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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationSun, 18 Dec 2011 11:48:21 -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/2011/Dec/18/t13242269320p6jh1gluzvfgvm.htm/, Retrieved Sun, 05 May 2024 16:10:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=157075, Retrieved Sun, 05 May 2024 16:10:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [Paper] [2011-12-18 16:48:21] [6e647d331a8f33aa61a2d78ef323178e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
248,85
249,68
251,13
251,24
253,24
254,66
255,85
256,93
258,99
258,30
260,53
260,65
260,98
262,09
263,18
262,62
263,18
264,91
265,20
266,14
268,15
270,62
272,65
274,50
274,37
277,85
280,15
280,67
281,42
283,23
283,34
284,09
285,47
287,27
287,96
289,05
289,84
292,68
294,61
296,22
296,70
300,82
303,57
304,32
304,52
306,69
308,73
308,30
309,67
311,68
312,62
315,18
320,19
325,96
330,45
329,16
327,53
326,87
326,52
326,65
329,25
333,11
334,51
336,21
339,91
344,53

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157075&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157075&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157075&T=0

As an alternative you can also use a QR Code:  
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' @ jenkins.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1248.85NANA-0.478187500000003NA
2249.68NANA0.334895833333334NA
3251.13251.2203125250.773750.446562499999992-0.0903125000000102
4251.24251.641729166667251.945-0.303270833333323-0.401729166666655
5253.24252.6793125253.1575-0.4781875000000030.560687499999972
6254.66254.793645833333254.458750.334895833333334-0.133645833333333
7255.85256.3353125255.888750.446562499999992-0.485312499999992
8256.93256.759229166667257.0625-0.3032708333333230.170770833333336
9258.99257.6243125258.1025-0.4781875000000031.36568749999998
10258.3259.487395833333259.15250.334895833333334-1.18739583333331
11260.53260.3128125259.866250.4465624999999920.217187499999966
12260.65260.285479166667260.58875-0.3032708333333230.364520833333302
13260.98260.9155625261.39375-0.4781875000000030.0644375000000537
14262.09262.306145833333261.971250.334895833333334-0.216145833333371
15263.18262.9390625262.49250.4465624999999920.24093750000003
16262.62262.816729166667263.12-0.303270833333323-0.196729166666671
17263.18263.2468125263.725-0.478187500000003-0.0668125000000259
18264.91264.752395833333264.41750.3348958333333340.157604166666658
19265.2265.9253125265.478750.446562499999992-0.725312499999973
20266.14266.510479166667266.81375-0.303270833333323-0.370479166666655
21268.15267.9805625268.45875-0.4781875000000030.169437499999958
22270.62270.769895833333270.4350.334895833333334-0.149895833333346
23272.65272.7040625272.25750.446562499999992-0.0540624999999864
24274.5273.635479166667273.93875-0.3032708333333230.864520833333302
25274.37275.3018125275.78-0.478187500000003-0.931812500000035
26277.85277.823645833333277.488750.3348958333333340.0263541666666924
27280.15279.5878125279.141250.4465624999999920.562187499999993
28280.67280.391729166667280.695-0.3032708333333230.278270833333352
29281.42281.2880625281.76625-0.4781875000000030.131937499999992
30283.23282.927395833333282.59250.3348958333333340.30260416666664
31283.34283.9728125283.526250.446562499999992-0.6328125
32284.09284.234229166667284.5375-0.303270833333323-0.144229166666662
33285.47285.1418125285.62-0.4781875000000030.328187500000013
