82 & 2.80568 & 2.78458 & 0.0210937 & 0.0143229 \tabularnewline
25 & 2.82 & 2.8079 & 2.79375 & 0.0141493 & 0.0121007 \tabularnewline
26 & 2.8 & 2.81012 & 2.80292 & 0.00720486 & -0.0101215 \tabularnewline
27 & 2.82 & 2.81873 & 2.81208 & 0.00664931 & 0.00126736 \tabularnewline
28 & 2.82 & 2.82283 & 2.82333 & -0.000503472 & -0.00282986 \tabularnewline
29 & 2.82 & 2.8279 & 2.835 & -0.00710069 & -0.00789931 \tabularnewline
30 & 2.82 & 2.83491 & 2.845 & -0.0100868 & -0.0149132 \tabularnewline
31 & 2.82 & 2.83582 & 2.85542 & -0.0196007 & -0.015816 \tabularnewline
32 & 2.82 & 2.84061 & 2.86708 & -0.0264757 & -0.0206076 \tabularnewline
33 & 2.85 & 2.85422 & 2.87917 & -0.0249479 & -0.00421875 \tabularnewline
34 & 2.93 & 2.90241 & 2.89083 & 0.0115799 & 0.0275868 \tabularnewline
35 & 2.94 & 2.93095 & 2.90292 & 0.0280382 & 0.00904514 \tabularnewline
36 & 2.94 & 2.93651 & 2.91542 & 0.0210937 & 0.00348958 \tabularnewline
37 & 2.95 & 2.94207 & 2.92792 & 0.0141493 & 0.00793403 \tabularnewline
38 & 2.95 & 2.94762 & 2.94042 & 0.00720486 & 0.00237847 \tabularnewline
39 & 2.96 & 2.95873 & 2.95208 & 0.00664931 & 0.00126736 \tabularnewline
40 & 2.96 & 2.96116 & 2.96167 & -0.000503472 & -0.00116319 \tabularnewline
41 & 2.97 & 2.96248 & 2.96958 & -0.00710069 & 0.00751736 \tabularnewline
42 & 2.97 & 2.967 & 2.97708 & -0.0100868 & 0.00300347 \tabularnewline
43 & 2.97 & 2.96457 & 2.98417 & -0.0196007 & 0.00543403 \tabularnewline
44 & 2.97 & 2.96477 & 2.99125 & -0.0264757 & 0.00522569 \tabularnewline
45 & 2.98 & 2.9738 & 2.99875 & -0.0249479 & 0.00619792 \tabularnewline
46 & 3.03 & 3.01825 & 3.00667 & 0.0115799 & 0.0117535 \tabularnewline
47 & 3.03 & 3.04262 & 3.01458 & 0.0280382 & -0.0126215 \tabularnewline
48 & 3.03 & 3.04359 & 3.0225 & 0.0210937 & -0.0135938 \tabularnewline
49 & 3.03 & 3.04498 & 3.03083 & 0.0141493 & -0.0149826 \tabularnewline
50 & 3.04 & 3.04637 & 3.03917 & 0.00720486 & -0.00637153 \tabularnewline
51 & 3.05 & 3.05248 & 3.04583 & 0.00664931 & -0.00248264 \tabularnewline
52 & 3.06 & 3.04908 & 3.04958 & -0.000503472 & 0.0109201 \tabularnewline
53 & 3.06 & 3.04623 & 3.05333 & -0.00710069 & 0.0137674 \tabularnewline
54 & 3.07 & 3.04825 & 3.05833 & -0.0100868 & 0.0217535 \tabularnewline
55 & 3.07 & 3.04373 & 3.06333 & -0.0196007 & 0.0262674 \tabularnewline
56 & 3.07 & 3.04144 & 3.06792 & -0.0264757 & 0.028559 \tabularnewline
57 & 3.04 & 3.04714 & 3.07208 & -0.0249479 & -0.00713542 \tabularnewline
58 & 3.06 & 3.08741 & 3.07583 & 0.0115799 & -0.0274132 \tabularnewline
59 & 3.09 & 3.1072 & 3.07917 & 0.0280382 & -0.0172049 \tabularnewline
