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Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationWed, 30 Apr 2008 07:08:50 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Apr/30/t1209562201j330c3fjvbtr3qz.htm/, Retrieved Thu, 31 Oct 2024 23:30:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=11101, Retrieved Thu, 31 Oct 2024 23:30:53 +0000
QR Codes:

Original text written by user:Er is duidelijk een seizoenseffect aanwezig dat om de 6 maanden terugkeert. Omdat het seizoenseffect niet lijkt toe te nemen verkoos ik een additief model. Volgens de tabel die de componenten weergeeft verklaart het seizoenseffect veel minder dan de trend. In periode 14 bv. telt het seizoenseffect voor ongeveer 17% van de voorspelde waarde. ( het seizoenseffect heeft een waarde van 7378.44675925926 terwijl de trendcomponent een waarde van 36991.2083333333 heeft). In de verklaring van de tijdreeks is het seizoenseffect dus niet te verwaarlozen.
IsPrivate?No (this computation is public)
User-defined keywordsadditive time series
Estimated Impact316
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [vraag 1: klassiek...] [2008-04-30 13:08:50] [22ba6e794bab0d6f675e50704b024283] [Current]
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Dataseries X:
56421
53152
53536
52408
41454
38271
35306
26414
31917
38030
27534
18387
50556
43901
48572
43899
37532
40357
35489
29027
34485
42598
30306
26451
47460
50104
61465
53726
39477
43895
31481
29896
33842
39120
33702
25094
51442
45594
52518
48564
41745
49585
32747
33379
35645
37034
35681
20972
58552
54955
65540
51570
51145
46641
35704
33253
35193
41668
34865
21210
56126
49231
59723
48103
47472
50497
40059
34149
36860
46356
36577




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=11101&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=11101&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=11101&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
156421NANA10725.9745370370NA
253152NANA7378.44675925926NA
353536NANA14881.9328703704NA
452408NANA9388.6550925926NA
541454NANA144.335648148148NA
638271NANA5022.94675925926NA
73530632660.780092592639158.125-6497.344907407412645.21990740741
82641429294.696759259338528.2916666667-9233.5949074074-2880.69675925926
93191732203.210648148137936-5732.78935185185-286.210648148146
103803036226.293981481537374.625-1148.331018518521803.70601851852
112753429058.391203703736856.6666666667-7798.27546296296-1524.39120370371
121838719648.210648148136780.1666666667-17131.9560185185-1261.21064814815
135055647600.682870370436874.708333333310725.97453703702955.31712962964
144390144369.655092592636991.20833333337378.44675925926-468.655092592591
154857252089.016203703737207.083333333314881.9328703704-3517.01620370371
164389946893.071759259337504.41666666679388.6550925926-2994.07175925926
173753237954.585648148137810.25144.335648148148-422.585648148146
184035743284.696759259338261.755022.94675925926-2927.69675925926
193548931971.405092592638468.75-6497.344907407413517.59490740741
202902729364.613425925938598.2083333333-9233.5949074074-337.613425925927
213448533661.085648148139393.875-5732.78935185185823.914351851854
224259839192.210648148140340.5416666667-1148.331018518523405.78935185185
233030633032.766203703740831.0416666667-7798.27546296296-2726.7662037037
242645123927.543981481541059.5-17131.95601851852523.45601851852
254746051765.891203703741039.916666666710725.9745370370-4305.8912037037
265010448287.571759259340909.1257378.446759259261816.42824074074
276146555800.47453703740918.541666666714881.93287037045664.52546296296