34287.27287.152395833333286.81750.3348958333333340.117604166666638
35287.96288.4303125287.983750.446562499999992-0.470312499999977
36289.05288.902979166667289.20625-0.3032708333333230.147020833333329
37289.84290.2355625290.71375-0.478187500000003-0.39556250000004
38292.68292.776145833333292.441250.334895833333334-0.0961458333333667
39294.61294.6415625294.1950.446562499999992-0.0315625000000068
40296.22295.766729166667296.07-0.3032708333333230.453270833333363
41296.7297.7293125298.2075-0.478187500000003-1.02931250000006
42300.82300.674895833333300.340.3348958333333340.145104166666613
43303.57302.7765625302.330.4465624999999920.793437500000039
44304.32303.737979166667304.04125-0.3032708333333230.582020833333331
45304.52304.9418125305.42-0.478187500000003-0.421812500000044
46306.69306.897395833333306.56250.334895833333334-0.207395833333351
47308.73308.1503125307.703750.4465624999999920.579687500000034
48308.3308.667979166667308.97125-0.303270833333323-0.367979166666657
49309.67309.6030625310.08125-0.4781875000000030.0669374999999945
50311.68311.762395833333311.42750.334895833333334-0.0823958333334076
51312.62314.0490625313.60250.446562499999992-1.42906249999999
52315.18316.399229166667316.7025-0.303270833333323-1.21922916666659
53320.19320.2380625320.71625-0.478187500000003-0.0480625000000146
54325.96325.027395833333324.69250.3348958333333340.932604166666636
55330.45327.8040625327.35750.4465624999999922.6459375
56329.16328.085479166667328.38875-0.3032708333333231.07452083333334
57327.53327.5330625328.01125-0.478187500000003-0.00306249999999864
58326.87327.541145833333327.206250.334895833333334-0.671145833333298
59326.52327.5540625327.10750.446562499999992-1.03406249999995
60326.65327.799229166667328.1025-0.303270833333323-1.14922916666666
61329.25329.4030625329.88125-0.478187500000003-0.153062499999976
62333.11332.409895833333332.0750.3348958333333340.700104166666677
63334.51335.0490625334.60250.446562499999992-0.539062499999943
64336.21337.059229166667337.3625-0.303270833333323-0.849229166666703
65339.91NANANANA
66344.53NANANANA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 248.85 & NA & NA & -0.478187500000003 & NA \tabularnewline
2 & 249.68 & NA & NA & 0.334895833333334 & NA \tabularnewline
3 & 251.13 & 251.2203125 & 250.77375 & 0.446562499999992 & -0.0903125000000102 \tabularnewline
4 & 251.24 & 251.641729166667 & 251.945 & -0.303270833333323 & -0.401729166666655 \tabularnewline
5 & 253.24 & 252.6793125 & 253.1575 & -0.478187500000003 & 0.560687499999972 \tabularnewline
6 & 254.66 & 254.793645833333 & 254.45875 & 0.334895833333334 & -0.133645833333333 \tabularnewline
7 & 255.85 & 256.3353125 & 255.88875 & 0.446562499999992 & -0.485312499999992 \tabularnewline
8 & 256.93 & 256.759229166667 & 257.0625 & -0.303270833333323 & 0.170770833333336 \tabularnewline
9 & 258.99 & 257.6243125 & 258.1025 & -0.478187500000003 & 1.36568749999998 \tabularnewline
10 & 258.3 & 259.487395833333 & 259.1525 & 0.334895833333334 & -1.18739583333331 \tabularnewline
11 & 260.53 & 260.3128125 & 259.86625 & 0.446562499999992 & 0.217187499999966 \tabularnewline
12 & 260.65 & 260.285479166667 & 260.58875 & -0.303270833333323 & 0.364520833333302 \tabularnewline
13 & 260.98 & 260.9155625 & 261.39375 & -0.478187500000003 & 0.0644375000000537 \tabularnewline
14 & 262.09 & 262.306145833333 & 261.97125 & 0.334895833333334 & -0.216145833333371 \tabularnewline
