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Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationThu, 05 Dec 2013 08:51:14 -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/05/t1386251515s2z5so4frbfq8i0.htm/, Retrieved Fri, 29 Mar 2024 06:13:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231120, Retrieved Fri, 29 Mar 2024 06:13:43 +0000
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Original text written by user:
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User-defined keywords
Estimated Impact60
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [] [2013-12-05 13:51:14] [c37684f2e387cbe6dfbcb0e59307bb9b] [Current]
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Dataseries X:
3.875
3.863
3.876
3.878
3.881
3.883
3.884
3.885
3.895
3.903
3.911
3.929
3.946
3.965
3.992
4.010
4.015
4.020
4.037
4.059
4.083
4.102
4.126
4.145
4.162
4.169
4.178
4.174
4.168
4.170
4.159
4.159
4.143
4.159
4.167
4.176
4.185
4.195
4.210
4.226
4.250
4.259
4.270
4.277
4.286
4.303
4.320
4.336
4.352
4.371
4.392
4.415
4.442
4.457
4.472
4.474
4.461
4.453
4.446
4.450
4.459
4.474
4.492
4.509
4.526
4.541
4.550
4.562
4.555
4.554
4.551
4.553




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=231120&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=231120&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231120&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
13.875NANA-0.00403819NA
23.863NANA-0.00122986NA
33.876NANA0.00562847NA
43.878NANA0.00870347NA
53.881NANA0.0113451NA
63.883NANA0.0100118NA
73.8843.89423.891540.00266181-0.0102035
83.8853.897853.89875-0.000896528-0.0128535
93.8953.899513.90783-0.00832153-0.00451181
103.9033.909853.91817-0.00831319-0.00685347
113.9113.92033.92925-0.00894653-0.00930347
123.9293.933943.94054-0.00660486-0.00493681
133.9463.948593.95262-0.00403819-0.00258681
143.9653.965023.96625-0.00122986-2.01389e-05
153.9923.986963.981330.005628470.00503819
164.014.006163.997460.008703470.00383819
174.0154.026054.014710.0113451-0.0110535
184.024.042684.032670.0100118-0.0226785
194.0374.053334.050670.00266181-0.0163285
204.0594.067274.06817-0.000896528-0.00827014
214.0834.07614.08442-0.008321530.00690486
224.1024.090694.099-0.008313190.0113132
234.1264.103264.11221-0.008946530.0227382
244.1454.118234.12483-0.006604860.0267715
254.1624.132134.13617-0.004038190.0298715
264.1694.144194.14542-0.001229860.0248132
274.1784.157714.152080.005628470.0202882
284.1744.165664.156960.008703470.00833819
294.1684.172394.161040.0113451-0.00438681
304.174.174054.164040.0100118-0.00405347
314.1594.168954.166290.00266181-0.00995347
324.1594.167444.16833-0.000896528-0.00843681
334.1434.162434.17075-0.00832153-0.0194285
344.1594.165944.17425-0.00831319-0.00693681
354.1674.170894.17983-0.00894653-0.00388681
