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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_decompose.wasp
Title produced by softwareClassical Decomposition
Date of computationThu, 02 May 2013 03:44:08 -0400
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/May/02/t1367480709lwq2wvbbucch694.htm/, Retrieved Thu, 21 May 2026 07:58:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=208645, Retrieved Thu, 21 May 2026 07:58:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact378

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Classical Decomposition] [addi eigen cijfers] [2013-05-02 07:44:08] [5f178b5bce8a01d64692a8a5c649399b] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
599
599
599
599
599
599
599
599
599
617,06
617,06
617,06
617,06
617,06
617,06
617,06
617,06
617,06
617,06
617,06
617,06
628,18
628,18
628,18
628,18
628,18
628,18
628,18
628,18
628,18
628,18
628,18
628,18
641,08
641,08
641,08
641,08
641,08
641,08
641,08
641,08
641,08
641,08
641,08
641,08
668,21
668,21
668,21
668,21
668,21
668,21
668,21
668,21
668,21
668,21
668,21
668,21
665,27
665,27
665,27
665,27
665,27
665,27
665,27
665,27
665,27
665,27
665,27
665,27
674,3
674,3
674,3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208645&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]3 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=208645&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208645&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1599NANA2.70481249999999NA
2599NANA1.6003125NA
3599NANA0.495812499999996NA
4599NANA-0.533437499999983NA
5599NANA-1.48743749999998NA
6599NANA-2.44143749999995NA
7599600.3453125604.2675-3.92218750000001-1.34531249999998
8599600.7458125605.7725-5.0266875-1.74581250000006
9599601.1463125607.2775-6.13118750000003-2.14631250000002
10617.06614.8008125608.78256.018312499999992.25918749999994
11617.06615.2013125610.28754.913812499999981.85868749999997
12617.06615.6018125611.79253.809312499999991.45818749999989
13617.06616.0023125613.29752.704812499999991.05768749999993
14617.06616.4028125614.80251.60031250.657187499999964
15617.06616.8033125616.30750.4958124999999960.256687499999998
16617.06616.989895833333617.523333333333-0.5334374999999830.0701041666666242
17617.06616.9625625618.45-1.487437499999980.0974374999999554
18617.06616.935229166667619.376666666667-2.441437499999950.1247708333334
19617.06616.381145833333620.303333333333-3.922187500000010.678854166666724
20617.06616.2033125621.23-5.02668750.856687500000021
21617.06616.025479166667622.156666666667-6.131187500000031.03452083333332
22628.18629.101645833333623.0833333333336.01831249999999-0.921645833333287
23628.18628.9238125624.014.91381249999998-0.743812499999876
24628.18628.745979166667624.9366666666663.80931249999999-0.56597916666658
25628.18628.568145833333625.8633333333332.70481249999999-0.388145833333169
26628.18628.3903125626.791.6003125-0.210312499999759
27628.18628.212479166666627.7166666666660.495812499999996-0.0324791666664623
28628.18628.1840625628.7175-0.533437499999983-0.00406249999991815
29628.18628.3050625629.7925-1.48743749999998-0.125062500000013
