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rwasp_decompose.wasp

Title produced by software

Classical Decomposition

Date of computation

Mon, 25 Apr 2016 21:57:38 +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/Apr/25/t1461617894fkkfylop8ysv8u6.htm/, Retrieved Mon, 06 May 2024 09:57:11 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=294797, Retrieved Mon, 06 May 2024 09:57:11 +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] [] [2016-04-25 20:57:38] [9d122f8260d20611f07666190c7f1fd6] [Current]
- R P     [Classical Decomposition] [] [2016-05-21 10:23:55] [984d31b28aa27320c9bb8a4be001f13a] 
- RMPD    [Exponential Smoothing] [] [2016-05-21 10:34:12] [984d31b28aa27320c9bb8a4be001f13a] 
- RMPD    [(Partial) Autocorrelation Function] [] [2016-05-21 10:45:38] [984d31b28aa27320c9bb8a4be001f13a] 
- RMPD    [(Partial) Autocorrelation Function] [] [2016-05-21 10:49:57] [984d31b28aa27320c9bb8a4be001f13a] 

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Dataseries X:

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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	'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=294797&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=294797&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294797&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	'Gwilym Jenkins' @ jenkins.wessa.net

Classical Decomposition by Moving Averages
t	Observations	Fit	Trend	Seasonal	Random
1	45564.6	NA	NA	-1613.77	NA
2	47295.5	NA	NA	83.734	NA
3	46465.5	NA	NA	-1951.01	NA
4	50679.5	NA	NA	2997.54	NA
5	47452.8	NA	NA	-1629.4	NA
6	49415.4	NA	NA	218.895	NA
7	48165.3	47569.6	49367.9	-1798.32	595.677
8	51814	52520.1	49774.6	2745.52	-706.126
9	49030.7	48778.7	50175.6	-1396.87	251.957
10	50820.8	51099.6	50559.3	540.273	-278.81
11	49729.5	49690	50952.5	-1262.55	39.5076
12	53501.6	54433.3	51367.3	3065.97	-931.711
13	50524.9	50161	51774.7	-1613.77	363.945
14	52095	52303	52219.2	83.734	-207.959
15	51290.3	50726.5	52677.5	-1951.01	563.776
16	55064	56132.2	53134.7	2997.54	-1068.2
17	52505.2	51990.7	53620.1	-1629.4	514.514
18	54318.3	54358.5	54139.6	218.895	-40.2163
19	53039.6	52883.9	54682.2	-1798.32	155.682
20	57607.6	57990.1	55244.6	2745.52	-382.513
21	54236.4	54413.2	55810.1	-1396.87	-176.823
22	56586.4	56933.4	56393.1	540.273	-346.96
23	55614	55731.4	56994	-1262.55	-117.438
24	60085.9	60657.1	57591.1	3065.97	-571.157
25	56963.5	56591.7	58205.5	-1613.77	371.774
26	59152.8	58934.1	58850.4	83.734	218.712
27	57804.6	57589.4	59540.4	-1951.01	215.247
28	62541.5	63262.2	60264.7	2997.54	-720.692
29	59449.3	59368.6	60998	-1629.4	80.6764
30	61704.7	61939.6	61720.7	218.895	-234.887
31	60399	60558.3	62356.6	-1798.32	-159.306
32	65724.7	65669.8	62924.3	2745.52	54.8618
33	62679.4	62101.8	63498.7	-1396.87	577.561
34	65526.5	64674	64133.7	540.273	852.49
35	64274.8	63501.9	64764.5	-1262.55	772.853
36	68769.1	68411.3	65345.3	3065.97	357.847
37	63542.8	64306.5	65920.2	-1613.77	-763.676
38	66198	66607.1	66523.4	83.734	-409.122
39	64544.9	65146.7	67097.7	-1951.01	-601.782
40	71041.8	70584.2	67586.7	2997.54	457.558
41	66087.2	66433.6	68063	-1629.4	-346.365
42	69005.8	68832.3	68613.4	218.895	173.505
43	66897	67455.6	69253.9	-1798.32	-558.598
44	73702	72634.8	69889.2	2745.52	1067.23
45	68485.3	69104.5	70501.4	-1396.87	-619.239
46	71457	71650.3	71110	540.273	-193.264
47	69774.6	70436.1	71698.6	-1262.55	-661.467
48	76479.7	75301.2	72235.3	3065.97	1178.48
49	71204.7	71143.3	72757.1	-1613.77	61.4118
50	73783.9	73352.1	73268.3	83.734	431.824
51	71651	71794.8	73745.8	-1951.01	-143.786
52	78541.6	77176.8	74179.3	2997.54	1364.8
53	72714.4	72929.8	74559.2	-1629.4	-215.369
54	75258	75122.9	74904	218.895	135.055
55	73168.1	NA	NA	-1798.32	NA
56	79701.6	NA	NA	2745.52	NA
57	73944.5	NA	NA	-1396.87	NA
58	76401.2	NA	NA	540.273	NA
59	73948.1	NA	NA	-1262.55	NA
60	80583.3	NA	NA	3065.97	NA

