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Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 04 Dec 2013 04:58:57 -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/04/t138615117886nd6yquzjss4ra.htm/, Retrieved Thu, 28 Mar 2024 23:24:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=230474, Retrieved Thu, 28 Mar 2024 23:24:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
4,69
4,69
4,69
4,69
4,69
4,69
4,69
4,73
4,78
4,79
4,79
4,8
4,8
4,81
5,16
5,26
5,29
5,29
5,29
5,3
5,3
5,3
5,3
5,3
5,3
5,3
5,3
5,35
5,44
5,47
5,47
5,48
5,48
5,48
5,48
5,48
5,48
5,48
5,5
5,55
5,57
5,58
5,58
5,58
5,59
5,59
5,59
5,55
5,61
5,61
5,61
5,63
5,69
5,7
5,7
5,7
5,7
5,7
5,7
5,7
5,7
5,7
5,7
5,71
5,74
5,77
5,79
5,79
5,8
5,8
5,8
5,8
5,8
5,81
5,81
5,83
5,94
5,98
5,99
6
6,02
6,02
6,02
6,02




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230474&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230474&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230474&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'Gwilym Jenkins' @ jenkins.wessa.net







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
14.69NANA-0.0452431NA
24.69NANA-0.0597569NA
34.69NANA-0.0155208NA
44.69NANA0.00899306NA
54.69NANA0.0485764NA
64.69NANA0.0515625NA
74.694.756634.731250.0253819-0.0666319
84.734.760734.740830.0198958-0.0307292
94.784.781424.765420.0160069-0.00142361
104.794.810734.808750.00197917-0.0207292
114.794.842884.8575-0.0146181-0.0528819
124.84.870244.9075-0.0372569-0.0702431
134.84.912264.9575-0.0452431-0.112257
144.814.946495.00625-0.0597569-0.136493
155.165.036155.05167-0.01552080.123854
165.265.103585.094580.008993060.156424
175.295.185665.137080.04857640.10434
185.295.230735.179170.05156250.0592708
195.295.246225.220830.02538190.0437847
205.35.281985.262080.01989580.0180208
215.35.304345.288330.0160069-0.00434028
225.35.29995.297920.001979170.000104167
235.35.29335.30792-0.01461810.00670139
245.35.284415.32167-0.03725690.0155903
255.35.291425.33667-0.04524310.00857639
265.35.291915.35167-0.05975690.00809028
275.35.351155.36667-0.0155208-0.0511458
285.355.390665.381670.00899306-0.0406597
295.445.445245.396670.0485764-0.00524306
305.475.463235.411670.05156250.00677083
315.475.452055.426670.02538190.0179514
325.485.461565.441670.01989580.0184375
335.485.473515.45750.01600690.00649306
345.485.476155.474170.001979170.00385417
355.485.47335.48792-0.01461810.00670139
365.485.460665.49792-0.03725690.0193403
375.485.461845.50708-0.04524310.0181597
385.485.456085.51583-0.05975690.0239236
395.55.509065.52458-0.0155208-0.0090625
405.555.542745.533750.008993060.00725694
415.575.591495.542920.0485764-0.0214931
