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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationMon, 09 Dec 2013 04:11:30 -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/09/t1386580322o0po7vawikjdtn4.htm/, Retrieved Thu, 28 Mar 2024 12:25:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231590, Retrieved Thu, 28 Mar 2024 12:25:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-09 09:11:30] [1b8ce37c5679a09a5286ac5230bb7f24] [Current]
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Dataseries X:
96.86
96.77
96.5
96.01
96.07
95.93
95.93
95.83
96.24
96.25
96.59
96.62
96.62
96.81
96.71
96.45
96.63
96.56
96.56
96.65
97.04
97.14
97.2
97.26
97.26
97.24
97.35
97.36
97.28
97.31
97.31
97.31
97.23
97.78
97.64
97.68
97.68
97.81
97.75
97.63
97.6
97.65
97.65
97.65
97.86
98.41
98.79
98.75
98.74
98.55
98.65
98.86
98.94
99.05
99.05
99.05
99.17
98.99
98.91
98.89
98.89
98.72
98.89
98.97
99.16
99.54
99.54
99.55
100.01
99.52
99.44
99.39
99.39
99.4
100.43
100.62
101.05
100.95
100.95
100.91
101.13
100.81
100.47
100.56





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=231590&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=231590&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231590&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 time8 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
196.86NANA0.99968NA
296.77NANA0.998894NA
396.5NANA1.00027NA
496.01NANA0.999777NA
596.07NANA1.00047NA
695.93NANA1.00059NA
795.9396.178696.290.9988430.997415
895.8396.13596.28170.9984770.996827
996.2496.348696.29211.000590.998872
1096.2596.40896.31921.000920.998361
1196.5996.464296.36081.001071.0013
1296.6296.449596.41041.000411.00177
1396.6296.432196.46290.999681.00195
1496.8196.416696.52330.9988941.00408
1596.7196.617396.59081.000271.00096
1696.4596.639796.66120.9997770.998037
1796.6396.769496.72381.000470.99856
1896.5696.833396.77581.000590.997178
1996.5696.717296.82920.9988430.998375
2096.6596.726296.87370.9984770.999212
2197.0496.975396.91831.000591.00067
2297.1497.072496.98291.000921.0007
2397.297.152197.04791.001071.00049
2497.2697.145697.10621.000411.00118
2597.2697.137797.16870.999681.00126
2697.2497.1297.22750.9988941.00124
2797.3597.289697.26291.000271.00062
2897.3697.275897.29750.9997771.00087
2997.2897.388497.34251.000470.998887
3097.3197.436197.37831.000590.998705
3197.3197.300797.41330.9988431.0001
3297.3197.306297.45460.9984771.00004
3397.2397.552397.4951.000590.996697
3497.7897.612997.52291.000921.00171
3597.6497.652297.54751.001070.999875
3697.6897.614697.5751.000411.00067
3797.6897.572197.60330.999681.00111
3897.8197.523797.63170.9988941.00294
3997.7597.698997.67211.000271.00052
4097.6397.702897.72460.9997770.999255
4197.697.844997.79881.000470.997497
4297.6597.949497.89121.000590.996944
4397.6597.866797.980.9988430.997786
4497.6597.905798.0550.9984770.997389
4597.8698.18198.12331.000590.996731
4698.4198.302798.21211.000921.00109
4798.7998.424798.31921.001071.00371