\begin{tabular}{lllllllll}
\hline
Classical Decomposition by Moving Averages \tabularnewline
t & Observations & Fit & Trend & Seasonal & Random \tabularnewline
1 & 45564.6 & NA & NA & -1613.77 & NA \tabularnewline
2 & 47295.5 & NA & NA & 83.734 & NA \tabularnewline
3 & 46465.5 & NA & NA & -1951.01 & NA \tabularnewline
4 & 50679.5 & NA & NA & 2997.54 & NA \tabularnewline
5 & 47452.8 & NA & NA & -1629.4 & NA \tabularnewline
6 & 49415.4 & NA & NA & 218.895 & NA \tabularnewline
7 & 48165.3 & 47569.6 & 49367.9 & -1798.32 & 595.677 \tabularnewline
8 & 51814 & 52520.1 & 49774.6 & 2745.52 & -706.126 \tabularnewline
9 & 49030.7 & 48778.7 & 50175.6 & -1396.87 & 251.957 \tabularnewline
10 & 50820.8 & 51099.6 & 50559.3 & 540.273 & -278.81 \tabularnewline
11 & 49729.5 & 49690 & 50952.5 & -1262.55 & 39.5076 \tabularnewline
12 & 53501.6 & 54433.3 & 51367.3 & 3065.97 & -931.711 \tabularnewline
13 & 50524.9 & 50161 & 51774.7 & -1613.77 & 363.945 \tabularnewline
14 & 52095 & 52303 & 52219.2 & 83.734 & -207.959 \tabularnewline
15 & 51290.3 & 50726.5 & 52677.5 & -1951.01 & 563.776 \tabularnewline
16 & 55064 & 56132.2 & 53134.7 & 2997.54 & -1068.2 \tabularnewline
17 & 52505.2 & 51990.7 & 53620.1 & -1629.4 & 514.514 \tabularnewline
18 & 54318.3 & 54358.5 & 54139.6 & 218.895 & -40.2163 \tabularnewline
19 & 53039.6 & 52883.9 & 54682.2 & -1798.32 & 155.682 \tabularnewline
20 & 57607.6 & 57990.1 & 55244.6 & 2745.52 & -382.513 \tabularnewline
21 & 54236.4 & 54413.2 & 55810.1 & -1396.87 & -176.823 \tabularnewline
22 & 56586.4 & 56933.4 & 56393.1 & 540.273 & -346.96 \tabularnewline
23 & 55614 & 55731.4 & 56994 & -1262.55 & -117.438 \tabularnewline
24 & 60085.9 & 60657.1 & 57591.1 & 3065.97 & -571.157 \tabularnewline
25 & 56963.5 & 56591.7 & 58205.5 & -1613.77 & 371.774 \tabularnewline
26 & 59152.8 & 58934.1 & 58850.4 & 83.734 & 218.712 \tabularnewline
27 & 57804.6 & 57589.4 & 59540.4 & -1951.01 & 215.247 \tabularnewline
28 & 62541.5 & 63262.2 & 60264.7 & 2997.54 & -720.692 \tabularnewline
29 & 59449.3 & 59368.6 & 60998 & -1629.4 & 80.6764 \tabularnewline
30 & 61704.7 & 61939.6 & 61720.7 & 218.895 & -234.887 \tabularnewline
31 & 60399 & 60558.3 & 62356.6 & -1798.32 & -159.306 \tabularnewline
32 & 65724.7 & 65669.8 & 62924.3 & 2745.52 & 54.8618 \tabularnewline
33 & 62679.4 & 62101.8 & 63498.7 & -1396.87 & 577.561 \tabularnewline
34 & 65526.5 & 64674 & 64133.7 & 540.273 & 852.49 \tabularnewline
35 & 64274.8 & 63501.9 & 64764.5 & -1262.55 & 772.853 \tabularnewline