285372650135.488425925940746.83333333339388.65509259263590.51157407408
293947740887.752314814840743.4166666667144.335648148148-1410.75231481481
304389545851.321759259340828.3755022.94675925926-1956.32175925925
313148134440.405092592640937.75-6497.34490740741-2959.4050925926
322989631682.155092592640915.75-9233.5949074074-1786.15509259259
333384234622.252314814840355.0416666667-5732.78935185185-780.25231481481
343912038618.835648148239767.1666666667-1148.33101851852501.164351851847
353370231848.307870370439646.5833333333-7798.275462962961853.69212962964
362509422846.210648148139978.1666666667-17131.95601851852247.78935185185
375144250993.9745370374026810725.9745370370448.025462962964
384559447844.321759259340465.8757378.44675925926-2250.32175925925
395251855568.057870370440686.12514881.9328703704-3050.05787037037
404856450062.988425925940674.33333333339388.6550925926-1498.98842592592
414174540814.210648148140669.875144.335648148148930.789351851854
424958545603.530092592640580.58333333335022.946759259263981.46990740741
433274734207.738425925940705.0833333333-6497.34490740741-1460.73842592592
443337932157.780092592641391.375-9233.59490740741221.21990740741
453564536591.210648148142324-5732.78935185185-946.210648148146
463703441843.502314814842991.8333333333-1148.33101851852-4809.50231481482
473568135710.474537037043508.75-7798.27546296296-29.4745370370365
482097226645.793981481543777.75-17131.9560185185-5673.79398148148
495855254504.266203703743778.291666666710725.97453703704047.7337962963
505495551274.696759259243896.257378.446759259263680.30324074076
516554058754.09953703743872.166666666714881.93287037046785.90046296297
525157053435.071759259344046.41666666679388.6550925926-1865.07175925926
535114544349.835648148144205.5144.3356481481486795.16435185185
544664149204.363425925944181.41666666675022.94675925926-2563.36342592593
553570437592.905092592644090.25-6497.34490740741-1888.90509259258
563325334517.071759259243750.6666666667-9233.5949074074-1264.07175925925
573519337537.002314814843269.7916666667-5732.78935185185-2344.00231481481
584166841734.627314814842882.9583333333-1148.33101851852-66.6273148148102
593486534787.182870370442585.4583333333-7798.2754629629677.8171296296277
602121025461.127314814842593.0833333333-17131.9560185185-4251.12731481482
6156126NA42935.2083333333NANA
6249231NA43154NANA
6359723NA43260.7916666667NANA
6448103NA43525.5833333333NANA
6547472NA43792.25NANA
6650497NANANANA
6740059NANANANA
6834149NANANANA
6936860NANANANA
7046356NANANANA
7136577NANANANA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 56421 & NA & NA & 10725.9745370370 & NA \tabularnewline
2 & 53152 & NA & NA & 7378.44675925926 & NA \tabularnewline
3 & 53536 & NA & NA & 14881.9328703704 & NA \tabularnewline
4 & 52408 & NA & NA & 9388.6550925926 & NA \tabularnewline
5 & 41454 & NA & NA & 144.335648148148 & NA \tabularnewline
6 & 38271 & NA & NA & 5022.94675925926 & NA \tabularnewline
7 & 35306 & 32660.7800925926 & 39158.125 & -6497.34490740741 & 2645.21990740741 \tabularnewline
8 & 26414 & 29294.6967592593 & 38528.2916666667 & -9233.5949074074 & -2880.69675925926 \tabularnewline
9 & 31917 & 32203.2106481481 & 37936 & -5732.78935185185 & -286.210648148146 \tabularnewline
10 & 38030 & 36226.2939814815 & 37374.625 & -1148.33101851852 & 1803.70601851852 \tabularnewline
11 & 27534 & 29058.3912037037 & 36856.6666666667 & -7798.27546296296 & -1524.39120370371 \tabularnewline
12 & 18387 & 19648.2106481481 & 36780.1666666667 & -17131.9560185185 & -1261.21064814815 \tabularnewline
13 & 50556 & 47600.6828703704 & 36874.7083333333 & 10725.9745370370 & 2955.31712962964 \tabularnewline