425.585.601985.550420.0515625-0.0219792
435.585.584135.558750.0253819-0.00413194
445.585.589485.569580.0198958-0.00947917
455.595.595595.579580.0160069-0.00559028
465.595.589485.58750.001979170.000520833
475.595.581225.59583-0.01461810.00878472
485.555.568585.60583-0.0372569-0.0185764
495.615.570595.61583-0.04524310.0394097
505.615.566085.62583-0.05975690.0439236
515.615.61995.63542-0.0155208-0.00989583
525.635.653585.644580.00899306-0.0235764
535.695.702335.653750.0485764-0.0123264
545.75.716155.664580.0515625-0.0161458
555.75.699975.674580.02538193.47222e-05
565.75.701985.682080.0198958-0.00197917
575.75.705595.689580.0160069-0.00559028
585.75.698655.696670.001979170.00135417
595.75.687475.70208-0.01461810.0125347
605.75.669835.70708-0.03725690.0301736
615.75.668515.71375-0.04524310.0314931
625.75.661495.72125-0.05975690.0385069
635.75.713655.72917-0.0155208-0.0136458
645.715.746495.73750.00899306-0.0364931
655.745.794415.745830.0485764-0.0544097
665.775.805735.754170.0515625-0.0357292
675.795.787885.76250.02538190.00211806
685.795.791155.771250.0198958-0.00114583
695.85.796425.780420.01600690.00357639
705.85.791985.790.001979170.00802083
715.85.788725.80333-0.01461810.0112847
725.85.783165.82042-0.03725690.0168403
735.85.792265.8375-0.04524310.00774306
745.815.794835.85458-0.05975690.0151736
755.815.856985.8725-0.0155208-0.0469792
765.835.899835.890830.00899306-0.0698264
775.945.957745.909170.0485764-0.0177431
785.985.979065.92750.05156250.0009375
795.99NANA0.0253819NA
806NANA0.0198958NA
816.02NANA0.0160069NA
826.02NANA0.00197917NA
836.02NANA-0.0146181NA
846.02NANA-0.0372569NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 4.69 & NA & NA & -0.0452431 & NA \tabularnewline
2 & 4.69 & NA & NA & -0.0597569 & NA \tabularnewline
3 & 4.69 & NA & NA & -0.0155208 & NA \tabularnewline
4 & 4.69 & NA & NA & 0.00899306 & NA \tabularnewline
5 & 4.69 & NA & NA & 0.0485764 & NA \tabularnewline
6 & 4.69 & NA & NA & 0.0515625 & NA \tabularnewline
7 & 4.69 & 4.75663 & 4.73125 & 0.0253819 & -0.0666319 \tabularnewline
8 & 4.73 & 4.76073 & 4.74083 & 0.0198958 & -0.0307292 \tabularnewline
9 & 4.78 & 4.78142 & 4.76542 & 0.0160069 & -0.00142361 \tabularnewline
10 & 4.79 & 4.81073 & 4.80875 & 0.00197917 & -0.0207292 \tabularnewline
11 & 4.79 & 4.84288 & 4.8575 & -0.0146181 & -0.0528819 \tabularnewline
12 & 4.8 & 4.87024 & 4.9075 & -0.0372569 & -0.0702431 \tabularnewline
13 & 4.8 & 4.91226 & 4.9575 & -0.0452431 & -0.112257 \tabularnewline
14 & 4.81 & 4.94649 & 5.00625 & -0.0597569 & -0.136493 \tabularnewline
15 & 5.16 & 5.03615 & 5.05167 & -0.0155208 & 0.123854 \tabularnewline