30628.18628.4260625630.8675-2.44143749999995-0.24606249999988
31628.18628.0203125631.9425-3.922187500000010.159687500000132
32628.18627.9908125633.0175-5.02668750.189187500000003
33628.18627.9613125634.0925-6.131187500000030.218687499999987
34641.08641.1858125635.16756.01831249999999-0.105812499999956
35641.08641.1563125636.24254.91381249999998-0.0763124999999718
36641.08641.1268125637.31753.80931249999999-0.046812500000101
37641.08641.0973125638.39252.70481249999999-0.0173125000001164
38641.08641.0678125639.46751.60031250.0121874999999818
39641.08641.0383125640.54250.4958124999999960.04168750000008
40641.08641.676979166667642.210416666667-0.533437499999983-0.596979166666642
41641.08642.9838125644.47125-1.48743749999998-1.90381250000007
42641.08644.290645833333646.732083333333-2.44143749999995-3.21064583333327
43641.08645.070729166667648.992916666667-3.92218750000001-3.9907291666666
44641.08646.2270625651.25375-5.0266875-5.14706249999995
45641.08647.383395833333653.514583333333-6.13118750000003-6.3033958333333
46668.21661.793729166667655.7754166666676.018312499999996.41627083333344
47668.21662.9500625658.036254.913812499999985.25993750000009
48668.21664.106395833333660.2970833333333.809312499999994.10360416666674
49668.21665.262729166667662.5579166666672.704812499999992.94727083333339
50668.21666.4190625664.818751.60031251.79093750000004
51668.21667.575395833333667.0795833333330.4958124999999960.634604166666691
52668.21667.5540625668.0875-0.5334374999999830.65593750000005
53668.21666.3550625667.8425-1.487437499999981.85493750000001
54668.21665.1560625667.5975-2.441437499999953.05393750000007
55668.21663.4303125667.3525-3.922187500000014.77968750000014
56668.21662.0808125667.1075-5.02668756.12918750000006
57668.21660.7313125666.8625-6.131187500000037.47868750000009
58665.27672.6358125666.61756.01831249999999-7.36581249999995
59665.27671.2863125666.37254.91381249999998-6.01631249999991
60665.27669.9368125666.12753.80931249999999-4.66681249999999
61665.27668.5873125665.88252.70481249999999-3.31731249999996
62665.27667.2378125665.63751.6003125-1.96781249999992
63665.27665.8883125665.39250.495812499999996-0.618312499999888
64665.27665.1128125665.64625-0.5334374999999830.157187500000077
65665.27664.9113125666.39875-1.487437499999980.358687500000087
66665.27664.7098125667.15125-2.441437499999950.560187500000097
67665.27NANA-3.92218750000001NA
68665.27NANA-5.0266875NA
69665.27NANA-6.13118750000003NA
70674.3NANA6.01831249999999NA
71674.3NANA4.91381249999998NA
72674.3NANA3.80931249999999NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 599 & NA & NA & 2.70481249999999 & NA \tabularnewline
2 & 599 & NA & NA & 1.6003125 & NA \tabularnewline
3 & 599 & NA & NA & 0.495812499999996 & NA \tabularnewline
4 & 599 & NA & NA & -0.533437499999983 & NA \tabularnewline
5 & 599 & NA & NA & -1.48743749999998 & NA \tabularnewline
6 & 599 & NA & NA & -2.44143749999995 & NA \tabularnewline
7 & 599 & 600.3453125 & 604.2675 & -3.92218750000001 & -1.34531249999998 \tabularnewline
8 & 599 & 600.7458125 & 605.7725 & -5.0266875 & -1.74581250000006 \tabularnewline