364.1764.180354.18696-0.00660486-0.00435347
374.1854.191254.19529-0.00403819-0.00625347
384.1954.20364.20483-0.00122986-0.00860347
394.214.221344.215710.00562847-0.0113368
404.2264.236374.227670.00870347-0.0103701
414.254.251394.240040.0113451-0.00138681
424.2594.26314.253080.0100118-0.00409514
434.274.269374.266710.002661810.000629861
444.2774.28014.281-0.000896528-0.00310347
454.2864.28764.29592-0.00832153-0.00159514
464.3034.303064.31138-0.00831319-6.18056e-05
474.324.31834.32725-0.008946530.00169653
484.3364.33694.3435-0.00660486-0.000895139
494.3524.356134.36017-0.00403819-0.00412847
504.3714.375564.37679-0.00122986-0.00456181
514.3924.397924.392290.00562847-0.00592014
524.4154.414544.405830.008703470.000463194
534.4424.428684.417330.01134510.0133215
544.4574.437354.427330.01001180.0196549
554.4724.43924.436540.002661810.0327965
564.4744.44444.44529-0.0008965280.0296049
574.4614.445434.45375-0.008321530.0155715
584.4534.453524.46183-0.00831319-0.000520139
594.4464.46034.46925-0.00894653-0.0143035
604.454.469654.47625-0.00660486-0.0196451
614.4594.478964.483-0.00403819-0.0199618
624.4744.488694.48992-0.00122986-0.0146868
634.4924.503134.49750.00562847-0.0111285
644.5094.514334.505620.00870347-0.00532847
654.5264.525554.514210.01134510.000446528
664.5414.532894.522880.01001180.00811319
674.55NANA0.00266181NA
684.562NANA-0.000896528NA
694.555NANA-0.00832153NA
704.554NANA-0.00831319NA
714.551NANA-0.00894653NA
724.553NANA-0.00660486NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 3.875 & NA & NA & -0.00403819 & NA \tabularnewline
2 & 3.863 & NA & NA & -0.00122986 & NA \tabularnewline
3 & 3.876 & NA & NA & 0.00562847 & NA \tabularnewline
4 & 3.878 & NA & NA & 0.00870347 & NA \tabularnewline
5 & 3.881 & NA & NA & 0.0113451 & NA \tabularnewline
6 & 3.883 & NA & NA & 0.0100118 & NA \tabularnewline
7 & 3.884 & 3.8942 & 3.89154 & 0.00266181 & -0.0102035 \tabularnewline
8 & 3.885 & 3.89785 & 3.89875 & -0.000896528 & -0.0128535 \tabularnewline
9 & 3.895 & 3.89951 & 3.90783 & -0.00832153 & -0.00451181 \tabularnewline
10 & 3.903 & 3.90985 & 3.91817 & -0.00831319 & -0.00685347 \tabularnewline
11 & 3.911 & 3.9203 & 3.92925 & -0.00894653 & -0.00930347 \tabularnewline
12 & 3.929 & 3.93394 & 3.94054 & -0.00660486 & -0.00493681 \tabularnewline
13 & 3.946 & 3.94859 & 3.95262 & -0.00403819 & -0.00258681 \tabularnewline
14 & 3.965 & 3.96502 & 3.96625 & -0.00122986 & -2.01389e-05 \tabularnewline
15 & 3.992 & 3.98696 & 3.98133 & 0.00562847 & 0.00503819 \tabularnewline
16 & 4.01 & 4.00616 & 3.99746 & 0.00870347 & 0.00383819 \tabularnewline
17 & 4.015 & 4.02605 & 4.01471 & 0.0113451 & -0.0110535 \tabularnewline
18 & 4.02 & 4.04268 & 4.03267 & 0.0100118 & -0.0226785 \tabularnewline