4898.7598.473298.43331.000411.00281
4998.7498.518598.550.999681.00225
5098.5598.557698.66670.9988940.999923
5198.6598.806798.77961.000270.998414
5298.8698.836398.85830.9997771.00024
5398.9498.934198.88751.000471.00006
5499.0598.95798.89831.000591.00094
5599.0598.79698.91040.9988431.00257
5699.0598.773198.92380.9984771.0028
5799.1798.998998.94081.000591.00173
5898.9999.046798.95541.000920.999427
5998.9199.075498.96921.001070.998331
6098.8999.038998.99871.000410.998497
6198.8999.007999.03960.999680.998809
6298.7298.971399.08080.9988940.997461
6398.8999.163999.13671.000270.997238
6498.9799.171699.19370.9997770.997967
6599.1699.284799.23791.000470.998744
6699.5499.339899.28081.000591.00202
6799.5499.207699.32250.9988431.00335
6899.5599.220399.37170.9984771.00332
69100.0199.522699.46421.000591.0049
7099.5299.68999.59711.000920.998305
7199.4499.851699.74461.001070.995878
7299.3999.922699.88211.000410.99467
7399.3999.967699.99960.999680.994222
7499.4100.004100.1150.9988940.993957
75100.43100.246100.2181.000271.00184
76100.62100.296100.3190.9997771.00323
77101.05100.463100.4151.000471.00585
78100.95100.567100.5071.000591.00381
79100.95NANA0.998843NA
80100.91NANA0.998477NA
81101.13NANA1.00059NA
82100.81NANA1.00092NA
83100.47NANA1.00107NA
84100.56NANA1.00041NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 96.86 & NA & NA & 0.99968 & NA \tabularnewline
2 & 96.77 & NA & NA & 0.998894 & NA \tabularnewline
3 & 96.5 & NA & NA & 1.00027 & NA \tabularnewline
4 & 96.01 & NA & NA & 0.999777 & NA \tabularnewline
5 & 96.07 & NA & NA & 1.00047 & NA \tabularnewline
6 & 95.93 & NA & NA & 1.00059 & NA \tabularnewline
7 & 95.93 & 96.1786 & 96.29 & 0.998843 & 0.997415 \tabularnewline
8 & 95.83 & 96.135 & 96.2817 & 0.998477 & 0.996827 \tabularnewline
9 & 96.24 & 96.3486 & 96.2921 & 1.00059 & 0.998872 \tabularnewline
10 & 96.25 & 96.408 & 96.3192 & 1.00092 & 0.998361 \tabularnewline
11 & 96.59 & 96.4642 & 96.3608 & 1.00107 & 1.0013 \tabularnewline
12 & 96.62 & 96.4495 & 96.4104 & 1.00041 & 1.00177 \tabularnewline
13 & 96.62 & 96.4321 & 96.4629 & 0.99968 & 1.00195 \tabularnewline
14 & 96.81 & 96.4166 & 96.5233 & 0.998894 & 1.00408 \tabularnewline
15 & 96.71 & 96.6173 & 96.5908 & 1.00027 & 1.00096 \tabularnewline
16 & 96.45 & 96.6397 & 96.6612 & 0.999777 & 0.998037 \tabularnewline
17 & 96.63 & 96.7694 & 96.7238 & 1.00047 & 0.99856 \tabularnewline
18 & 96.56 & 96.8333 & 96.7758 & 1.00059 & 0.997178 \tabularnewline
19 & 96.56 & 96.7172 & 96.8292 & 0.998843 & 0.998375 \tabularnewline
20 & 96.65 & 96.7262 & 96.8737 & 0.998477 & 0.999212 \tabularnewline
21 & 97.04 & 96.9753 & 96.9183 & 1.00059 & 1.00067 \tabularnewline
22 & 97.14 & 97.0724 & 96.9829 & 1.00092 & 1.0007 \tabularnewline
23 & 97.2 & 97.1521 & 97.0479 & 1.00107 & 1.00049 \tabularnewline
24 & 97.26 & 97.1456 & 97.1062 & 1.00041 & 1.00118 \tabularnewline
25 & 97.26 & 97.1377 & 97.1687 & 0.99968 & 1.00126 \tabularnewline
26 & 97.24 & 97.12 & 97.2275 & 0.998894 & 1.00124 \tabularnewline