36 & 68769.1 & 68411.3 & 65345.3 & 3065.97 & 357.847 \tabularnewline
37 & 63542.8 & 64306.5 & 65920.2 & -1613.77 & -763.676 \tabularnewline
38 & 66198 & 66607.1 & 66523.4 & 83.734 & -409.122 \tabularnewline
39 & 64544.9 & 65146.7 & 67097.7 & -1951.01 & -601.782 \tabularnewline
40 & 71041.8 & 70584.2 & 67586.7 & 2997.54 & 457.558 \tabularnewline
41 & 66087.2 & 66433.6 & 68063 & -1629.4 & -346.365 \tabularnewline
42 & 69005.8 & 68832.3 & 68613.4 & 218.895 & 173.505 \tabularnewline
43 & 66897 & 67455.6 & 69253.9 & -1798.32 & -558.598 \tabularnewline
44 & 73702 & 72634.8 & 69889.2 & 2745.52 & 1067.23 \tabularnewline
45 & 68485.3 & 69104.5 & 70501.4 & -1396.87 & -619.239 \tabularnewline
46 & 71457 & 71650.3 & 71110 & 540.273 & -193.264 \tabularnewline
47 & 69774.6 & 70436.1 & 71698.6 & -1262.55 & -661.467 \tabularnewline
48 & 76479.7 & 75301.2 & 72235.3 & 3065.97 & 1178.48 \tabularnewline
49 & 71204.7 & 71143.3 & 72757.1 & -1613.77 & 61.4118 \tabularnewline
50 & 73783.9 & 73352.1 & 73268.3 & 83.734 & 431.824 \tabularnewline
51 & 71651 & 71794.8 & 73745.8 & -1951.01 & -143.786 \tabularnewline
52 & 78541.6 & 77176.8 & 74179.3 & 2997.54 & 1364.8 \tabularnewline
53 & 72714.4 & 72929.8 & 74559.2 & -1629.4 & -215.369 \tabularnewline
54 & 75258 & 75122.9 & 74904 & 218.895 & 135.055 \tabularnewline
55 & 73168.1 & NA & NA & -1798.32 & NA \tabularnewline
56 & 79701.6 & NA & NA & 2745.52 & NA \tabularnewline
57 & 73944.5 & NA & NA & -1396.87 & NA \tabularnewline
58 & 76401.2 & NA & NA & 540.273 & NA \tabularnewline
59 & 73948.1 & NA & NA & -1262.55 & NA \tabularnewline
60 & 80583.3 & NA & NA & 3065.97 & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=294797&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]45564.6[/C][C]NA[/C][C]NA[/C][C]-1613.77[/C][C]NA[/C][/ROW]
[ROW][C]2[/C][C]47295.5[/C][C]NA[/C][C]NA[/C][C]83.734[/C][C]NA[/C][/ROW]
[ROW][C]3[/C][C]46465.5[/C][C]NA[/C][C]NA[/C][C]-1951.01[/C][C]NA[/C][/ROW]
[ROW][C]4[/C][C]50679.5[/C][C]NA[/C][C]NA[/C][C]2997.54[/C][C]NA[/C][/ROW]
[ROW][C]5[/C][C]47452.8[/C][C]NA[/C][C]NA[/C][C]-1629.4[/C][C]NA[/C][/ROW]
[ROW][C]6[/C][C]49415.4[/C][C]NA[/C][C]NA[/C][C]218.895[/C][C]NA[/C][/ROW]
[ROW][C]7[/C][C]48165.3[/C][C]47569.6[/C][C]49367.9[/C][C]-1798.32[/C][C]595.677[/C][/ROW]
[ROW][C]8[/C][C]51814[/C][C]52520.1[/C][C]49774.6[/C][C]2745.52[/C][C]-706.126[/C][/ROW]
[ROW][C]9[/C][C]49030.7[/C][C]48778.7[/C][C]50175.6[/C][C]-1396.87[/C][C]251.957[/C][/ROW]
[ROW][C]10[/C][C]50820.8[/C][C]51099.6[/C][C]50559.3[/C][C]540.273[/C][C]-278.81[/C][/ROW]