15 & 263.18 & 262.9390625 & 262.4925 & 0.446562499999992 & 0.24093750000003 \tabularnewline
16 & 262.62 & 262.816729166667 & 263.12 & -0.303270833333323 & -0.196729166666671 \tabularnewline
17 & 263.18 & 263.2468125 & 263.725 & -0.478187500000003 & -0.0668125000000259 \tabularnewline
18 & 264.91 & 264.752395833333 & 264.4175 & 0.334895833333334 & 0.157604166666658 \tabularnewline
19 & 265.2 & 265.9253125 & 265.47875 & 0.446562499999992 & -0.725312499999973 \tabularnewline
20 & 266.14 & 266.510479166667 & 266.81375 & -0.303270833333323 & -0.370479166666655 \tabularnewline
21 & 268.15 & 267.9805625 & 268.45875 & -0.478187500000003 & 0.169437499999958 \tabularnewline
22 & 270.62 & 270.769895833333 & 270.435 & 0.334895833333334 & -0.149895833333346 \tabularnewline
23 & 272.65 & 272.7040625 & 272.2575 & 0.446562499999992 & -0.0540624999999864 \tabularnewline
24 & 274.5 & 273.635479166667 & 273.93875 & -0.303270833333323 & 0.864520833333302 \tabularnewline
25 & 274.37 & 275.3018125 & 275.78 & -0.478187500000003 & -0.931812500000035 \tabularnewline
26 & 277.85 & 277.823645833333 & 277.48875 & 0.334895833333334 & 0.0263541666666924 \tabularnewline
27 & 280.15 & 279.5878125 & 279.14125 & 0.446562499999992 & 0.562187499999993 \tabularnewline
28 & 280.67 & 280.391729166667 & 280.695 & -0.303270833333323 & 0.278270833333352 \tabularnewline
29 & 281.42 & 281.2880625 & 281.76625 & -0.478187500000003 & 0.131937499999992 \tabularnewline
30 & 283.23 & 282.927395833333 & 282.5925 & 0.334895833333334 & 0.30260416666664 \tabularnewline
31 & 283.34 & 283.9728125 & 283.52625 & 0.446562499999992 & -0.6328125 \tabularnewline
32 & 284.09 & 284.234229166667 & 284.5375 & -0.303270833333323 & -0.144229166666662 \tabularnewline
33 & 285.47 & 285.1418125 & 285.62 & -0.478187500000003 & 0.328187500000013 \tabularnewline
34 & 287.27 & 287.152395833333 & 286.8175 & 0.334895833333334 & 0.117604166666638 \tabularnewline
35 & 287.96 & 288.4303125 & 287.98375 & 0.446562499999992 & -0.470312499999977 \tabularnewline
36 & 289.05 & 288.902979166667 & 289.20625 & -0.303270833333323 & 0.147020833333329 \tabularnewline
37 & 289.84 & 290.2355625 & 290.71375 & -0.478187500000003 & -0.39556250000004 \tabularnewline
38 & 292.68 & 292.776145833333 & 292.44125 & 0.334895833333334 & -0.0961458333333667 \tabularnewline
39 & 294.61 & 294.6415625 & 294.195 & 0.446562499999992 & -0.0315625000000068 \tabularnewline
40 & 296.22 & 295.766729166667 & 296.07 & -0.303270833333323 & 0.453270833333363 \tabularnewline
41 & 296.7 & 297.7293125 & 298.2075 & -0.478187500000003 & -1.02931250000006 \tabularnewline
42 & 300.82 & 300.674895833333 & 300.34 & 0.334895833333334 & 0.145104166666613 \tabularnewline
43 & 303.57 & 302.7765625 & 302.33 & 0.446562499999992 & 0.793437500000039 \tabularnewline
44 & 304.32 & 303.737979166667 & 304.04125 & -0.303270833333323 & 0.582020833333331 \tabularnewline
45 & 304.52 & 304.9418125 & 305.42 & -0.478187500000003 & -0.421812500000044 \tabularnewline
46 & 306.69 & 306.897395833333 & 306.5625 & 0.334895833333334 & -0.207395833333351 \tabularnewline
47 & 308.73 & 308.1503125 & 307.70375 & 0.446562499999992 & 0.579687500000034 \tabularnewline
48 & 308.3 & 308.667979166667 & 308.97125 & -0.303270833333323 & -0.367979166666657 \tabularnewline
49 & 309.67 & 309.6030625 & 310.08125 & -0.478187500000003 & 0.0669374999999945 \tabularnewline
50 & 311.68 & 311.762395833333 & 311.4275 & 0.334895833333334 & -0.0823958333334076 \tabularnewline