27 & 97.35 & 97.2896 & 97.2629 & 1.00027 & 1.00062 \tabularnewline
28 & 97.36 & 97.2758 & 97.2975 & 0.999777 & 1.00087 \tabularnewline
29 & 97.28 & 97.3884 & 97.3425 & 1.00047 & 0.998887 \tabularnewline
30 & 97.31 & 97.4361 & 97.3783 & 1.00059 & 0.998705 \tabularnewline
31 & 97.31 & 97.3007 & 97.4133 & 0.998843 & 1.0001 \tabularnewline
32 & 97.31 & 97.3062 & 97.4546 & 0.998477 & 1.00004 \tabularnewline
33 & 97.23 & 97.5523 & 97.495 & 1.00059 & 0.996697 \tabularnewline
34 & 97.78 & 97.6129 & 97.5229 & 1.00092 & 1.00171 \tabularnewline
35 & 97.64 & 97.6522 & 97.5475 & 1.00107 & 0.999875 \tabularnewline
36 & 97.68 & 97.6146 & 97.575 & 1.00041 & 1.00067 \tabularnewline
37 & 97.68 & 97.5721 & 97.6033 & 0.99968 & 1.00111 \tabularnewline
38 & 97.81 & 97.5237 & 97.6317 & 0.998894 & 1.00294 \tabularnewline
39 & 97.75 & 97.6989 & 97.6721 & 1.00027 & 1.00052 \tabularnewline
40 & 97.63 & 97.7028 & 97.7246 & 0.999777 & 0.999255 \tabularnewline
41 & 97.6 & 97.8449 & 97.7988 & 1.00047 & 0.997497 \tabularnewline
42 & 97.65 & 97.9494 & 97.8912 & 1.00059 & 0.996944 \tabularnewline
43 & 97.65 & 97.8667 & 97.98 & 0.998843 & 0.997786 \tabularnewline
44 & 97.65 & 97.9057 & 98.055 & 0.998477 & 0.997389 \tabularnewline
45 & 97.86 & 98.181 & 98.1233 & 1.00059 & 0.996731 \tabularnewline
46 & 98.41 & 98.3027 & 98.2121 & 1.00092 & 1.00109 \tabularnewline
47 & 98.79 & 98.4247 & 98.3192 & 1.00107 & 1.00371 \tabularnewline
48 & 98.75 & 98.4732 & 98.4333 & 1.00041 & 1.00281 \tabularnewline
49 & 98.74 & 98.5185 & 98.55 & 0.99968 & 1.00225 \tabularnewline
50 & 98.55 & 98.5576 & 98.6667 & 0.998894 & 0.999923 \tabularnewline
51 & 98.65 & 98.8067 & 98.7796 & 1.00027 & 0.998414 \tabularnewline
52 & 98.86 & 98.8363 & 98.8583 & 0.999777 & 1.00024 \tabularnewline
53 & 98.94 & 98.9341 & 98.8875 & 1.00047 & 1.00006 \tabularnewline
54 & 99.05 & 98.957 & 98.8983 & 1.00059 & 1.00094 \tabularnewline
55 & 99.05 & 98.796 & 98.9104 & 0.998843 & 1.00257 \tabularnewline
56 & 99.05 & 98.7731 & 98.9238 & 0.998477 & 1.0028 \tabularnewline
57 & 99.17 & 98.9989 & 98.9408 & 1.00059 & 1.00173 \tabularnewline
58 & 98.99 & 99.0467 & 98.9554 & 1.00092 & 0.999427 \tabularnewline
59 & 98.91 & 99.0754 & 98.9692 & 1.00107 & 0.998331 \tabularnewline
60 & 98.89 & 99.0389 & 98.9987 & 1.00041 & 0.998497 \tabularnewline
61 & 98.89 & 99.0079 & 99.0396 & 0.99968 & 0.998809 \tabularnewline
62 & 98.72 & 98.9713 & 99.0808 & 0.998894 & 0.997461 \tabularnewline
63 & 98.89 & 99.1639 & 99.1367 & 1.00027 & 0.997238 \tabularnewline
64 & 98.97 & 99.1716 & 99.1937 & 0.999777 & 0.997967 \tabularnewline
65 & 99.16 & 99.2847 & 99.2379 & 1.00047 & 0.998744 \tabularnewline
66 & 99.54 & 99.3398 & 99.2808 & 1.00059 & 1.00202 \tabularnewline
67 & 99.54 & 99.2076 & 99.3225 & 0.998843 & 1.00335 \tabularnewline
68 & 99.55 & 99.2203 & 99.3717 & 0.998477 & 1.00332 \tabularnewline
69 & 100.01 & 99.5226 & 99.4642 & 1.00059 & 1.0049 \tabularnewline
70 & 99.52 & 99.689 & 99.5971 & 1.00092 & 0.998305 \tabularnewline