16 & 5.26 & 5.10358 & 5.09458 & 0.00899306 & 0.156424 \tabularnewline
17 & 5.29 & 5.18566 & 5.13708 & 0.0485764 & 0.10434 \tabularnewline
18 & 5.29 & 5.23073 & 5.17917 & 0.0515625 & 0.0592708 \tabularnewline
19 & 5.29 & 5.24622 & 5.22083 & 0.0253819 & 0.0437847 \tabularnewline
20 & 5.3 & 5.28198 & 5.26208 & 0.0198958 & 0.0180208 \tabularnewline
21 & 5.3 & 5.30434 & 5.28833 & 0.0160069 & -0.00434028 \tabularnewline
22 & 5.3 & 5.2999 & 5.29792 & 0.00197917 & 0.000104167 \tabularnewline
23 & 5.3 & 5.2933 & 5.30792 & -0.0146181 & 0.00670139 \tabularnewline
24 & 5.3 & 5.28441 & 5.32167 & -0.0372569 & 0.0155903 \tabularnewline
25 & 5.3 & 5.29142 & 5.33667 & -0.0452431 & 0.00857639 \tabularnewline
26 & 5.3 & 5.29191 & 5.35167 & -0.0597569 & 0.00809028 \tabularnewline
27 & 5.3 & 5.35115 & 5.36667 & -0.0155208 & -0.0511458 \tabularnewline
28 & 5.35 & 5.39066 & 5.38167 & 0.00899306 & -0.0406597 \tabularnewline
29 & 5.44 & 5.44524 & 5.39667 & 0.0485764 & -0.00524306 \tabularnewline
30 & 5.47 & 5.46323 & 5.41167 & 0.0515625 & 0.00677083 \tabularnewline
31 & 5.47 & 5.45205 & 5.42667 & 0.0253819 & 0.0179514 \tabularnewline
32 & 5.48 & 5.46156 & 5.44167 & 0.0198958 & 0.0184375 \tabularnewline
33 & 5.48 & 5.47351 & 5.4575 & 0.0160069 & 0.00649306 \tabularnewline
34 & 5.48 & 5.47615 & 5.47417 & 0.00197917 & 0.00385417 \tabularnewline
35 & 5.48 & 5.4733 & 5.48792 & -0.0146181 & 0.00670139 \tabularnewline
36 & 5.48 & 5.46066 & 5.49792 & -0.0372569 & 0.0193403 \tabularnewline
37 & 5.48 & 5.46184 & 5.50708 & -0.0452431 & 0.0181597 \tabularnewline
38 & 5.48 & 5.45608 & 5.51583 & -0.0597569 & 0.0239236 \tabularnewline
39 & 5.5 & 5.50906 & 5.52458 & -0.0155208 & -0.0090625 \tabularnewline
40 & 5.55 & 5.54274 & 5.53375 & 0.00899306 & 0.00725694 \tabularnewline
41 & 5.57 & 5.59149 & 5.54292 & 0.0485764 & -0.0214931 \tabularnewline
42 & 5.58 & 5.60198 & 5.55042 & 0.0515625 & -0.0219792 \tabularnewline
43 & 5.58 & 5.58413 & 5.55875 & 0.0253819 & -0.00413194 \tabularnewline
44 & 5.58 & 5.58948 & 5.56958 & 0.0198958 & -0.00947917 \tabularnewline
45 & 5.59 & 5.59559 & 5.57958 & 0.0160069 & -0.00559028 \tabularnewline
46 & 5.59 & 5.58948 & 5.5875 & 0.00197917 & 0.000520833 \tabularnewline
47 & 5.59 & 5.58122 & 5.59583 & -0.0146181 & 0.00878472 \tabularnewline
48 & 5.55 & 5.56858 & 5.60583 & -0.0372569 & -0.0185764 \tabularnewline
49 & 5.61 & 5.57059 & 5.61583 & -0.0452431 & 0.0394097 \tabularnewline
50 & 5.61 & 5.56608 & 5.62583 & -0.0597569 & 0.0439236 \tabularnewline
51 & 5.61 & 5.6199 & 5.63542 & -0.0155208 & -0.00989583 \tabularnewline
52 & 5.63 & 5.65358 & 5.64458 & 0.00899306 & -0.0235764 \tabularnewline
53 & 5.69 & 5.70233 & 5.65375 & 0.0485764 & -0.0123264 \tabularnewline