9 & 599 & 601.1463125 & 607.2775 & -6.13118750000003 & -2.14631250000002 \tabularnewline
10 & 617.06 & 614.8008125 & 608.7825 & 6.01831249999999 & 2.25918749999994 \tabularnewline
11 & 617.06 & 615.2013125 & 610.2875 & 4.91381249999998 & 1.85868749999997 \tabularnewline
12 & 617.06 & 615.6018125 & 611.7925 & 3.80931249999999 & 1.45818749999989 \tabularnewline
13 & 617.06 & 616.0023125 & 613.2975 & 2.70481249999999 & 1.05768749999993 \tabularnewline
14 & 617.06 & 616.4028125 & 614.8025 & 1.6003125 & 0.657187499999964 \tabularnewline
15 & 617.06 & 616.8033125 & 616.3075 & 0.495812499999996 & 0.256687499999998 \tabularnewline
16 & 617.06 & 616.989895833333 & 617.523333333333 & -0.533437499999983 & 0.0701041666666242 \tabularnewline
17 & 617.06 & 616.9625625 & 618.45 & -1.48743749999998 & 0.0974374999999554 \tabularnewline
18 & 617.06 & 616.935229166667 & 619.376666666667 & -2.44143749999995 & 0.1247708333334 \tabularnewline
19 & 617.06 & 616.381145833333 & 620.303333333333 & -3.92218750000001 & 0.678854166666724 \tabularnewline
20 & 617.06 & 616.2033125 & 621.23 & -5.0266875 & 0.856687500000021 \tabularnewline
21 & 617.06 & 616.025479166667 & 622.156666666667 & -6.13118750000003 & 1.03452083333332 \tabularnewline
22 & 628.18 & 629.101645833333 & 623.083333333333 & 6.01831249999999 & -0.921645833333287 \tabularnewline
23 & 628.18 & 628.9238125 & 624.01 & 4.91381249999998 & -0.743812499999876 \tabularnewline
24 & 628.18 & 628.745979166667 & 624.936666666666 & 3.80931249999999 & -0.56597916666658 \tabularnewline
25 & 628.18 & 628.568145833333 & 625.863333333333 & 2.70481249999999 & -0.388145833333169 \tabularnewline
26 & 628.18 & 628.3903125 & 626.79 & 1.6003125 & -0.210312499999759 \tabularnewline
27 & 628.18 & 628.212479166666 & 627.716666666666 & 0.495812499999996 & -0.0324791666664623 \tabularnewline
28 & 628.18 & 628.1840625 & 628.7175 & -0.533437499999983 & -0.00406249999991815 \tabularnewline
29 & 628.18 & 628.3050625 & 629.7925 & -1.48743749999998 & -0.125062500000013 \tabularnewline
30 & 628.18 & 628.4260625 & 630.8675 & -2.44143749999995 & -0.24606249999988 \tabularnewline
31 & 628.18 & 628.0203125 & 631.9425 & -3.92218750000001 & 0.159687500000132 \tabularnewline
32 & 628.18 & 627.9908125 & 633.0175 & -5.0266875 & 0.189187500000003 \tabularnewline
33 & 628.18 & 627.9613125 & 634.0925 & -6.13118750000003 & 0.218687499999987 \tabularnewline
34 & 641.08 & 641.1858125 & 635.1675 & 6.01831249999999 & -0.105812499999956 \tabularnewline
35 & 641.08 & 641.1563125 & 636.2425 & 4.91381249999998 & -0.0763124999999718 \tabularnewline
36 & 641.08 & 641.1268125 & 637.3175 & 3.80931249999999 & -0.046812500000101 \tabularnewline
37 & 641.08 & 641.0973125 & 638.3925 & 2.70481249999999 & -0.0173125000001164 \tabularnewline
38 & 641.08 & 641.0678125 & 639.4675 & 1.6003125 & 0.0121874999999818 \tabularnewline
39 & 641.08 & 641.0383125 & 640.5425 & 0.495812499999996 & 0.04168750000008 \tabularnewline
40 & 641.08 & 641.676979166667 & 642.210416666667 & -0.533437499999983 & -0.596979166666642 \tabularnewline
41 & 641.08 & 642.9838125 & 644.47125 & -1.48743749999998 & -1.90381250000007 \tabularnewline