19 & 4.037 & 4.05333 & 4.05067 & 0.00266181 & -0.0163285 \tabularnewline
20 & 4.059 & 4.06727 & 4.06817 & -0.000896528 & -0.00827014 \tabularnewline
21 & 4.083 & 4.0761 & 4.08442 & -0.00832153 & 0.00690486 \tabularnewline
22 & 4.102 & 4.09069 & 4.099 & -0.00831319 & 0.0113132 \tabularnewline
23 & 4.126 & 4.10326 & 4.11221 & -0.00894653 & 0.0227382 \tabularnewline
24 & 4.145 & 4.11823 & 4.12483 & -0.00660486 & 0.0267715 \tabularnewline
25 & 4.162 & 4.13213 & 4.13617 & -0.00403819 & 0.0298715 \tabularnewline
26 & 4.169 & 4.14419 & 4.14542 & -0.00122986 & 0.0248132 \tabularnewline
27 & 4.178 & 4.15771 & 4.15208 & 0.00562847 & 0.0202882 \tabularnewline
28 & 4.174 & 4.16566 & 4.15696 & 0.00870347 & 0.00833819 \tabularnewline
29 & 4.168 & 4.17239 & 4.16104 & 0.0113451 & -0.00438681 \tabularnewline
30 & 4.17 & 4.17405 & 4.16404 & 0.0100118 & -0.00405347 \tabularnewline
31 & 4.159 & 4.16895 & 4.16629 & 0.00266181 & -0.00995347 \tabularnewline
32 & 4.159 & 4.16744 & 4.16833 & -0.000896528 & -0.00843681 \tabularnewline
33 & 4.143 & 4.16243 & 4.17075 & -0.00832153 & -0.0194285 \tabularnewline
34 & 4.159 & 4.16594 & 4.17425 & -0.00831319 & -0.00693681 \tabularnewline
35 & 4.167 & 4.17089 & 4.17983 & -0.00894653 & -0.00388681 \tabularnewline
36 & 4.176 & 4.18035 & 4.18696 & -0.00660486 & -0.00435347 \tabularnewline
37 & 4.185 & 4.19125 & 4.19529 & -0.00403819 & -0.00625347 \tabularnewline
38 & 4.195 & 4.2036 & 4.20483 & -0.00122986 & -0.00860347 \tabularnewline
39 & 4.21 & 4.22134 & 4.21571 & 0.00562847 & -0.0113368 \tabularnewline
40 & 4.226 & 4.23637 & 4.22767 & 0.00870347 & -0.0103701 \tabularnewline
41 & 4.25 & 4.25139 & 4.24004 & 0.0113451 & -0.00138681 \tabularnewline
42 & 4.259 & 4.2631 & 4.25308 & 0.0100118 & -0.00409514 \tabularnewline
43 & 4.27 & 4.26937 & 4.26671 & 0.00266181 & 0.000629861 \tabularnewline
44 & 4.277 & 4.2801 & 4.281 & -0.000896528 & -0.00310347 \tabularnewline
45 & 4.286 & 4.2876 & 4.29592 & -0.00832153 & -0.00159514 \tabularnewline
46 & 4.303 & 4.30306 & 4.31138 & -0.00831319 & -6.18056e-05 \tabularnewline
47 & 4.32 & 4.3183 & 4.32725 & -0.00894653 & 0.00169653 \tabularnewline
48 & 4.336 & 4.3369 & 4.3435 & -0.00660486 & -0.000895139 \tabularnewline
49 & 4.352 & 4.35613 & 4.36017 & -0.00403819 & -0.00412847 \tabularnewline
50 & 4.371 & 4.37556 & 4.37679 & -0.00122986 & -0.00456181 \tabularnewline
51 & 4.392 & 4.39792 & 4.39229 & 0.00562847 & -0.00592014 \tabularnewline
52 & 4.415 & 4.41454 & 4.40583 & 0.00870347 & 0.000463194 \tabularnewline
53 & 4.442 & 4.42868 & 4.41733 & 0.0113451 & 0.0133215 \tabularnewline
54 & 4.457 & 4.43735 & 4.42733 & 0.0100118 & 0.0196549 \tabularnewline
55 & 4.472 & 4.4392 & 4.43654 & 0.00266181 & 0.0327965 \tabularnewline
56 & 4.474 & 4.4444 & 4.44529 & -0.000896528 & 0.0296049 \tabularnewline