54 & 5.7 & 5.71615 & 5.66458 & 0.0515625 & -0.0161458 \tabularnewline
55 & 5.7 & 5.69997 & 5.67458 & 0.0253819 & 3.47222e-05 \tabularnewline
56 & 5.7 & 5.70198 & 5.68208 & 0.0198958 & -0.00197917 \tabularnewline
57 & 5.7 & 5.70559 & 5.68958 & 0.0160069 & -0.00559028 \tabularnewline
58 & 5.7 & 5.69865 & 5.69667 & 0.00197917 & 0.00135417 \tabularnewline
59 & 5.7 & 5.68747 & 5.70208 & -0.0146181 & 0.0125347 \tabularnewline
60 & 5.7 & 5.66983 & 5.70708 & -0.0372569 & 0.0301736 \tabularnewline
61 & 5.7 & 5.66851 & 5.71375 & -0.0452431 & 0.0314931 \tabularnewline
62 & 5.7 & 5.66149 & 5.72125 & -0.0597569 & 0.0385069 \tabularnewline
63 & 5.7 & 5.71365 & 5.72917 & -0.0155208 & -0.0136458 \tabularnewline
64 & 5.71 & 5.74649 & 5.7375 & 0.00899306 & -0.0364931 \tabularnewline
65 & 5.74 & 5.79441 & 5.74583 & 0.0485764 & -0.0544097 \tabularnewline
66 & 5.77 & 5.80573 & 5.75417 & 0.0515625 & -0.0357292 \tabularnewline
67 & 5.79 & 5.78788 & 5.7625 & 0.0253819 & 0.00211806 \tabularnewline
68 & 5.79 & 5.79115 & 5.77125 & 0.0198958 & -0.00114583 \tabularnewline
69 & 5.8 & 5.79642 & 5.78042 & 0.0160069 & 0.00357639 \tabularnewline
70 & 5.8 & 5.79198 & 5.79 & 0.00197917 & 0.00802083 \tabularnewline
71 & 5.8 & 5.78872 & 5.80333 & -0.0146181 & 0.0112847 \tabularnewline
72 & 5.8 & 5.78316 & 5.82042 & -0.0372569 & 0.0168403 \tabularnewline
73 & 5.8 & 5.79226 & 5.8375 & -0.0452431 & 0.00774306 \tabularnewline
74 & 5.81 & 5.79483 & 5.85458 & -0.0597569 & 0.0151736 \tabularnewline
75 & 5.81 & 5.85698 & 5.8725 & -0.0155208 & -0.0469792 \tabularnewline
76 & 5.83 & 5.89983 & 5.89083 & 0.00899306 & -0.0698264 \tabularnewline
77 & 5.94 & 5.95774 & 5.90917 & 0.0485764 & -0.0177431 \tabularnewline
78 & 5.98 & 5.97906 & 5.9275 & 0.0515625 & 0.0009375 \tabularnewline
79 & 5.99 & NA & NA & 0.0253819 & NA \tabularnewline
80 & 6 & NA & NA & 0.0198958 & NA \tabularnewline
81 & 6.02 & NA & NA & 0.0160069 & NA \tabularnewline
82 & 6.02 & NA & NA & 0.00197917 & NA \tabularnewline
83 & 6.02 & NA & NA & -0.0146181 & NA \tabularnewline
84 & 6.02 & NA & NA & -0.0372569 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=230474&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]4.69[/C][C]NA[/C][C]NA[/C][C]-0.0452431[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]4.69[/C][C]NA[/C][C]NA[/C][C]-0.0597569[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]4.69[/C][C]NA[/C][C]NA[/C][C]-0.0155208[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]4.69[/C][C]NA[/C][C]NA[/C][C]0.00899306[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]4.69[/C][C]NA[/C][C]NA[/C][C]0.0485764[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]4.69[/C][C]NA[/C][C]NA[/C][C]0.0515625[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]4.69[/C][C]4.75663[/C][C]4.73125[/C][C]0.0253819[/C][C]-0.0666319[/C][/ROW]
