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rwasp_decompose.wasp

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Classical Decomposition

Date of computation

Fri, 16 Dec 2016 21:41:06 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/16/t1481921559r0bgje82513vi5w.htm/, Retrieved Thu, 02 May 2024 22:06:27 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=300557, Retrieved Thu, 02 May 2024 22:06:27 +0000

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Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Classical Decomposition] [multiple regressi...] [2016-12-16 20:41:06] [ca14e1566745fb922befb698831e7d61] [Current]
- RM D    [Multiple Regression] [multiple regressi...] [2016-12-16 20:55:26] [15f3778596b3a039df0348fb43372a09] 
-    D    [Classical Decomposition] [classical decompo...] [2016-12-16 21:35:46] [15f3778596b3a039df0348fb43372a09] 
- RM D    [Exponential Smoothing] [exponential smoot...] [2016-12-16 21:42:16] [15f3778596b3a039df0348fb43372a09] 
- RM      [Structural Time Series Models] [zonder dummie 4] [2016-12-16 21:57:52] [15f3778596b3a039df0348fb43372a09] 
-    D    [Classical Decomposition] [data5] [2016-12-16 22:06:01] [15f3778596b3a039df0348fb43372a09] 

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300557&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

2 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=300557&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300557&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	Big Analytics Cloud Computing Center

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	11285	NA	NA	285.727	NA
2	11218	NA	NA	51.842	NA
3	11195	NA	NA	-503.71	NA
4	11145	NA	NA	-375.293	NA
5	11153	NA	NA	-85.3142	NA
6	11230	NA	NA	13.0503	NA
7	11133	11148.1	11138.8	9.27951	-15.1128
8	11217	11165.5	11108.2	57.3712	51.4622
9	11148	11146.7	11075	71.6878	1.31215
10	11095	11146.4	11043.5	102.915	-51.3733
11	11023	11167.3	11011.3	156.009	-144.3
12	11006	11182.8	10966.4	216.436	-176.811
13	10921	11196.7	10911	285.727	-275.727
14	10846	10898.4	10846.5	51.842	-52.3837
15	10771	10269.9	10773.6	-503.71	501.085
16	10812	10322.4	10697.7	-375.293	489.627
17	10714	10535.5	10620.8	-85.3142	178.523
18	10591	10553.8	10540.7	13.0503	37.2413
19	10443	10471.8	10462.5	9.27951	-28.7795
20	10360	10439.8	10382.4	57.3712	-79.7878
21	10255	10365.5	10293.8	71.6878	-110.48
22	10165	10304.2	10201.3	102.915	-139.207
23	10108	10297.4	10141.4	156.009	-189.425
24	9999	10345.8	10129.3	216.436	-346.769
25	10051	10411.1	10125.3	285.727	-360.061
26	9794	10170.8	10119	51.842	-376.8
27	9696	9613.41	10117.1	-503.71	82.5851
28	9667	9739.08	10114.4	-375.293	-72.0816
29	10422	10024.5	10109.8	-85.3142	397.523
30	10593	10124.6	10111.5	13.0503	468.408