51 & 312.62 & 314.0490625 & 313.6025 & 0.446562499999992 & -1.42906249999999 \tabularnewline
52 & 315.18 & 316.399229166667 & 316.7025 & -0.303270833333323 & -1.21922916666659 \tabularnewline
53 & 320.19 & 320.2380625 & 320.71625 & -0.478187500000003 & -0.0480625000000146 \tabularnewline
54 & 325.96 & 325.027395833333 & 324.6925 & 0.334895833333334 & 0.932604166666636 \tabularnewline
55 & 330.45 & 327.8040625 & 327.3575 & 0.446562499999992 & 2.6459375 \tabularnewline
56 & 329.16 & 328.085479166667 & 328.38875 & -0.303270833333323 & 1.07452083333334 \tabularnewline
57 & 327.53 & 327.5330625 & 328.01125 & -0.478187500000003 & -0.00306249999999864 \tabularnewline
58 & 326.87 & 327.541145833333 & 327.20625 & 0.334895833333334 & -0.671145833333298 \tabularnewline
59 & 326.52 & 327.5540625 & 327.1075 & 0.446562499999992 & -1.03406249999995 \tabularnewline
60 & 326.65 & 327.799229166667 & 328.1025 & -0.303270833333323 & -1.14922916666666 \tabularnewline
61 & 329.25 & 329.4030625 & 329.88125 & -0.478187500000003 & -0.153062499999976 \tabularnewline
62 & 333.11 & 332.409895833333 & 332.075 & 0.334895833333334 & 0.700104166666677 \tabularnewline
63 & 334.51 & 335.0490625 & 334.6025 & 0.446562499999992 & -0.539062499999943 \tabularnewline
64 & 336.21 & 337.059229166667 & 337.3625 & -0.303270833333323 & -0.849229166666703 \tabularnewline
65 & 339.91 & NA & NA & NA & NA \tabularnewline
66 & 344.53 & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=157075&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]248.85[/C][C]NA[/C][C]NA[/C][C]-0.478187500000003[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]249.68[/C][C]NA[/C][C]NA[/C][C]0.334895833333334[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]251.13[/C][C]251.2203125[/C][C]250.77375[/C][C]0.446562499999992[/C][C]-0.0903125000000102[/C][/ROW]
[ROW][C]4[/C][C]251.24[/C][C]251.641729166667[/C][C]251.945[/C][C]-0.303270833333323[/C][C]-0.401729166666655[/C][/ROW]
[ROW][C]5[/C][C]253.24[/C][C]252.6793125[/C][C]253.1575[/C][C]-0.478187500000003[/C][C]0.560687499999972[/C][/ROW]
[ROW][C]6[/C][C]254.66[/C][C]254.793645833333[/C][C]254.45875[/C][C]0.334895833333334[/C][C]-0.133645833333333[/C][/ROW]
[ROW][C]7[/C][C]255.85[/C][C]256.3353125[/C][C]255.88875[/C][C]0.446562499999992[/C][C]-0.485312499999992[/C][/ROW]
[ROW][C]8[/C][C]256.93[/C][C]256.759229166667[/C][C]257.0625[/C][C]-0.303270833333323[/C][C]0.170770833333336[/C][/ROW]
[ROW][C]9[/C][C]258.99[/C][C]257.6243125[/C][C]258.1025[/C][C]-0.478187500000003[/C][C]1.36568749999998[/C][/ROW]
[ROW][C]10[/C][C]258.3[/C][C]259.487395833333[/C][C]259.1525[/C][C]0.334895833333334[/C][C]-1.18739583333331[/C][/ROW]
[ROW][C]11[/C][C]260.53[/C][C]260.3128125[/C][C]259.86625[/C][C]0.446562499999992[/C][C]0.217187499999966[/C][/ROW]
[ROW][C]12[/C][C]260.65[/C][C]260.285479166667[/C][C]260.58875[/C][C]-0.303270833333323[/C][C]0.364520833333302[/C][/ROW]
[ROW][C]13[/C][C]260.98[/C][C]260.9155625[/C][C]261.39375[/C][C]-0.478187500000003[/C][C]0.0644375000000537[/C][/ROW]
[ROW][C]14[/C][C]262.09[/C][C]262.306145833333[/C][C]261.97125[/C][C]0.334895833333334[/C][C]-0.216145833333371[/C][/ROW]
[ROW][C]15[/C][C]263.18[/C][C]262.9390625[/C][C]262.4925[/C][C]0.446562499999992[/C][C]0.24093750000003[/C][/ROW]
[ROW][C]16[/C][C]262.62[/C][C]262.816729166667[/C][C]263.12[/C][C]-0.303270833333323[/C][C]-0.196729166666671[/C][/ROW]
[ROW][C]17[/C][C]263.18[/C][C]263.2468125[/C][C]263.725[/C][C]-0.478187500000003[/C][C]-0.0668125000000259[/C][/ROW]