[ROW][C]8[/C][C]4.73[/C][C]4.76073[/C][C]4.74083[/C][C]0.0198958[/C][C]-0.0307292[/C][/ROW]
[ROW][C]9[/C][C]4.78[/C][C]4.78142[/C][C]4.76542[/C][C]0.0160069[/C][C]-0.00142361[/C][/ROW]
[ROW][C]10[/C][C]4.79[/C][C]4.81073[/C][C]4.80875[/C][C]0.00197917[/C][C]-0.0207292[/C][/ROW]
[ROW][C]11[/C][C]4.79[/C][C]4.84288[/C][C]4.8575[/C][C]-0.0146181[/C][C]-0.0528819[/C][/ROW]
[ROW][C]12[/C][C]4.8[/C][C]4.87024[/C][C]4.9075[/C][C]-0.0372569[/C][C]-0.0702431[/C][/ROW]
[ROW][C]13[/C][C]4.8[/C][C]4.91226[/C][C]4.9575[/C][C]-0.0452431[/C][C]-0.112257[/C][/ROW]
[ROW][C]14[/C][C]4.81[/C][C]4.94649[/C][C]5.00625[/C][C]-0.0597569[/C][C]-0.136493[/C][/ROW]
[ROW][C]15[/C][C]5.16[/C][C]5.03615[/C][C]5.05167[/C][C]-0.0155208[/C][C]0.123854[/C][/ROW]
[ROW][C]16[/C][C]5.26[/C][C]5.10358[/C][C]5.09458[/C][C]0.00899306[/C][C]0.156424[/C][/ROW]
[ROW][C]17[/C][C]5.29[/C][C]5.18566[/C][C]5.13708[/C][C]0.0485764[/C][C]0.10434[/C][/ROW]
[ROW][C]18[/C][C]5.29[/C][C]5.23073[/C][C]5.17917[/C][C]0.0515625[/C][C]0.0592708[/C][/ROW]
[ROW][C]19[/C][C]5.29[/C][C]5.24622[/C][C]5.22083[/C][C]0.0253819[/C][C]0.0437847[/C][/ROW]
[ROW][C]20[/C][C]5.3[/C][C]5.28198[/C][C]5.26208[/C][C]0.0198958[/C][C]0.0180208[/C][/ROW]
[ROW][C]21[/C][C]5.3[/C][C]5.30434[/C][C]5.28833[/C][C]0.0160069[/C][C]-0.00434028[/C][/ROW]
[ROW][C]22[/C][C]5.3[/C][C]5.2999[/C][C]5.29792[/C][C]0.00197917[/C][C]0.000104167[/C][/ROW]
[ROW][C]23[/C][C]5.3[/C][C]5.2933[/C][C]5.30792[/C][C]-0.0146181[/C][C]0.00670139[/C][/ROW]
[ROW][C]24[/C][C]5.3[/C][C]5.28441[/C][C]5.32167[/C][C]-0.0372569[/C][C]0.0155903[/C][/ROW]
[ROW][C]25[/C][C]5.3[/C][C]5.29142[/C][C]5.33667[/C][C]-0.0452431[/C][C]0.00857639[/C][/ROW]
[ROW][C]26[/C][C]5.3[/C][C]5.29191[/C][C]5.35167[/C][C]-0.0597569[/C][C]0.00809028[/C][/ROW]
[ROW][C]27[/C][C]5.3[/C][C]5.35115[/C][C]5.36667[/C][C]-0.0155208[/C][C]-0.0511458[/C][/ROW]
[ROW][C]28[/C][C]5.35[/C][C]5.39066[/C][C]5.38167[/C][C]0.00899306[/C][C]-0.0406597[/C][/ROW]
[ROW][C]29[/C][C]5.44[/C][C]5.44524[/C][C]5.39667[/C][C]0.0485764[/C][C]-0.00524306[/C][/ROW]
[ROW][C]30[/C][C]5.47[/C][C]5.46323[/C][C]5.41167[/C][C]0.0515625[/C][C]0.00677083[/C][/ROW]
[ROW][C]31[/C][C]5.47[/C][C]5.45205[/C][C]5.42667[/C][C]0.0253819[/C][C]0.0179514[/C][/ROW]
[ROW][C]32[/C][C]5.48[/C][C]5.46156[/C][C]5.44167[/C][C]0.0198958[/C][C]0.0184375[/C][/ROW]
[ROW][C]33[/C][C]5.48[/C][C]5.47351[/C][C]5.4575[/C][C]0.0160069[/C][C]0.00649306[/C][/ROW]