31	10345	10123.4	10114.1	9.27951	221.637
32	10305	10140.4	10083	57.3712	164.629
33	10266	9995.52	9923.83	71.6878	270.479
34	10088	9770.46	9667.54	102.915	317.543
35	10075	9544.93	9388.92	156.009	530.075
36	10074	9292.98	9076.54	216.436	781.023
37	10037	9050.23	8764.5	285.727	986.773
38	9062	8515.26	8463.42	51.842	546.741
39	6608	7655.04	8158.75	-503.71	-1047.04
40	6604	7481.21	7856.5	-375.293	-877.207
41	6798	7473.85	7559.17	-85.3142	-675.852
42	6720	7271.97	7258.92	13.0503	-551.967
43	6729	6965.36	6956.08	9.27951	-236.363
44	6695	6748.45	6691.08	57.3712	-53.4545
45	6564	6638.15	6566.46	71.6878	-74.1462
46	6536	6649.91	6547	102.915	-113.915
47	6491	6674.3	6518.29	156.009	-183.3
48	6452	6696.39	6479.96	216.436	-244.394
49	6391	6728.94	6443.21	285.727	-337.936
50	6348	6452.51	6400.67	51.842	-104.509
51	6331	5854.58	6358.29	-503.71	476.418
52	6414	5941.29	6316.58	-375.293	472.71
53	6299	6186.14	6271.46	-85.3142	112.856
54	6299	6239.63	6226.58	13.0503	59.3663
55	6268	6193.07	6183.79	9.27951	74.9288
56	6135	6201.54	6144.17	57.3712	-66.5378
57	6107	6177.85	6106.17	71.6878	-70.8545
58	5992	NA	NA	102.915	NA
59	5952	NA	NA	156.009	NA
60	5914	NA	NA	216.436	NA
61	5902	NA	NA	285.727	NA
62	5886	NA	NA	51.842	NA
63	5881	NA	NA	-503.71	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 11285 & NA & NA & 285.727 & NA \tabularnewline
2 & 11218 & NA & NA & 51.842 & NA \tabularnewline
3 & 11195 & NA & NA & -503.71 & NA \tabularnewline
4 & 11145 & NA & NA & -375.293 & NA \tabularnewline
5 & 11153 & NA & NA & -85.3142 & NA \tabularnewline
6 & 11230 & NA & NA & 13.0503 & NA \tabularnewline
7 & 11133 & 11148.1 & 11138.8 & 9.27951 & -15.1128 \tabularnewline
8 & 11217 & 11165.5 & 11108.2 & 57.3712 & 51.4622 \tabularnewline
9 & 11148 & 11146.7 & 11075 & 71.6878 & 1.31215 \tabularnewline
10 & 11095 & 11146.4 & 11043.5 & 102.915 & -51.3733 \tabularnewline
11 & 11023 & 11167.3 & 11011.3 & 156.009 & -144.3 \tabularnewline
12 & 11006 & 11182.8 & 10966.4 & 216.436 & -176.811 \tabularnewline
13 & 10921 & 11196.7 & 10911 & 285.727 & -275.727 \tabularnewline
14 & 10846 & 10898.4 & 10846.5 & 51.842 & -52.3837 \tabularnewline
15 & 10771 & 10269.9 & 10773.6 & -503.71 & 501.085 \tabularnewline
16 & 10812 & 10322.4 & 10697.7 & -375.293 & 489.627 \tabularnewline
17 & 10714 & 10535.5 & 10620.8 & -85.3142 & 178.523 \tabularnewline
18 & 10591 & 10553.8 & 10540.7 & 13.0503 & 37.2413 \tabularnewline
19 & 10443 & 10471.8 & 10462.5 & 9.27951 & -28.7795 \tabularnewline
20 & 10360 & 10439.8 & 10382.4 & 57.3712 & -79.7878 \tabularnewline
21 & 10255 & 10365.5 & 10293.8 & 71.6878 & -110.48 \tabularnewline
22 & 10165 & 10304.2 & 10201.3 & 102.915 & -139.207 \tabularnewline
23 & 10108 & 10297.4 & 10141.4 & 156.009 & -189.425 \tabularnewline