42 & 641.08 & 644.290645833333 & 646.732083333333 & -2.44143749999995 & -3.21064583333327 \tabularnewline
43 & 641.08 & 645.070729166667 & 648.992916666667 & -3.92218750000001 & -3.9907291666666 \tabularnewline
44 & 641.08 & 646.2270625 & 651.25375 & -5.0266875 & -5.14706249999995 \tabularnewline
45 & 641.08 & 647.383395833333 & 653.514583333333 & -6.13118750000003 & -6.3033958333333 \tabularnewline
46 & 668.21 & 661.793729166667 & 655.775416666667 & 6.01831249999999 & 6.41627083333344 \tabularnewline
47 & 668.21 & 662.9500625 & 658.03625 & 4.91381249999998 & 5.25993750000009 \tabularnewline
48 & 668.21 & 664.106395833333 & 660.297083333333 & 3.80931249999999 & 4.10360416666674 \tabularnewline
49 & 668.21 & 665.262729166667 & 662.557916666667 & 2.70481249999999 & 2.94727083333339 \tabularnewline
50 & 668.21 & 666.4190625 & 664.81875 & 1.6003125 & 1.79093750000004 \tabularnewline
51 & 668.21 & 667.575395833333 & 667.079583333333 & 0.495812499999996 & 0.634604166666691 \tabularnewline
52 & 668.21 & 667.5540625 & 668.0875 & -0.533437499999983 & 0.65593750000005 \tabularnewline
53 & 668.21 & 666.3550625 & 667.8425 & -1.48743749999998 & 1.85493750000001 \tabularnewline
54 & 668.21 & 665.1560625 & 667.5975 & -2.44143749999995 & 3.05393750000007 \tabularnewline
55 & 668.21 & 663.4303125 & 667.3525 & -3.92218750000001 & 4.77968750000014 \tabularnewline
56 & 668.21 & 662.0808125 & 667.1075 & -5.0266875 & 6.12918750000006 \tabularnewline
57 & 668.21 & 660.7313125 & 666.8625 & -6.13118750000003 & 7.47868750000009 \tabularnewline
58 & 665.27 & 672.6358125 & 666.6175 & 6.01831249999999 & -7.36581249999995 \tabularnewline
59 & 665.27 & 671.2863125 & 666.3725 & 4.91381249999998 & -6.01631249999991 \tabularnewline
60 & 665.27 & 669.9368125 & 666.1275 & 3.80931249999999 & -4.66681249999999 \tabularnewline
61 & 665.27 & 668.5873125 & 665.8825 & 2.70481249999999 & -3.31731249999996 \tabularnewline
62 & 665.27 & 667.2378125 & 665.6375 & 1.6003125 & -1.96781249999992 \tabularnewline
63 & 665.27 & 665.8883125 & 665.3925 & 0.495812499999996 & -0.618312499999888 \tabularnewline
64 & 665.27 & 665.1128125 & 665.64625 & -0.533437499999983 & 0.157187500000077 \tabularnewline
65 & 665.27 & 664.9113125 & 666.39875 & -1.48743749999998 & 0.358687500000087 \tabularnewline
66 & 665.27 & 664.7098125 & 667.15125 & -2.44143749999995 & 0.560187500000097 \tabularnewline
67 & 665.27 & NA & NA & -3.92218750000001 & NA \tabularnewline
68 & 665.27 & NA & NA & -5.0266875 & NA \tabularnewline
69 & 665.27 & NA & NA & -6.13118750000003 & NA \tabularnewline
70 & 674.3 & NA & NA & 6.01831249999999 & NA \tabularnewline
71 & 674.3 & NA & NA & 4.91381249999998 & NA \tabularnewline