[ROW][C]34[/C][C]5.48[/C][C]5.47615[/C][C]5.47417[/C][C]0.00197917[/C][C]0.00385417[/C][/ROW]
[ROW][C]35[/C][C]5.48[/C][C]5.4733[/C][C]5.48792[/C][C]-0.0146181[/C][C]0.00670139[/C][/ROW]
[ROW][C]36[/C][C]5.48[/C][C]5.46066[/C][C]5.49792[/C][C]-0.0372569[/C][C]0.0193403[/C][/ROW]
[ROW][C]37[/C][C]5.48[/C][C]5.46184[/C][C]5.50708[/C][C]-0.0452431[/C][C]0.0181597[/C][/ROW]
[ROW][C]38[/C][C]5.48[/C][C]5.45608[/C][C]5.51583[/C][C]-0.0597569[/C][C]0.0239236[/C][/ROW]
[ROW][C]39[/C][C]5.5[/C][C]5.50906[/C][C]5.52458[/C][C]-0.0155208[/C][C]-0.0090625[/C][/ROW]
[ROW][C]40[/C][C]5.55[/C][C]5.54274[/C][C]5.53375[/C][C]0.00899306[/C][C]0.00725694[/C][/ROW]
[ROW][C]41[/C][C]5.57[/C][C]5.59149[/C][C]5.54292[/C][C]0.0485764[/C][C]-0.0214931[/C][/ROW]
[ROW][C]42[/C][C]5.58[/C][C]5.60198[/C][C]5.55042[/C][C]0.0515625[/C][C]-0.0219792[/C][/ROW]
[ROW][C]43[/C][C]5.58[/C][C]5.58413[/C][C]5.55875[/C][C]0.0253819[/C][C]-0.00413194[/C][/ROW]
[ROW][C]44[/C][C]5.58[/C][C]5.58948[/C][C]5.56958[/C][C]0.0198958[/C][C]-0.00947917[/C][/ROW]
[ROW][C]45[/C][C]5.59[/C][C]5.59559[/C][C]5.57958[/C][C]0.0160069[/C][C]-0.00559028[/C][/ROW]
[ROW][C]46[/C][C]5.59[/C][C]5.58948[/C][C]5.5875[/C][C]0.00197917[/C][C]0.000520833[/C][/ROW]
[ROW][C]47[/C][C]5.59[/C][C]5.58122[/C][C]5.59583[/C][C]-0.0146181[/C][C]0.00878472[/C][/ROW]
[ROW][C]48[/C][C]5.55[/C][C]5.56858[/C][C]5.60583[/C][C]-0.0372569[/C][C]-0.0185764[/C][/ROW]
[ROW][C]49[/C][C]5.61[/C][C]5.57059[/C][C]5.61583[/C][C]-0.0452431[/C][C]0.0394097[/C][/ROW]
[ROW][C]50[/C][C]5.61[/C][C]5.56608[/C][C]5.62583[/C][C]-0.0597569[/C][C]0.0439236[/C][/ROW]
[ROW][C]51[/C][C]5.61[/C][C]5.6199[/C][C]5.63542[/C][C]-0.0155208[/C][C]-0.00989583[/C][/ROW]
[ROW][C]52[/C][C]5.63[/C][C]5.65358[/C][C]5.64458[/C][C]0.00899306[/C][C]-0.0235764[/C][/ROW]
[ROW][C]53[/C][C]5.69[/C][C]5.70233[/C][C]5.65375[/C][C]0.0485764[/C][C]-0.0123264[/C][/ROW]
[ROW][C]54[/C][C]5.7[/C][C]5.71615[/C][C]5.66458[/C][C]0.0515625[/C][C]-0.0161458[/C][/ROW]
[ROW][C]55[/C][C]5.7[/C][C]5.69997[/C][C]5.67458[/C][C]0.0253819[/C][C]3.47222e-05[/C][/ROW]
[ROW][C]56[/C][C]5.7[/C][C]5.70198[/C][C]5.68208[/C][C]0.0198958[/C][C]-0.00197917[/C][/ROW]
[ROW][C]57[/C][C]5.7[/C][C]5.70559[/C][C]5.68958[/C][C]0.0160069[/C][C]-0.00559028[/C][/ROW]
[ROW][C]58[/C][C]5.7[/C][C]5.69865[/C][C]5.69667[/C][C]0.00197917[/C][C]0.00135417[/C][/ROW]
[ROW][C]59[/C][C]5.7[/C][C]5.68747[/C][C]5.70208[/C][C]-0.0146181[/C][C]0.0125347[/C][/ROW]
[ROW][C]60[/C][C]5.7[/C][C]5.66983[/C][C]5.70708[/C][C]-0.0372569[/C][C]0.0301736[/C][/ROW]
[ROW][C]61[/C][C]5.7[/C][C]5.66851[/C][C]5.71375[/C][C]-0.0452431[/C][C]0.0314931[/C][/ROW]
[ROW][C]62[/C][C]5.7[/C][C]5.66149[/C][C]5.72125[/C][C]-0.0597569[/C][C]0.0385069[/C][/ROW]