14 & 43901 & 44369.6550925926 & 36991.2083333333 & 7378.44675925926 & -468.655092592591 \tabularnewline
15 & 48572 & 52089.0162037037 & 37207.0833333333 & 14881.9328703704 & -3517.01620370371 \tabularnewline
16 & 43899 & 46893.0717592593 & 37504.4166666667 & 9388.6550925926 & -2994.07175925926 \tabularnewline
17 & 37532 & 37954.5856481481 & 37810.25 & 144.335648148148 & -422.585648148146 \tabularnewline
18 & 40357 & 43284.6967592593 & 38261.75 & 5022.94675925926 & -2927.69675925926 \tabularnewline
19 & 35489 & 31971.4050925926 & 38468.75 & -6497.34490740741 & 3517.59490740741 \tabularnewline
20 & 29027 & 29364.6134259259 & 38598.2083333333 & -9233.5949074074 & -337.613425925927 \tabularnewline
21 & 34485 & 33661.0856481481 & 39393.875 & -5732.78935185185 & 823.914351851854 \tabularnewline
22 & 42598 & 39192.2106481481 & 40340.5416666667 & -1148.33101851852 & 3405.78935185185 \tabularnewline
23 & 30306 & 33032.7662037037 & 40831.0416666667 & -7798.27546296296 & -2726.7662037037 \tabularnewline
24 & 26451 & 23927.5439814815 & 41059.5 & -17131.9560185185 & 2523.45601851852 \tabularnewline
25 & 47460 & 51765.8912037037 & 41039.9166666667 & 10725.9745370370 & -4305.8912037037 \tabularnewline
26 & 50104 & 48287.5717592593 & 40909.125 & 7378.44675925926 & 1816.42824074074 \tabularnewline
27 & 61465 & 55800.474537037 & 40918.5416666667 & 14881.9328703704 & 5664.52546296296 \tabularnewline
28 & 53726 & 50135.4884259259 & 40746.8333333333 & 9388.6550925926 & 3590.51157407408 \tabularnewline
29 & 39477 & 40887.7523148148 & 40743.4166666667 & 144.335648148148 & -1410.75231481481 \tabularnewline
30 & 43895 & 45851.3217592593 & 40828.375 & 5022.94675925926 & -1956.32175925925 \tabularnewline
31 & 31481 & 34440.4050925926 & 40937.75 & -6497.34490740741 & -2959.4050925926 \tabularnewline
32 & 29896 & 31682.1550925926 & 40915.75 & -9233.5949074074 & -1786.15509259259 \tabularnewline
33 & 33842 & 34622.2523148148 & 40355.0416666667 & -5732.78935185185 & -780.25231481481 \tabularnewline
34 & 39120 & 38618.8356481482 & 39767.1666666667 & -1148.33101851852 & 501.164351851847 \tabularnewline
35 & 33702 & 31848.3078703704 & 39646.5833333333 & -7798.27546296296 & 1853.69212962964 \tabularnewline
36 & 25094 & 22846.2106481481 & 39978.1666666667 & -17131.9560185185 & 2247.78935185185 \tabularnewline
37 & 51442 & 50993.974537037 & 40268 & 10725.9745370370 & 448.025462962964 \tabularnewline
38 & 45594 & 47844.3217592593 & 40465.875 & 7378.44675925926 & -2250.32175925925 \tabularnewline
39 & 52518 & 55568.0578703704 & 40686.125 & 14881.9328703704 & -3050.05787037037 \tabularnewline
40 & 48564 & 50062.9884259259 & 40674.3333333333 & 9388.6550925926 & -1498.98842592592 \tabularnewline
41 & 41745 & 40814.2106481481 & 40669.875 & 144.335648148148 & 930.789351851854 \tabularnewline
42 & 49585 & 45603.5300925926 & 40580.5833333333 & 5022.94675925926 & 3981.46990740741 \tabularnewline
43 & 32747 & 34207.7384259259 & 40705.0833333333 & -6497.34490740741 & -1460.73842592592 \tabularnewline
44 & 33379 & 32157.7800925926 & 41391.375 & -9233.5949074074 & 1221.21990740741 \tabularnewline
45 & 35645 & 36591.2106481481 & 42324 & -5732.78935185185 & -946.210648148146 \tabularnewline
46 & 37034 & 41843.5023148148 & 42991.8333333333 & -1148.33101851852 & -4809.50231481482 \tabularnewline
47 & 35681 & 35710.4745370370 & 43508.75 & -7798.27546296296 & -29.4745370370365 \tabularnewline
48 & 20972 & 26645.7939814815 & 43777.75 & -17131.9560185185 & -5673.79398148148 \tabularnewline
49 & 58552 & 54504.2662037037 & 43778.2916666667 & 10725.9745370370 & 4047.7337962963 \tabularnewline