72 & 674.3 & NA & NA & 3.80931249999999 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=208645&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]599[/C][C]NA[/C][C]NA[/C][C]2.70481249999999[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]599[/C][C]NA[/C][C]NA[/C][C]1.6003125[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]599[/C][C]NA[/C][C]NA[/C][C]0.495812499999996[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]599[/C][C]NA[/C][C]NA[/C][C]-0.533437499999983[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]599[/C][C]NA[/C][C]NA[/C][C]-1.48743749999998[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]599[/C][C]NA[/C][C]NA[/C][C]-2.44143749999995[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]599[/C][C]600.3453125[/C][C]604.2675[/C][C]-3.92218750000001[/C][C]-1.34531249999998[/C][/ROW]
[ROW][C]8[/C][C]599[/C][C]600.7458125[/C][C]605.7725[/C][C]-5.0266875[/C][C]-1.74581250000006[/C][/ROW]
[ROW][C]9[/C][C]599[/C][C]601.1463125[/C][C]607.2775[/C][C]-6.13118750000003[/C][C]-2.14631250000002[/C][/ROW]
[ROW][C]10[/C][C]617.06[/C][C]614.8008125[/C][C]608.7825[/C][C]6.01831249999999[/C][C]2.25918749999994[/C][/ROW]
[ROW][C]11[/C][C]617.06[/C][C]615.2013125[/C][C]610.2875[/C][C]4.91381249999998[/C][C]1.85868749999997[/C][/ROW]
[ROW][C]12[/C][C]617.06[/C][C]615.6018125[/C][C]611.7925[/C][C]3.80931249999999[/C][C]1.45818749999989[/C][/ROW]
[ROW][C]13[/C][C]617.06[/C][C]616.0023125[/C][C]613.2975[/C][C]2.70481249999999[/C][C]1.05768749999993[/C][/ROW]
[ROW][C]14[/C][C]617.06[/C][C]616.4028125[/C][C]614.8025[/C][C]1.6003125[/C][C]0.657187499999964[/C][/ROW]
[ROW][C]15[/C][C]617.06[/C][C]616.8033125[/C][C]616.3075[/C][C]0.495812499999996[/C][C]0.256687499999998[/C][/ROW]
[ROW][C]16[/C][C]617.06[/C][C]616.989895833333[/C][C]617.523333333333[/C][C]-0.533437499999983[/C][C]0.0701041666666242[/C][/ROW]
[ROW][C]17[/C][C]617.06[/C][C]616.9625625[/C][C]618.45[/C][C]-1.48743749999998[/C][C]0.0974374999999554[/C][/ROW]
[ROW][C]18[/C][C]617.06[/C][C]616.935229166667[/C][C]619.376666666667[/C][C]-2.44143749999995[/C][C]0.1247708333334[/C][/ROW]
[ROW][C]19[/C][C]617.06[/C][C]616.381145833333[/C][C]620.303333333333[/C][C]-3.92218750000001[/C][C]0.678854166666724[/C][/ROW]
[ROW][C]20[/C][C]617.06[/C][C]616.2033125[/C][C]621.23[/C][C]-5.0266875[/C][C]0.856687500000021[/C][/ROW]
[ROW][C]21[/C][C]617.06[/C][C]616.025479166667[/C][C]622.156666666667[/C][C]-6.13118750000003[/C][C]1.03452083333332[/C][/ROW]
[ROW][C]22[/C][C]628.18[/C][C]629.101645833333[/C][C]623.083333333333[/C][C]6.01831249999999[/C][C]-0.921645833333287[/C][/ROW]
[ROW][C]23[/C][C]628.18[/C][C]628.9238125[/C][C]624.01[/C][C]4.91381249999998[/C][C]-0.743812499999876[/C][/ROW]
[ROW][C]24[/C][C]628.18[/C][C]628.745979166667[/C][C]624.936666666666[/C][C]3.80931249999999[/C][C]-0.56597916666658[/C][/ROW]
[ROW][C]25[/C][C]628.18[/C][C]628.568145833333[/C][C]625.863333333333[/C][C]2.70481249999999[/C][C]-0.388145833333169[/C][/ROW]
[ROW][C]26[/C][C]628.18[/C][C]628.3903125[/C][C]626.79[/C][C]1.6003125[/C][C]-0.210312499999759[/C][/ROW]
[ROW][C]27[/C][C]628.18[/C][C]628.212479166666[/C][C]627.716666666666[/C][C]0.495812499999996[/C][C]-0.0324791666664623[/C][/ROW]
[ROW][C]28[/C][C]628.18[/C][C]628.1840625[/C][C]628.7175[/C][C]-0.533437499999983[/C][C]-0.00406249999991815[/C][/ROW]
[ROW][C]29[/C][C]628.18[/C][C]628.3050625[/C][C]629.7925[/C][C]-1.48743749999998[/C][C]-0.125062500000013[/C][/ROW]