[ROW][C]63[/C][C]5.7[/C][C]5.71365[/C][C]5.72917[/C][C]-0.0155208[/C][C]-0.0136458[/C][/ROW]
[ROW][C]64[/C][C]5.71[/C][C]5.74649[/C][C]5.7375[/C][C]0.00899306[/C][C]-0.0364931[/C][/ROW]
[ROW][C]65[/C][C]5.74[/C][C]5.79441[/C][C]5.74583[/C][C]0.0485764[/C][C]-0.0544097[/C][/ROW]
[ROW][C]66[/C][C]5.77[/C][C]5.80573[/C][C]5.75417[/C][C]0.0515625[/C][C]-0.0357292[/C][/ROW]
[ROW][C]67[/C][C]5.79[/C][C]5.78788[/C][C]5.7625[/C][C]0.0253819[/C][C]0.00211806[/C][/ROW]
[ROW][C]68[/C][C]5.79[/C][C]5.79115[/C][C]5.77125[/C][C]0.0198958[/C][C]-0.00114583[/C][/ROW]
[ROW][C]69[/C][C]5.8[/C][C]5.79642[/C][C]5.78042[/C][C]0.0160069[/C][C]0.00357639[/C][/ROW]
[ROW][C]70[/C][C]5.8[/C][C]5.79198[/C][C]5.79[/C][C]0.00197917[/C][C]0.00802083[/C][/ROW]
[ROW][C]71[/C][C]5.8[/C][C]5.78872[/C][C]5.80333[/C][C]-0.0146181[/C][C]0.0112847[/C][/ROW]
[ROW][C]72[/C][C]5.8[/C][C]5.78316[/C][C]5.82042[/C][C]-0.0372569[/C][C]0.0168403[/C][/ROW]
[ROW][C]73[/C][C]5.8[/C][C]5.79226[/C][C]5.8375[/C][C]-0.0452431[/C][C]0.00774306[/C][/ROW]
[ROW][C]74[/C][C]5.81[/C][C]5.79483[/C][C]5.85458[/C][C]-0.0597569[/C][C]0.0151736[/C][/ROW]
[ROW][C]75[/C][C]5.81[/C][C]5.85698[/C][C]5.8725[/C][C]-0.0155208[/C][C]-0.0469792[/C][/ROW]
[ROW][C]76[/C][C]5.83[/C][C]5.89983[/C][C]5.89083[/C][C]0.00899306[/C][C]-0.0698264[/C][/ROW]
[ROW][C]77[/C][C]5.94[/C][C]5.95774[/C][C]5.90917[/C][C]0.0485764[/C][C]-0.0177431[/C][/ROW]
[ROW][C]78[/C][C]5.98[/C][C]5.97906[/C][C]5.9275[/C][C]0.0515625[/C][C]0.0009375[/C][/ROW]
[ROW][C]79[/C][C]5.99[/C][C]NA[/C][C]NA[/C][C]0.0253819[/C][C]NA[/C][/ROW]
[ROW][C]80[/C][C]6[/C][C]NA[/C][C]NA[/C][C]0.0198958[/C][C]NA[/C][/ROW]
[ROW][C]81[/C][C]6.02[/C][C]NA[/C][C]NA[/C][C]0.0160069[/C][C]NA[/C][/ROW]
[ROW][C]82[/C][C]6.02[/C][C]NA[/C][C]NA[/C][C]0.00197917[/C][C]NA[/C][/ROW]
[ROW][C]83[/C][C]6.02[/C][C]NA[/C][C]NA[/C][C]-0.0146181[/C][C]NA[/C][/ROW]
[ROW][C]84[/C][C]6.02[/C][C]NA[/C][C]NA[/C][C]-0.0372569[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=230474&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=230474&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
14.69NANA-0.0452431NA
24.69NANA-0.0597569NA
34.69NANA-0.0155208NA
44.69NANA0.00899306NA
54.69NANA0.0485764NA
64.69NANA0.0515625NA
74.694.756634.731250.0253819-0.0666319
84.734.760734.740830.0198958-0.0307292
94.784.781424.765420.0160069-0.00142361
104.794.810734.808750.00197917-0.0207292
114.794.842884.8575-0.0146181-0.0528819
124.84.870244.9075-0.0372569-0.0702431
134.84.912264.9575-0.0452431-0.112257
144.814.946495.00625-0.0597569-0.136493
155.165.036155.05167-0.01552080.123854
165.265.103585.094580.008993060.156424