50 & 54955 & 51274.6967592592 & 43896.25 & 7378.44675925926 & 3680.30324074076 \tabularnewline
51 & 65540 & 58754.099537037 & 43872.1666666667 & 14881.9328703704 & 6785.90046296297 \tabularnewline
52 & 51570 & 53435.0717592593 & 44046.4166666667 & 9388.6550925926 & -1865.07175925926 \tabularnewline
53 & 51145 & 44349.8356481481 & 44205.5 & 144.335648148148 & 6795.16435185185 \tabularnewline
54 & 46641 & 49204.3634259259 & 44181.4166666667 & 5022.94675925926 & -2563.36342592593 \tabularnewline
55 & 35704 & 37592.9050925926 & 44090.25 & -6497.34490740741 & -1888.90509259258 \tabularnewline
56 & 33253 & 34517.0717592592 & 43750.6666666667 & -9233.5949074074 & -1264.07175925925 \tabularnewline
57 & 35193 & 37537.0023148148 & 43269.7916666667 & -5732.78935185185 & -2344.00231481481 \tabularnewline
58 & 41668 & 41734.6273148148 & 42882.9583333333 & -1148.33101851852 & -66.6273148148102 \tabularnewline
59 & 34865 & 34787.1828703704 & 42585.4583333333 & -7798.27546296296 & 77.8171296296277 \tabularnewline
60 & 21210 & 25461.1273148148 & 42593.0833333333 & -17131.9560185185 & -4251.12731481482 \tabularnewline
61 & 56126 & NA & 42935.2083333333 & NA & NA \tabularnewline
62 & 49231 & NA & 43154 & NA & NA \tabularnewline
63 & 59723 & NA & 43260.7916666667 & NA & NA \tabularnewline
64 & 48103 & NA & 43525.5833333333 & NA & NA \tabularnewline
65 & 47472 & NA & 43792.25 & NA & NA \tabularnewline
66 & 50497 & NA & NA & NA & NA \tabularnewline
67 & 40059 & NA & NA & NA & NA \tabularnewline
68 & 34149 & NA & NA & NA & NA \tabularnewline
69 & 36860 & NA & NA & NA & NA \tabularnewline
70 & 46356 & NA & NA & NA & NA \tabularnewline
71 & 36577 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=11101&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]56421[/C][C]NA[/C][C]NA[/C][C]10725.9745370370[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]53152[/C][C]NA[/C][C]NA[/C][C]7378.44675925926[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]53536[/C][C]NA[/C][C]NA[/C][C]14881.9328703704[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]52408[/C][C]NA[/C][C]NA[/C][C]9388.6550925926[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]41454[/C][C]NA[/C][C]NA[/C][C]144.335648148148[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]38271[/C][C]NA[/C][C]NA[/C][C]5022.94675925926[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]35306[/C][C]32660.7800925926[/C][C]39158.125[/C][C]-6497.34490740741[/C][C]2645.21990740741[/C][/ROW]
[ROW][C]8[/C][C]26414[/C][C]29294.6967592593[/C][C]38528.2916666667[/C][C]-9233.5949074074[/C][C]-2880.69675925926[/C][/ROW]
[ROW][C]9[/C][C]31917[/C][C]32203.2106481481[/C][C]37936[/C][C]-5732.78935185185[/C][C]-286.210648148146[/C][/ROW]
[ROW][C]10[/C][C]38030[/C][C]36226.2939814815[/C][C]37374.625[/C][C]-1148.33101851852[/C][C]1803.70601851852[/C][/ROW]
[ROW][C]11[/C][C]27534[/C][C]29058.3912037037[/C][C]36856.6666666667[/C][C]-7798.27546296296[/C][C]-1524.39120370371[/C][/ROW]
[ROW][C]12[/C][C]18387[/C][C]19648.2106481481[/C][C]36780.1666666667[/C][C]-17131.9560185185[/C][C]-1261.21064814815[/C][/ROW]
[ROW][C]13[/C][C]50556[/C][C]47600.6828703704[/C][C]36874.7083333333[/C][C]10725.9745370370[/C][C]2955.31712962964[/C][/ROW]
[ROW][C]14[/C][C]43901[/C][C]44369.6550925926[/C][C]36991.2083333333[/C][C]7378.44675925926[/C][C]-468.655092592591[/C][/ROW]
[ROW][C]15[/C][C]48572[/C][C]52089.0162037037[/C][C]37207.0833333333[/C][C]14881.9328703704[/C][C]-3517.01620370371[/C][/ROW]
[ROW][C]16[/C][C]43899[/C][C]46893.0717592593[/C][C]37504.4166666667[/C][C]9388.6550925926[/C][C]-2994.07175925926[/C][/ROW]