[ROW][C]11[/C][C]49729.5[/C][C]49690[/C][C]50952.5[/C][C]-1262.55[/C][C]39.5076[/C][/ROW]
[ROW][C]12[/C][C]53501.6[/C][C]54433.3[/C][C]51367.3[/C][C]3065.97[/C][C]-931.711[/C][/ROW]
[ROW][C]13[/C][C]50524.9[/C][C]50161[/C][C]51774.7[/C][C]-1613.77[/C][C]363.945[/C][/ROW]
[ROW][C]14[/C][C]52095[/C][C]52303[/C][C]52219.2[/C][C]83.734[/C][C]-207.959[/C][/ROW]
[ROW][C]15[/C][C]51290.3[/C][C]50726.5[/C][C]52677.5[/C][C]-1951.01[/C][C]563.776[/C][/ROW]
[ROW][C]16[/C][C]55064[/C][C]56132.2[/C][C]53134.7[/C][C]2997.54[/C][C]-1068.2[/C][/ROW]
[ROW][C]17[/C][C]52505.2[/C][C]51990.7[/C][C]53620.1[/C][C]-1629.4[/C][C]514.514[/C][/ROW]
[ROW][C]18[/C][C]54318.3[/C][C]54358.5[/C][C]54139.6[/C][C]218.895[/C][C]-40.2163[/C][/ROW]
[ROW][C]19[/C][C]53039.6[/C][C]52883.9[/C][C]54682.2[/C][C]-1798.32[/C][C]155.682[/C][/ROW]
[ROW][C]20[/C][C]57607.6[/C][C]57990.1[/C][C]55244.6[/C][C]2745.52[/C][C]-382.513[/C][/ROW]
[ROW][C]21[/C][C]54236.4[/C][C]54413.2[/C][C]55810.1[/C][C]-1396.87[/C][C]-176.823[/C][/ROW]
[ROW][C]22[/C][C]56586.4[/C][C]56933.4[/C][C]56393.1[/C][C]540.273[/C][C]-346.96[/C][/ROW]
[ROW][C]23[/C][C]55614[/C][C]55731.4[/C][C]56994[/C][C]-1262.55[/C][C]-117.438[/C][/ROW]
[ROW][C]24[/C][C]60085.9[/C][C]60657.1[/C][C]57591.1[/C][C]3065.97[/C][C]-571.157[/C][/ROW]
[ROW][C]25[/C][C]56963.5[/C][C]56591.7[/C][C]58205.5[/C][C]-1613.77[/C][C]371.774[/C][/ROW]
[ROW][C]26[/C][C]59152.8[/C][C]58934.1[/C][C]58850.4[/C][C]83.734[/C][C]218.712[/C][/ROW]
[ROW][C]27[/C][C]57804.6[/C][C]57589.4[/C][C]59540.4[/C][C]-1951.01[/C][C]215.247[/C][/ROW]
[ROW][C]28[/C][C]62541.5[/C][C]63262.2[/C][C]60264.7[/C][C]2997.54[/C][C]-720.692[/C][/ROW]
[ROW][C]29[/C][C]59449.3[/C][C]59368.6[/C][C]60998[/C][C]-1629.4[/C][C]80.6764[/C][/ROW]
[ROW][C]30[/C][C]61704.7[/C][C]61939.6[/C][C]61720.7[/C][C]218.895[/C][C]-234.887[/C][/ROW]
[ROW][C]31[/C][C]60399[/C][C]60558.3[/C][C]62356.6[/C][C]-1798.32[/C][C]-159.306[/C][/ROW]
[ROW][C]32[/C][C]65724.7[/C][C]65669.8[/C][C]62924.3[/C][C]2745.52[/C][C]54.8618[/C][/ROW]
[ROW][C]33[/C][C]62679.4[/C][C]62101.8[/C][C]63498.7[/C][C]-1396.87[/C][C]577.561[/C][/ROW]
[ROW][C]34[/C][C]65526.5[/C][C]64674[/C][C]64133.7[/C][C]540.273[/C][C]852.49[/C][/ROW]
[ROW][C]35[/C][C]64274.8[/C][C]63501.9[/C][C]64764.5[/C][C]-1262.55[/C][C]772.853[/C][/ROW]
[ROW][C]36[/C][C]68769.1[/C][C]68411.3[/C][C]65345.3[/C][C]3065.97[/C][C]357.847[/C][/ROW]
[ROW][C]37[/C][C]63542.8[/C][C]64306.5[/C][C]65920.2[/C][C]-1613.77[/C][C]-763.676[/C][/ROW]
[ROW][C]38[/C][C]66198[/C][C]66607.1[/C][C]66523.4[/C][C]83.734[/C][C]-409.122[/C][/ROW]