60 & 3.09 & 3.10318 & 3.08208 & 0.0210937 & -0.0131771 \tabularnewline
61 & 3.09 & 3.09873 & 3.08458 & 0.0141493 & -0.00873264 \tabularnewline
62 & 3.09 & 3.0947 & 3.0875 & 0.00720486 & -0.00470486 \tabularnewline
63 & 3.1 & 3.09873 & 3.09208 & 0.00664931 & 0.00126736 \tabularnewline
64 & 3.1 & 3.09908 & 3.09958 & -0.000503472 & 0.000920139 \tabularnewline
65 & 3.1 & 3.10123 & 3.10833 & -0.00710069 & -0.00123264 \tabularnewline
66 & 3.1 & 3.10658 & 3.11667 & -0.0100868 & -0.00657986 \tabularnewline
67 & 3.1 & 3.1054 & 3.125 & -0.0196007 & -0.00539931 \tabularnewline
68 & 3.11 & 3.10686 & 3.13333 & -0.0264757 & 0.00314236 \tabularnewline
69 & 3.11 & 3.1163 & 3.14125 & -0.0249479 & -0.00630208 \tabularnewline
70 & 3.17 & 3.16033 & 3.14875 & 0.0115799 & 0.00967014 \tabularnewline
71 & 3.19 & 3.18429 & 3.15625 & 0.0280382 & 0.00571181 \tabularnewline
72 & 3.19 & 3.18484 & 3.16375 & 0.0210937 & 0.00515625 \tabularnewline
73 & 3.19 & 3.1854 & 3.17125 & 0.0141493 & 0.00460069 \tabularnewline
74 & 3.19 & 3.18554 & 3.17833 & 0.00720486 & 0.00446181 \tabularnewline
75 & 3.19 & 3.19415 & 3.1875 & 0.00664931 & -0.00414931 \tabularnewline
76 & 3.19 & 3.19533 & 3.19583 & -0.000503472 & -0.00532986 \tabularnewline
77 & 3.19 & 3.19332 & 3.20042 & -0.00710069 & -0.00331597 \tabularnewline
78 & 3.19 & 3.1945 & 3.20458 & -0.0100868 & -0.00449653 \tabularnewline
79 & 3.19 & NA & NA & -0.0196007 & NA \tabularnewline
80 & 3.19 & NA & NA & -0.0264757 & NA \tabularnewline
81 & 3.25 & NA & NA & -0.0249479 & NA \tabularnewline
82 & 3.23 & NA & NA & 0.0115799 & NA \tabularnewline
83 & 3.24 & NA & NA & 0.0280382 & NA \tabularnewline
84 & 3.24 & NA & NA & 0.0210937 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=232143&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]2.57[/C][C]NA[/C][C]NA[/C][C]0.0141493[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]2.58[/C][C]NA[/C][C]NA[/C][C]0.00720486[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]2.58[/C][C]NA[/C][C]NA[/C][C]0.00664931[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]2.58[/C][C]NA[/C][C]NA[/C][C]-0.000503472[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]2.57[/C][C]NA[/C][C]NA[/C][C]-0.00710069[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]2.57[/C][C]NA[/C][C]NA[/C][C]-0.0100868[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]2.57[/C][C]2.57873[/C][C]2.59833[/C][C]-0.0196007[/C][C]-0.00873264[/C][/ROW]
[ROW][C]8[/C][C]2.57[/C][C]2.58061[/C][C]2.60708[/C][C]-0.0264757[/C][C]-0.0106076[/C][/ROW]
[ROW][C]9[/C][C]2.59[/C][C]2.5913[/C][C]2.61625[/C][C]-0.0249479[/C][C]-0.00130208[/C][/ROW]