[ROW][C]17[/C][C]37532[/C][C]37954.5856481481[/C][C]37810.25[/C][C]144.335648148148[/C][C]-422.585648148146[/C][/ROW]
[ROW][C]18[/C][C]40357[/C][C]43284.6967592593[/C][C]38261.75[/C][C]5022.94675925926[/C][C]-2927.69675925926[/C][/ROW]
[ROW][C]19[/C][C]35489[/C][C]31971.4050925926[/C][C]38468.75[/C][C]-6497.34490740741[/C][C]3517.59490740741[/C][/ROW]
[ROW][C]20[/C][C]29027[/C][C]29364.6134259259[/C][C]38598.2083333333[/C][C]-9233.5949074074[/C][C]-337.613425925927[/C][/ROW]
[ROW][C]21[/C][C]34485[/C][C]33661.0856481481[/C][C]39393.875[/C][C]-5732.78935185185[/C][C]823.914351851854[/C][/ROW]
[ROW][C]22[/C][C]42598[/C][C]39192.2106481481[/C][C]40340.5416666667[/C][C]-1148.33101851852[/C][C]3405.78935185185[/C][/ROW]
[ROW][C]23[/C][C]30306[/C][C]33032.7662037037[/C][C]40831.0416666667[/C][C]-7798.27546296296[/C][C]-2726.7662037037[/C][/ROW]
[ROW][C]24[/C][C]26451[/C][C]23927.5439814815[/C][C]41059.5[/C][C]-17131.9560185185[/C][C]2523.45601851852[/C][/ROW]
[ROW][C]25[/C][C]47460[/C][C]51765.8912037037[/C][C]41039.9166666667[/C][C]10725.9745370370[/C][C]-4305.8912037037[/C][/ROW]
[ROW][C]26[/C][C]50104[/C][C]48287.5717592593[/C][C]40909.125[/C][C]7378.44675925926[/C][C]1816.42824074074[/C][/ROW]
[ROW][C]27[/C][C]61465[/C][C]55800.474537037[/C][C]40918.5416666667[/C][C]14881.9328703704[/C][C]5664.52546296296[/C][/ROW]
[ROW][C]28[/C][C]53726[/C][C]50135.4884259259[/C][C]40746.8333333333[/C][C]9388.6550925926[/C][C]3590.51157407408[/C][/ROW]
[ROW][C]29[/C][C]39477[/C][C]40887.7523148148[/C][C]40743.4166666667[/C][C]144.335648148148[/C][C]-1410.75231481481[/C][/ROW]
[ROW][C]30[/C][C]43895[/C][C]45851.3217592593[/C][C]40828.375[/C][C]5022.94675925926[/C][C]-1956.32175925925[/C][/ROW]
[ROW][C]31[/C][C]31481[/C][C]34440.4050925926[/C][C]40937.75[/C][C]-6497.34490740741[/C][C]-2959.4050925926[/C][/ROW]
[ROW][C]32[/C][C]29896[/C][C]31682.1550925926[/C][C]40915.75[/C][C]-9233.5949074074[/C][C]-1786.15509259259[/C][/ROW]
[ROW][C]33[/C][C]33842[/C][C]34622.2523148148[/C][C]40355.0416666667[/C][C]-5732.78935185185[/C][C]-780.25231481481[/C][/ROW]
[ROW][C]34[/C][C]39120[/C][C]38618.8356481482[/C][C]39767.1666666667[/C][C]-1148.33101851852[/C][C]501.164351851847[/C][/ROW]
[ROW][C]35[/C][C]33702[/C][C]31848.3078703704[/C][C]39646.5833333333[/C][C]-7798.27546296296[/C][C]1853.69212962964[/C][/ROW]
[ROW][C]36[/C][C]25094[/C][C]22846.2106481481[/C][C]39978.1666666667[/C][C]-17131.9560185185[/C][C]2247.78935185185[/C][/ROW]
[ROW][C]37[/C][C]51442[/C][C]50993.974537037[/C][C]40268[/C][C]10725.9745370370[/C][C]448.025462962964[/C][/ROW]
[ROW][C]38[/C][C]45594[/C][C]47844.3217592593[/C][C]40465.875[/C][C]7378.44675925926[/C][C]-2250.32175925925[/C][/ROW]
[ROW][C]39[/C][C]52518[/C][C]55568.0578703704[/C][C]40686.125[/C][C]14881.9328703704[/C][C]-3050.05787037037[/C][/ROW]
[ROW][C]40[/C][C]48564[/C][C]50062.9884259259[/C][C]40674.3333333333[/C][C]9388.6550925926[/C][C]-1498.98842592592[/C][/ROW]
[ROW][C]41[/C][C]41745[/C][C]40814.2106481481[/C][C]40669.875[/C][C]144.335648148148[/C][C]930.789351851854[/C][/ROW]
[ROW][C]42[/C][C]49585[/C][C]45603.5300925926[/C][C]40580.5833333333[/C][C]5022.94675925926[/C][C]3981.46990740741[/C][/ROW]
[ROW][C]43[/C][C]32747[/C][C]34207.7384259259[/C][C]40705.0833333333[/C][C]-6497.34490740741[/C][C]-1460.73842592592[/C][/ROW]
[ROW][C]44[/C][C]33379[/C][C]32157.7800925926[/C][C]41391.375[/C][C]-9233.5949074074[/C][C]1221.21990740741[/C][/ROW]
[ROW][C]45[/C][C]35645[/C][C]36591.2106481481[/C][C]42324[/C][C]-5732.78935185185[/C][C]-946.210648148146[/C][/ROW]