175.295.185665.137080.04857640.10434
185.295.230735.179170.05156250.0592708
195.295.246225.220830.02538190.0437847
205.35.281985.262080.01989580.0180208
215.35.304345.288330.0160069-0.00434028
225.35.29995.297920.001979170.000104167
235.35.29335.30792-0.01461810.00670139
245.35.284415.32167-0.03725690.0155903
255.35.291425.33667-0.04524310.00857639
265.35.291915.35167-0.05975690.00809028
275.35.351155.36667-0.0155208-0.0511458
285.355.390665.381670.00899306-0.0406597
295.445.445245.396670.0485764-0.00524306
305.475.463235.411670.05156250.00677083
315.475.452055.426670.02538190.0179514
325.485.461565.441670.01989580.0184375
335.485.473515.45750.01600690.00649306
345.485.476155.474170.001979170.00385417
355.485.47335.48792-0.01461810.00670139
365.485.460665.49792-0.03725690.0193403
375.485.461845.50708-0.04524310.0181597
385.485.456085.51583-0.05975690.0239236
395.55.509065.52458-0.0155208-0.0090625
405.555.542745.533750.008993060.00725694
415.575.591495.542920.0485764-0.0214931
425.585.601985.550420.0515625-0.0219792
435.585.584135.558750.0253819-0.00413194
445.585.589485.569580.0198958-0.00947917
455.595.595595.579580.0160069-0.00559028
465.595.589485.58750.001979170.000520833
475.595.581225.59583-0.01461810.00878472
485.555.568585.60583-0.0372569-0.0185764
495.615.570595.61583-0.04524310.0394097
505.615.566085.62583-0.05975690.0439236
515.615.61995.63542-0.0155208-0.00989583
525.635.653585.644580.00899306-0.0235764
535.695.702335.653750.0485764-0.0123264
545.75.716155.664580.0515625-0.0161458
555.75.699975.674580.02538193.47222e-05
565.75.701985.682080.0198958-0.00197917
575.75.705595.689580.0160069-0.00559028
585.75.698655.696670.001979170.00135417
595.75.687475.70208-0.01461810.0125347
605.75.669835.70708-0.03725690.0301736
615.75.668515.71375-0.04524310.0314931
625.75.661495.72125-0.05975690.0385069
635.75.713655.72917-0.0155208-0.0136458
645.715.746495.73750.00899306-0.0364931
655.745.794415.745830.0485764-0.0544097
665.775.805735.754170.0515625-0.0357292
675.795.787885.76250.02538190.00211806
685.795.791155.771250.0198958-0.00114583
695.85.796425.780420.01600690.00357639
705.85.791985.790.001979170.00802083
715.85.788725.80333-0.01461810.0112847
725.85.783165.82042-0.03725690.0168403
735.85.792265.8375-0.04524310.00774306
745.815.794835.85458-0.05975690.0151736
755.815.856985.8725-0.0155208-0.0469792
765.835.899835.890830.00899306-0.0698264
775.945.957745.909170.0485764-0.0177431
785.985.979065.92750.05156250.0009375
795.99NANA0.0253819NA
806NANA0.0198958NA
816.02NANA0.0160069NA
826.02NANA0.00197917NA
836.02NANA-0.0146181NA
846.02NANA-0.0372569NA



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')