24 & 9999 & 10345.8 & 10129.3 & 216.436 & -346.769 \tabularnewline
25 & 10051 & 10411.1 & 10125.3 & 285.727 & -360.061 \tabularnewline
26 & 9794 & 10170.8 & 10119 & 51.842 & -376.8 \tabularnewline
27 & 9696 & 9613.41 & 10117.1 & -503.71 & 82.5851 \tabularnewline
28 & 9667 & 9739.08 & 10114.4 & -375.293 & -72.0816 \tabularnewline
29 & 10422 & 10024.5 & 10109.8 & -85.3142 & 397.523 \tabularnewline
30 & 10593 & 10124.6 & 10111.5 & 13.0503 & 468.408 \tabularnewline
31 & 10345 & 10123.4 & 10114.1 & 9.27951 & 221.637 \tabularnewline
32 & 10305 & 10140.4 & 10083 & 57.3712 & 164.629 \tabularnewline
33 & 10266 & 9995.52 & 9923.83 & 71.6878 & 270.479 \tabularnewline
34 & 10088 & 9770.46 & 9667.54 & 102.915 & 317.543 \tabularnewline
35 & 10075 & 9544.93 & 9388.92 & 156.009 & 530.075 \tabularnewline
36 & 10074 & 9292.98 & 9076.54 & 216.436 & 781.023 \tabularnewline
37 & 10037 & 9050.23 & 8764.5 & 285.727 & 986.773 \tabularnewline
38 & 9062 & 8515.26 & 8463.42 & 51.842 & 546.741 \tabularnewline
39 & 6608 & 7655.04 & 8158.75 & -503.71 & -1047.04 \tabularnewline
40 & 6604 & 7481.21 & 7856.5 & -375.293 & -877.207 \tabularnewline
41 & 6798 & 7473.85 & 7559.17 & -85.3142 & -675.852 \tabularnewline
42 & 6720 & 7271.97 & 7258.92 & 13.0503 & -551.967 \tabularnewline
43 & 6729 & 6965.36 & 6956.08 & 9.27951 & -236.363 \tabularnewline
44 & 6695 & 6748.45 & 6691.08 & 57.3712 & -53.4545 \tabularnewline
45 & 6564 & 6638.15 & 6566.46 & 71.6878 & -74.1462 \tabularnewline
46 & 6536 & 6649.91 & 6547 & 102.915 & -113.915 \tabularnewline
47 & 6491 & 6674.3 & 6518.29 & 156.009 & -183.3 \tabularnewline
48 & 6452 & 6696.39 & 6479.96 & 216.436 & -244.394 \tabularnewline
49 & 6391 & 6728.94 & 6443.21 & 285.727 & -337.936 \tabularnewline
50 & 6348 & 6452.51 & 6400.67 & 51.842 & -104.509 \tabularnewline
51 & 6331 & 5854.58 & 6358.29 & -503.71 & 476.418 \tabularnewline
52 & 6414 & 5941.29 & 6316.58 & -375.293 & 472.71 \tabularnewline
53 & 6299 & 6186.14 & 6271.46 & -85.3142 & 112.856 \tabularnewline
54 & 6299 & 6239.63 & 6226.58 & 13.0503 & 59.3663 \tabularnewline
55 & 6268 & 6193.07 & 6183.79 & 9.27951 & 74.9288 \tabularnewline
56 & 6135 & 6201.54 & 6144.17 & 57.3712 & -66.5378 \tabularnewline
57 & 6107 & 6177.85 & 6106.17 & 71.6878 & -70.8545 \tabularnewline
58 & 5992 & NA & NA & 102.915 & NA \tabularnewline
59 & 5952 & NA & NA & 156.009 & NA \tabularnewline
60 & 5914 & NA & NA & 216.436 & NA \tabularnewline
61 & 5902 & NA & NA & 285.727 & NA \tabularnewline
62 & 5886 & NA & NA & 51.842 & NA \tabularnewline