[ROW][C]39[/C][C]64544.9[/C][C]65146.7[/C][C]67097.7[/C][C]-1951.01[/C][C]-601.782[/C][/ROW]
[ROW][C]40[/C][C]71041.8[/C][C]70584.2[/C][C]67586.7[/C][C]2997.54[/C][C]457.558[/C][/ROW]
[ROW][C]41[/C][C]66087.2[/C][C]66433.6[/C][C]68063[/C][C]-1629.4[/C][C]-346.365[/C][/ROW]
[ROW][C]42[/C][C]69005.8[/C][C]68832.3[/C][C]68613.4[/C][C]218.895[/C][C]173.505[/C][/ROW]
[ROW][C]43[/C][C]66897[/C][C]67455.6[/C][C]69253.9[/C][C]-1798.32[/C][C]-558.598[/C][/ROW]
[ROW][C]44[/C][C]73702[/C][C]72634.8[/C][C]69889.2[/C][C]2745.52[/C][C]1067.23[/C][/ROW]
[ROW][C]45[/C][C]68485.3[/C][C]69104.5[/C][C]70501.4[/C][C]-1396.87[/C][C]-619.239[/C][/ROW]
[ROW][C]46[/C][C]71457[/C][C]71650.3[/C][C]71110[/C][C]540.273[/C][C]-193.264[/C][/ROW]
[ROW][C]47[/C][C]69774.6[/C][C]70436.1[/C][C]71698.6[/C][C]-1262.55[/C][C]-661.467[/C][/ROW]
[ROW][C]48[/C][C]76479.7[/C][C]75301.2[/C][C]72235.3[/C][C]3065.97[/C][C]1178.48[/C][/ROW]
[ROW][C]49[/C][C]71204.7[/C][C]71143.3[/C][C]72757.1[/C][C]-1613.77[/C][C]61.4118[/C][/ROW]
[ROW][C]50[/C][C]73783.9[/C][C]73352.1[/C][C]73268.3[/C][C]83.734[/C][C]431.824[/C][/ROW]
[ROW][C]51[/C][C]71651[/C][C]71794.8[/C][C]73745.8[/C][C]-1951.01[/C][C]-143.786[/C][/ROW]
[ROW][C]52[/C][C]78541.6[/C][C]77176.8[/C][C]74179.3[/C][C]2997.54[/C][C]1364.8[/C][/ROW]
[ROW][C]53[/C][C]72714.4[/C][C]72929.8[/C][C]74559.2[/C][C]-1629.4[/C][C]-215.369[/C][/ROW]
[ROW][C]54[/C][C]75258[/C][C]75122.9[/C][C]74904[/C][C]218.895[/C][C]135.055[/C][/ROW]
[ROW][C]55[/C][C]73168.1[/C][C]NA[/C][C]NA[/C][C]-1798.32[/C][C]NA[/C][/ROW]
[ROW][C]56[/C][C]79701.6[/C][C]NA[/C][C]NA[/C][C]2745.52[/C][C]NA[/C][/ROW]
[ROW][C]57[/C][C]73944.5[/C][C]NA[/C][C]NA[/C][C]-1396.87[/C][C]NA[/C][/ROW]
[ROW][C]58[/C][C]76401.2[/C][C]NA[/C][C]NA[/C][C]540.273[/C][C]NA[/C][/ROW]
[ROW][C]59[/C][C]73948.1[/C][C]NA[/C][C]NA[/C][C]-1262.55[/C][C]NA[/C][/ROW]
[ROW][C]60[/C][C]80583.3[/C][C]NA[/C][C]NA[/C][C]3065.97[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=294797&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=294797&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	45564.6	NA	NA	-1613.77	NA
2	47295.5	NA	NA	83.734	NA
3	46465.5	NA	NA	-1951.01	NA
4	50679.5	NA	NA	2997.54	NA
5	47452.8	NA	NA	-1629.4	NA
6	49415.4	NA	NA	218.895	NA
7	48165.3	47569.6	49367.9	-1798.32	595.677
8	51814	52520.1	49774.6	2745.52	-706.126
9	49030.7	48778.7	50175.6	-1396.87	251.957
10	50820.8	51099.6	50559.3	540.273	-278.81
11	49729.5	49690	50952.5	-1262.55	39.5076
12	53501.6	54433.3	51367.3	3065.97	-931.711