[ROW][C]18[/C][C]264.91[/C][C]264.752395833333[/C][C]264.4175[/C][C]0.334895833333334[/C][C]0.157604166666658[/C][/ROW]
[ROW][C]19[/C][C]265.2[/C][C]265.9253125[/C][C]265.47875[/C][C]0.446562499999992[/C][C]-0.725312499999973[/C][/ROW]
[ROW][C]20[/C][C]266.14[/C][C]266.510479166667[/C][C]266.81375[/C][C]-0.303270833333323[/C][C]-0.370479166666655[/C][/ROW]
[ROW][C]21[/C][C]268.15[/C][C]267.9805625[/C][C]268.45875[/C][C]-0.478187500000003[/C][C]0.169437499999958[/C][/ROW]
[ROW][C]22[/C][C]270.62[/C][C]270.769895833333[/C][C]270.435[/C][C]0.334895833333334[/C][C]-0.149895833333346[/C][/ROW]
[ROW][C]23[/C][C]272.65[/C][C]272.7040625[/C][C]272.2575[/C][C]0.446562499999992[/C][C]-0.0540624999999864[/C][/ROW]
[ROW][C]24[/C][C]274.5[/C][C]273.635479166667[/C][C]273.93875[/C][C]-0.303270833333323[/C][C]0.864520833333302[/C][/ROW]
[ROW][C]25[/C][C]274.37[/C][C]275.3018125[/C][C]275.78[/C][C]-0.478187500000003[/C][C]-0.931812500000035[/C][/ROW]
[ROW][C]26[/C][C]277.85[/C][C]277.823645833333[/C][C]277.48875[/C][C]0.334895833333334[/C][C]0.0263541666666924[/C][/ROW]
[ROW][C]27[/C][C]280.15[/C][C]279.5878125[/C][C]279.14125[/C][C]0.446562499999992[/C][C]0.562187499999993[/C][/ROW]
[ROW][C]28[/C][C]280.67[/C][C]280.391729166667[/C][C]280.695[/C][C]-0.303270833333323[/C][C]0.278270833333352[/C][/ROW]
[ROW][C]29[/C][C]281.42[/C][C]281.2880625[/C][C]281.76625[/C][C]-0.478187500000003[/C][C]0.131937499999992[/C][/ROW]
[ROW][C]30[/C][C]283.23[/C][C]282.927395833333[/C][C]282.5925[/C][C]0.334895833333334[/C][C]0.30260416666664[/C][/ROW]
[ROW][C]31[/C][C]283.34[/C][C]283.9728125[/C][C]283.52625[/C][C]0.446562499999992[/C][C]-0.6328125[/C][/ROW]
[ROW][C]32[/C][C]284.09[/C][C]284.234229166667[/C][C]284.5375[/C][C]-0.303270833333323[/C][C]-0.144229166666662[/C][/ROW]
[ROW][C]33[/C][C]285.47[/C][C]285.1418125[/C][C]285.62[/C][C]-0.478187500000003[/C][C]0.328187500000013[/C][/ROW]
[ROW][C]34[/C][C]287.27[/C][C]287.152395833333[/C][C]286.8175[/C][C]0.334895833333334[/C][C]0.117604166666638[/C][/ROW]
[ROW][C]35[/C][C]287.96[/C][C]288.4303125[/C][C]287.98375[/C][C]0.446562499999992[/C][C]-0.470312499999977[/C][/ROW]
[ROW][C]36[/C][C]289.05[/C][C]288.902979166667[/C][C]289.20625[/C][C]-0.303270833333323[/C][C]0.147020833333329[/C][/ROW]
[ROW][C]37[/C][C]289.84[/C][C]290.2355625[/C][C]290.71375[/C][C]-0.478187500000003[/C][C]-0.39556250000004[/C][/ROW]
[ROW][C]38[/C][C]292.68[/C][C]292.776145833333[/C][C]292.44125[/C][C]0.334895833333334[/C][C]-0.0961458333333667[/C][/ROW]
[ROW][C]39[/C][C]294.61[/C][C]294.6415625[/C][C]294.195[/C][C]0.446562499999992[/C][C]-0.0315625000000068[/C][/ROW]
[ROW][C]40[/C][C]296.22[/C][C]295.766729166667[/C][C]296.07[/C][C]-0.303270833333323[/C][C]0.453270833333363[/C][/ROW]
[ROW][C]41[/C][C]296.7[/C][C]297.7293125[/C][C]298.2075[/C][C]-0.478187500000003[/C][C]-1.02931250000006[/C][/ROW]
[ROW][C]42[/C][C]300.82[/C][C]300.674895833333[/C][C]300.34[/C][C]0.334895833333334[/C][C]0.145104166666613[/C][/ROW]
[ROW][C]43[/C][C]303.57[/C][C]302.7765625[/C][C]302.33[/C][C]0.446562499999992[/C][C]0.793437500000039[/C][/ROW]
[ROW][C]44[/C][C]304.32[/C][C]303.737979166667[/C][C]304.04125[/C][C]-0.303270833333323[/C][C]0.582020833333331[/C][/ROW]
[ROW][C]45[/C][C]304.52[/C][C]304.9418125[/C][C]305.42[/C][C]-0.478187500000003[/C][C]-0.421812500000044[/C][/ROW]
[ROW][C]46[/C][C]306.69[/C][C]306.897395833333[/C][C]306.5625[/C][C]0.334895833333334[/C][C]-0.207395833333351[/C][/ROW]