[ROW][C]10[/C][C]2.62[/C][C]2.637[/C][C]2.62542[/C][C]0.0115799[/C][C]-0.0169965[/C][/ROW]
[ROW][C]11[/C][C]2.66[/C][C]2.66304[/C][C]2.635[/C][C]0.0280382[/C][C]-0.00303819[/C][/ROW]
[ROW][C]12[/C][C]2.67[/C][C]2.66693[/C][C]2.64583[/C][C]0.0210937[/C][C]0.00307292[/C][/ROW]
[ROW][C]13[/C][C]2.67[/C][C]2.67165[/C][C]2.6575[/C][C]0.0141493[/C][C]-0.00164931[/C][/ROW]
[ROW][C]14[/C][C]2.69[/C][C]2.67637[/C][C]2.66917[/C][C]0.00720486[/C][C]0.0136285[/C][/ROW]
[ROW][C]15[/C][C]2.69[/C][C]2.6879[/C][C]2.68125[/C][C]0.00664931[/C][C]0.00210069[/C][/ROW]
[ROW][C]16[/C][C]2.69[/C][C]2.69325[/C][C]2.69375[/C][C]-0.000503472[/C][C]-0.00324653[/C][/ROW]
[ROW][C]17[/C][C]2.69[/C][C]2.69957[/C][C]2.70667[/C][C]-0.00710069[/C][C]-0.00956597[/C][/ROW]
[ROW][C]18[/C][C]2.71[/C][C]2.7095[/C][C]2.71958[/C][C]-0.0100868[/C][C]0.000503472[/C][/ROW]
[ROW][C]19[/C][C]2.71[/C][C]2.71248[/C][C]2.73208[/C][C]-0.0196007[/C][C]-0.00248264[/C][/ROW]
[ROW][C]20[/C][C]2.71[/C][C]2.71644[/C][C]2.74292[/C][C]-0.0264757[/C][C]-0.00644097[/C][/ROW]
[ROW][C]21[/C][C]2.74[/C][C]2.72797[/C][C]2.75292[/C][C]-0.0249479[/C][C]0.0120313[/C][/ROW]
[ROW][C]22[/C][C]2.77[/C][C]2.77533[/C][C]2.76375[/C][C]0.0115799[/C][C]-0.00532986[/C][/ROW]
[ROW][C]23[/C][C]2.82[/C][C]2.80262[/C][C]2.77458[/C][C]0.0280382[/C][C]0.0173785[/C][/ROW]
[ROW][C]24[/C][C]2.82[/C][C]2.80568[/C][C]2.78458[/C][C]0.0210937[/C][C]0.0143229[/C][/ROW]
[ROW][C]25[/C][C]2.82[/C][C]2.8079[/C][C]2.79375[/C][C]0.0141493[/C][C]0.0121007[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]2.81012[/C][C]2.80292[/C][C]0.00720486[/C][C]-0.0101215[/C][/ROW]
[ROW][C]27[/C][C]2.82[/C][C]2.81873[/C][C]2.81208[/C][C]0.00664931[/C][C]0.00126736[/C][/ROW]
[ROW][C]28[/C][C]2.82[/C][C]2.82283[/C][C]2.82333[/C][C]-0.000503472[/C][C]-0.00282986[/C][/ROW]
[ROW][C]29[/C][C]2.82[/C][C]2.8279[/C][C]2.835[/C][C]-0.00710069[/C][C]-0.00789931[/C][/ROW]
[ROW][C]30[/C][C]2.82[/C][C]2.83491[/C][C]2.845[/C][C]-0.0100868[/C][C]-0.0149132[/C][/ROW]
[ROW][C]31[/C][C]2.82[/C][C]2.83582[/C][C]2.85542[/C][C]-0.0196007[/C][C]-0.015816[/C][/ROW]
[ROW][C]32[/C][C]2.82[/C][C]2.84061[/C][C]2.86708[/C][C]-0.0264757[/C][C]-0.0206076[/C][/ROW]
[ROW][C]33[/C][C]2.85[/C][C]2.85422[/C][C]2.87917[/C][C]-0.0249479[/C][C]-0.00421875[/C][/ROW]
[ROW][C]34[/C][C]2.93[/C][C]2.90241[/C][C]2.89083[/C][C]0.0115799[/C][C]0.0275868[/C][/ROW]
[ROW][C]35[/C][C]2.94[/C][C]2.93095[/C][C]2.90292[/C][C]0.0280382[/C][C]0.00904514[/C][/ROW]
[ROW][C]36[/C][C]2.94[/C][C]2.93651[/C][C]2.91542[/C][C]0.0210937[/C][C]0.00348958[/C][/ROW]
[ROW][C]37[/C][C]2.95[/C][C]2.94207[/C][C]2.92792[/C][C]0.0141493[/C][C]0.00793403[/C][/ROW]