[ROW][C]46[/C][C]37034[/C][C]41843.5023148148[/C][C]42991.8333333333[/C][C]-1148.33101851852[/C][C]-4809.50231481482[/C][/ROW]
[ROW][C]47[/C][C]35681[/C][C]35710.4745370370[/C][C]43508.75[/C][C]-7798.27546296296[/C][C]-29.4745370370365[/C][/ROW]
[ROW][C]48[/C][C]20972[/C][C]26645.7939814815[/C][C]43777.75[/C][C]-17131.9560185185[/C][C]-5673.79398148148[/C][/ROW]
[ROW][C]49[/C][C]58552[/C][C]54504.2662037037[/C][C]43778.2916666667[/C][C]10725.9745370370[/C][C]4047.7337962963[/C][/ROW]
[ROW][C]50[/C][C]54955[/C][C]51274.6967592592[/C][C]43896.25[/C][C]7378.44675925926[/C][C]3680.30324074076[/C][/ROW]
[ROW][C]51[/C][C]65540[/C][C]58754.099537037[/C][C]43872.1666666667[/C][C]14881.9328703704[/C][C]6785.90046296297[/C][/ROW]
[ROW][C]52[/C][C]51570[/C][C]53435.0717592593[/C][C]44046.4166666667[/C][C]9388.6550925926[/C][C]-1865.07175925926[/C][/ROW]
[ROW][C]53[/C][C]51145[/C][C]44349.8356481481[/C][C]44205.5[/C][C]144.335648148148[/C][C]6795.16435185185[/C][/ROW]
[ROW][C]54[/C][C]46641[/C][C]49204.3634259259[/C][C]44181.4166666667[/C][C]5022.94675925926[/C][C]-2563.36342592593[/C][/ROW]
[ROW][C]55[/C][C]35704[/C][C]37592.9050925926[/C][C]44090.25[/C][C]-6497.34490740741[/C][C]-1888.90509259258[/C][/ROW]
[ROW][C]56[/C][C]33253[/C][C]34517.0717592592[/C][C]43750.6666666667[/C][C]-9233.5949074074[/C][C]-1264.07175925925[/C][/ROW]
[ROW][C]57[/C][C]35193[/C][C]37537.0023148148[/C][C]43269.7916666667[/C][C]-5732.78935185185[/C][C]-2344.00231481481[/C][/ROW]
[ROW][C]58[/C][C]41668[/C][C]41734.6273148148[/C][C]42882.9583333333[/C][C]-1148.33101851852[/C][C]-66.6273148148102[/C][/ROW]
[ROW][C]59[/C][C]34865[/C][C]34787.1828703704[/C][C]42585.4583333333[/C][C]-7798.27546296296[/C][C]77.8171296296277[/C][/ROW]
[ROW][C]60[/C][C]21210[/C][C]25461.1273148148[/C][C]42593.0833333333[/C][C]-17131.9560185185[/C][C]-4251.12731481482[/C][/ROW]
[ROW][C]61[/C][C]56126[/C][C]NA[/C][C]42935.2083333333[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]62[/C][C]49231[/C][C]NA[/C][C]43154[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]63[/C][C]59723[/C][C]NA[/C][C]43260.7916666667[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]64[/C][C]48103[/C][C]NA[/C][C]43525.5833333333[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]65[/C][C]47472[/C][C]NA[/C][C]43792.25[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]66[/C][C]50497[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]67[/C][C]40059[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]34149[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]36860[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]46356[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]36577[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=11101&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=11101&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
156421NANA10725.9745370370NA
253152NANA7378.44675925926NA
353536NANA14881.9328703704NA
452408NANA9388.6550925926NA
541454NANA144.335648148148NA
638271NANA5022.94675925926NA
73530632660.780092592639158.125-6497.344907407412645.21990740741
82641429294.696759259338528.2916666667-9233.5949074074-2880.69675925926
93191732203.210648148137936-5732.78935185185-286.210648148146
103803036226.293981481537374.625-1148.331018518521803.70601851852
112753429058.391203703736856.6666666667-7798.27546296296-1524.39120370371
121838719648.210648148136780.1666666667-17131.9560185185-1261.21064814815
135055647600.682870370436874.708333333310725.97453703702955.31712962964
144390144369.655092592636991.20833333337378.44675925926-468.655092592591