63 & 5881 & NA & NA & -503.71 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=300557&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]11285[/C][C]NA[/C][C]NA[/C][C]285.727[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]11218[/C][C]NA[/C][C]NA[/C][C]51.842[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]11195[/C][C]NA[/C][C]NA[/C][C]-503.71[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]11145[/C][C]NA[/C][C]NA[/C][C]-375.293[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]11153[/C][C]NA[/C][C]NA[/C][C]-85.3142[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]11230[/C][C]NA[/C][C]NA[/C][C]13.0503[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]11133[/C][C]11148.1[/C][C]11138.8[/C][C]9.27951[/C][C]-15.1128[/C][/ROW]
[ROW][C]8[/C][C]11217[/C][C]11165.5[/C][C]11108.2[/C][C]57.3712[/C][C]51.4622[/C][/ROW]
[ROW][C]9[/C][C]11148[/C][C]11146.7[/C][C]11075[/C][C]71.6878[/C][C]1.31215[/C][/ROW]
[ROW][C]10[/C][C]11095[/C][C]11146.4[/C][C]11043.5[/C][C]102.915[/C][C]-51.3733[/C][/ROW]
[ROW][C]11[/C][C]11023[/C][C]11167.3[/C][C]11011.3[/C][C]156.009[/C][C]-144.3[/C][/ROW]
[ROW][C]12[/C][C]11006[/C][C]11182.8[/C][C]10966.4[/C][C]216.436[/C][C]-176.811[/C][/ROW]
[ROW][C]13[/C][C]10921[/C][C]11196.7[/C][C]10911[/C][C]285.727[/C][C]-275.727[/C][/ROW]
[ROW][C]14[/C][C]10846[/C][C]10898.4[/C][C]10846.5[/C][C]51.842[/C][C]-52.3837[/C][/ROW]
[ROW][C]15[/C][C]10771[/C][C]10269.9[/C][C]10773.6[/C][C]-503.71[/C][C]501.085[/C][/ROW]
[ROW][C]16[/C][C]10812[/C][C]10322.4[/C][C]10697.7[/C][C]-375.293[/C][C]489.627[/C][/ROW]
[ROW][C]17[/C][C]10714[/C][C]10535.5[/C][C]10620.8[/C][C]-85.3142[/C][C]178.523[/C][/ROW]
[ROW][C]18[/C][C]10591[/C][C]10553.8[/C][C]10540.7[/C][C]13.0503[/C][C]37.2413[/C][/ROW]
[ROW][C]19[/C][C]10443[/C][C]10471.8[/C][C]10462.5[/C][C]9.27951[/C][C]-28.7795[/C][/ROW]
[ROW][C]20[/C][C]10360[/C][C]10439.8[/C][C]10382.4[/C][C]57.3712[/C][C]-79.7878[/C][/ROW]
[ROW][C]21[/C][C]10255[/C][C]10365.5[/C][C]10293.8[/C][C]71.6878[/C][C]-110.48[/C][/ROW]
[ROW][C]22[/C][C]10165[/C][C]10304.2[/C][C]10201.3[/C][C]102.915[/C][C]-139.207[/C][/ROW]
[ROW][C]23[/C][C]10108[/C][C]10297.4[/C][C]10141.4[/C][C]156.009[/C][C]-189.425[/C][/ROW]
[ROW][C]24[/C][C]9999[/C][C]10345.8[/C][C]10129.3[/C][C]216.436[/C][C]-346.769[/C][/ROW]
[ROW][C]25[/C][C]10051[/C][C]10411.1[/C][C]10125.3[/C][C]285.727[/C][C]-360.061[/C][/ROW]
[ROW][C]26[/C][C]9794[/C][C]10170.8[/C][C]10119[/C][C]51.842[/C][C]-376.8[/C][/ROW]
[ROW][C]27[/C][C]9696[/C][C]9613.41[/C][C]10117.1[/C][C]-503.71[/C][C]82.5851[/C][/ROW]
[ROW][C]28[/C][C]9667[/C][C]9739.08[/C][C]10114.4[/C][C]-375.293[/C][C]-72.0816[/C][/ROW]
[ROW][C]29[/C][C]10422[/C][C]10024.5[/C][C]10109.8[/C][C]-85.3142[/C][C]397.523[/C][/ROW]
[ROW][C]30[/C][C]10593[/C][C]10124.6[/C][C]10111.5[/C][C]13.0503[/C][C]468.408[/C][/ROW]
[ROW][C]31[/C][C]10345[/C][C]10123.4[/C][C]10114.1[/C][C]9.27951[/C][C]221.637[/C][/ROW]
[ROW][C]32[/C][C]10305[/C][C]10140.4[/C][C]10083[/C][C]57.3712[/C][C]164.629[/C][/ROW]