71 & 99.44 & 99.8516 & 99.7446 & 1.00107 & 0.995878 \tabularnewline
72 & 99.39 & 99.9226 & 99.8821 & 1.00041 & 0.99467 \tabularnewline
73 & 99.39 & 99.9676 & 99.9996 & 0.99968 & 0.994222 \tabularnewline
74 & 99.4 & 100.004 & 100.115 & 0.998894 & 0.993957 \tabularnewline
75 & 100.43 & 100.246 & 100.218 & 1.00027 & 1.00184 \tabularnewline
76 & 100.62 & 100.296 & 100.319 & 0.999777 & 1.00323 \tabularnewline
77 & 101.05 & 100.463 & 100.415 & 1.00047 & 1.00585 \tabularnewline
78 & 100.95 & 100.567 & 100.507 & 1.00059 & 1.00381 \tabularnewline
79 & 100.95 & NA & NA & 0.998843 & NA \tabularnewline
80 & 100.91 & NA & NA & 0.998477 & NA \tabularnewline
81 & 101.13 & NA & NA & 1.00059 & NA \tabularnewline
82 & 100.81 & NA & NA & 1.00092 & NA \tabularnewline
83 & 100.47 & NA & NA & 1.00107 & NA \tabularnewline
84 & 100.56 & NA & NA & 1.00041 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231590&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]96.86[/C][C]NA[/C][C]NA[/C][C]0.99968[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]96.77[/C][C]NA[/C][C]NA[/C][C]0.998894[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]96.5[/C][C]NA[/C][C]NA[/C][C]1.00027[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]96.01[/C][C]NA[/C][C]NA[/C][C]0.999777[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]96.07[/C][C]NA[/C][C]NA[/C][C]1.00047[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]95.93[/C][C]NA[/C][C]NA[/C][C]1.00059[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]95.93[/C][C]96.1786[/C][C]96.29[/C][C]0.998843[/C][C]0.997415[/C][/ROW]
[ROW][C]8[/C][C]95.83[/C][C]96.135[/C][C]96.2817[/C][C]0.998477[/C][C]0.996827[/C][/ROW]
[ROW][C]9[/C][C]96.24[/C][C]96.3486[/C][C]96.2921[/C][C]1.00059[/C][C]0.998872[/C][/ROW]
[ROW][C]10[/C][C]96.25[/C][C]96.408[/C][C]96.3192[/C][C]1.00092[/C][C]0.998361[/C][/ROW]
[ROW][C]11[/C][C]96.59[/C][C]96.4642[/C][C]96.3608[/C][C]1.00107[/C][C]1.0013[/C][/ROW]
[ROW][C]12[/C][C]96.62[/C][C]96.4495[/C][C]96.4104[/C][C]1.00041[/C][C]1.00177[/C][/ROW]
[ROW][C]13[/C][C]96.62[/C][C]96.4321[/C][C]96.4629[/C][C]0.99968[/C][C]1.00195[/C][/ROW]
[ROW][C]14[/C][C]96.81[/C][C]96.4166[/C][C]96.5233[/C][C]0.998894[/C][C]1.00408[/C][/ROW]
[ROW][C]15[/C][C]96.71[/C][C]96.6173[/C][C]96.5908[/C][C]1.00027[/C][C]1.00096[/C][/ROW]
[ROW][C]16[/C][C]96.45[/C][C]96.6397[/C][C]96.6612[/C][C]0.999777[/C][C]0.998037[/C][/ROW]
[ROW][C]17[/C][C]96.63[/C][C]96.7694[/C][C]96.7238[/C][C]1.00047[/C][C]0.99856[/C][/ROW]
[ROW][C]18[/C][C]96.56[/C][C]96.8333[/C][C]96.7758[/C][C]1.00059[/C][C]0.997178[/C][/ROW]
[ROW][C]19[/C][C]96.56[/C][C]96.7172[/C][C]96.8292[/C][C]0.998843[/C][C]0.998375[/C][/ROW]
[ROW][C]20[/C][C]96.65[/C][C]96.7262[/C][C]96.8737[/C][C]0.998477[/C][C]0.999212[/C][/ROW]
[ROW][C]21[/C][C]97.04[/C][C]96.9753[/C][C]96.9183[/C][C]1.00059[/C][C]1.00067[/C][/ROW]
[ROW][C]22[/C][C]97.14[/C][C]97.0724[/C][C]96.9829[/C][C]1.00092[/C][C]1.0007[/C][/ROW]