13	50524.9	50161	51774.7	-1613.77	363.945
14	52095	52303	52219.2	83.734	-207.959
15	51290.3	50726.5	52677.5	-1951.01	563.776
16	55064	56132.2	53134.7	2997.54	-1068.2
17	52505.2	51990.7	53620.1	-1629.4	514.514
18	54318.3	54358.5	54139.6	218.895	-40.2163
19	53039.6	52883.9	54682.2	-1798.32	155.682
20	57607.6	57990.1	55244.6	2745.52	-382.513
21	54236.4	54413.2	55810.1	-1396.87	-176.823
22	56586.4	56933.4	56393.1	540.273	-346.96
23	55614	55731.4	56994	-1262.55	-117.438
24	60085.9	60657.1	57591.1	3065.97	-571.157
25	56963.5	56591.7	58205.5	-1613.77	371.774
26	59152.8	58934.1	58850.4	83.734	218.712
27	57804.6	57589.4	59540.4	-1951.01	215.247
28	62541.5	63262.2	60264.7	2997.54	-720.692
29	59449.3	59368.6	60998	-1629.4	80.6764
30	61704.7	61939.6	61720.7	218.895	-234.887
31	60399	60558.3	62356.6	-1798.32	-159.306
32	65724.7	65669.8	62924.3	2745.52	54.8618
33	62679.4	62101.8	63498.7	-1396.87	577.561
34	65526.5	64674	64133.7	540.273	852.49
35	64274.8	63501.9	64764.5	-1262.55	772.853
36	68769.1	68411.3	65345.3	3065.97	357.847
37	63542.8	64306.5	65920.2	-1613.77	-763.676
38	66198	66607.1	66523.4	83.734	-409.122
39	64544.9	65146.7	67097.7	-1951.01	-601.782
40	71041.8	70584.2	67586.7	2997.54	457.558
41	66087.2	66433.6	68063	-1629.4	-346.365
42	69005.8	68832.3	68613.4	218.895	173.505
43	66897	67455.6	69253.9	-1798.32	-558.598
44	73702	72634.8	69889.2	2745.52	1067.23
45	68485.3	69104.5	70501.4	-1396.87	-619.239
46	71457	71650.3	71110	540.273	-193.264
47	69774.6	70436.1	71698.6	-1262.55	-661.467
48	76479.7	75301.2	72235.3	3065.97	1178.48
49	71204.7	71143.3	72757.1	-1613.77	61.4118
50	73783.9	73352.1	73268.3	83.734	431.824
51	71651	71794.8	73745.8	-1951.01	-143.786
52	78541.6	77176.8	74179.3	2997.54	1364.8
53	72714.4	72929.8	74559.2	-1629.4	-215.369
54	75258	75122.9	74904	218.895	135.055
55	73168.1	NA	NA	-1798.32	NA
56	79701.6	NA	NA	2745.52	NA
57	73944.5	NA	NA	-1396.87	NA
58	76401.2	NA	NA	540.273	NA
59	73948.1	NA	NA	-1262.55	NA
60	80583.3	NA	NA	3065.97	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 = additive ; par2 = 12 ;

Parameters (R input):

par1 = additive ; par2 = 12 ;

R code (references can be found in the software module):

par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,signif(m$trend[i]+m$seasonal[i],6)) else a<-table.element(a,signif(m$trend[i]*m$seasonal[i],6))
a<-table.element(a,signif(m$trend[i],6))
a<-table.element(a,signif(m$seasonal[i],6))
a<-table.element(a,signif(m$random[i],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code