57 & 4.461 & 4.44543 & 4.45375 & -0.00832153 & 0.0155715 \tabularnewline
58 & 4.453 & 4.45352 & 4.46183 & -0.00831319 & -0.000520139 \tabularnewline
59 & 4.446 & 4.4603 & 4.46925 & -0.00894653 & -0.0143035 \tabularnewline
60 & 4.45 & 4.46965 & 4.47625 & -0.00660486 & -0.0196451 \tabularnewline
61 & 4.459 & 4.47896 & 4.483 & -0.00403819 & -0.0199618 \tabularnewline
62 & 4.474 & 4.48869 & 4.48992 & -0.00122986 & -0.0146868 \tabularnewline
63 & 4.492 & 4.50313 & 4.4975 & 0.00562847 & -0.0111285 \tabularnewline
64 & 4.509 & 4.51433 & 4.50562 & 0.00870347 & -0.00532847 \tabularnewline
65 & 4.526 & 4.52555 & 4.51421 & 0.0113451 & 0.000446528 \tabularnewline
66 & 4.541 & 4.53289 & 4.52288 & 0.0100118 & 0.00811319 \tabularnewline
67 & 4.55 & NA & NA & 0.00266181 & NA \tabularnewline
68 & 4.562 & NA & NA & -0.000896528 & NA \tabularnewline
69 & 4.555 & NA & NA & -0.00832153 & NA \tabularnewline
70 & 4.554 & NA & NA & -0.00831319 & NA \tabularnewline
71 & 4.551 & NA & NA & -0.00894653 & NA \tabularnewline
72 & 4.553 & NA & NA & -0.00660486 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231120&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]3.875[/C][C]NA[/C][C]NA[/C][C]-0.00403819[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]3.863[/C][C]NA[/C][C]NA[/C][C]-0.00122986[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]3.876[/C][C]NA[/C][C]NA[/C][C]0.00562847[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]3.878[/C][C]NA[/C][C]NA[/C][C]0.00870347[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]3.881[/C][C]NA[/C][C]NA[/C][C]0.0113451[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]3.883[/C][C]NA[/C][C]NA[/C][C]0.0100118[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]3.884[/C][C]3.8942[/C][C]3.89154[/C][C]0.00266181[/C][C]-0.0102035[/C][/ROW]
[ROW][C]8[/C][C]3.885[/C][C]3.89785[/C][C]3.89875[/C][C]-0.000896528[/C][C]-0.0128535[/C][/ROW]
[ROW][C]9[/C][C]3.895[/C][C]3.89951[/C][C]3.90783[/C][C]-0.00832153[/C][C]-0.00451181[/C][/ROW]
[ROW][C]10[/C][C]3.903[/C][C]3.90985[/C][C]3.91817[/C][C]-0.00831319[/C][C]-0.00685347[/C][/ROW]
[ROW][C]11[/C][C]3.911[/C][C]3.9203[/C][C]3.92925[/C][C]-0.00894653[/C][C]-0.00930347[/C][/ROW]
[ROW][C]12[/C][C]3.929[/C][C]3.93394[/C][C]3.94054[/C][C]-0.00660486[/C][C]-0.00493681[/C][/ROW]
[ROW][C]13[/C][C]3.946[/C][C]3.94859[/C][C]3.95262[/C][C]-0.00403819[/C][C]-0.00258681[/C][/ROW]
[ROW][C]14[/C][C]3.965[/C][C]3.96502[/C][C]3.96625[/C][C]-0.00122986[/C][C]-2.01389e-05[/C][/ROW]
[ROW][C]15[/C][C]3.992[/C][C]3.98696[/C][C]3.98133[/C][C]0.00562847[/C][C]0.00503819[/C][/ROW]
[ROW][C]16[/C][C]4.01[/C][C]4.00616[/C][C]3.99746[/C][C]0.00870347[/C][C]0.00383819[/C][/ROW]
[ROW][C]17[/C][C]4.015[/C][C]4.02605[/C][C]4.01471[/C][C]0.0113451[/C][C]-0.0110535[/C][/ROW]