[ROW][C]23[/C][C]97.2[/C][C]97.1521[/C][C]97.0479[/C][C]1.00107[/C][C]1.00049[/C][/ROW]
[ROW][C]24[/C][C]97.26[/C][C]97.1456[/C][C]97.1062[/C][C]1.00041[/C][C]1.00118[/C][/ROW]
[ROW][C]25[/C][C]97.26[/C][C]97.1377[/C][C]97.1687[/C][C]0.99968[/C][C]1.00126[/C][/ROW]
[ROW][C]26[/C][C]97.24[/C][C]97.12[/C][C]97.2275[/C][C]0.998894[/C][C]1.00124[/C][/ROW]
[ROW][C]27[/C][C]97.35[/C][C]97.2896[/C][C]97.2629[/C][C]1.00027[/C][C]1.00062[/C][/ROW]
[ROW][C]28[/C][C]97.36[/C][C]97.2758[/C][C]97.2975[/C][C]0.999777[/C][C]1.00087[/C][/ROW]
[ROW][C]29[/C][C]97.28[/C][C]97.3884[/C][C]97.3425[/C][C]1.00047[/C][C]0.998887[/C][/ROW]
[ROW][C]30[/C][C]97.31[/C][C]97.4361[/C][C]97.3783[/C][C]1.00059[/C][C]0.998705[/C][/ROW]
[ROW][C]31[/C][C]97.31[/C][C]97.3007[/C][C]97.4133[/C][C]0.998843[/C][C]1.0001[/C][/ROW]
[ROW][C]32[/C][C]97.31[/C][C]97.3062[/C][C]97.4546[/C][C]0.998477[/C][C]1.00004[/C][/ROW]
[ROW][C]33[/C][C]97.23[/C][C]97.5523[/C][C]97.495[/C][C]1.00059[/C][C]0.996697[/C][/ROW]
[ROW][C]34[/C][C]97.78[/C][C]97.6129[/C][C]97.5229[/C][C]1.00092[/C][C]1.00171[/C][/ROW]
[ROW][C]35[/C][C]97.64[/C][C]97.6522[/C][C]97.5475[/C][C]1.00107[/C][C]0.999875[/C][/ROW]
[ROW][C]36[/C][C]97.68[/C][C]97.6146[/C][C]97.575[/C][C]1.00041[/C][C]1.00067[/C][/ROW]
[ROW][C]37[/C][C]97.68[/C][C]97.5721[/C][C]97.6033[/C][C]0.99968[/C][C]1.00111[/C][/ROW]
[ROW][C]38[/C][C]97.81[/C][C]97.5237[/C][C]97.6317[/C][C]0.998894[/C][C]1.00294[/C][/ROW]
[ROW][C]39[/C][C]97.75[/C][C]97.6989[/C][C]97.6721[/C][C]1.00027[/C][C]1.00052[/C][/ROW]
[ROW][C]40[/C][C]97.63[/C][C]97.7028[/C][C]97.7246[/C][C]0.999777[/C][C]0.999255[/C][/ROW]
[ROW][C]41[/C][C]97.6[/C][C]97.8449[/C][C]97.7988[/C][C]1.00047[/C][C]0.997497[/C][/ROW]
[ROW][C]42[/C][C]97.65[/C][C]97.9494[/C][C]97.8912[/C][C]1.00059[/C][C]0.996944[/C][/ROW]
[ROW][C]43[/C][C]97.65[/C][C]97.8667[/C][C]97.98[/C][C]0.998843[/C][C]0.997786[/C][/ROW]
[ROW][C]44[/C][C]97.65[/C][C]97.9057[/C][C]98.055[/C][C]0.998477[/C][C]0.997389[/C][/ROW]
[ROW][C]45[/C][C]97.86[/C][C]98.181[/C][C]98.1233[/C][C]1.00059[/C][C]0.996731[/C][/ROW]
[ROW][C]46[/C][C]98.41[/C][C]98.3027[/C][C]98.2121[/C][C]1.00092[/C][C]1.00109[/C][/ROW]
[ROW][C]47[/C][C]98.79[/C][C]98.4247[/C][C]98.3192[/C][C]1.00107[/C][C]1.00371[/C][/ROW]
[ROW][C]48[/C][C]98.75[/C][C]98.4732[/C][C]98.4333[/C][C]1.00041[/C][C]1.00281[/C][/ROW]
[ROW][C]49[/C][C]98.74[/C][C]98.5185[/C][C]98.55[/C][C]0.99968[/C][C]1.00225[/C][/ROW]
[ROW][C]50[/C][C]98.55[/C][C]98.5576[/C][C]98.6667[/C][C]0.998894[/C][C]0.999923[/C][/ROW]
[ROW][C]51[/C][C]98.65[/C][C]98.8067[/C][C]98.7796[/C][C]1.00027[/C][C]0.998414[/C][/ROW]
[ROW][C]52[/C][C]98.86[/C][C]98.8363[/C][C]98.8583[/C][C]0.999777[/C][C]1.00024[/C][/ROW]
[ROW][C]53[/C][C]98.94[/C][C]98.9341[/C][C]98.8875[/C][C]1.00047[/C][C]1.00006[/C][/ROW]
[ROW][C]54[/C][C]99.05[/C][C]98.957[/C][C]98.8983[/C][C]1.00059[/C][C]1.00094[/C][/ROW]
[ROW][C]55[/C][C]99.05[/C][C]98.796[/C][C]98.9104[/C][C]0.998843[/C][C]1.00257[/C][/ROW]