154857252089.016203703737207.083333333314881.9328703704-3517.01620370371
164389946893.071759259337504.41666666679388.6550925926-2994.07175925926
173753237954.585648148137810.25144.335648148148-422.585648148146
184035743284.696759259338261.755022.94675925926-2927.69675925926
193548931971.405092592638468.75-6497.344907407413517.59490740741
202902729364.613425925938598.2083333333-9233.5949074074-337.613425925927
213448533661.085648148139393.875-5732.78935185185823.914351851854
224259839192.210648148140340.5416666667-1148.331018518523405.78935185185
233030633032.766203703740831.0416666667-7798.27546296296-2726.7662037037
242645123927.543981481541059.5-17131.95601851852523.45601851852
254746051765.891203703741039.916666666710725.9745370370-4305.8912037037
265010448287.571759259340909.1257378.446759259261816.42824074074
276146555800.47453703740918.541666666714881.93287037045664.52546296296
285372650135.488425925940746.83333333339388.65509259263590.51157407408
293947740887.752314814840743.4166666667144.335648148148-1410.75231481481
304389545851.321759259340828.3755022.94675925926-1956.32175925925
313148134440.405092592640937.75-6497.34490740741-2959.4050925926
322989631682.155092592640915.75-9233.5949074074-1786.15509259259
333384234622.252314814840355.0416666667-5732.78935185185-780.25231481481
343912038618.835648148239767.1666666667-1148.33101851852501.164351851847
353370231848.307870370439646.5833333333-7798.275462962961853.69212962964
362509422846.210648148139978.1666666667-17131.95601851852247.78935185185
375144250993.9745370374026810725.9745370370448.025462962964
384559447844.321759259340465.8757378.44675925926-2250.32175925925
395251855568.057870370440686.12514881.9328703704-3050.05787037037
404856450062.988425925940674.33333333339388.6550925926-1498.98842592592
414174540814.210648148140669.875144.335648148148930.789351851854
424958545603.530092592640580.58333333335022.946759259263981.46990740741
433274734207.738425925940705.0833333333-6497.34490740741-1460.73842592592
443337932157.780092592641391.375-9233.59490740741221.21990740741
453564536591.210648148142324-5732.78935185185-946.210648148146
463703441843.502314814842991.8333333333-1148.33101851852-4809.50231481482
473568135710.474537037043508.75-7798.27546296296-29.4745370370365
482097226645.793981481543777.75-17131.9560185185-5673.79398148148
495855254504.266203703743778.291666666710725.97453703704047.7337962963
505495551274.696759259243896.257378.446759259263680.30324074076
516554058754.09953703743872.166666666714881.93287037046785.90046296297
525157053435.071759259344046.41666666679388.6550925926-1865.07175925926
535114544349.835648148144205.5144.3356481481486795.16435185185
544664149204.363425925944181.41666666675022.94675925926-2563.36342592593
553570437592.905092592644090.25-6497.34490740741-1888.90509259258
563325334517.071759259243750.6666666667-9233.5949074074-1264.07175925925
573519337537.002314814843269.7916666667-5732.78935185185-2344.00231481481
584166841734.627314814842882.9583333333-1148.33101851852-66.6273148148102
593486534787.182870370442585.4583333333-7798.2754629629677.8171296296277
602121025461.127314814842593.0833333333-17131.9560185185-4251.12731481482
6156126NA42935.2083333333NANA
6249231NA43154NANA
6359723NA43260.7916666667NANA
6448103NA43525.5833333333NANA
6547472NA43792.25NANA
6650497NANANANA
6740059NANANANA
6834149NANANANA
6936860NANANANA
7046356NANANANA
7136577NANANANA



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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
a<-table.element(a,m$trend[i]+m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')