[ROW][C]33[/C][C]10266[/C][C]9995.52[/C][C]9923.83[/C][C]71.6878[/C][C]270.479[/C][/ROW]
[ROW][C]34[/C][C]10088[/C][C]9770.46[/C][C]9667.54[/C][C]102.915[/C][C]317.543[/C][/ROW]
[ROW][C]35[/C][C]10075[/C][C]9544.93[/C][C]9388.92[/C][C]156.009[/C][C]530.075[/C][/ROW]
[ROW][C]36[/C][C]10074[/C][C]9292.98[/C][C]9076.54[/C][C]216.436[/C][C]781.023[/C][/ROW]
[ROW][C]37[/C][C]10037[/C][C]9050.23[/C][C]8764.5[/C][C]285.727[/C][C]986.773[/C][/ROW]
[ROW][C]38[/C][C]9062[/C][C]8515.26[/C][C]8463.42[/C][C]51.842[/C][C]546.741[/C][/ROW]
[ROW][C]39[/C][C]6608[/C][C]7655.04[/C][C]8158.75[/C][C]-503.71[/C][C]-1047.04[/C][/ROW]
[ROW][C]40[/C][C]6604[/C][C]7481.21[/C][C]7856.5[/C][C]-375.293[/C][C]-877.207[/C][/ROW]
[ROW][C]41[/C][C]6798[/C][C]7473.85[/C][C]7559.17[/C][C]-85.3142[/C][C]-675.852[/C][/ROW]
[ROW][C]42[/C][C]6720[/C][C]7271.97[/C][C]7258.92[/C][C]13.0503[/C][C]-551.967[/C][/ROW]
[ROW][C]43[/C][C]6729[/C][C]6965.36[/C][C]6956.08[/C][C]9.27951[/C][C]-236.363[/C][/ROW]
[ROW][C]44[/C][C]6695[/C][C]6748.45[/C][C]6691.08[/C][C]57.3712[/C][C]-53.4545[/C][/ROW]
[ROW][C]45[/C][C]6564[/C][C]6638.15[/C][C]6566.46[/C][C]71.6878[/C][C]-74.1462[/C][/ROW]
[ROW][C]46[/C][C]6536[/C][C]6649.91[/C][C]6547[/C][C]102.915[/C][C]-113.915[/C][/ROW]
[ROW][C]47[/C][C]6491[/C][C]6674.3[/C][C]6518.29[/C][C]156.009[/C][C]-183.3[/C][/ROW]
[ROW][C]48[/C][C]6452[/C][C]6696.39[/C][C]6479.96[/C][C]216.436[/C][C]-244.394[/C][/ROW]
[ROW][C]49[/C][C]6391[/C][C]6728.94[/C][C]6443.21[/C][C]285.727[/C][C]-337.936[/C][/ROW]
[ROW][C]50[/C][C]6348[/C][C]6452.51[/C][C]6400.67[/C][C]51.842[/C][C]-104.509[/C][/ROW]
[ROW][C]51[/C][C]6331[/C][C]5854.58[/C][C]6358.29[/C][C]-503.71[/C][C]476.418[/C][/ROW]
[ROW][C]52[/C][C]6414[/C][C]5941.29[/C][C]6316.58[/C][C]-375.293[/C][C]472.71[/C][/ROW]
[ROW][C]53[/C][C]6299[/C][C]6186.14[/C][C]6271.46[/C][C]-85.3142[/C][C]112.856[/C][/ROW]
[ROW][C]54[/C][C]6299[/C][C]6239.63[/C][C]6226.58[/C][C]13.0503[/C][C]59.3663[/C][/ROW]
[ROW][C]55[/C][C]6268[/C][C]6193.07[/C][C]6183.79[/C][C]9.27951[/C][C]74.9288[/C][/ROW]
[ROW][C]56[/C][C]6135[/C][C]6201.54[/C][C]6144.17[/C][C]57.3712[/C][C]-66.5378[/C][/ROW]
[ROW][C]57[/C][C]6107[/C][C]6177.85[/C][C]6106.17[/C][C]71.6878[/C][C]-70.8545[/C][/ROW]
[ROW][C]58[/C][C]5992[/C][C]NA[/C][C]NA[/C][C]102.915[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]5952[/C][C]NA[/C][C]NA[/C][C]156.009[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]5914[/C][C]NA[/C][C]NA[/C][C]216.436[/C][C]NA[/C][/ROW]
[ROW][C]61[/C][C]5902[/C][C]NA[/C][C]NA[/C][C]285.727[/C][C]NA[/C][/ROW]
[ROW][C]62[/C][C]5886[/C][C]NA[/C][C]NA[/C][C]51.842[/C][C]NA[/C][/ROW]