[ROW][C]47[/C][C]308.73[/C][C]308.1503125[/C][C]307.70375[/C][C]0.446562499999992[/C][C]0.579687500000034[/C][/ROW]
[ROW][C]48[/C][C]308.3[/C][C]308.667979166667[/C][C]308.97125[/C][C]-0.303270833333323[/C][C]-0.367979166666657[/C][/ROW]
[ROW][C]49[/C][C]309.67[/C][C]309.6030625[/C][C]310.08125[/C][C]-0.478187500000003[/C][C]0.0669374999999945[/C][/ROW]
[ROW][C]50[/C][C]311.68[/C][C]311.762395833333[/C][C]311.4275[/C][C]0.334895833333334[/C][C]-0.0823958333334076[/C][/ROW]
[ROW][C]51[/C][C]312.62[/C][C]314.0490625[/C][C]313.6025[/C][C]0.446562499999992[/C][C]-1.42906249999999[/C][/ROW]
[ROW][C]52[/C][C]315.18[/C][C]316.399229166667[/C][C]316.7025[/C][C]-0.303270833333323[/C][C]-1.21922916666659[/C][/ROW]
[ROW][C]53[/C][C]320.19[/C][C]320.2380625[/C][C]320.71625[/C][C]-0.478187500000003[/C][C]-0.0480625000000146[/C][/ROW]
[ROW][C]54[/C][C]325.96[/C][C]325.027395833333[/C][C]324.6925[/C][C]0.334895833333334[/C][C]0.932604166666636[/C][/ROW]
[ROW][C]55[/C][C]330.45[/C][C]327.8040625[/C][C]327.3575[/C][C]0.446562499999992[/C][C]2.6459375[/C][/ROW]
[ROW][C]56[/C][C]329.16[/C][C]328.085479166667[/C][C]328.38875[/C][C]-0.303270833333323[/C][C]1.07452083333334[/C][/ROW]
[ROW][C]57[/C][C]327.53[/C][C]327.5330625[/C][C]328.01125[/C][C]-0.478187500000003[/C][C]-0.00306249999999864[/C][/ROW]
[ROW][C]58[/C][C]326.87[/C][C]327.541145833333[/C][C]327.20625[/C][C]0.334895833333334[/C][C]-0.671145833333298[/C][/ROW]
[ROW][C]59[/C][C]326.52[/C][C]327.5540625[/C][C]327.1075[/C][C]0.446562499999992[/C][C]-1.03406249999995[/C][/ROW]
[ROW][C]60[/C][C]326.65[/C][C]327.799229166667[/C][C]328.1025[/C][C]-0.303270833333323[/C][C]-1.14922916666666[/C][/ROW]
[ROW][C]61[/C][C]329.25[/C][C]329.4030625[/C][C]329.88125[/C][C]-0.478187500000003[/C][C]-0.153062499999976[/C][/ROW]
[ROW][C]62[/C][C]333.11[/C][C]332.409895833333[/C][C]332.075[/C][C]0.334895833333334[/C][C]0.700104166666677[/C][/ROW]
[ROW][C]63[/C][C]334.51[/C][C]335.0490625[/C][C]334.6025[/C][C]0.446562499999992[/C][C]-0.539062499999943[/C][/ROW]
[ROW][C]64[/C][C]336.21[/C][C]337.059229166667[/C][C]337.3625[/C][C]-0.303270833333323[/C][C]-0.849229166666703[/C][/ROW]
[ROW][C]65[/C][C]339.91[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]66[/C][C]344.53[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=157075&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=157075&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1248.85NANA-0.478187500000003NA
2249.68NANA0.334895833333334NA
3251.13251.2203125250.773750.446562499999992-0.0903125000000102
4251.24251.641729166667251.945-0.303270833333323-0.401729166666655
5253.24252.6793125253.1575-0.4781875000000030.560687499999972
6254.66254.793645833333254.458750.334895833333334-0.133645833333333
7255.85256.3353125255.888750.446562499999992-0.485312499999992
8256.93256.759229166667257.0625-0.3032708333333230.170770833333336
9258.99257.6243125258.1025-0.4781875000000031.36568749999998
10258.3259.487395833333259.15250.334895833333334-1.18739583333331
11260.53260.3128125259.866250.4465624999999920.217187499999966
12260.65260.285479166667260.58875-0.3032708333333230.364520833333302
13260.98260.9155625261.39375-0.4781875000000030.0644375000000537
14262.09262.306145833333261.971250.334895833333334-0.216145833333371
15263.18262.9390625262.49250.4465624999999920.24093750000003
16262.62262.816729166667263.12-0.303270833333323-0.196729166666671