[ROW][C]30[/C][C]628.18[/C][C]628.4260625[/C][C]630.8675[/C][C]-2.44143749999995[/C][C]-0.24606249999988[/C][/ROW]
[ROW][C]31[/C][C]628.18[/C][C]628.0203125[/C][C]631.9425[/C][C]-3.92218750000001[/C][C]0.159687500000132[/C][/ROW]
[ROW][C]32[/C][C]628.18[/C][C]627.9908125[/C][C]633.0175[/C][C]-5.0266875[/C][C]0.189187500000003[/C][/ROW]
[ROW][C]33[/C][C]628.18[/C][C]627.9613125[/C][C]634.0925[/C][C]-6.13118750000003[/C][C]0.218687499999987[/C][/ROW]
[ROW][C]34[/C][C]641.08[/C][C]641.1858125[/C][C]635.1675[/C][C]6.01831249999999[/C][C]-0.105812499999956[/C][/ROW]
[ROW][C]35[/C][C]641.08[/C][C]641.1563125[/C][C]636.2425[/C][C]4.91381249999998[/C][C]-0.0763124999999718[/C][/ROW]
[ROW][C]36[/C][C]641.08[/C][C]641.1268125[/C][C]637.3175[/C][C]3.80931249999999[/C][C]-0.046812500000101[/C][/ROW]
[ROW][C]37[/C][C]641.08[/C][C]641.0973125[/C][C]638.3925[/C][C]2.70481249999999[/C][C]-0.0173125000001164[/C][/ROW]
[ROW][C]38[/C][C]641.08[/C][C]641.0678125[/C][C]639.4675[/C][C]1.6003125[/C][C]0.0121874999999818[/C][/ROW]
[ROW][C]39[/C][C]641.08[/C][C]641.0383125[/C][C]640.5425[/C][C]0.495812499999996[/C][C]0.04168750000008[/C][/ROW]
[ROW][C]40[/C][C]641.08[/C][C]641.676979166667[/C][C]642.210416666667[/C][C]-0.533437499999983[/C][C]-0.596979166666642[/C][/ROW]
[ROW][C]41[/C][C]641.08[/C][C]642.9838125[/C][C]644.47125[/C][C]-1.48743749999998[/C][C]-1.90381250000007[/C][/ROW]
[ROW][C]42[/C][C]641.08[/C][C]644.290645833333[/C][C]646.732083333333[/C][C]-2.44143749999995[/C][C]-3.21064583333327[/C][/ROW]
[ROW][C]43[/C][C]641.08[/C][C]645.070729166667[/C][C]648.992916666667[/C][C]-3.92218750000001[/C][C]-3.9907291666666[/C][/ROW]
[ROW][C]44[/C][C]641.08[/C][C]646.2270625[/C][C]651.25375[/C][C]-5.0266875[/C][C]-5.14706249999995[/C][/ROW]
[ROW][C]45[/C][C]641.08[/C][C]647.383395833333[/C][C]653.514583333333[/C][C]-6.13118750000003[/C][C]-6.3033958333333[/C][/ROW]
[ROW][C]46[/C][C]668.21[/C][C]661.793729166667[/C][C]655.775416666667[/C][C]6.01831249999999[/C][C]6.41627083333344[/C][/ROW]
[ROW][C]47[/C][C]668.21[/C][C]662.9500625[/C][C]658.03625[/C][C]4.91381249999998[/C][C]5.25993750000009[/C][/ROW]
[ROW][C]48[/C][C]668.21[/C][C]664.106395833333[/C][C]660.297083333333[/C][C]3.80931249999999[/C][C]4.10360416666674[/C][/ROW]
[ROW][C]49[/C][C]668.21[/C][C]665.262729166667[/C][C]662.557916666667[/C][C]2.70481249999999[/C][C]2.94727083333339[/C][/ROW]
[ROW][C]50[/C][C]668.21[/C][C]666.4190625[/C][C]664.81875[/C][C]1.6003125[/C][C]1.79093750000004[/C][/ROW]
[ROW][C]51[/C][C]668.21[/C][C]667.575395833333[/C][C]667.079583333333[/C][C]0.495812499999996[/C][C]0.634604166666691[/C][/ROW]
[ROW][C]52[/C][C]668.21[/C][C]667.5540625[/C][C]668.0875[/C][C]-0.533437499999983[/C][C]0.65593750000005[/C][/ROW]
[ROW][C]53[/C][C]668.21[/C][C]666.3550625[/C][C]667.8425[/C][C]-1.48743749999998[/C][C]1.85493750000001[/C][/ROW]
[ROW][C]54[/C][C]668.21[/C][C]665.1560625[/C][C]667.5975[/C][C]-2.44143749999995[/C][C]3.05393750000007[/C][/ROW]
[ROW][C]55[/C][C]668.21[/C][C]663.4303125[/C][C]667.3525[/C][C]-3.92218750000001[/C][C]4.77968750000014[/C][/ROW]
[ROW][C]56[/C][C]668.21[/C][C]662.0808125[/C][C]667.1075[/C][C]-5.0266875[/C][C]6.12918750000006[/C][/ROW]