[ROW][C]38[/C][C]2.95[/C][C]2.94762[/C][C]2.94042[/C][C]0.00720486[/C][C]0.00237847[/C][/ROW]
[ROW][C]39[/C][C]2.96[/C][C]2.95873[/C][C]2.95208[/C][C]0.00664931[/C][C]0.00126736[/C][/ROW]
[ROW][C]40[/C][C]2.96[/C][C]2.96116[/C][C]2.96167[/C][C]-0.000503472[/C][C]-0.00116319[/C][/ROW]
[ROW][C]41[/C][C]2.97[/C][C]2.96248[/C][C]2.96958[/C][C]-0.00710069[/C][C]0.00751736[/C][/ROW]
[ROW][C]42[/C][C]2.97[/C][C]2.967[/C][C]2.97708[/C][C]-0.0100868[/C][C]0.00300347[/C][/ROW]
[ROW][C]43[/C][C]2.97[/C][C]2.96457[/C][C]2.98417[/C][C]-0.0196007[/C][C]0.00543403[/C][/ROW]
[ROW][C]44[/C][C]2.97[/C][C]2.96477[/C][C]2.99125[/C][C]-0.0264757[/C][C]0.00522569[/C][/ROW]
[ROW][C]45[/C][C]2.98[/C][C]2.9738[/C][C]2.99875[/C][C]-0.0249479[/C][C]0.00619792[/C][/ROW]
[ROW][C]46[/C][C]3.03[/C][C]3.01825[/C][C]3.00667[/C][C]0.0115799[/C][C]0.0117535[/C][/ROW]
[ROW][C]47[/C][C]3.03[/C][C]3.04262[/C][C]3.01458[/C][C]0.0280382[/C][C]-0.0126215[/C][/ROW]
[ROW][C]48[/C][C]3.03[/C][C]3.04359[/C][C]3.0225[/C][C]0.0210937[/C][C]-0.0135938[/C][/ROW]
[ROW][C]49[/C][C]3.03[/C][C]3.04498[/C][C]3.03083[/C][C]0.0141493[/C][C]-0.0149826[/C][/ROW]
[ROW][C]50[/C][C]3.04[/C][C]3.04637[/C][C]3.03917[/C][C]0.00720486[/C][C]-0.00637153[/C][/ROW]
[ROW][C]51[/C][C]3.05[/C][C]3.05248[/C][C]3.04583[/C][C]0.00664931[/C][C]-0.00248264[/C][/ROW]
[ROW][C]52[/C][C]3.06[/C][C]3.04908[/C][C]3.04958[/C][C]-0.000503472[/C][C]0.0109201[/C][/ROW]
[ROW][C]53[/C][C]3.06[/C][C]3.04623[/C][C]3.05333[/C][C]-0.00710069[/C][C]0.0137674[/C][/ROW]
[ROW][C]54[/C][C]3.07[/C][C]3.04825[/C][C]3.05833[/C][C]-0.0100868[/C][C]0.0217535[/C][/ROW]
[ROW][C]55[/C][C]3.07[/C][C]3.04373[/C][C]3.06333[/C][C]-0.0196007[/C][C]0.0262674[/C][/ROW]
[ROW][C]56[/C][C]3.07[/C][C]3.04144[/C][C]3.06792[/C][C]-0.0264757[/C][C]0.028559[/C][/ROW]
[ROW][C]57[/C][C]3.04[/C][C]3.04714[/C][C]3.07208[/C][C]-0.0249479[/C][C]-0.00713542[/C][/ROW]
[ROW][C]58[/C][C]3.06[/C][C]3.08741[/C][C]3.07583[/C][C]0.0115799[/C][C]-0.0274132[/C][/ROW]
[ROW][C]59[/C][C]3.09[/C][C]3.1072[/C][C]3.07917[/C][C]0.0280382[/C][C]-0.0172049[/C][/ROW]
[ROW][C]60[/C][C]3.09[/C][C]3.10318[/C][C]3.08208[/C][C]0.0210937[/C][C]-0.0131771[/C][/ROW]
[ROW][C]61[/C][C]3.09[/C][C]3.09873[/C][C]3.08458[/C][C]0.0141493[/C][C]-0.00873264[/C][/ROW]
[ROW][C]62[/C][C]3.09[/C][C]3.0947[/C][C]3.0875[/C][C]0.00720486[/C][C]-0.00470486[/C][/ROW]
[ROW][C]63[/C][C]3.1[/C][C]3.09873[/C][C]3.09208[/C][C]0.00664931[/C][C]0.00126736[/C][/ROW]
[ROW][C]64[/C][C]3.1[/C][C]3.09908[/C][C]3.09958[/C][C]-0.000503472[/C][C]0.000920139[/C][/ROW]
[ROW][C]65[/C][C]3.1[/C][C]3.10123[/C][C]3.10833[/C][C]-0.00710069[/C][C]-0.00123264[/C][/ROW]