[ROW][C]56[/C][C]99.05[/C][C]98.7731[/C][C]98.9238[/C][C]0.998477[/C][C]1.0028[/C][/ROW]
[ROW][C]57[/C][C]99.17[/C][C]98.9989[/C][C]98.9408[/C][C]1.00059[/C][C]1.00173[/C][/ROW]
[ROW][C]58[/C][C]98.99[/C][C]99.0467[/C][C]98.9554[/C][C]1.00092[/C][C]0.999427[/C][/ROW]
[ROW][C]59[/C][C]98.91[/C][C]99.0754[/C][C]98.9692[/C][C]1.00107[/C][C]0.998331[/C][/ROW]
[ROW][C]60[/C][C]98.89[/C][C]99.0389[/C][C]98.9987[/C][C]1.00041[/C][C]0.998497[/C][/ROW]
[ROW][C]61[/C][C]98.89[/C][C]99.0079[/C][C]99.0396[/C][C]0.99968[/C][C]0.998809[/C][/ROW]
[ROW][C]62[/C][C]98.72[/C][C]98.9713[/C][C]99.0808[/C][C]0.998894[/C][C]0.997461[/C][/ROW]
[ROW][C]63[/C][C]98.89[/C][C]99.1639[/C][C]99.1367[/C][C]1.00027[/C][C]0.997238[/C][/ROW]
[ROW][C]64[/C][C]98.97[/C][C]99.1716[/C][C]99.1937[/C][C]0.999777[/C][C]0.997967[/C][/ROW]
[ROW][C]65[/C][C]99.16[/C][C]99.2847[/C][C]99.2379[/C][C]1.00047[/C][C]0.998744[/C][/ROW]
[ROW][C]66[/C][C]99.54[/C][C]99.3398[/C][C]99.2808[/C][C]1.00059[/C][C]1.00202[/C][/ROW]
[ROW][C]67[/C][C]99.54[/C][C]99.2076[/C][C]99.3225[/C][C]0.998843[/C][C]1.00335[/C][/ROW]
[ROW][C]68[/C][C]99.55[/C][C]99.2203[/C][C]99.3717[/C][C]0.998477[/C][C]1.00332[/C][/ROW]
[ROW][C]69[/C][C]100.01[/C][C]99.5226[/C][C]99.4642[/C][C]1.00059[/C][C]1.0049[/C][/ROW]
[ROW][C]70[/C][C]99.52[/C][C]99.689[/C][C]99.5971[/C][C]1.00092[/C][C]0.998305[/C][/ROW]
[ROW][C]71[/C][C]99.44[/C][C]99.8516[/C][C]99.7446[/C][C]1.00107[/C][C]0.995878[/C][/ROW]
[ROW][C]72[/C][C]99.39[/C][C]99.9226[/C][C]99.8821[/C][C]1.00041[/C][C]0.99467[/C][/ROW]
[ROW][C]73[/C][C]99.39[/C][C]99.9676[/C][C]99.9996[/C][C]0.99968[/C][C]0.994222[/C][/ROW]
[ROW][C]74[/C][C]99.4[/C][C]100.004[/C][C]100.115[/C][C]0.998894[/C][C]0.993957[/C][/ROW]
[ROW][C]75[/C][C]100.43[/C][C]100.246[/C][C]100.218[/C][C]1.00027[/C][C]1.00184[/C][/ROW]
[ROW][C]76[/C][C]100.62[/C][C]100.296[/C][C]100.319[/C][C]0.999777[/C][C]1.00323[/C][/ROW]
[ROW][C]77[/C][C]101.05[/C][C]100.463[/C][C]100.415[/C][C]1.00047[/C][C]1.00585[/C][/ROW]
[ROW][C]78[/C][C]100.95[/C][C]100.567[/C][C]100.507[/C][C]1.00059[/C][C]1.00381[/C][/ROW]
[ROW][C]79[/C][C]100.95[/C][C]NA[/C][C]NA[/C][C]0.998843[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]100.91[/C][C]NA[/C][C]NA[/C][C]0.998477[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]101.13[/C][C]NA[/C][C]NA[/C][C]1.00059[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]100.81[/C][C]NA[/C][C]NA[/C][C]1.00092[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]100.47[/C][C]NA[/C][C]NA[/C][C]1.00107[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]100.56[/C][C]NA[/C][C]NA[/C][C]1.00041[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231590&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231590&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
196.86NANA0.99968NA
296.77NANA0.998894NA
396.5NANA1.00027NA
496.01NANA0.999777NA
596.07NANA1.00047NA
695.93NANA1.00059NA
795.9396.178696.290.9988430.997415
895.8396.13596.28170.9984770.996827