[ROW][C]57[/C][C]668.21[/C][C]660.7313125[/C][C]666.8625[/C][C]-6.13118750000003[/C][C]7.47868750000009[/C][/ROW]
[ROW][C]58[/C][C]665.27[/C][C]672.6358125[/C][C]666.6175[/C][C]6.01831249999999[/C][C]-7.36581249999995[/C][/ROW]
[ROW][C]59[/C][C]665.27[/C][C]671.2863125[/C][C]666.3725[/C][C]4.91381249999998[/C][C]-6.01631249999991[/C][/ROW]
[ROW][C]60[/C][C]665.27[/C][C]669.9368125[/C][C]666.1275[/C][C]3.80931249999999[/C][C]-4.66681249999999[/C][/ROW]
[ROW][C]61[/C][C]665.27[/C][C]668.5873125[/C][C]665.8825[/C][C]2.70481249999999[/C][C]-3.31731249999996[/C][/ROW]
[ROW][C]62[/C][C]665.27[/C][C]667.2378125[/C][C]665.6375[/C][C]1.6003125[/C][C]-1.96781249999992[/C][/ROW]
[ROW][C]63[/C][C]665.27[/C][C]665.8883125[/C][C]665.3925[/C][C]0.495812499999996[/C][C]-0.618312499999888[/C][/ROW]
[ROW][C]64[/C][C]665.27[/C][C]665.1128125[/C][C]665.64625[/C][C]-0.533437499999983[/C][C]0.157187500000077[/C][/ROW]
[ROW][C]65[/C][C]665.27[/C][C]664.9113125[/C][C]666.39875[/C][C]-1.48743749999998[/C][C]0.358687500000087[/C][/ROW]
[ROW][C]66[/C][C]665.27[/C][C]664.7098125[/C][C]667.15125[/C][C]-2.44143749999995[/C][C]0.560187500000097[/C][/ROW]
[ROW][C]67[/C][C]665.27[/C][C]NA[/C][C]NA[/C][C]-3.92218750000001[/C][C]NA[/C][/ROW]
[ROW][C]68[/C][C]665.27[/C][C]NA[/C][C]NA[/C][C]-5.0266875[/C][C]NA[/C][/ROW]
[ROW][C]69[/C][C]665.27[/C][C]NA[/C][C]NA[/C][C]-6.13118750000003[/C][C]NA[/C][/ROW]
[ROW][C]70[/C][C]674.3[/C][C]NA[/C][C]NA[/C][C]6.01831249999999[/C][C]NA[/C][/ROW]
[ROW][C]71[/C][C]674.3[/C][C]NA[/C][C]NA[/C][C]4.91381249999998[/C][C]NA[/C][/ROW]
[ROW][C]72[/C][C]674.3[/C][C]NA[/C][C]NA[/C][C]3.80931249999999[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=208645&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=208645&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
1599NANA2.70481249999999NA
2599NANA1.6003125NA
3599NANA0.495812499999996NA
4599NANA-0.533437499999983NA
5599NANA-1.48743749999998NA
6599NANA-2.44143749999995NA
7599600.3453125604.2675-3.92218750000001-1.34531249999998
8599600.7458125605.7725-5.0266875-1.74581250000006
9599601.1463125607.2775-6.13118750000003-2.14631250000002
10617.06614.8008125608.78256.018312499999992.25918749999994
11617.06615.2013125610.28754.913812499999981.85868749999997
12617.06615.6018125611.79253.809312499999991.45818749999989
13617.06616.0023125613.29752.704812499999991.05768749999993
14617.06616.4028125614.80251.60031250.657187499999964
15617.06616.8033125616.30750.4958124999999960.256687499999998
16617.06616.989895833333617.523333333333-0.5334374999999830.0701041666666242
17617.06616.9625625618.45-1.487437499999980.0974374999999554
18617.06616.935229166667619.376666666667-2.441437499999950.1247708333334
19617.06616.381145833333620.303333333333-3.922187500000010.678854166666724
20617.06616.2033125621.23-5.02668750.856687500000021
21617.06616.025479166667622.156666666667-6.131187500000031.03452083333332
22628.18629.101645833333623.0833333333336.01831249999999-0.921645833333287
23628.18628.9238125624.014.91381249999998-0.743812499999876
24628.18628.745979166667624.9366666666663.80931249999999-0.56597916666658