[ROW][C]66[/C][C]3.1[/C][C]3.10658[/C][C]3.11667[/C][C]-0.0100868[/C][C]-0.00657986[/C][/ROW]
[ROW][C]67[/C][C]3.1[/C][C]3.1054[/C][C]3.125[/C][C]-0.0196007[/C][C]-0.00539931[/C][/ROW]
[ROW][C]68[/C][C]3.11[/C][C]3.10686[/C][C]3.13333[/C][C]-0.0264757[/C][C]0.00314236[/C][/ROW]
[ROW][C]69[/C][C]3.11[/C][C]3.1163[/C][C]3.14125[/C][C]-0.0249479[/C][C]-0.00630208[/C][/ROW]
[ROW][C]70[/C][C]3.17[/C][C]3.16033[/C][C]3.14875[/C][C]0.0115799[/C][C]0.00967014[/C][/ROW]
[ROW][C]71[/C][C]3.19[/C][C]3.18429[/C][C]3.15625[/C][C]0.0280382[/C][C]0.00571181[/C][/ROW]
[ROW][C]72[/C][C]3.19[/C][C]3.18484[/C][C]3.16375[/C][C]0.0210937[/C][C]0.00515625[/C][/ROW]
[ROW][C]73[/C][C]3.19[/C][C]3.1854[/C][C]3.17125[/C][C]0.0141493[/C][C]0.00460069[/C][/ROW]
[ROW][C]74[/C][C]3.19[/C][C]3.18554[/C][C]3.17833[/C][C]0.00720486[/C][C]0.00446181[/C][/ROW]
[ROW][C]75[/C][C]3.19[/C][C]3.19415[/C][C]3.1875[/C][C]0.00664931[/C][C]-0.00414931[/C][/ROW]
[ROW][C]76[/C][C]3.19[/C][C]3.19533[/C][C]3.19583[/C][C]-0.000503472[/C][C]-0.00532986[/C][/ROW]
[ROW][C]77[/C][C]3.19[/C][C]3.19332[/C][C]3.20042[/C][C]-0.00710069[/C][C]-0.00331597[/C][/ROW]
[ROW][C]78[/C][C]3.19[/C][C]3.1945[/C][C]3.20458[/C][C]-0.0100868[/C][C]-0.00449653[/C][/ROW]
[ROW][C]79[/C][C]3.19[/C][C]NA[/C][C]NA[/C][C]-0.0196007[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]3.19[/C][C]NA[/C][C]NA[/C][C]-0.0264757[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]3.25[/C][C]NA[/C][C]NA[/C][C]-0.0249479[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]3.23[/C][C]NA[/C][C]NA[/C][C]0.0115799[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]3.24[/C][C]NA[/C][C]NA[/C][C]0.0280382[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]3.24[/C][C]NA[/C][C]NA[/C][C]0.0210937[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=232143&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=232143&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
12.57NANA0.0141493NA
22.58NANA0.00720486NA
32.58NANA0.00664931NA
42.58NANA-0.000503472NA
52.57NANA-0.00710069NA
62.57NANA-0.0100868NA
72.572.578732.59833-0.0196007-0.00873264
82.572.580612.60708-0.0264757-0.0106076
92.592.59132.61625-0.0249479-0.00130208
102.622.6372.625420.0115799-0.0169965
112.662.663042.6350.0280382-0.00303819
122.672.666932.645830.02109370.00307292
132.672.671652.65750.0141493-0.00164931
142.692.676372.669170.007204860.0136285
152.692.68792.681250.006649310.00210069
162.692.693252.69375-0.000503472-0.00324653
172.692.699572.70667-0.00710069-0.00956597
182.712.70952.71958-0.01008680.000503472
192.712.712482.73208-0.0196007-0.00248264