[ROW][C]18[/C][C]4.02[/C][C]4.04268[/C][C]4.03267[/C][C]0.0100118[/C][C]-0.0226785[/C][/ROW]
[ROW][C]19[/C][C]4.037[/C][C]4.05333[/C][C]4.05067[/C][C]0.00266181[/C][C]-0.0163285[/C][/ROW]
[ROW][C]20[/C][C]4.059[/C][C]4.06727[/C][C]4.06817[/C][C]-0.000896528[/C][C]-0.00827014[/C][/ROW]
[ROW][C]21[/C][C]4.083[/C][C]4.0761[/C][C]4.08442[/C][C]-0.00832153[/C][C]0.00690486[/C][/ROW]
[ROW][C]22[/C][C]4.102[/C][C]4.09069[/C][C]4.099[/C][C]-0.00831319[/C][C]0.0113132[/C][/ROW]
[ROW][C]23[/C][C]4.126[/C][C]4.10326[/C][C]4.11221[/C][C]-0.00894653[/C][C]0.0227382[/C][/ROW]
[ROW][C]24[/C][C]4.145[/C][C]4.11823[/C][C]4.12483[/C][C]-0.00660486[/C][C]0.0267715[/C][/ROW]
[ROW][C]25[/C][C]4.162[/C][C]4.13213[/C][C]4.13617[/C][C]-0.00403819[/C][C]0.0298715[/C][/ROW]
[ROW][C]26[/C][C]4.169[/C][C]4.14419[/C][C]4.14542[/C][C]-0.00122986[/C][C]0.0248132[/C][/ROW]
[ROW][C]27[/C][C]4.178[/C][C]4.15771[/C][C]4.15208[/C][C]0.00562847[/C][C]0.0202882[/C][/ROW]
[ROW][C]28[/C][C]4.174[/C][C]4.16566[/C][C]4.15696[/C][C]0.00870347[/C][C]0.00833819[/C][/ROW]
[ROW][C]29[/C][C]4.168[/C][C]4.17239[/C][C]4.16104[/C][C]0.0113451[/C][C]-0.00438681[/C][/ROW]
[ROW][C]30[/C][C]4.17[/C][C]4.17405[/C][C]4.16404[/C][C]0.0100118[/C][C]-0.00405347[/C][/ROW]
[ROW][C]31[/C][C]4.159[/C][C]4.16895[/C][C]4.16629[/C][C]0.00266181[/C][C]-0.00995347[/C][/ROW]
[ROW][C]32[/C][C]4.159[/C][C]4.16744[/C][C]4.16833[/C][C]-0.000896528[/C][C]-0.00843681[/C][/ROW]
[ROW][C]33[/C][C]4.143[/C][C]4.16243[/C][C]4.17075[/C][C]-0.00832153[/C][C]-0.0194285[/C][/ROW]
[ROW][C]34[/C][C]4.159[/C][C]4.16594[/C][C]4.17425[/C][C]-0.00831319[/C][C]-0.00693681[/C][/ROW]
[ROW][C]35[/C][C]4.167[/C][C]4.17089[/C][C]4.17983[/C][C]-0.00894653[/C][C]-0.00388681[/C][/ROW]
[ROW][C]36[/C][C]4.176[/C][C]4.18035[/C][C]4.18696[/C][C]-0.00660486[/C][C]-0.00435347[/C][/ROW]
[ROW][C]37[/C][C]4.185[/C][C]4.19125[/C][C]4.19529[/C][C]-0.00403819[/C][C]-0.00625347[/C][/ROW]
[ROW][C]38[/C][C]4.195[/C][C]4.2036[/C][C]4.20483[/C][C]-0.00122986[/C][C]-0.00860347[/C][/ROW]
[ROW][C]39[/C][C]4.21[/C][C]4.22134[/C][C]4.21571[/C][C]0.00562847[/C][C]-0.0113368[/C][/ROW]
[ROW][C]40[/C][C]4.226[/C][C]4.23637[/C][C]4.22767[/C][C]0.00870347[/C][C]-0.0103701[/C][/ROW]
[ROW][C]41[/C][C]4.25[/C][C]4.25139[/C][C]4.24004[/C][C]0.0113451[/C][C]-0.00138681[/C][/ROW]
[ROW][C]42[/C][C]4.259[/C][C]4.2631[/C][C]4.25308[/C][C]0.0100118[/C][C]-0.00409514[/C][/ROW]
[ROW][C]43[/C][C]4.27[/C][C]4.26937[/C][C]4.26671[/C][C]0.00266181[/C][C]0.000629861[/C][/ROW]
[ROW][C]44[/C][C]4.277[/C][C]4.2801[/C][C]4.281[/C][C]-0.000896528[/C][C]-0.00310347[/C][/ROW]
[ROW][C]45[/C][C]4.286[/C][C]4.2876[/C][C]4.29592[/C][C]-0.00832153[/C][C]-0.00159514[/C][/ROW]
[ROW][C]46[/C][C]4.303[/C][C]4.30306[/C][C]4.31138[/C][C]-0.00831319[/C][C]-6.18056e-05[/C][/ROW]