25628.18628.568145833333625.8633333333332.70481249999999-0.388145833333169
26628.18628.3903125626.791.6003125-0.210312499999759
27628.18628.212479166666627.7166666666660.495812499999996-0.0324791666664623
28628.18628.1840625628.7175-0.533437499999983-0.00406249999991815
29628.18628.3050625629.7925-1.48743749999998-0.125062500000013
30628.18628.4260625630.8675-2.44143749999995-0.24606249999988
31628.18628.0203125631.9425-3.922187500000010.159687500000132
32628.18627.9908125633.0175-5.02668750.189187500000003
33628.18627.9613125634.0925-6.131187500000030.218687499999987
34641.08641.1858125635.16756.01831249999999-0.105812499999956
35641.08641.1563125636.24254.91381249999998-0.0763124999999718
36641.08641.1268125637.31753.80931249999999-0.046812500000101
37641.08641.0973125638.39252.70481249999999-0.0173125000001164
38641.08641.0678125639.46751.60031250.0121874999999818
39641.08641.0383125640.54250.4958124999999960.04168750000008
40641.08641.676979166667642.210416666667-0.533437499999983-0.596979166666642
41641.08642.9838125644.47125-1.48743749999998-1.90381250000007
42641.08644.290645833333646.732083333333-2.44143749999995-3.21064583333327
43641.08645.070729166667648.992916666667-3.92218750000001-3.9907291666666
44641.08646.2270625651.25375-5.0266875-5.14706249999995
45641.08647.383395833333653.514583333333-6.13118750000003-6.3033958333333
46668.21661.793729166667655.7754166666676.018312499999996.41627083333344
47668.21662.9500625658.036254.913812499999985.25993750000009
48668.21664.106395833333660.2970833333333.809312499999994.10360416666674
49668.21665.262729166667662.5579166666672.704812499999992.94727083333339
50668.21666.4190625664.818751.60031251.79093750000004
51668.21667.575395833333667.0795833333330.4958124999999960.634604166666691
52668.21667.5540625668.0875-0.5334374999999830.65593750000005
53668.21666.3550625667.8425-1.487437499999981.85493750000001
54668.21665.1560625667.5975-2.441437499999953.05393750000007
55668.21663.4303125667.3525-3.922187500000014.77968750000014
56668.21662.0808125667.1075-5.02668756.12918750000006
57668.21660.7313125666.8625-6.131187500000037.47868750000009
58665.27672.6358125666.61756.01831249999999-7.36581249999995
59665.27671.2863125666.37254.91381249999998-6.01631249999991
60665.27669.9368125666.12753.80931249999999-4.66681249999999
61665.27668.5873125665.88252.70481249999999-3.31731249999996
62665.27667.2378125665.63751.6003125-1.96781249999992
63665.27665.8883125665.39250.495812499999996-0.618312499999888
64665.27665.1128125665.64625-0.5334374999999830.157187500000077
65665.27664.9113125666.39875-1.487437499999980.358687500000087
66665.27664.7098125667.15125-2.441437499999950.560187500000097
67665.27NANA-3.92218750000001NA
68665.27NANA-5.0266875NA
69665.27NANA-6.13118750000003NA
70674.3NANA6.01831249999999NA
71674.3NANA4.91381249999998NA
72674.3NANA3.80931249999999NA


Figures (Output of Computation)

Input Parameters & R Code

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$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$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')