17263.18263.2468125263.725-0.478187500000003-0.0668125000000259
18264.91264.752395833333264.41750.3348958333333340.157604166666658
19265.2265.9253125265.478750.446562499999992-0.725312499999973
20266.14266.510479166667266.81375-0.303270833333323-0.370479166666655
21268.15267.9805625268.45875-0.4781875000000030.169437499999958
22270.62270.769895833333270.4350.334895833333334-0.149895833333346
23272.65272.7040625272.25750.446562499999992-0.0540624999999864
24274.5273.635479166667273.93875-0.3032708333333230.864520833333302
25274.37275.3018125275.78-0.478187500000003-0.931812500000035
26277.85277.823645833333277.488750.3348958333333340.0263541666666924
27280.15279.5878125279.141250.4465624999999920.562187499999993
28280.67280.391729166667280.695-0.3032708333333230.278270833333352
29281.42281.2880625281.76625-0.4781875000000030.131937499999992
30283.23282.927395833333282.59250.3348958333333340.30260416666664
31283.34283.9728125283.526250.446562499999992-0.6328125
32284.09284.234229166667284.5375-0.303270833333323-0.144229166666662
33285.47285.1418125285.62-0.4781875000000030.328187500000013
34287.27287.152395833333286.81750.3348958333333340.117604166666638
35287.96288.4303125287.983750.446562499999992-0.470312499999977
36289.05288.902979166667289.20625-0.3032708333333230.147020833333329
37289.84290.2355625290.71375-0.478187500000003-0.39556250000004
38292.68292.776145833333292.441250.334895833333334-0.0961458333333667
39294.61294.6415625294.1950.446562499999992-0.0315625000000068
40296.22295.766729166667296.07-0.3032708333333230.453270833333363
41296.7297.7293125298.2075-0.478187500000003-1.02931250000006
42300.82300.674895833333300.340.3348958333333340.145104166666613
43303.57302.7765625302.330.4465624999999920.793437500000039
44304.32303.737979166667304.04125-0.3032708333333230.582020833333331
45304.52304.9418125305.42-0.478187500000003-0.421812500000044
46306.69306.897395833333306.56250.334895833333334-0.207395833333351
47308.73308.1503125307.703750.4465624999999920.579687500000034
48308.3308.667979166667308.97125-0.303270833333323-0.367979166666657
49309.67309.6030625310.08125-0.4781875000000030.0669374999999945
50311.68311.762395833333311.42750.334895833333334-0.0823958333334076
51312.62314.0490625313.60250.446562499999992-1.42906249999999
52315.18316.399229166667316.7025-0.303270833333323-1.21922916666659
53320.19320.2380625320.71625-0.478187500000003-0.0480625000000146
54325.96325.027395833333324.69250.3348958333333340.932604166666636
55330.45327.8040625327.35750.4465624999999922.6459375
56329.16328.085479166667328.38875-0.3032708333333231.07452083333334
57327.53327.5330625328.01125-0.478187500000003-0.00306249999999864
58326.87327.541145833333327.206250.334895833333334-0.671145833333298
59326.52327.5540625327.10750.446562499999992-1.03406249999995
60326.65327.799229166667328.1025-0.303270833333323-1.14922916666666
61329.25329.4030625329.88125-0.478187500000003-0.153062499999976
62333.11332.409895833333332.0750.3348958333333340.700104166666677
63334.51335.0490625334.60250.446562499999992-0.539062499999943
64336.21337.059229166667337.3625-0.303270833333323-0.849229166666703
65339.91NANANANA
66344.53NANANANA


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = additive ; par2 = 4 ;
Parameters (R input):
par1 = additive ; par2 = 4 ;
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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$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')