[ROW][C]63[/C][C]5881[/C][C]NA[/C][C]NA[/C][C]-503.71[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=300557&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=300557&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
t	Observations	Fit	Trend	Seasonal	Random
1	11285	NA	NA	285.727	NA
2	11218	NA	NA	51.842	NA
3	11195	NA	NA	-503.71	NA
4	11145	NA	NA	-375.293	NA
5	11153	NA	NA	-85.3142	NA
6	11230	NA	NA	13.0503	NA
7	11133	11148.1	11138.8	9.27951	-15.1128
8	11217	11165.5	11108.2	57.3712	51.4622
9	11148	11146.7	11075	71.6878	1.31215
10	11095	11146.4	11043.5	102.915	-51.3733
11	11023	11167.3	11011.3	156.009	-144.3
12	11006	11182.8	10966.4	216.436	-176.811
13	10921	11196.7	10911	285.727	-275.727
14	10846	10898.4	10846.5	51.842	-52.3837
15	10771	10269.9	10773.6	-503.71	501.085
16	10812	10322.4	10697.7	-375.293	489.627
17	10714	10535.5	10620.8	-85.3142	178.523
18	10591	10553.8	10540.7	13.0503	37.2413
19	10443	10471.8	10462.5	9.27951	-28.7795
20	10360	10439.8	10382.4	57.3712	-79.7878
21	10255	10365.5	10293.8	71.6878	-110.48
22	10165	10304.2	10201.3	102.915	-139.207
23	10108	10297.4	10141.4	156.009	-189.425
24	9999	10345.8	10129.3	216.436	-346.769
25	10051	10411.1	10125.3	285.727	-360.061
26	9794	10170.8	10119	51.842	-376.8
27	9696	9613.41	10117.1	-503.71	82.5851
28	9667	9739.08	10114.4	-375.293	-72.0816
29	10422	10024.5	10109.8	-85.3142	397.523
30	10593	10124.6	10111.5	13.0503	468.408
31	10345	10123.4	10114.1	9.27951	221.637
32	10305	10140.4	10083	57.3712	164.629
33	10266	9995.52	9923.83	71.6878	270.479
34	10088	9770.46	9667.54	102.915	317.543
35	10075	9544.93	9388.92	156.009	530.075
36	10074	9292.98	9076.54	216.436	781.023
37	10037	9050.23	8764.5	285.727	986.773
38	9062	8515.26	8463.42	51.842	546.741
39	6608	7655.04	8158.75	-503.71	-1047.04
40	6604	7481.21	7856.5	-375.293	-877.207
41	6798	7473.85	7559.17	-85.3142	-675.852
42	6720	7271.97	7258.92	13.0503	-551.967
43	6729	6965.36	6956.08	9.27951	-236.363
44	6695	6748.45	6691.08	57.3712	-53.4545
45	6564	6638.15	6566.46	71.6878	-74.1462
46	6536	6649.91	6547	102.915	-113.915
47	6491	6674.3	6518.29	156.009	-183.3
48	6452	6696.39	6479.96	216.436	-244.394
49	6391	6728.94	6443.21	285.727	-337.936
50	6348	6452.51	6400.67	51.842	-104.509
51	6331	5854.58	6358.29	-503.71	476.418
52	6414	5941.29	6316.58	-375.293	472.71
53	6299	6186.14	6271.46	-85.3142	112.856
54	6299	6239.63	6226.58	13.0503	59.3663
55	6268	6193.07	6183.79	9.27951	74.9288
56	6135	6201.54	6144.17	57.3712	-66.5378
57	6107	6177.85	6106.17	71.6878	-70.8545
58	5992	NA	NA	102.915	NA
59	5952	NA	NA	156.009	NA
60	5914	NA	NA	216.436	NA
61	5902	NA	NA	285.727	NA
62	5886	NA	NA	51.842	NA
63	5881	NA	NA	-503.71	NA

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 12 ; par2 = periodic ; par3 = 0 ; par5 = 1 ; par7 = 1 ; par8 = FALSE ;

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code