202.712.716442.74292-0.0264757-0.00644097
212.742.727972.75292-0.02494790.0120313
222.772.775332.763750.0115799-0.00532986
232.822.802622.774580.02803820.0173785
242.822.805682.784580.02109370.0143229
252.822.80792.793750.01414930.0121007
262.82.810122.802920.00720486-0.0101215
272.822.818732.812080.006649310.00126736
282.822.822832.82333-0.000503472-0.00282986
292.822.82792.835-0.00710069-0.00789931
302.822.834912.845-0.0100868-0.0149132
312.822.835822.85542-0.0196007-0.015816
322.822.840612.86708-0.0264757-0.0206076
332.852.854222.87917-0.0249479-0.00421875
342.932.902412.890830.01157990.0275868
352.942.930952.902920.02803820.00904514
362.942.936512.915420.02109370.00348958
372.952.942072.927920.01414930.00793403
382.952.947622.940420.007204860.00237847
392.962.958732.952080.006649310.00126736
402.962.961162.96167-0.000503472-0.00116319
412.972.962482.96958-0.007100690.00751736
422.972.9672.97708-0.01008680.00300347
432.972.964572.98417-0.01960070.00543403
442.972.964772.99125-0.02647570.00522569
452.982.97382.99875-0.02494790.00619792
463.033.018253.006670.01157990.0117535
473.033.042623.014580.0280382-0.0126215
483.033.043593.02250.0210937-0.0135938
493.033.044983.030830.0141493-0.0149826
503.043.046373.039170.00720486-0.00637153
513.053.052483.045830.00664931-0.00248264
523.063.049083.04958-0.0005034720.0109201
533.063.046233.05333-0.007100690.0137674
543.073.048253.05833-0.01008680.0217535
553.073.043733.06333-0.01960070.0262674
563.073.041443.06792-0.02647570.028559
573.043.047143.07208-0.0249479-0.00713542
583.063.087413.075830.0115799-0.0274132
593.093.10723.079170.0280382-0.0172049
603.093.103183.082080.0210937-0.0131771
613.093.098733.084580.0141493-0.00873264
623.093.09473.08750.00720486-0.00470486
633.13.098733.092080.006649310.00126736
643.13.099083.09958-0.0005034720.000920139
653.13.101233.10833-0.00710069-0.00123264
663.13.106583.11667-0.0100868-0.00657986
673.13.10543.125-0.0196007-0.00539931
683.113.106863.13333-0.02647570.00314236
693.113.11633.14125-0.0249479-0.00630208
703.173.160333.148750.01157990.00967014
713.193.184293.156250.02803820.00571181
723.193.184843.163750.02109370.00515625
733.193.18543.171250.01414930.00460069
743.193.185543.178330.007204860.00446181
753.193.194153.18750.00664931-0.00414931
763.193.195333.19583-0.000503472-0.00532986
773.193.193323.20042-0.00710069-0.00331597
783.193.19453.20458-0.0100868-0.00449653
793.19NANA-0.0196007NA
803.19NANA-0.0264757NA
813.25NANA-0.0249479NA
823.23NANA0.0115799NA
833.24NANA0.0280382NA
843.24NANA0.0210937NA



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')