[ROW][C]47[/C][C]4.32[/C][C]4.3183[/C][C]4.32725[/C][C]-0.00894653[/C][C]0.00169653[/C][/ROW]
[ROW][C]48[/C][C]4.336[/C][C]4.3369[/C][C]4.3435[/C][C]-0.00660486[/C][C]-0.000895139[/C][/ROW]
[ROW][C]49[/C][C]4.352[/C][C]4.35613[/C][C]4.36017[/C][C]-0.00403819[/C][C]-0.00412847[/C][/ROW]
[ROW][C]50[/C][C]4.371[/C][C]4.37556[/C][C]4.37679[/C][C]-0.00122986[/C][C]-0.00456181[/C][/ROW]
[ROW][C]51[/C][C]4.392[/C][C]4.39792[/C][C]4.39229[/C][C]0.00562847[/C][C]-0.00592014[/C][/ROW]
[ROW][C]52[/C][C]4.415[/C][C]4.41454[/C][C]4.40583[/C][C]0.00870347[/C][C]0.000463194[/C][/ROW]
[ROW][C]53[/C][C]4.442[/C][C]4.42868[/C][C]4.41733[/C][C]0.0113451[/C][C]0.0133215[/C][/ROW]
[ROW][C]54[/C][C]4.457[/C][C]4.43735[/C][C]4.42733[/C][C]0.0100118[/C][C]0.0196549[/C][/ROW]
[ROW][C]55[/C][C]4.472[/C][C]4.4392[/C][C]4.43654[/C][C]0.00266181[/C][C]0.0327965[/C][/ROW]
[ROW][C]56[/C][C]4.474[/C][C]4.4444[/C][C]4.44529[/C][C]-0.000896528[/C][C]0.0296049[/C][/ROW]
[ROW][C]57[/C][C]4.461[/C][C]4.44543[/C][C]4.45375[/C][C]-0.00832153[/C][C]0.0155715[/C][/ROW]
[ROW][C]58[/C][C]4.453[/C][C]4.45352[/C][C]4.46183[/C][C]-0.00831319[/C][C]-0.000520139[/C][/ROW]
[ROW][C]59[/C][C]4.446[/C][C]4.4603[/C][C]4.46925[/C][C]-0.00894653[/C][C]-0.0143035[/C][/ROW]
[ROW][C]60[/C][C]4.45[/C][C]4.46965[/C][C]4.47625[/C][C]-0.00660486[/C][C]-0.0196451[/C][/ROW]
[ROW][C]61[/C][C]4.459[/C][C]4.47896[/C][C]4.483[/C][C]-0.00403819[/C][C]-0.0199618[/C][/ROW]
[ROW][C]62[/C][C]4.474[/C][C]4.48869[/C][C]4.48992[/C][C]-0.00122986[/C][C]-0.0146868[/C][/ROW]
[ROW][C]63[/C][C]4.492[/C][C]4.50313[/C][C]4.4975[/C][C]0.00562847[/C][C]-0.0111285[/C][/ROW]
[ROW][C]64[/C][C]4.509[/C][C]4.51433[/C][C]4.50562[/C][C]0.00870347[/C][C]-0.00532847[/C][/ROW]
[ROW][C]65[/C][C]4.526[/C][C]4.52555[/C][C]4.51421[/C][C]0.0113451[/C][C]0.000446528[/C][/ROW]
[ROW][C]66[/C][C]4.541[/C][C]4.53289[/C][C]4.52288[/C][C]0.0100118[/C][C]0.00811319[/C][/ROW]
[ROW][C]67[/C][C]4.55[/C][C]NA[/C][C]NA[/C][C]0.00266181[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]4.562[/C][C]NA[/C][C]NA[/C][C]-0.000896528[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]4.555[/C][C]NA[/C][C]NA[/C][C]-0.00832153[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]4.554[/C][C]NA[/C][C]NA[/C][C]-0.00831319[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]4.551[/C][C]NA[/C][C]NA[/C][C]-0.00894653[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]4.553[/C][C]NA[/C][C]NA[/C][C]-0.00660486[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231120&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231120&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
13.875NANA-0.00403819NA
23.863NANA-0.00122986NA
33.876NANA0.00562847NA
43.878NANA0.00870347NA
53.881NANA0.0113451NA
63.883NANA0.0100118NA