996.2496.348696.29211.000590.998872
1096.2596.40896.31921.000920.998361
1196.5996.464296.36081.001071.0013
1296.6296.449596.41041.000411.00177
1396.6296.432196.46290.999681.00195
1496.8196.416696.52330.9988941.00408
1596.7196.617396.59081.000271.00096
1696.4596.639796.66120.9997770.998037
1796.6396.769496.72381.000470.99856
1896.5696.833396.77581.000590.997178
1996.5696.717296.82920.9988430.998375
2096.6596.726296.87370.9984770.999212
2197.0496.975396.91831.000591.00067
2297.1497.072496.98291.000921.0007
2397.297.152197.04791.001071.00049
2497.2697.145697.10621.000411.00118
2597.2697.137797.16870.999681.00126
2697.2497.1297.22750.9988941.00124
2797.3597.289697.26291.000271.00062
2897.3697.275897.29750.9997771.00087
2997.2897.388497.34251.000470.998887
3097.3197.436197.37831.000590.998705
3197.3197.300797.41330.9988431.0001
3297.3197.306297.45460.9984771.00004
3397.2397.552397.4951.000590.996697
3497.7897.612997.52291.000921.00171
3597.6497.652297.54751.001070.999875
3697.6897.614697.5751.000411.00067
3797.6897.572197.60330.999681.00111
3897.8197.523797.63170.9988941.00294
3997.7597.698997.67211.000271.00052
4097.6397.702897.72460.9997770.999255
4197.697.844997.79881.000470.997497
4297.6597.949497.89121.000590.996944
4397.6597.866797.980.9988430.997786
4497.6597.905798.0550.9984770.997389
4597.8698.18198.12331.000590.996731
4698.4198.302798.21211.000921.00109
4798.7998.424798.31921.001071.00371
4898.7598.473298.43331.000411.00281
4998.7498.518598.550.999681.00225
5098.5598.557698.66670.9988940.999923
5198.6598.806798.77961.000270.998414
5298.8698.836398.85830.9997771.00024
5398.9498.934198.88751.000471.00006
5499.0598.95798.89831.000591.00094
5599.0598.79698.91040.9988431.00257
5699.0598.773198.92380.9984771.0028
5799.1798.998998.94081.000591.00173
5898.9999.046798.95541.000920.999427
5998.9199.075498.96921.001070.998331
6098.8999.038998.99871.000410.998497
6198.8999.007999.03960.999680.998809
6298.7298.971399.08080.9988940.997461
6398.8999.163999.13671.000270.997238
6498.9799.171699.19370.9997770.997967
6599.1699.284799.23791.000470.998744
6699.5499.339899.28081.000591.00202
6799.5499.207699.32250.9988431.00335
6899.5599.220399.37170.9984771.00332
69100.0199.522699.46421.000591.0049
7099.5299.68999.59711.000920.998305
7199.4499.851699.74461.001070.995878
7299.3999.922699.88211.000410.99467
7399.3999.967699.99960.999680.994222
7499.4100.004100.1150.9988940.993957
75100.43100.246100.2181.000271.00184
76100.62100.296100.3190.9997771.00323
77101.05100.463100.4151.000471.00585
78100.95100.567100.5071.000591.00381
79100.95NANA0.998843NA
80100.91NANA0.998477NA
81101.13NANA1.00059NA
82100.81NANA1.00092NA
83100.47NANA1.00107NA
84100.56NANA1.00041NA



Parameters (Session):
par1 = multiplicative ; par2 = 12 ;
Parameters (R input):
par1 = multiplicative ; par2 = 12 ;
R code (references can be found in the software module):
par2 <- '12'
par1 <- 'multiplicative'
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')