73.8843.89423.891540.00266181-0.0102035
83.8853.897853.89875-0.000896528-0.0128535
93.8953.899513.90783-0.00832153-0.00451181
103.9033.909853.91817-0.00831319-0.00685347
113.9113.92033.92925-0.00894653-0.00930347
123.9293.933943.94054-0.00660486-0.00493681
133.9463.948593.95262-0.00403819-0.00258681
143.9653.965023.96625-0.00122986-2.01389e-05
153.9923.986963.981330.005628470.00503819
164.014.006163.997460.008703470.00383819
174.0154.026054.014710.0113451-0.0110535
184.024.042684.032670.0100118-0.0226785
194.0374.053334.050670.00266181-0.0163285
204.0594.067274.06817-0.000896528-0.00827014
214.0834.07614.08442-0.008321530.00690486
224.1024.090694.099-0.008313190.0113132
234.1264.103264.11221-0.008946530.0227382
244.1454.118234.12483-0.006604860.0267715
254.1624.132134.13617-0.004038190.0298715
264.1694.144194.14542-0.001229860.0248132
274.1784.157714.152080.005628470.0202882
284.1744.165664.156960.008703470.00833819
294.1684.172394.161040.0113451-0.00438681
304.174.174054.164040.0100118-0.00405347
314.1594.168954.166290.00266181-0.00995347
324.1594.167444.16833-0.000896528-0.00843681
334.1434.162434.17075-0.00832153-0.0194285
344.1594.165944.17425-0.00831319-0.00693681
354.1674.170894.17983-0.00894653-0.00388681
364.1764.180354.18696-0.00660486-0.00435347
374.1854.191254.19529-0.00403819-0.00625347
384.1954.20364.20483-0.00122986-0.00860347
394.214.221344.215710.00562847-0.0113368
404.2264.236374.227670.00870347-0.0103701
414.254.251394.240040.0113451-0.00138681
424.2594.26314.253080.0100118-0.00409514
434.274.269374.266710.002661810.000629861
444.2774.28014.281-0.000896528-0.00310347
454.2864.28764.29592-0.00832153-0.00159514
464.3034.303064.31138-0.00831319-6.18056e-05
474.324.31834.32725-0.008946530.00169653
484.3364.33694.3435-0.00660486-0.000895139
494.3524.356134.36017-0.00403819-0.00412847
504.3714.375564.37679-0.00122986-0.00456181
514.3924.397924.392290.00562847-0.00592014
524.4154.414544.405830.008703470.000463194
534.4424.428684.417330.01134510.0133215
544.4574.437354.427330.01001180.0196549
554.4724.43924.436540.002661810.0327965
564.4744.44444.44529-0.0008965280.0296049
574.4614.445434.45375-0.008321530.0155715
584.4534.453524.46183-0.00831319-0.000520139
594.4464.46034.46925-0.00894653-0.0143035
604.454.469654.47625-0.00660486-0.0196451
614.4594.478964.483-0.00403819-0.0199618
624.4744.488694.48992-0.00122986-0.0146868
634.4924.503134.49750.00562847-0.0111285
644.5094.514334.505620.00870347-0.00532847
654.5264.525554.514210.01134510.000446528
664.5414.532894.522880.01001180.00811319
674.55NANA0.00266181NA
684.562NANA-0.000896528NA
694.555NANA-0.00832153NA
704.554NANA-0.00831319NA
714.551NANA-0.00894653NA
724.553NANA-0.00660486NA



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')